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Data-Modelling-with-PostgreSQL | data modeling with postgresql img align right width 25 height 25 src https fedingo com wp content uploads 2021 09 python postgresql jpg this project is part of the course relational databases in the data engineering nanodegree on udacity the final version of the repository will be submitted to udacity for review and grading course topics relational databases sql olap oltp database systems normalization normal forms denormalization fact and dimension tables star and snowflake schemas project description in this project i applied what i ve learned on data modeling with postgres and build an etl pipeline using python to complete the project i needed to define fact and dimension tables for a star schema for a particular analytic focus and write an etl pipeline that transfers data from files in two local directories into these tables in postgres using python and sql project context a startup called sparkify wants to analyze the data they ve been collecting on songs and user activity on their new music streaming app the analytics team is particularly interested in understanding what songs users are listening to currently they don t have an easy way to query their data which resides in a directory of json logs on user activity on the app as well as a directory with json metadata on the songs in their app they d like a data engineer to create a postgres database with tables designed to optimize queries on song play analysis and bring you on the project your role is to create a database schema and etl pipeline for this analysis you ll be able to test your database and etl pipeline by running queries given to you by the analytics team from sparkify and compare your results with their expected results dependencies pandas psycopg2 files create tables py allows for the creation of the database tables sql queries py contains all postgesql insert create statements etl ipynb a notebook for testing purposes to test out sql statements before running them on all files etl py the code to process all the data and insert it in the tables by using the queries defined in sql queries py test ipynb a notebook to check the contents of the database to validate whether the data is indeed correctly inserted license the project license usage to use the code written to create the database and tables use the following in a terminal bash python create tables py to insert the data from the data directory in the database use the following bash python etl py contributing pull requests to show improvements of the existing code are welcome for major changes please open an issue first to discuss what you would like to change license mit https choosealicense com licenses mit | postgres python pandas | server |
insight | this proyect has been replaced by bitcore node at https github com bitpay bitcore | blockchain |
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cognitive-services-javascript-computer-vision-tutorial | microsoft cognitive services javascript computer vision tutorial project this tutorial shows the features of the microsoft cognitive services computer vision rest api by using javascript html and css getting started installation 1 clone the repository or download the repository as a zip file and extract the zip file into an empty directory 1 drag and drop the analyze html file into a browser 1 drag and drop the analyze html file into a text editor you are now ready to begin the tutorial instructions the instructions for the tutorial can be found at computer vision api javascript tutorial https docs microsoft com en us azure cognitive services computer vision tutorials javascript tutorial | ai |
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Feature-level-fusion-of-Palm-print-and-Palm-vein | feature level fusion of palm print and palm vein biometric systems have become a major part of research due its application of identification code provides a multimodal biometric system using palm prints modality combined with palm print modality dct transformation is applied initially into input image the proposed methodology uses standard deviation of pre defined block of dct coefficient as feature vector in this way single image is converted into feature vector of 1 x 39 recognition process is being done by performing distance measurement between feature vector of testing and training data set results show that the false acceptance rate far of feature level fusion is less than that of uni modal systems hence having multimodality is advantageous testing and training is done on database of 500 students of college of engineering pune pune india canberra distance shows best result when compared to euclidean or manhatten distance | server |
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LINUX_BOARD | embedded linux board running custom linux distribution project info at91sam9n12 microprocessor with memory management unit based embedded linux board hardware and operating system files it has 133 mhz ram and 4 gb nand flash and qspi flash interfaces this design is the custom version of sam9n12 ek the custom design has a ram with different speed and latency also it is using nand flash as a memory therefore at91bootstrap hardware initialization files must be customized accordingly briefly the pll and clocking settings as well as ram latency values and nand interface pinouts are changed accordingly to see the details of the changes please check linux board linux files at91bootstrap files at91sam9n12ek c and h files buildroot is used for custom linux distribution check step by step guide below for details follow ubuntu u boot and buildroot versions strictly as indicated below to make it compile properly final board screenshots img 20200227 002110 https user images githubusercontent com 61315249 75391102 8bdff500 58fa 11ea 9e2c 1d8b6b9425e5 jpg linux board https user images githubusercontent com 61315249 82347687 9cd36a80 9a00 11ea 9536 8540351ce7bf jpg untitled https user images githubusercontent com 61315249 75391308 e1b49d00 58fa 11ea 8e9b d573de76c693 png important notes use teraterm on windows or picocom on mac osx to be able to set the baud rate to 118200 8bit no parity 1 stop the reason of this baudrate is that in at91bootstrap cpu clock is configured from 400mhz to 300mhz for changing ram speed from 133mhz to 100mhz since pll multipliers are not strickly generating 300 and 100 mhz the actual speeds are 301 333 mhz and 101 333 this main clock difference changes all other peripherals clocking as result of this characters are not received correctly when the baudrate is set to 115200 when the baudrate is set to 118200 on host side the hardware and the host side can talk without any issue however in buildroot u boot and at91bootstrap files the uart is configured to 115200 8n1 no parity the steps below are tested with ubuntu 16 04 6 and 18 04 2 lts step by step guide for flashing board to run linux 1 this part describes how board is connected over usb and what commands needed to see on sam ba program if linux board is new and fresh then connect over usb to linux virtual computer if linux board is flashed previously with files then connect nandflash s pin41 to ground and plug usb linux will see device as dev ttyacm0 but sam ba cannot see so type command it is required every time sudo chmod a rw dev ttyacm0 or add read write permission to usb device to not write above command every time to do that go to etc udev rules d and create a rule file such as myusb rules file and add the line sudo nano etc udev rules d filename rules kernel ttyacm 0 9 mode 0666 now sam ba can see board as dev ttyacm0 2 before start install these libraries 1 sudo apt install libncurses5 libncursesw5 dev libncurses5 dev 2 sudo apt install make 3 sudo apt install gcc arm linux gnueabi 4 sudo apt install gcc 5 sudo apt install g 6 sudo apt install python these might no be needed 7 sudo apt install make guile 8 sudo apt install gcc multilib 9 sudo apt install gcc arm files required for board and in this part they will be created a at91sambootstrap initialises sam9n12e hardware like clock nano interface scram gpio etc b u boot initialises sam9n12e hardware clk ram and etc c buildroot linux kernel rootfs our python c compiler nano etc apps are in rootfs 2 1 how to compile and create at91sambootstrap files i put at91bootstrap 3 3 8 tar gz it is the latest version and works unzip to ubuntu and use it for any case download link is below http repository timesys com buildsources a at91bootstrap 3 go to at91bootstrap 3 3 8 folder and change at91bootstrap 3 3 8 board at91sam9n12ek at91sam9n12ek c and h files with at91bootstrap files at91sam9n12ek c and h go to the at91bootstrap 3 3 8 folder location over terminal and apply commands 1 make mrproper 2 make at91sam9n12eknf uboot defconfig nf means it will be loaded to nandflash and u boot means it will run u boot 3 make menuconfig this will open a configuration window 3 1 go to ram configuration and change ram size to 64m 3 2 go to nand flash configuration and change pmecc error correction bits to 4bits sector to 512 3 3 go to u boot image storage setup and change external ram address to load u boot image from 0x26f00000 to 0x22000000 3 4 save an alternate file and it will create config file in at91bootstrap 3 3 8 it is invisible nano config opens if needed 4 make cross compile arm linux gnueabi result the at91sam9n12ek nandflashboot uboot 3 8 beta1 bin file is ready in binaries file 2 2 how to compile and create u boot files i put u boot 2015 1 tar gz to folder no need to download newer versions since include files naming are changed and they are not compiling without error unzip it to ubuntu and use for any case download link is below http repository timesys com buildsources u u boot go to folder and change u boot 2015 01 board atmel at91sam9n12ek at91sam9n12ek c file with u boot files at91sam9n12ek c and u boot 2015 01 include configs at91sam9n12ek h file with u boot files at91sam9n12ek h also for ubuntu 16 04 gcc5 h for ubuntu 18 04 gcc7 h add they have same code inside as well u boot files compiler gcc5 h or gcc 7 file to u boot 2015 01 include linux folder go to u boot 2015 01 folder with terminal and apply commands 1 make mrproper 2 make at91sam9n12ek nandflash defconfig nandflash means this file will be written to nandflash 3 make cross compile arm linux gnueabi result the u boot bin is in u boot 2015 01 folder 2 3 how to compile and create build root files i put buildroot 2015 2 tar gz to folder and this version works for any case download link is below https buildroot org downloads before start buildroot might create errors during compile these are mostly package version collision type errors i look for error file and try to correct them if i cannot then i disable that package with make menuconfig window update when ubuntu 16 04 is used in buildroot 2015 2 it gave error i added correction files update when ubuntu 18 04 is used in buildroot 2015 2 it gave error i added correction files i used ready linux binary tree files with config files read linux embedded notes 5 device tree part for details of device tree dts file go to buildroot files folder and copy 3 files from buildroot files to buildroot 2015 02 main folder and then rename config buildroot file as config it will be invisible after name change use terminal to see go to the buildroot 2015 02 folder and apply commands to configure linux kernel to enable drivers use make linux menuconfig command for example wifi usb dongle drivers are enabled in linux menuconfig 1 make mrproper or make clean 2 make menuconfig if i want to add or change something 3 make result the files are in buildroot 2015 02 output images correction if ubuntu 2016 04 is used these can be applied when they occur during compilation if an error not listed below happens first try to remove the installed package from output build and make again a ncrses copy the code in buildroot correction ncrse5 9 correction mklib gen sh and paste inside of do not copy paste files itself copy paste code buildroot 2015 02 output build host crse5 9 ncrse base mklib gen sh c host mtd1 5 1 do this to host mtd not mtd folder copy the code buildroot correction mkfsutil correction and paste to do not copy paste file buildroot 2015 02 output build host mtd1 5 1 mkfs ubifs hashtable correction if ubuntu 2018 04 is with gcc 5 used these can be applied when they occur during compilation all error corrections are added below if could not make it work use ubuntu 16 04 a libffi3 1 change buildroot correction libffi3 1 correction automake file with buildroot 2015 02 output host user bin automake b libglib2 42 do this to libglib not host libglib folder change buildroot correction libglib2 42 correction gdate c file with buildroot 2015 02 output build libglib2 42 glib gdate c c lzop1 03 copy the code buildroot correction lzop1 03 correction miniacc f file and paste to do not copy paste file buildroot 2015 02 output build host lzop1 03 src miniacc h d ncrses copy the code buildroot correction ncrse5 9 correction mklib gen sh and paste to do not copy paste file buildroot 2015 02 output build host crse5 9 ncrse base mklib gen sh e host mtd1 5 1 do this to host mtd not mtd folder copy the code buildroot correction mkfsutil correction and paste to do not copy paste file buildroot 2015 02 output build host mtd1 5 1 mkfs ubifs hashtable 3 how to flash files to board memory locations of files at91sam9n12ek nandflashboot uboot 3 8 bin 0x00 u boot bin 0x40000 at91sam9n12 sam board 3 15 dtb 0x180000 uimage bin 0x200000 rootfs ubi 0x800000 unzip sam ba to ubuntu and connect board over usb see 1 then click connect select nandflash tab and from scripts select enable nandflash click execute button it should print as below i nandflash init trace level 4 i loading applet applet nandflash sam9n12 bin at address 0x20000000 i memory size 0x20000000 bytes i buffer address 0x20011404 i buffer size 0x20000 bytes i applet initialization done then again from scripts select pmecc configuration click execute button i think it reads nandflash auto but do anyway because when wrong parameters selected it gives error select ecc bit 4 select sector size 512 it should print as below i ecc type is 2 ecc status is 2 i configure trimffs 0 i pmecc c0082805 to be configured i pmecc header configration successful i pmecc configure c0082805 3 1 set address to 0x00 and from scripts select send boot file click execute button select at91sam9n12ek nandflashboot uboot 3 8 bin file and click open it should print as below i sending boot file done from now on execute button will not be used rest of the files will send with send file name folder selector and address setting 3 2 set address to 0x40000 and from send file name folder selector select u boot bin file and click send file it should print as below i send file home ck desktop u boot bin at address 0x40000 generic sendfile home ck desktop u boot bin at address 0x40000 i file size 0x5cd30 byte s i writing 0x20000 bytes at 0x40000 buffer addr 0x20011404 i 0x20000 bytes written by applet i writing 0x20000 bytes at 0x60000 buffer addr 0x20011404 i 0x20000 bytes written by applet i writing 0x1cd30 bytes at 0x80000 buffer addr 0x20011404 i 0x1cd30 bytes written by applet 3 3 set address to 0x180000 and from send file name folder selector select at91sam9n12 sam board 3 15 dtb file and click send file it should print as below i send file home ck desktop at91sam9n12 sam board 3 15 dtb at address 0x180000 generic sendfile home ck desktop at91sam9n12 sam board 3 15 dtb at address 0x180000 i file size 0x2966 byte s i writing 0x2966 bytes at 0x180000 buffer addr 0x20011404 i 0x20000 bytes written by applet 3 4 set address to 0x200000 and from send file name folder selector select uimage bin file and click send file it should print as below i send file home ck desktop uimage bin at address 0x200000 generic sendfile home ck desktop uimage bin at address 0x200000 i file size 0x2b91f0 byte s i writing 0x20000 bytes at 0x200000 buffer addr 0x20011404 i 0x20000 bytes written by applet goes like this i writing 0x191f0 bytes at 0x4a0000 buffer addr 0x20011404 i 0x191f0 bytes written by applet 3 5 set address to 0x800000 and from send file name folder selector select rootfs ubi file and click send file | os |
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Fake_News_Detection | fake news detection a web application for detecting fake news from real ones utilizing natural language processing this is the flask verison please see the other branch for the django version the app is deployed on heroku with link https fakenewsdetectorflask herokuapp com feel free to use the api i published on rapidapi https rapidapi com fangyiyu api fake news detection1 | flask machine-learning text-classification django | ai |
worker-vllm | div align center h1 vllm endpoint serverless worker h1 ci test worker https github com runpod workers worker template actions workflows ci test worker yml badge svg https github com runpod workers worker template actions workflows ci test worker yml nbsp docker image https github com runpod workers worker template actions workflows cd docker dev yml badge svg https github com runpod workers worker template actions workflows cd docker dev yml this serverless worker utilizes vllm very large language model behind the scenes and is integrated into runpod s serverless environment it supports dynamic auto scaling using the built in runpod autoscaling feature div docker arguments 1 hugging face hub token your private hugging face token this token is required for downloading models that necessitate agreement to an end user license agreement eula such as the llama2 family of models 2 model name the hugging face model to use please ensure that the chosen model is supported by vllm refer to the list of supported models for compatibility 3 tokenizer optional the specified tokenizer to use if you want to use the default tokenizer for the model do not provide this docker argument at all 4 streaming whether to use http streaming or not specify true if you want to enable http streaming otherwise omit this argument llama2 7b chat docker build platform linux amd64 build arg hugging face hub token your hugging face token here build arg model name meta llama llama 2 7b chat hf build arg tokenizer hf internal testing llama tokenizer build arg streaming true llama2 13b chat docker build platform linux amd64 build arg hugging face hub token your hugging face token here build arg model name meta llama llama 2 13b chat hf build arg tokenizer hf internal testing llama tokenizer build arg streaming true please make sure to replace your hugging face token here with your actual hugging face token to enable model downloads that require it ensure that you have docker installed and properly set up before running the docker build commands once built you can deploy this serverless worker in your desired environment with confidence that it will automatically scale based on demand for further inquiries or assistance feel free to contact our support team model inputs argument type default description n int 1 number of output sequences to return for the given prompt best of optional int none number of output sequences that are generated from the prompt from these best of sequences the top n sequences are returned best of must be greater than or equal to n this is treated as the beam width when use beam search is true by default best of is set to n presence penalty float 0 0 float that penalizes new tokens based on whether they appear in the generated text so far values 0 encourage the model to use new tokens while values 0 encourage the model to repeat tokens frequency penalty float 0 0 float that penalizes new tokens based on their frequency in the generated text so far values 0 encourage the model to use new tokens while values 0 encourage the model to repeat tokens temperature float 1 0 float that controls the randomness of the sampling lower values make the model more deterministic while higher values make the model more random zero means greedy sampling top p float 1 0 float that controls the cumulative probability of the top tokens to consider must be in 0 1 set to 1 to consider all tokens top k int 1 integer that controls the number of top tokens to consider set to 1 to consider all tokens use beam search bool false whether to use beam search instead of sampling stop union none str list str none list of strings that stop the generation when they are generated the returned output will not contain the stop strings ignore eos bool false whether to ignore the eos token and continue generating tokens after the eos token is generated max tokens int 256 maximum number of tokens to generate per output sequence logprobs optional int none number of log probabilities to return per output token test inputs the following inputs can be used for testing the model json input prompt who is the president of the united states sampling params max tokens 100 | ai |
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DIY-Data-Science | diy data science diy logo https mir s3 cdn cf behance net project modules disp 9336839151265 560c985e288ea png compilation of data science resources optimized for self education the best way to learn data science is to do it yourself theory research seq2seq https github com jxieeducation diy data science blob master research seq2seq md visual qa https github com jxieeducation diy data science blob master research visual qa md frameworks libraries xgboost https github com jxieeducation diy data science blob master frameworks xgboost md chainer https github com jxieeducation diy data science blob master frameworks chainer md gensim https github com jxieeducation diy data science blob master frameworks gensim md pyldavis https github com jxieeducation diy data science blob master frameworks pyldavis md pyevolve https github com jxieeducation diy data science blob master frameworks pyevolve md deep learning paper notes papernotes https github com jxieeducation diy data science blob master papernotes contributing everything is appreciated from writing a guide to sharing a great link contributing is a great way to teach yourself and others data science | ai |
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BENR3523-Ass-ES-SpeedClassification | ass es speedclassification ass speedfan datalogging is used to import the raw accelerometer data into nanoedge ai studio to automatically find the best bundle of signal processing machine learning model and hyperparametrization given the input signals provided the resulting nanoegde ai library will be the best fit for the use case selected and it will be untrained the library libneai a nanoedgeai h knowledge h are placed in the path acc lib of ass speedfan nanoedgelibclassification to classify input signals with the trained knowledge the class of speed of fan that can be detected is off speed1 speed2 | os |
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SQL-Employee-Database | sql employee database for this project used postgresql to create an employee database using data from six csv files on employees of a corporation from the 1980s and 1990s the following were used for this project 1 data modeling 2 data engineering 3 data analysis data modeling inspect the csvs and sketch out an entity relationship diagram erd of the tables data engineering use the information to create a table schema for each of the six csv files specify data types primary keys foreign keys and other constraints import each csv file into the corresponding sql table data analysis perform sql queries to answer the following questions 1 list the following details of each employee employee number last name first name gender and salary 2 list employees who were hired in 1986 3 list the manager of each department with the following information department number department name the manager s employee number last name first name and start and end employment dates 4 list the department of each employee with the following information employee number last name first name and department name 5 list all employees whose first name is hercules and last names begin with b 6 list all employees in the sales department including their employee number last name first name and department name 7 list all employees in the sales and development departments including their employee number last name first name and department name 8 in descending order list the frequency count of employee last names i e how many employees share each last name examples of queries and results query 4 images query2 png query 6 images query1 png | server |
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olinjs | olin js course description olin js is a project oriented approach to learning modern web application development with server and client technology advancing so rapidly the modern website looks very different from that of even a few years ago and the web will only become a bigger part of our lives in the near future olin js will quickly familiarize students with node js as a web server framework and es5 javascript as a responsive client side language with three weeks of introductory instruction and four weeks of partner labs along the way students will learn the basic layout of the web how to deploy and maintain large applications important development strategies skills and technologies and how to design and manage complex multi developer software projects students will spend the last six weeks of the course putting their knowledge and skills to the test by designing and developing their own web applications learn web development if you stumbled across this repository and feel like learning some web development please follow along with us this repository contains all of the lesson material and assignments to get you going feel free to submit a github issue if you find any problems in the course and we ll do our best to fix it up course contents lesson 0 getting ready for the course lessons 00 getting ready lesson 1 course introduction and the internet lessons 01 welcome internet lesson 2 express and templating lessons 02 express templating lesson 3 mongodb lessons 03 mongo lesson 4 the client lessons 04 client jquery ajax lesson 5 burger app help lessons 05 flex burger help lesson 6 sessions users errors css and debugging lessons 06 css development style lesson 7 application integration lessons 07 apis debugging lesson 8 unit testing and task running lessons 08 tests tasks lesson 9 application frameworks lessons 09 clientside frameworks lesson 10 deployment and scaling lessons 10 deployment scaling lesson 11 security lessons 11 security lesson 12 agile project management lessons 12 agile lesson 13 architecture and diagramming lessons 13 designs diagrams mini project miniproject final project finalproject learning objectives 1 understand the modern internet and web applications 2 learn software design skills and strategies such as javascript node and database technologies that are relevant to web infrastructure 3 find connections between current coursework with future professional applications 4 learn software industry related processes with a focuses on things such as working on a team and communication of code and software projects 5 balance scope and design decisions to complete a viable web application course syllabus syllabus md | javascript education olin | front_end |
TrueChain-Stellar-Fabric | stellar truechain dashboard this is the frontend codebase of truechain smart contract management tool build setup bash install dependencies npm install serve with hot reload at localhost 8080 npm run dev build for production with minification npm run build build for production and view the bundle analyzer report npm run build report to join stellar and publish chaincode please follow the steps below 1 go to https stellar truechain pro and apply for an invitation code under sign up page fill in personal enterprise information and the blockchain dapp description after review stellar product team is going to send back invitation code via email and text message 2 visit stellar at stellar truechain pro 3 register for a new users and enter invitation code 4 log into the system 5 submit chaincode | front_end |
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swe381-lab | swe381 embedded system design lab applications welcome to the repository for the swe381 embedded system design lab applications this repository contains the source code and documentation for the various applications developed as part of the swe381 course focusing on embedded system design using arduino table of contents introduction introduction getting started getting started prerequisites prerequisites installation installation applications applications license license introduction the swe381 embedded system design lab applications repository is a collection of applications developed by students for the swe381 course which covers the fundamentals of embedded system design using arduino these applications serve as practical examples to reinforce the concepts learned throughout the course each application focuses on a specific aspect of embedded systems such as sensors actuators communication protocols and data processing by exploring the source code and documentation provided you can gain insights into how to design and implement embedded systems using arduino getting started to get started with the swe381 embedded system design lab applications follow the steps below prerequisites arduino ide make sure you have the arduino ide installed on your computer you can download it from the official arduino website https www arduino cc en software installation 1 clone the repository bash git clone https github com karabunar swe381 lab git 2 open the arduino ide 3 open the desired application sketch ino file from the cloned repository 4 connect your arduino board to your computer via usb 5 select the appropriate board and port in the arduino ide 6 upload the sketch to your arduino board applications this repository contains multiple applications developed as part of the swe381 embedded system design lab course each application is stored in a separate directory and includes the following source code the arduino sketch file ino containing the code for the application documentation a readme file providing details about the application its functionality and usage instructions schematics circuit diagrams or connection diagrams illustrating the hardware setup required for the application feel free to explore the applications available in this repository and use them as a reference or starting point for your own embedded system design projects license the swe381 embedded system design lab applications repository is licensed under the mit license license you are free to use modify and distribute the code for personal or commercial purposes however please include appropriate attribution and a link back to this repository | os |
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llm-rankers | llm rankers pointwise listwise pairwise and setwise https arxiv org pdf 2310 09497 pdf document ranking with large language models note the current code base only supports t5 style open source llms and openai apis for several methods we are in the process of implementing support for more llms installation git clone this repository then pip install the following libraries bash torch 2 0 1 transformers 4 31 0 pyserini 0 21 0 ir datasets 0 5 5 openai 0 27 10 tiktoken 0 4 0 accelerate 0 22 0 note the code base is tested with python 3 9 conda environment you may also need to install some pyserini dependencies such as faiss we refer to pyserini installation doc link https github com castorini pyserini blob master docs installation md development installation first stage runs we use llms to re rank top documents retrieved by a first stage retriever in this repo we take bm25 as the retriever we rely on pyserini https github com castorini pyserini ir toolkit to get bm25 ranking here is an example of using pyserini command lines to generate bm25 run files on trec dl 2019 bash python m pyserini search lucene threads 16 batch size 128 index msmarco v1 passage topics dl19 passage output run msmarco v1 passage bm25 default dl19 txt bm25 k1 0 9 b 0 4 to evaluate ndcg 10 scores of bm25 bash python m pyserini eval trec eval c l 2 m ndcg cut 10 dl19 passage run msmarco v1 passage bm25 default dl19 txt results ndcg cut 10 all 0 5058 you can find the command line examples for full trec dl datasets here https castorini github io pyserini 2cr msmarco v1 passage html similarly you can find command lines for obtaining bm25 results on beir datasets here https castorini github io pyserini 2cr beir html in this repository we use dl 2019 as an example that is we always re rank run msmarco v1 passage bm25 default dl19 txt with llms prompting methods for zero shot document ranking with llms python code example python from rankers setwise import setwisellmranker from rankers rankers import searchresult docs searchresult docid i text f this is passage i score none for i in range 100 query give me passage 34 ranker setwisellmranker model name or path google flan t5 large tokenizer name or path google flan t5 large device cuda num child 10 scoring generation method heapsort k 10 print ranker rerank query docs 0 command lines examples details summary pointwise summary we have two pointwise methods implemented so far yes no llms are prompted to generate whether the provided candidate document is relevant to the query candidate documents are re ranked based on the normalized likelihood of generating a yes response qlm query likelihood modelling qlm llms are prompted to produce a relevant query for each candidate document the documents are then re ranked based on the likelihood of generating the given query 1 these methods rely on access to the model output logits to compute relevance scores command line example bash cuda visible devices 0 python3 run py run model name or path google flan t5 large tokenizer name or path google flan t5 large run path run msmarco v1 passage bm25 default dl19 txt save path run pointwise yes no txt ir dataset name msmarco passage trec dl 2019 hits 100 query length 32 passage length 128 device cuda pointwise method yes no batch size 32 bash evaluation python m pyserini eval trec eval c l 2 m ndcg cut 10 dl19 passage run pointwise qlm txt results ndcg cut 10 all 0 6544 change method yes no to method qlm for qlm pointwise ranking you can also set larger batch size that you gpu can afford for faster inference we also have implemented supervised monot5 https github com castorini pygaggle pointwise re ranker simply set model name or path and tokenizer name or path to castorini monot5 3b msmarco or other monot5 models listed in here https huggingface co castorini details details summary listwise summary our implementation of listwise approach is following rankgpt https github com sunnweiwei rankgpt 2 it uses a sliding window sorting algorithm to re rank documents bash cuda visible devices 0 python3 run py run model name or path google flan t5 large tokenizer name or path google flan t5 large run path run msmarco v1 passage bm25 default dl19 txt save path run liswise generation txt ir dataset name msmarco passage trec dl 2019 hits 100 query length 32 passage length 100 scoring generation device cuda listwise window size 4 step size 2 num repeat 5 bash evaluation python m pyserini eval trec eval c l 2 m ndcg cut 10 dl19 passage run liswise generation txt results ndcg cut 10 all 0 5612 use window size step size and num repeat to configure sliding window process we also provide openai api implementation simply do bash python3 run py run model name or path gpt 3 5 turbo openai key your key run path run msmarco v1 passage bm25 default dl19 txt save path run iswise generation openai txt ir dataset name msmarco passage trec dl 2019 hits 100 query length 32 passage length 100 scoring generation listwise window size 4 step size 2 num repeat 5 the above two listwise runs are relying on llm generated tokens to do the sliding window however if we have local model for example flan t5 we can use setwise prompting proposed in our paper https arxiv org abs 2310 09497 3 to estimate the likehood of document rankings to do the sliding window bash cuda visible devices 0 python3 run py run model name or path google flan t5 large tokenizer name or path google flan t5 large run path run msmarco v1 passage bm25 default dl19 txt save path run liswise likelihood txt ir dataset name msmarco passage trec dl 2019 hits 100 query length 32 passage length 100 scoring likelihood device cuda listwise window size 4 step size 2 num repeat 5 bash evaluation python m pyserini eval trec eval c l 2 m ndcg cut 10 dl19 passage run liswise likelihood txt results ndcg cut 10 all 0 6691 details details summary pairwise summary we implement pairwise prompting method proposed in 4 bash cuda visible devices 0 python3 run py run model name or path google flan t5 large tokenizer name or path google flan t5 large run path run msmarco v1 passage bm25 default dl19 txt save path run pairwise heapsort txt ir dataset name msmarco passage trec dl 2019 hits 100 query length 32 passage length 128 scoring generation device cuda pairwise method heapsort k 10 bash evaluation python m pyserini eval trec eval c l 2 m ndcg cut 10 dl19 passage run pairwise heapsort txt results ndcg cut 10 all 0 6571 method heapsort does pairwise inferences with heap sort algorithm change to method bubblesort for bubble sort algorithm you can set method allpair for comparing all possible pairs in this case you can set batch size for batching inference but allpair is very expensive we also have supervised duot5 https github com castorini pygaggle pairwise ranking model implemented simply set model name or path and tokenizer name or path to castorini duot5 3b msmarco or other duot5 models listed in here https huggingface co castorini details details summary setwise summary our proposed setwise prompting can considerably speed up the sorting based pairwise methods check our paper here https arxiv org abs 2310 09497 for more details bash cuda visible devices 0 python3 run py run model name or path google flan t5 large tokenizer name or path google flan t5 large run path run msmarco v1 passage bm25 default dl19 txt save path run setwise heapsort txt ir dataset name msmarco passage trec dl 2019 hits 100 query length 32 passage length 128 scoring generation device cuda setwise num child 2 method heapsort k 10 bash evaluation python m pyserini eval trec eval c l 2 m ndcg cut 10 dl19 passage run setwise heapsort txt results ndcg cut 10 all 0 6697 num child 2 means comparing two child node documents one parent node document 3 documents in total to compare in the prompt increasing num child will give more efficiency gain but you may need to truncate documents more by setting a small passage length otherwise prompt may exceed input limitation you can also set scoring likelihood for faster inference we also have openai api implementation for setwise method bash python3 run py run model name or path gpt 3 5 turbo openai key your key run path run msmarco v1 passage bm25 default dl19 txt save path run setwise heapsort openai txt ir dataset name msmarco passage trec dl 2019 hits 100 query length 32 passage length 128 scoring generation setwise num child 2 method heapsort k 10 details details summary beir experiments summary for beir datasets experiments change ir dataset name to pyserini index with pyserini pre build index for example bash dataset trec covid change to trec covid robust04 webis touche2020 scifact signal1m trec news dbpedia entity nfcorpus for other experiments in the paper get bm25 first stage results python m pyserini search lucene index beir v1 0 0 dataset flat topics beir v1 0 0 dataset test output run bm25 dataset txt output format trec batch 36 threads 12 hits 1000 bm25 remove query python m pyserini eval trec eval c m ndcg cut 10 beir v1 0 0 dataset test run bm25 dataset txt results ndcg cut 10 all 0 5947 setwise with heapsort cuda visible devices 0 python3 run py run model name or path google flan t5 large tokenizer name or path google flan t5 large run path run bm25 dataset txt save path run setwise heapsort dataset txt pyserini index beir v1 0 0 dataset hits 100 query length 32 passage length 128 scoring generation device cuda setwise num child 2 method heapsort k 10 python m pyserini eval trec eval c m ndcg cut 10 beir v1 0 0 dataset test run setwise heapsort dataset txt results ndcg cut 10 all 0 7675 details note if you remove cuda visible devices 0 our code should automatically perform multi gpu inference but we may observe slight changes in the ndcg 10 scores references 1 devendra sachan mike lewis mandar joshi armen aghajanyan wen tau yih joelle pineau and luke zettlemoyer 2022 improving passage retrieval with zero shot question generation 2 weiwei sun lingyong yan xinyu ma pengjie ren dawei yin and zhaochun ren 2023 is chatgpt good at search 3 shengyao zhuang honglei zhuang bevan koopman and guido zuccon 2023 a setwise approach for effective and highly efficient zero shot ranking with large language models 4 zhen qin rolf jagerman kai hui honglei zhuang junru wu jiaming shen tianqi liu jialu liu donald metzler xuanhui wang and michael bendersky 2023 large language models are effective text rankers with pairwise ranking prompting if you used our code for your research please consider to cite our paper text article zhuang2023setwise title a setwise approach for effective and highly efficient zero shot ranking with large language models author zhuang shengyao and zhuang honglei and koopman bevan and zuccon guido journal arxiv preprint arxiv 2310 09497 year 2023 | ai |
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EtherCAT-FreeRTOS | freertos ethercat project 1 freertos ethercat etherccat 2 freertos ecat task ethercat ethercat adc task adc control task b exit c exit h lan9252 irq 500hz 3 3 1 ecat task ethercat mainloop 20ms 3 2 adc task adc read ad ecat task 3 3 control task b 4 4 1 1 void led init void led c 2 uint8 hw init void stm32 lan9252 fsmc 0 ecatappl c 3 void adc change init api c 4 uint16 maininit void ethercat ecatapp c 5 void ecat init void maininit ecatslv c 6 void coe objinit void maininit coeappl c 7 void spi1 init void stm32 ads8343 spi spi1 c 4 2ethercat 1 void mainloop void ecatapp c 2 void ecat application void appl application ecatappl c 3 void appl application void mainloop el9800appl c 4 void coe main void mainloop coeappl c 5 void ecat main void mainloop al ecatslv c 6 void coe objinit void coeapp c 7 uint16 coe addobjecttodic tobject objmem pnewobjentry coeapp c 8 uint16 coe objdictionaryinit void coeapp c 9 void coe clearobjdictionary void coeapp c 10 void handlebuscyclecalculation void sm ecatapp c 11 uint8 coe serviceind tcoembx mbxmem pcoembx coe ecatcoe c 12 uint8 checksmsettings uint8 maxchannel sm ecatslv c 13 uint16 startinputhandler void sm ecatslv c 14 uint16 startoutputhandler void ecatslv c 15 void al controlind uint8 alcontrol uint16 alstatuscode ethercat ecatslv c 16 void al controlres void ecatslv c 17 void dc checkwatchdog void sync ecatslv c 18 void ecat statechange uint8 alstatus uint16 alstatuscode ecatslv c 4 3ethercat ecatslv ethercat a1control al controlind sm sm ecatappl ethercat ethercat main esc mailbox ethercat ecatcoe coe canopen overethercat coe coe coe sdoserv sdo sdo sdo sdo sdo el9800appl coe coeappl coe coe coe 5 api 1 main freertos int main void nvic prioritygroupconfig nvic prioritygroup 4 4 delay init 168 uart init 115200 led init led hw init adc change init maininit spi1 init brunapplication true paout 4 1 xtaskcreate taskfunction t start task const char start task uint16 t start stk size void null ubasetype t start task prio taskhandle t starttask handler vtaskstartscheduler 2 adc read setchannelandmodebyte spi uint32 t read int channel adc mode mode union uint8 t b 2 uint16 t dw data uint32 t result cs 0 setchannelandmodebyte channel mode data dw spi readwrite 2byte 0x00 return data dw 3 adc void adc change init x y k i void adc change init int i for i 0 i 10 i y i i 1 1 25 i i 4 1 5 i for i 0 i 10 i k i 1 y i 1 y i i i 1 i i 4 int16 a2x float x int16 a2x float x int16 y if x 1 3 y 0x8000 else if x 3 y 0x8fff else if x i 1 y k 1 x i 0 y 0 else if x i 2 y k 2 x i 1 y 1 else if x i 3 y k 3 x i 2 y 2 else if x i 4 y k 4 x i 3 y 3 else if x i 5 y k 5 x i 4 y 4 else if x i 6 y k 6 x i 5 y 5 else if x i 7 y k 7 x i 6 y 6 else if x i 8 y k 8 x i 7 y 7 else if x i 9 y k 9 x i 8 y 8 else if x 20 y k 10 x i 0 y 9 else y 0x7fff return y 5 a int count1 0 count2 0 count3 0 count4 0 void ecatisr void pdi isr count1 if count1 500 ad switch status out ch1 frequency ch1 current float read 0 single 163 4 count1 0 count2 if count2 500 ad switch status out ch2 frequency ch2 current float read 1 single 163 6 count2 0 count3 if count3 500 ad switch status out ch3 frequency ch3 current float read 2 single 163 4 count3 0 count4 if count4 500 ad switch status out ch4 frequency ch4 current float read 3 single 165 count4 0 reset the interrupt flag ack esc int b int count1 0 count2 0 count3 0 count4 0 int second count 0 int diancifa time1 0 diancifa time2 0 diancifa time3 0 diancifa time4 0 int diancifa state1 0 diancifa state2 0 diancifa state3 0 diancifa state4 0 extern float ch1 current ch2 current ch3 current ch4 current extern tobj6010 ad switch status out extern tobj6000 ad inputs void ecatisr void pdi isr count1 if count1 1000 ad switch status out ch1 frequency float temp float read 0 single 163 4 read ad if temp 20 ch1 current temp ad inputs ch1 a2x ch1 current a2x count1 0 count2 if count2 1000 ad switch status out ch2 frequency float temp float read 1 single 163 6 if temp 20 ch2 current temp ad inputs ch2 a2x ch2 current count2 0 count3 if count3 1000 ad switch status out ch3 frequency float temp float read 2 single 163 4 if temp 20 ch3 current temp ad inputs ch3 a2x ch3 current count3 0 count4 if count4 1000 ad switch status out ch4 frequency float temp float read 1 single 165 if temp 20 ch4 current temp ad inputs ch4 a2x ch4 current count4 0 reset the interrupt flag second count if second count 1000 second count 0 diancifa status 6030 diancifa1 time diancifa status 6030 diancifa2 time diancifa status 6030 diancifa3 time diancifa status 6030 diancifa4 time if diancifa status 6030 diancifa1 time diancifa cmd diancifa1 time if diancifa state1 diancifa cmd diancifa1 diancifa state1 diancifa cmd diancifa1 relay1 diancifa cmd diancifa1 diancifa status 6030 diancifa1 time 0 if diancifa status 6030 diancifa2 time diancifa cmd diancifa2 time if diancifa state2 diancifa cmd diancifa2 diancifa state2 diancifa cmd diancifa2 relay2 diancifa cmd diancifa2 diancifa status 6030 diancifa2 time 0 if diancifa status 6030 diancifa3 time diancifa cmd diancifa3 time if diancifa state3 diancifa cmd diancifa3 diancifa state3 diancifa cmd diancifa3 relay3 diancifa cmd diancifa3 diancifa status 6030 diancifa3 time 0 if diancifa status 6030 diancifa4 time diancifa cmd diancifa4 time if diancifa state4 diancifa cmd diancifa4 diancifa state4 diancifa cmd diancifa4 relay4 diancifa cmd diancifa4 diancifa status 6030 diancifa4 time 0 diancifa status 6030 diancifa1 state fbo1 diancifa status 6030 diancifa2 state fbo2 diancifa status 6030 diancifa3 state fbo3 diancifa status 6030 diancifa4 state fbo4 ack esc int | os |
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edf | edf means event driven framework event driven programming is a common pattern in embedded systems however if you develop software directly on top of rtos using event driven mode is a bit more complicated the framework is designed in c fully adopts message mechanism and implements event driven model through subscriber publisher pattern the framework also provides a finite state machine implementation method | event-driven rtos state-machine subscriber-publisher | os |
AI_Artist | overview a project that trains a convolutional neural network over a dataset to repaint an novel image in the style of a given painting this is the implementation of neural style transfer from the paper a neural algorithm of artistic style http arxiv org abs 1508 06576 in keras 1 0 2 this is also the code for build an ai artist on youtube https youtu be 9mxw ilpvwa alt text https raw githubusercontent com titu1994 neural style transfer master images blue 20moon 20lake gif dependencies numpy http www numpy org keras http keras io installation scipy https www scipy org install html theano http deeplearning net software theano install html install h5py http docs h5py org en latest build html sklearn http scikit learn org stable install html pillow https pillow readthedocs io en latest installation html cuda gpu http docs nvidia com cuda cuda getting started guide for mac os x cudnn gpu https developer nvidia com cudnn vgg16 file https drive google com file d 0bz7kyqmugsilt0j5dmrcm0rovhc view usp sharing use pip https pypi python org pypi pip to install any missing dependencies if you have dependency version issues use virtualenv http docs python guide org en latest dev virtualenvs basic usage there are 3 images to identify when we run the script 1 your base image to artify 2 your reference image the art to learn from 3 your generated image run the following comand to generate an image in your chosen style python network py base image path path to your image style reference image path path to your painting result prefix path to generated file you create other optional commands are image size size of your output image content weight how much to weigh the content style weight how much to weigh the style style scale how much to scale the style total variation weight uniformity of the generated image num iter nmber of iterations rescale image to rescale or not to rescale rescale method rescale algorithm maintain aspect ratio to maintain aspect ratio or not content layer which layer to focus on for content generation i d run this on aws but you can run this locally too if you have a gpu on a 980m gpu the time required for each epoch depends on mainly image size gram matrix size for a 400x400 gram matrix each epoch takes approximately 11 13 seconds for a 512x512 gram matrix each epoch takes approximately 18 22 seconds for a 600x600 gram matrix each epoch takes approximately 28 30 seconds credits credit for the vast majority of code here goes to somsubra majumdar https github com titu1994 i ve merely created a wrapper around all of the important functions to get people started | ai |
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98.css | 98 css npm https 98badges now sh api version http npm im 98 css gzip size https 98badges now sh api size https unpkg com 98 css a design system for building faithful recreations of old uis img alt a screenshot of a window with the title my first vb4 program and two buttons ok and cancel styled like a windows 98 dialog src https github com jdan 98 css blob main docs window png raw true height 133 img alt a magnified view showing pixel perfect borders on a scrollbar and button element src https github com jdan 98 css blob main docs zoom png raw true raw true height 133 98 css is a css file that takes semantic html and makes it look pretty it does not ship with any javascript so it is compatible with your frontend framework of choice be sure to check out xp css https botoxparty github io xp css and 7 css https khang nd github io 7 css as well installation usage the easiest way to use 98 css is to import it from unpkg https unpkg com html doctype html html head title 98 css example title meta charset utf 8 link rel stylesheet href https unpkg com 98 css head body div class window style margin 32px width 250px div class title bar div class title bar text my first vb4 program div div div class window body p hello world p div div body html alternatively you can grab 98 css for the releases page https github com jdan 98 css releases or npm https www npmjs com package 98 css npm install 98 css here is an example of 98 css being used with react https codesandbox io s objective chandrasekhar t5t6h file src index js and an example with vanilla javascript https codesandbox io s late sound miqho file index html refer to the documentation page https jdan github io 98 css for specific instructions on this library s components developing first run npm install style css https github com jdan 98 css blob main style css is where everything happens you can use npm start to start a development environment that will watch for file changes and rebuild 98 css reloading your browser in the process you can run a build manually with npm run build this will write to the dist directory issues contributing etc refer to the github issues page https github com jdan 98 css issues to see bugs in my css or report new ones i d really like to see your pull requests especially those new to open source and will happily provide code review 98 css is a fun silly project and i d like to make it a fun place to build your open source muscle thank you for checking my little project out i hope it brought you some joy today consider starring following along on github https github com jdan 98 css stargazers and maybe subscribing to more fun things on my twitter https twitter com jdan publishing building the docs site npm run deploy docs publishing to npm npm run release license mit https github com jdan 98 css blob main license | css design-system windows98 guy-fieri | os |
Credit-Card-Management-System | credit card management system the credit card management system is a java based program that can display a list of credit card transactions and modify a customer s personal information from a mysql database via a jdbc driver this respository not only includes the source code for the credit card management system it also includes other files that are useful for creating a data pipeline to the hadoop file system hortonworks sandbox table of contents 1 credit card management system bin src mysql connector java 5 1 45 cdw sapp updated sql zip change authentication sql 2 data extraction and transportation using sqoop sqoop txt 3 data loading with hive hive txt 4 automating the process with oozie job properties oozie workflow sqoop jobs java json jar 5 optimizing the process optimized job properties oozie workflow optimized sqoop jobs optimized java json jar 6 visualization of dataset data visualization txt top 20 zips by total transactions png total transaction value for each transaction by quarter 2018 png 7 requirements casestudy flow 1 2 pdf mapping document xlsx credit card management system srd pdf deliverable xlsx functional requirements pdf for instructions on how to use the contents of each folder except credit card management system simply click on the link of these folders and read the readme md file to use the credit card management system 1 download the credit card management system folder 2 unzip the cdw sapp updated sql zip folder and open it using mysql workbench under the root user run the file so that you will have the cdw sapp database 3 open the credit card management system via eclipse then run the code note make sure eclipse references the mysql driver file as it is connect to the mysql database moreover when running the java code the program will ask for a user name and password when accessing the transaction module or the customer module in that case the user name is root and the password is whatever password you used for root user if it doesn t then you can change the type of password for root user to mysql native password see change authentication sql in the credit card management system folder for other projects feel free to take a look at my personal github https github com xnell90 | server |
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penguin | penguin build status https secure travis ci org bq penguin png branch master http travis ci org bq penguin penguin is a lightweight and extensible front end framework built with sass to kickstart any web project jump to section quickstart quickstart what s included what s included usage usage components components quickstart back to top jump to section penguin is very easy to install and configure you have following options to get penguin download the last release https github com bq penguin releases install with bower http bower io bower install penguin save repository clone this repository inside your project assets git github com bq penguin git what s included back to top jump to section penguin bower components jquery lib css penguin css penguin min css js components alert js dropdown js modal js spinner js tab js penguin js penguin min js node modules src css base components main scss js test usage back to top jump to section after downloading penguin follow these simple steps to get started customize with sass if you choose this way then you should build using a task runner penguin uses grunt for compile our code first of all you should install node and grunt if you want to build the project locally you have to run the grunt http gruntjs com task grunt build this task will concat and minify the sources and create the readme md before all this you do not forget to run npm install and bower install compiled and minified css and javascript you should add to project stylesheet and link it in the header of your html file like so this is how you would link your penguin stylesheet link rel stylesheet href lib css penguin min css head if you are using any penguin javascript components this needs to be loaded on the page you do not forget add jquery to project penguin components require jquery to be included we recommend at the end before your closing body tags like so script src js jquery min js script script src penguin js penguin min js script script body modal script body also you can load only the components you need for your project script src js jquery min js script alerts script src penguin js components alert js script dropdowns script src penguin js components dropdown js script modal script src penguin js components modal js script spinner script src penguin js components spinner js script body working with requirejs http requirejs org use strict require config paths penguin path to penguin penguin lib penguin js you can also add a single javasript component use strict require config paths penguin modal path to penguin penguin src js modal js how to include penguin in your project quick example of how to import penguin to your project overriding it and adding your own theme styles the example file would be your main scss file main scss note path to penguin directory where penguin is for example app bower components penguin path to theme directory where your project theme is for example app css main scss base variables import path to penguin base variables import path to theme base variables components variables import path to penguin components variables import path to theme components variables base scaffolding optional import path to penguin base scaffolding import path to theme base scaffolding base mixins import path to penguin base mixins base reset import path to penguin base reset base print import path to penguin base print base utils import path to penguin base utils base responsive utilities import path to penguin base responsive base grid system import path to penguin base grid components animations import path to penguin base animations components alert import path to penguin components alert import path to theme components alert components text import path to penguin components text import path to theme components text components dropdown import path to penguin components dropdown import path to theme components dropdown components button import path to penguin components button import path to theme components button components navigation import path to penguin components navigation import path to theme components navigation components tab import path to penguin components tab import path to theme components tab components breadcumb import path to penguin components breadcrumb import path to theme components breadcrumb components table import path to penguin components table import path to theme components table components form import path to penguin components form import path to theme components form ui components icon import path to penguin components icon import path to theme components icon ui components modal import path to penguin components modal import path to theme components modal components banner import path to penguin components banner import path to theme components banner components spinner import path to penguin components spinner import path to theme components spinner components paginator import path to penguin components paginator import path to theme components paginator components panel import path to penguin components panel import path to theme components panel components back to top jump to section see all components on penguin themes http penguin docs bqws io small this readme has been automatically generated by readme generator https github com aponxi grunt readme generator on fri may 22 2015 17 32 13 gmt 0200 cest small | front_end |
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api | github read me header https user images githubusercontent com 8016073 203381867 f7b56861 04ca 4ae4 a5e6 53e97804817a png moonstream website https moonstream to join our discord https discord gg pye65funsz what is moonstream moonstream creates economic infrastructure for web3 projects with a focus on blockchain games this repository contains moonstream s complete data analysis stack the emphasis of it is on collecting actionable data related to the blockchain the repository contains 1 database management tools 2 blockchain node management tools 3 blockchain data crawlers 4 access controlled api which exposes collected data important resources 1 documentation https docs moonstream to 2 status page https moonstream to status 3 on chain mechanics https github com bugout dev engine 4 how to create a dashboard to analyze a smart contract https voracious gerbil 120 notion site creating dashboard for a smart contract 288b1bfa64984b109b79895f69129fce who uses moonstream people from different backgrounds who are interested in data crypto and code moonstream tools are often used by game designers and economists data scientists smart contract developers backend engineers and teams managing loyalty programs for blockchain projects some projects currently using moonstream 1 laguna games https laguna games makers of crypto unicorns https cryptounicorns fun 2 game7 https game7 io 3 champions ascension https www champions io please read the game master s guide to moonstream solutions https docs google com document d 1mjff8sgrrazvtcvvxb2qnsucbbmrh6dteysmfhkdegc view if you want to know how moonstream tools are applied in web3 games moonworm tool https github com bugout dev moonworm is used to build datasets of on chain data related to market activity the dataset with on chain activity from the ethereum nft market april 1 to september 25 2021 is available on kaggle https www kaggle com datasets simiotic ethereum nfts the full report on it is published on github https github com bugout dev moonstream blob main datasets nfts papers ethereum nfts pdf free software proprietary technologies are not inclusive technologies and we believe in inclusion all of our technology is open source this repository contains all the code that powers https moonstream to the code is licensed with the apache license version 2 0 https www apache org licenses license 2 0 you are and will always be free to host your own instance of moonstream architecture this monorepo contains the following components 1 frontend frontend a web frontend for moonstream allows users to create dashboards and monitor the activity of accounts and smart contracts on multiple blockchains built in react https reactjs org 2 backend backend the moonstream api allows users to programmatically consume data about transactions and events taking place on blockchains crawled by moonstream built in python https www python org using fast api https fastapi tiangolo com 3 crawlers crawlers this part of the code base contains workers which extract data from blockchains transaction pools and other sources we have many crawlers and each crawler can utilize a different tech stack 4 db db moonstream stores blockchain data in postgres https www postgresql org this directory contains the code we use to manage the schema in our postgres database for sources that send higher volumes of data we use a separate postgres database and interface with it using bugout https bugout dev installation and setup run server with docker compose if you want to deploy moonstream in isolation against live services then docker compose is your choice run script backend configs docker generate env bash which prepare for you backend configs docker moonstreamapi env with environment variables run script db configs docker generate env bash which prepare for you db configs alembic moonstreamdb ini with postgresql uri bash backend configs docker generate env bash db configs docker generate env bash run local setup bash docker compose up build contributing we are working on contributing guidelines in the meantime please reach out to zomglings on the moonstream discord https discord gg pye65funsz | protocol crypto data trading web3 solana polygon matic mempool nft dao dex analytics smartcontract economy defi blockchain hack | blockchain |
carbon-icons | carbon icons build status https travis ci org carbon design system carbon icons svg branch master https travis ci org carbon design system carbon icons icons for the carbon design system getting started run the following command using npm https www npmjs com bash npm install s carbon icons if you prefer yarn https yarnpkg com en use the following command instead bash yarn add carbon icons books docs usage https github com carbon design system carbon icons blob master docs usage md contributing https github com carbon design system carbon icons blob master github contributing md migration guides https github com carbon design system carbon icons tree master docs migration guides v3 https github com carbon design system carbon icons tree master docs migration guides migration v3 md v4 https github com carbon design system carbon icons tree master docs migration guides migration v4 md v5 https github com carbon design system carbon icons tree master docs migration guides migration v5 md v6 https github com carbon design system carbon icons tree master docs migration guides migration v6 md v7 https github com carbon design system carbon icons tree master docs migration guides migration v7 md links cdn unpkg https unpkg com carbon icons dist carbon design system iconography http carbondesignsystem com guidelines iconography library | carbon-design-system icons svg | os |
Flix | project 2 flix flix is a movies app using the the movie database api http docs themoviedb apiary io time spent 8 hours spent in total user stories the following required functionality is complete x user sees an app icon on the home screen and a styled launch screen x user can view a list of movies currently playing in theaters from the movie database x poster images are loaded using the uiimageview category in the afnetworking library x user sees a loading state while waiting for the movies api x user can pull to refresh the movie list x user sees an error message when there s a networking error x user can tap a tab bar button to view a grid layout of movie posters using a collectionview the following optional features are implemented x user can tap a poster in the collection view to see a detail screen of that movie x user can search for a movie x user can view the large movie poster by tapping on a cell they can see the movie trailer x customize the selection effect of the cell set to no selection effect x customize the navigation bar dark mode and changed icons x customize the ui dark mode x run your app on a real device all images fade in as they are loading for the large poster load the low resolution image first and then switch to the high resolution image when complete user can view the app on various device sizes and orientations please list two areas of the assignment you d like to discuss further with your peers during the next class examples include better ways to implement something how to extend your app in certain ways etc 1 i would have liked to implement a settings option that can change the app from dark mode to light mode and change the language of the app 2 i want to change how i implemented the collectionview to include some sort of tag on each cell so that i can search in the grid view as well video walkthrough img src flixreal gif title video walkthrough width alt video walkthrough width 200 height 500 gif created with kap https getkap co notes i can use this project as a reference for setting up a table view put tableview in storyboard create outlet put necessary roles in next to viewcontroller title implement 2 necessary methods reloaddata on didload creating a network request using cocoapods in repository pod init open podfile and add what you need pod install use the xcworkspace file from now on to open project good imageview settings content mode aspect fill clip to bounds should be checked pull to refresh featuer using uirefreshcontrol create segue used show segue to create movie detail screen navigation segue method to pass information between two view controllers remember that what you want to pass between them has to go in the header file create a basic loading screen uiactivityindicator create an alert using uialertcontroller create a collectionview very similar process to tableview and configuring layout creating a webview credits list an 3rd party libraries icons graphics or other assets you used in your app afnetworking https github com afnetworking afnetworking networking task library license copyright yyyy name of copyright owner licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license | os |
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honeybits | honeybits a simple poc tool designed to enhance the effectiveness of your traps by spreading breadcrumbs honeytokens across your production servers and workstations to lure the attacker toward your honeypots author adel 0x4d31 karimi the windows version of this project honeybits win https github com 0x4d31 honeybits win background although honeypots are used by security researchers to study the attackers tools techniques and motives for many years they still have not been widely accepted and deployed in production environments one reason is that the traditional implementation of honeypots is static and success is based on an attacker discovering it which usually requires network scanning taking a look at the mitre att ck matrix https attack mitre org wiki main page you will see that network service scanning is only one of the many different post compromise activities the more you plant false or misleading information in response to the post compromise techniques specially the techniques under credential access discovery and lateral movement tactics in att ck matrix the greater the chance of catching the attackers honeybits helps you automate the creation of breadcrumbs honeytokens on your production servers and workstations these honeytokens or breadcrumbs include fake bash history commands such as ssh ftp rsync scp mysql wget awscli fake aws credentials and config files you required to create fake aws iam users with no permissions and generate access keys for them configuration backup and connection files such as rdp and vpn fake entries in hosts arp table etc fake browser history bookmarks and saved passwords injected fake credentials into lsass fake registry keys honeybits https github com 0x4d31 honeybits blob master docs honeybits png features creating honeyfiles and monitoring the access to these traps using go audit or auditd template based content generator for honeyfiles insert honeybits into aws config and credentials file insert honeybits into etc hosts reading config from a remote key value store such as consul or etcd insert different honeybits into bash history including the following sample commands ssh sshpass p 123456 ssh p 2222 root 192 168 1 66 ftp ftp ftp backup b123 192 168 1 66 2121 rsync rsync avz e ssh p 2222 root 192 168 1 66 var db backup tar gz tmp backup tar gz scp scp p 2222 root 192 168 1 66 var db backup tar gz tmp backup tar gz mysql mysql h 192 168 1 66 p 3306 u dbadmin p12345 e show databases wget wget http 192 168 1 66 8080 backup zip any custom commands nano tmp backup credentials txt aws export aws access key id akiaiosfodnn7example export aws secret access key wjalrxutnfemi k7mdeng bpxrficyexamplekey aws ec2 describe instances profile devops region us east 2 requirements go lang 1 7 https golang org dl viper go get github com spf13 viper crypt go get github com xordataexchange crypt config go audit https github com slackhq go audit or auditd if you want to monitor the honeyfiles usage go build sudo honeybits failed reading remote config reading the local config file local config file loaded failed honeyfile already exists at this path tmp secret txt done go audit rule for home test secret txt is added done honeyfile is created home test secret txt done go audit rule for opt secret txt is added done sshpass honeybit is inserted done wget honeybit is inserted done ftp honeybit is inserted done rsync honeybit is inserted done scp honeybit is inserted done mysql honeybit is inserted failed aws honeybit already exists done hostsconf honeybit is inserted done awsconf honeybit is inserted done awscred honeybit is inserted done custom honeybit is inserted todo rewrite the whole code current code is crap just a poc improve the content generator more traps including beacon documents keepass file with entries kdbx database files backups sqlite mysql fake security scan results such as nmap output binary files with hardcoded ip credentials more network traps fake pcap network traffic containing credentials and etc fake arp table entries monitoring network traps using go audit complete the windows version honeybits win documentation | golang go honeybits honeypot honeytrap deception honeytoken security breadcrumbs trap | os |
DbScour | dbscour a net solution for sql database discovery and searches intended for assisting with reverse engineering efforts | server |
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CPE301_FinalProject | cpe 301 embedded systems design final project final project for cpe 301 fall 2022 author bradley sullivan bradleysullivan nevada unr edu project overview this project aims to implement the base functionality of the systems commonly found in evaporation cooler units these units rely on a few systems and inputs to function correctly this project implements the following systems temperature humidity and water level monitoring main fan control output vent adjustments user control start stop reset vent control status led output s serial data communication timestamped event reporting hardware description this project was built upon the arduino atmega 2560 microcontroller devices and sensors used with datasheets atmega 2560 https pdf1 alldatasheet com datasheet pdf view 107092 atmel atmega2560 html 16 pin lcd display https components101 com sites default files component datasheet 16x2 20lcd 20datasheet pdf ds3231 rtc module https pdf1 alldatasheet com datasheet pdf view 254832 maxim ds3231 html 28byj 48 5v dc stepper motor w control board https components101 com sites default files component datasheet 28byj48 step motor datasheet pdf 5v dc motor w fan blade dht11 sensor https components101 com sites default files component datasheet dht11 temperature sensor pdf resistive water level sensor 3x axial push buttons 4x leds green yellow red and blue 10k potentiometer 1x 220 ohm resistor 4x 330 ohm resistors 3x 1k ohm resistors 2n2222 npn transistor https components101 com sites default files component datasheet 2n2222 20npn transistor 20datasheet pdf 1n4007 diode https components101 com sites default files component datasheet 1n4001 pdf 5v powersupply 3x breadboards large medium and small sized tons of jumper wires schematic basic schematic layout for the assembled system image https user images githubusercontent com 77858921 206936878 f80d21e6 93ac 4dc4 8dc5 648256d079a4 png operation demonstration a link to a video demonstration of the assembled project can be found below https youtu be vfcblb3sc3c operation description this system operates within 1 of 4 states the system can operate in the disabled state in this state many of the main functions are disabled indicated by a yellow status led and the disabled message displayed to the lcd temperature humidity and water level monitoring is disabled the fan motor is turned off system will exit this state when the start stop button is pressed enters running idle state in this state most functions are enabled indicated by a green status led and the idle message displayed to the lcd temperature humidity and water level monitoring is enabled and output to the lcd the fan motor is turned off system can exit this state in several ways exits upon press of the start stop button enters disabled exits upon temperature falling outside of the threshold enters running exits upon water level falling outside of the threshold enters error error state in this state most functions are disabled indicated by a red status led and the error message displayed to the lcd temperature humidity and water level monitoring is disabled the fan motor is turned off an error message is displayed to the lcd system can exit this tate in several ways exits upon press of the start stop button enters disabled exits upon press of the reset button if water level threshold is satisfied enters idle running state in this state all functions are enabled indicated by a blue status led and the running message displayed to the lcd temperature humidity and water level monitoring is enabled and output to the lcd the fan motor is turned on system can exit this tate in several ways exits upon press of the start stop button enters disabled exits when temperature threshold is satisfied enters idle exits when water level threshold is violated enters error state transitions are reported to the serial monitor with time and date stamps in all states control of the vent positioning is enabled system images below are a few images of the system in the various states of operation disabled 20221211 142903 https user images githubusercontent com 77858921 206932733 c10a1581 e187 4e18 bf46 669b94b86a0b jpg idle 20221211 143042 https user images githubusercontent com 77858921 206932739 ed94518c 3d0b 4c1c a671 015a03b79f5f jpg error 20221211 142923 https user images githubusercontent com 77858921 206932744 ccde95c3 66c8 4dfe bba6 ec2353cc1c42 jpg running 20221211 142854 https user images githubusercontent com 77858921 206932737 e274a78b 19de 4d82 bde8 9b9964b7bac2 jpg below is a screenshot of the serial output which records system state changes image https user images githubusercontent com 77858921 206932961 ebe597a3 1ccb 40cc 962f 3f4ce0e365f8 png | os |
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cordova-plugin-braintree | warning no longer in development i ended up using the javascript version of the braintree sdk in my project and therefore have never gotten back to finishing this plugin please check out the fork by taracque https github com taracque which is located at https github com taracque cordova plugin braintree no maintenance intended http unmaintained tech badge svg http unmaintained tech braintree cordova plugin this is a cordova http cordova apache org plugin for the braintree https www braintreepayments com mobile payment processing sdk this version of the plugin uses versions 4 1 3 ios and 2 1 2 android of the braintree mobile sdk documentation for the braintree sdk can be found here https developers braintreepayments com start overview this plugin is still in development install to add the plugin to your cordova project simply add the plugin from the npm registry cordova plugin add cordova plugin braintree alternatively you can install the latest version of the plugin directly from git cordova plugin add https github com justin credible cordova plugin braintree note this plugin uses a build hook that uses execsync which requires node 0 12 or newer usage the plugin is available via a global variable named braintreeplugin it exposes the following properties and functions all functions accept optional success and failure callbacks as their last two arguments where the failure callback will receive an error string as an argument unless otherwise noted a typescript definition file for the javascript interface is available in the typings directory as well as on definitelytyped https github com borisyankov definitelytyped via the tsd tool initialize braintree client used to initialize the braintree client the client must be initialized before other methods can be used method signature initialize token successcallback failurecallback parameters token string the unique client token or static tokenization key to use example usage var token your token braintreeplugin initialize token function console log init ok function error console error error show drop in payment ui used to show braintree s drop in ui for accepting payments method signature presentdropinpaymentui options successcallback failurecallback parameters options object an optional argument used to configure the payment ui see type definition for parameters example usage var options canceltext cancel title purchase ctatext select payment method amount 49 99 primarydescription your item secondarydescription free shipping braintreeplugin presentdropinpaymentui options function result if result usercancelled console debug user cancelled payment dialog else console info user completed payment dialog console info payment nonce result nonce console debug payment result result | cordova-plugin payment-processing archived | front_end |
spring-cloud-build | spring cloud build practical engineering base spring cloud | spring-cloud spring-boot redis | cloud |
mjvmk | mjvmk the micro java virtual machine kernel is a portable preemptive real time kernel with an integrated cldc java virtual machine designed for embedded systems view project page https seancfoley github io mjvmk provides a full fledged java development environment for your real time embedded system capable of running any combination of native and java tasks in tandem preemptive real time micro operating system preemptive priority based scheduling and weighted cpu time sharing synchronization and wait lists with notification and timeouts enabled by monitors real time with low interrupt latency and response dual purpose heap for explicit memory allocation and release as well as exact mark and sweep garbage collection java me cldc java virtual machine brings the java language to your wireless or embedded platform full support for the cldc virtual machine and class librairies floating point supported and configurable to the processor s floating point capabilities advanced threading and process model for a complete separation of java applications into distinct memory spaces additional features very small size built in romizing support fully self contained no additional libraries required a second java thread scheduler enables jvm to be run on an existing o s platforms written in portable code for all platforms and targeted for embedded code that would need to be ported for each platform such as interrupts are separated developed in visual studio on windows you can easily import it into your visual studio environment | os |
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sight | p align center br img src https github com rish 16 sight blob master assets logo png raw true width 200 br p p align center a href https pypi org project sightseer img alt pypi src https img shields io pypi v sightseer color 231dd1a1 a a href https pepy tech project sightseer img alr pypi downloads src https pepy tech badge sightseer a a href https github com rish 16 sight blob master license img alt pypi license src https img shields io pypi l sightseer color 23feca57 a p h3 align center p state of the art computer vision and object detection for tensorflow p h3 h5 align center p made by rishabh anand a href https rish 16 github io https rish 16 github io a p h5 sightseer provides state of the art general purpose architectures yolov3 maskrcnn fast faster rcnn ssd for computer vision and object detection tasks with 30 pretrained models written in tensorflow 1 15 i d like to fully credit huynh ngoc anh https github com experiencor for their yolov3 model architecture code i ve repackaged that chunk as a callable python api wrapper under the model zoo this project would not be possible without their contribution installation sightseer is written in python 3 5 and tensorflow 1 15 ideally sightseer should be installed in a virtual environments https docs python org 3 library venv html if you re unfamiliar with python virtual environments check out this tutorial https packaging python org guides installing using pip and virtual environments on getting started via pypi to use sightseer you must first have tensorflow installed to do so follow the instructions on the tensorflow installation page https www tensorflow org install pip lang python3 when your virtual environment is set up with tensorflow you can install sightseer using pip bash pip install sightseer model clients as of now 1 yolov3client darknet by joseph redmon by popular demand tiny yolo will be out in the v1 2 0 release for more information on model release check out the roadmap https github com rish 16 sight blob master roadmap md components of sightseer the package comes with 4 major components that help with different parts of the object detection process all the way from preparing your raw data to getting predictions and displaying them component description sightseer obtains image data or video footage proc provides image frame wise annotation and inter format conversion tools zoo stores the wrappers over all state of the art models and configs serve provides deployment and model serving protocols and services if not using custom datasets sightseer and zoo are the submodules majorly used for generic predictions from pre trained models when there is custom data involved you can use proc to annotate your datasets and even convert them between xml json csv tfrecord formats serve is an experimental productionising submodule that helps deploy your models on cloud services like aws and gcp for more details on future tools and services check out the roadmap https github com rish 16 sight blob master roadmap md features footage or raw images can be rendered using sightseer before being ingested into models or further preprocessed strong 1a loading images strong python from sightseer import sightseer ss sightseer image ss load image path to image return numpy array representation of image strong 1b loading videos strong python from sightseer import sightseer ss sightseer frames ss load vidsource path to video returns nested array of frames support for video webcam footage and screen recording will be out in the coming v1 2 0 release strong 2 using models from sightseer zoo strong once installed any model offered by sightseer can be accessed in less than 10 lines of code for instance the code to use the yolov3 darknet model is as follows python from sightseer import sightseer from sightseer zoo import yolov3client yolo yolov3client yolo load model downloads weights loading image from local system ss sightseer image ss load image assets road jpg getting labels confidence scores and bounding box data preds pred img yolo predict image return img true ss render image pred img to run the model on frames from a video you can use the framewise predict method python from sightseer import sightseer from sightseer zoo import yolov3client yolo yolov3client yolo load model downloads weights loading video from local system ss sightseer frames ss load vidsource assets video mp4 for best results run on a gpu getting labels confidence scores and bounding box data preds pred frames yolo framewise predict frames ss render footage pred frames plays the video and saves the footage the module can even be repurposed into a command line interface cli app using the argparse https docs python org 3 library argparse html library contributing suggestions improvements and enhancements are always welcome if you have any issues please do raise one in the issues section if you have an improvement do file an issue to discuss the suggestion before creating a pr all ideas no matter how outrageous welcome before committing please check the roadmap https github com rish 16 sight blob master roadmap md to see if proposed features are already in development or not note please commit all changes to the development experimentation branch instead of master licence apache licencse 2 0 https github com rish 16 sight blob master license | tensorflow computer-vision state-of-the-art object-detection | ai |
public-api-lists | public api lists stars https img shields io github stars public api lists public api lists style flat square https github com public api lists public api lists stargazers fork https img shields io github forks public api lists public api lists style flat square https github com public api lists public api lists fork issues https img shields io github issues public api lists public api lists style flat square https github com public api lists public api lists issues new prs welcome https img shields io badge prs welcome brightgreen svg style flat square https github com public api lists public api lists pulls a collective list of free apis for use in software and web development for information on contributing to this project please see the contributing guide github contributing md index public api lists public api lists index index animals animals anime anime anti malware anti malware art design art design books books business business calendar calendar cloud storage file sharing cloud storage file sharing continuous integration continuous integration cryptocurrency cryptocurrency currency exchange currency exchange data validation data validation development development dictionaries dictionaries disasters disasters documents productivity documents productivity education education environment environment events events finance finance food drink food drink fraud prevention fraud prevention games comics games comics geocoding geocoding government government health health jobs jobs machine learning machine learning music music news news open data open data open source projects open source projects patent patent personality personality photography photography science math science math security security shopping shopping social social sports fitness sports fitness test data test data text analysis text analysis tracking tracking transportation transportation url shorteners url shorteners vehicle vehicle video video weather weather animals api description auth https cors cat facts https alexwohlbruck github io cat facts daily cat facts no yes no cats https docs thecatapi com pictures of cats from tumblr apikey yes unknown cataas https cataas com cat as a service cats pictures and gifs no yes unknown dogs https dog ceo dog api based on the stanford dogs dataset no yes yes foxes https foxes cool random fox images with different tags no yes yes httpcat https http cat cat for every http status no yes unknown http dogs https http dog dogs for every http status code no yes unknown iucn http apiv3 iucnredlist org api v3 docs iucn red list of threatened species apikey no unknown movebank https github com movebank movebank api doc movement and migration data of animals no yes unknown petfinder https www petfinder com developers v2 docs adoption oauth yes yes randombigcat https randombig cat roar json random pictures of big cats no yes yes randomcat https aws random cat meow random pictures of cats no yes yes randomdog https random dog woof json random pictures of dogs no yes yes randomfox https randomfox ca floof random pictures of foxes no yes no rescuegroups https userguide rescuegroups org display apidg api developers guide home adoption no yes unknown shibe online http shibe online random pictures of shibu inu cats or birds no yes yes back to index index anime api description auth https cors anilist https github com anilist apiv2 graphql docs anime discovery tracking oauth yes unknown animechan https animechan vercel app docs random anime quote generation no yes unknown animenewsnetwork https www animenewsnetwork com encyclopedia api php anime industry news no yes yes jikan https jikan moe unofficial myanimelist api no yes yes kitsu http docs kitsu apiary io anime discovery platform oauth yes unknown what anime https soruly github io trace moe api scan anime image to get specific detail apikey yes unknown mangadex https api mangadex org docs ad free manga reader offering high quality images no yes unknown back to index index anti malware api description auth https cors abuseipdb https docs abuseipdb com ip domain url reputation apikey yes unknown google safe browsing https developers google com safe browsing google link domain flagging apikey yes unknown metacert https metacert com metacert link flagging apikey yes unknown virustotal https www virustotal com en documentation public api virustotal file url analysis apikey yes unknown web of trust wot https www mywot com en api website reputation apikey yes unknown back to index index art design api description auth https cors behance https www behance net dev design apikey yes unknown cooper hewitt https collection cooperhewitt org api smithsonian design museum apikey yes unknown dribbble http developer dribbble com v2 design oauth no unknown europeana https pro europeana eu resources apis search european museum and galleries content apikey yes unknown harvard art museums https github com harvardartmuseums api docs art apikey no unknown iconfinder https developer iconfinder com icons apikey yes unknown icons8 http docs icons8 apiary io reference 0 meta icons oauth yes unknown noun project http api thenounproject com index html icons oauth no unknown rijksmuseum https www rijksmuseum nl en api art apikey yes unknown metropolitan museum of art https metmuseum github io art no yes unknown back to index index books api description auth https cors bhagavad gita https bhagavadgita io api bhagavad gita text oauth yes yes british national bibliography http bnb data bl uk books no no unknown goodreads https www goodreads com api books apikey yes unknown google books https developers google com books books oauth yes unknown open library https openlibrary org developers api books book covers and related data no yes unknown penguin publishing http www penguinrandomhouse biz webservices rest books book covers and related data no yes unknown rig veda https aninditabasu github io indica gods and poets their categories and the verse meters with the mandal and sukta number no yes unknown vedic society https aninditabasu github io indica html vs html descriptions of all nouns names places animals things from vedic literature no yes unknown back to index index business api description auth https cors charity search http charityapi orghunter com non profit charity data apikey no unknown clearbit logo https clearbit com docs logo api search for company logos and embed them in your projects apikey yes unknown domain labstack https labstack com domain search domain for availability lookup dns whois record of a domain apikey yes yes domainsdb info https domainsdb info registered domain names search no yes unknown freelancer https developers freelancer com hire freelancers to get work done oauth yes unknown gmail https developers google com gmail api flexible restful access to the user s inbox oauth yes unknown google analytics https developers google com analytics collect configure and analyze your data to reach the right audience oauth yes unknown mailboxvalidator https www mailboxvalidator com api single validation validate email address to improve deliverability apikey yes unknown mailgun https www mailgun com email service apikey yes unknown mailjet https www mailjet com email service apikey yes unknown markerapi http www markerapi com trademark search no no unknown repetiti https developers repetiti com repetiti 3d printer management service access and control 3d printers easily apikey yes yes ticksel https ticksel com friendly website analytics made for humans no yes unknown trello https developers trello com boards lists and cards to help you organize and prioritize your projects oauth yes unknown tomba email finder https tomba io email finder for b2b sales and email marketing apikey yes yes back to index index calendar api description auth https cors abstract s holiday api https www abstractapi com holidays api national regional and religious holidays for 120 countries 100 years yes yes yes byabbe https byabbe se on this day default get month day events json seach histories from wikipedia for a particular day no yes unknown calendar index https www calendarindex com worldwide holidays and working days apikey yes yes church calendar http calapi inadiutorium cz catholic liturgical calendar no no unknown czech namedays calendar http svatky adresa info lookup for a name and returns nameday date no no unknown google calendar https developers google com google apps calendar display create and modify google calendar events oauth yes unknown hebrew calendar https www hebcal com home developer apis convert between gregorian and hebrew fetch shabbat and holiday times etc no no unknown holiday api https holidayapi pl enterprise grade holiday and observance api for developers by developers apikey yes yes holidays https holidayapi com historical data regarding holidays apikey yes unknown non working days https isdayoff ru simple api for checking working non working or short days for russia ukraine belarus and kazakhstan no yes yes nager date https date nager at public holidays for more than 90 countries no yes no namedays calendar https api abalin net provides namedays for multiple countries no yes yes non working days https github com gadael icsdb database of ics files for non working days no yes unknown russian calendar https github com egno work calendar check if a date is a russian holiday or not no yes no timezones ical library https tz add to calendar technology com database of official time zones and corresponding ical vtimezone blocks no yes unknown back to index index cloud storage file sharing api description auth https cors anonfiles https anonfiles com docs api file sharing and storage no yes unknown box https developer box com file sharing and storage oauth yes unknown dropbox https www dropbox com developers file sharing and storage oauth yes unknown google drive https developers google com drive file sharing and storage oauth yes unknown onedrive https dev onedrive com file sharing and storage oauth yes unknown pastebin https pastebin com api plain text storage apikey yes unknown wetransfer https developers wetransfer com file sharing apikey yes yes back to index index continuous integration api description auth https cors circleci https circleci com docs api v1 reference automate the software development process using continuous integration and continuous delivery apikey yes unknown codeship https apidocs codeship com codeship is a continuous integration platform in the cloud apikey yes unknown travis ci https docs travis ci com api sync your github projects with travis ci to test your code in minutes apikey yes unknown back to index index cryptocurrency api description auth https cors binance https github com binance exchange binance official api docs exchange for trading cryptocurrencies based in china apikey yes unknown bitcoinaverage https apiv2 bitcoinaverage com digital asset price data for the blockchain industry apikey yes unknown bitcoincharts https bitcoincharts com about exchanges financial and technical data related to the bitcoin network no yes unknown bitmex https www bitmex com app apioverview real time cryptocurrency derivatives trading platform based in hong kong apikey yes unknown bithumb https apidocs bithumb com cryptocurrency trading platform apikey yes unknown block https www block io docs basic bitcoin payment wallet transaction data apikey yes unknown blockchain https www blockchain info api bitcoin payment wallet transaction data no yes unknown blockfacts https blockfacts io real time crypto data from multiple exchanges via a single unified api and much more apikey yes unknown coinapi https docs coinapi io all currency exchanges integrate under a single api apikey yes no coinbase https developers coinbase com bitcoin bitcoin cash litecoin and ethereum prices apikey yes unknown coinbase pro https docs pro coinbase com api cryptocurrency trading platform apikey yes unknown coindesk http www coindesk com api bitcoin price index no no unknown coingecko http www coingecko com api cryptocurrency price market and developer social data no yes yes coinigy https coinigy docs apiary io interacting with coinigy accounts and exchange directly apikey yes unknown coinlayer https coinlayer com real time crypto currency exchange rates apikey yes unknown coinlib https coinlib io apidocs crypto currency prices apikey yes unknown coinlore https www coinlore com cryptocurrency data api cryptocurrencies prices volume and more no yes unknown coinmarketcap https coinmarketcap com api cryptocurrencies prices apikey yes unknown coinpaprika https api coinpaprika com cryptocurrencies prices volume and more no yes yes coinranking https docs coinranking com live cryptocurrency data no yes unknown covalent https www covalenthq com docs api multi blockchain data aggregator yes yes unknown cryptocompare https www cryptocompare com api cryptocurrencies comparison no yes unknown cryptonator https www cryptonator com api cryptocurrencies exchange rates no yes unknown currencyapi https currencyapi com currency conversion api apikey yes unknown gates io https www gate io api2 blockchain assets exchange no yes unknown gemini https docs gemini com rest api cryptocurrencies exchange no yes unknown mexc global https mxcdevelop github io apidocs crypto asset exchange for trading marketplace apikey yes unknown nexchange https nexchange2 docs apiary io automated cryptocurrency exchange service no no yes nicehash https docs nicehash com largest crypto mining marketplace apikey yes unknown poloniex https poloniex com support api us based digital asset exchange apikey yes unknown worldcoinindex https www worldcoinindex com apiservice cryptocurrencies prices apikey yes unknown back to index index currency exchange api description auth https cors 1forge https 1forge com forex data api api documentation forex currency market data apikey yes unknown currency labstack https labstack com currency convert one currency to another with reliable exchanges rates apikey yes yes currencylayer https currencylayer com documentation exchange rates and currency conversion apikey yes unknown czech national bank https www cnb cz cs financni trhy devizovy trh kurzy devizoveho trhu denni kurz xml a collection of exchange rates no yes unknown exchangerate api https www exchangerate api com free currency conversion apikey yes yes exchangeratesapi io https exchangeratesapi io exchange rates with currency conversion apikey yes yes fixer io http fixer io exchange rates and currency conversion apikey yes unknown frankfurter https www frankfurter app docs exchange rates currency conversion and time series no yes yes fxratesapi https fxratesapi com real time exchange rates historical rates and currency conversion apikey yes no ratesapi https ratesapi io free exchange rates and historical rates no yes unknown back to index index data validation api description auth https cors cloudmersive validate https cloudmersive com validate api validate email addresses phone numbers vat numbers and domain names apikey yes yes email labstack https labstack com email instant email address verification to filter legit addresses apikey yes yes languagelayer https languagelayer com language detection no yes unknown lob com https lob com us address verification apikey yes unknown mailboxlayer https mailboxlayer com email address validation no yes unknown numvalidate https numvalidate com open source phone number validation no yes unknown numverify https numverify com phone number validation no yes unknown purgomalum http www purgomalum com content validator against profanity obscenity no no unknown us autocomplete https smartystreets com docs cloud us autocomplete api enter address data quickly with real time address suggestions apikey yes yes us extract https smartystreets com products apis us extract api extract postal addresses from any text including emails apikey yes yes us street address https smartystreets com docs cloud us street api validate and append data for any us postal address apikey yes yes vatlayer https vatlayer com vat number validation no yes unknown veriphone https veriphone io phone number validation carrier lookup apikey yes yes scraper run https scraper run email address validation no yes yes back to index index development api description auth https cors 24 pull requests https 24pullrequests com api project to promote open source collaboration during december no yes no agify io https agify io estimates the age from a first name no yes yes apiflash https apiflash com chrome based screenshot api for developers apikey yes unknown apis guru https apis guru api doc wikipedia for web apis openapi swagger specs for public apis no yes unknown bettermeta http bettermeta io return a site s meta tags in json format x mashape key yes unknown bored https www boredapi com find random activities to fight boredom no yes unknown browshot https browshot com api documentation easily make screenshots of web pages in any screen size as any device apikey yes unknown cdnjs https api cdnjs com libraries jquery library info on cdnjs no yes unknown changelogs md https changelogs md structured changelog metadata from open source projects no yes unknown countapi https countapi xyz free and simple counting service you can use it to track page hits and specific events no yes yes digitalocean status https status digitalocean com api v2 status of all digitalocean services no yes unknown dynamic qr code https rapidapi com updeploy tools api qr code dynamic and static1 details generate dynamic and static qr codes x rapidapi key yes yes extendsclass https extendsclass com json storage html a simple json store api no yes yes faceplusplus https www faceplusplus com a tool to detect face oauth yes unknown form2channel https form2channel com send static html form submissions to google sheets email slack or telegram no yes yes genderize io https genderize io estimates a gender from a first name no yes yes github https developer github com v3 make use of github repositories code and user info programmatically oauth yes yes gitlab https docs gitlab com ee api automate gitlab interaction programmatically oauth yes unknown gitter https github com gitterhq docs chat for github oauth yes unknown http2 pro https http2 pro doc api test endpoints for client and server http 2 protocol support no yes unknown ibm text to speech https console bluemix net docs services text to speech getting started html convert text to speech apikey yes yes image charts https www image charts com generate charts qr codes and graph images no yes yes import io http api docs import io retrieve structured data from a website or rss feed apikey yes unknown ip2whois information lookup https www ip2whois com whois domain name lookup apikey yes unknown ipapi is https ipapi is developers html public ip address api with company asn and hosting detection support no yes yes ipgeo https api techniknews net ipgeo ip adress domain unlimited free ip address api with useful information no yes unknown ipify https www ipify org a simple ip address api no yes unknown ipinfo https ipinfo io developers another simple ip address api no yes unknown jsdelivr https github com jsdelivr data jsdelivr com package info and download stats on jsdelivr cdn no yes yes json 2 jsonp https json2jsonp com convert json to jsonp on the fly for easy cross domain data requests using client side javascript no yes unknown jsonbin io https jsonbin io free json storage service ideal for small scale web apps websites and mobile apps apikey yes yes judge0 https api judge0 com compile and run source code no yes unknown let s validate https github com letsvalidate api uncovers the technologies used on websites and url to thumbnail no yes unknown license api https github com cmccandless license api blob master readme md unofficial rest api for choosealicense com no yes no liveedu https www liveedu tv developer applications live coding streaming oauth yes unknown mac address vendor lookup https macaddress io retrieve vendor details and other information regarding a given mac address or an oui apikey yes yes nationalize io https nationalize io estimate the nationality of a first name no yes yes oopspam https oopspam com multiple spam filtering service no yes yes postman https docs api getpostman com tool for testing apis apikey yes unknown proxycrawl https proxycrawl com scraping and crawling anticaptcha service apikey yes unknown public apis https github com davemachado public api a collective list of free json apis for use in web development no yes unknown pusher beams https pusher com beams push notifications for android ios apikey yes unknown qr code https fungenerators com api qrcode create new qr code or decode existing one apikey yes yes qr code http qrtag net api create an easy to read qr code and url shortener no yes yes qr code http goqr me api generate and decode read qr code graphics no yes unknown quickchart https quickchart io generate chart and graph images no yes yes reqres https reqres in a hosted rest api ready to respond to your ajax requests no yes unknown scraperapi https www scraperapi com easily build scalable web scrapers apikey yes unknown screenshotapi net https screenshotapi net create pixel perfect website screenshots apikey yes yes screenshots https screenshotson click free capture a full screenshot of the website with high resolution and without watermarks apikey yes yes shadify https github com cheatsnake shadify service for generating data and executing logic to create various games and puzzles no yes yes shoutcloud http shoutcloud io all caps as a service no no unknown stackexchange https api stackexchange com q a forum for developers oauth yes unknown unixtime https unixtime co za convert unixtime to datetime and vice versa no yes yes xml to json https developers wso2apistore com integration developer utility apis no yes unknown image to link https www contentful com developers docs references images api generate link from image integration developer utility apis no yes yes ipfinder https ipfinder io geolocation api asn api ip ranges api ip firewall api domain api yes yes yes scraper run https scraper run geolocation api lookup dns whois record of a domain no yes yes searchapi https www searchapi io real time search engines serp api apikey yes no back to index index dictionaries api description auth https cors lingua robot https www linguarobot io word definitions pronunciations synonyms antonyms and others apikey yes yes merriam webster https dictionaryapi com dictionary and thesaurus data apikey yes unknown owlbot https owlbot info definitions with example sentence and photo if available apikey yes yes oxford https developer oxforddictionaries com dictionary data apikey yes no wordnik http developer wordnik com dictionary data apikey no unknown words https www wordsapi com definitions and synonyms for more than 150 000 words apikey yes unknown back to index index disasters api description auth https cors usgs https earthquake usgs gov fdsnws event 1 earthquake data apikey yes unknown rwlabs https apidoc rwlabs org api use all types of disaster data apikey yes unknown predicthq https developer predicthq com events and natural disasters data oauth yes unknown back to index index documents productivity api description auth https cors cloudmersive document and data conversion https cloudmersive com convert api html url to pdf png office documents to pdf image conversion apikey yes yes base https www base api io reference authentication emails sending file uploading and shearing forms filluping apikey yes unknown dynamicdocs https advicement io generate dynamic pdfs with json to pdf api based on latex apikey yes unknown file io https www file io file sharing no yes unknown mercury https mercury postlight com web parser web parser apikey yes unknown pdfendpoint https pdfendpoint com html or url to pdf apikey yes no pdflayer https pdflayer com html url to pdf apikey yes unknown pocket https getpocket com developer bookmarking service oauth yes unknown prexview https prexview com data from xml or json to pdf html or image apikey yes unknown restpack https restpack io provides screenshot html to pdf and content extraction apis apikey yes unknown todoist https developer todoist com todo lists oauth yes unknown vector express http vector express free vector file converting api no no yes vertopal https www vertopal com en developer api convert files using vertopal api apikey yes no wakatime https wakatime com developers automated time tracking leaderboards for programmers no yes unknown webpage labstack https labstack com webpage high resolution retina display and responsive screenshot apikey yes yes back to index index education api description auth https cors current affairs https rapidapi com malaithiru370 api current affairs of india current international affairs quizzes and more apikey yes no back to index index environment api description auth https cors airvisual https airvisual com api air quality and weather data apikey yes unknown gr nstromindex https www corrently de hintergrund gruenstromindex index html green power index for germany gr nstromindex gsi no no yes openaq https docs openaq org open air quality data apikey yes unknown aqicn http aqicn org api real time air quality index apikey no unknown pvwatts https developer nrel gov docs solar pvwatts v6 energy production photovoltaic pv energy systems apikey yes unknown uk carbon intensity https carbon intensity github io api definitions carbon intensity api v1 0 0 the official carbon intensity api for great britain developed by national grid no yes unknown back to index index events api description auth https cors eventbrite https www eventbrite com developer v3 find events oauth yes unknown picatic http developer picatic com utm medium web utm source github utm campaign public apis 20repo utm content toddmotto sell tickets anywhere apikey yes unknown seatgeek https platform seatgeek com search events venues and performers apikey yes unknown ticketmaster http developer ticketmaster com products and docs apis getting started search events attractions or venues apikey yes unknown back to index index finance api description auth https cors alpha vantage https www alphavantage co realtime and historical stock data apikey yes unknown barchart ondemand https www barchartondemand com free stock futures and forex market data apikey yes unknown financial modeling prep https financialmodelingprep com stock information and data no yes unknown iex https iextrading com developer realtime stock data apikey yes yes iex cloud https iexcloud io realtime historical stock and market data apikey yes yes ig https labs ig com gettingstarted spreadbetting and cfd market data apikey yes unknown plaid https plaid com connect with users bank accounts and access transaction data apikey yes unknown razorpay ifsc https ifsc razorpay com indian financial systems code bank branch codes no yes unknown indian bank data api https github com kaustubhk24 indian banks data all banks ifsc code data search by ifsc or other details no yes unknown routingnumbers info https www routingnumbers info api index html ach nacha bank routing numbers no yes unknown tradier https developer tradier com us equity option market data delayed intraday historical oauth yes yes world trading data https www worldtradingdata com market data provider apikey yes unknown ynab https api youneedabudget com budgeting planning oauth yes yes back to index index food drink api description auth https cors edamam https developer edamam com recipe search apikey yes unknown lcbo https lcboapi com alcohol apikey yes unknown open brewery db https www openbrewerydb org breweries cideries and craft beer bottle shops no yes yes open food facts https world openfoodfacts org data food products database no yes unknown punkapi https punkapi com brewdog beer recipes no yes unknown recipe puppy http www recipepuppy com about api food no no unknown spoonacular https spoonacular com food api food and recipes apikey yes unknown tacofancy https github com evz tacofancy api community driven taco database no no unknown the report of the week https github com andyklimczak thereportoftheweek api food drink reviews no yes unknown thecocktaildb https www thecocktaildb com api php cocktail recipes apikey yes yes themealdb https www themealdb com api php meal recipes apikey yes yes what s on the menu http nypl github io menus api nypl human transcribed historical menu collection apikey no unknown whiskyhunter https whiskyhunter net api past online whisky auctions statistical data no yes unknown zestful https zestfuldata com parse recipe ingredients apikey yes yes back to index index fraud prevention api description auth https cors fraudlabs pro https www fraudlabspro com developer api screen order screen order information using ai to detect frauds apikey yes unknown whitepages pro https pro whitepages com developer documentation identity check api global identity verification with phone address email and ip apikey yes unknown whitepages pro https pro whitepages com developer documentation phone reputation api phone reputation to detect spammy phones apikey yes unknown whitepages pro https pro whitepages com developer documentation reverse phone api get an owner s name address demographics based on the phone number apikey yes unknown whitepages pro https pro whitepages com developer documentation phone intelligence api phone number validation line type carrier append apikey yes unknown whitepages pro https pro whitepages com developer documentation reverse address api get normalized physical address residents address type and validity apikey yes unknown back to index index games comics api description auth https cors amiiboapi http www amiiboapi com amiibo information no no yes battle net https dev battle net blizzard entertainment apikey yes unknown blue archive https github com arufars api blue archive blue archive game data api characters no yes unknown chuck norris database http www icndb com api jokes no no unknown clash of clans https developer clashofclans com clash of clans game information apikey yes unknown clash royale https developer clashroyale com clash royale game information apikey yes unknown comic vine https comicvine gamespot com api documentation comics no yes unknown deck of cards http deckofcardsapi com deck of cards no no unknown destiny the game https github com bungie net api bungie platform api apikey yes unknown dota 2 https docs opendota com provides information about player stats match stats rankings for dota 2 no yes unknown dungeons and dragons http www dnd5eapi co reference for 5th edition spells classes monsters and more no no no eve online https esi evetech net ui third party developer documentation oauth yes unknown final fantasy xiv https xivapi com final fantasy xiv game data api no yes yes fortnite https fortniteapi com fortnite stats cosmetics apikey yes yes fortnite https fortnitetracker com site api fortnite stats apikey yes unknown forza https docs forza api tk random images of forza cars no yes unknown giant bomb https www giantbomb com api documentation video games no yes unknown guild wars 2 https wiki guildwars2 com wiki api main guild wars 2 game information apikey yes unknown halo https developer haloapi com halo 5 and halo wars 2 information apikey yes unknown hearthstone http hearthstoneapi com hearthstone cards information x mashape key yes unknown hypixel https api hypixel net hypixel player stats apikey yes unknown igdb com https api igdb com video game database apikey yes unknown jokes one https jokes one api joke joke of the day and large category of jokes accessible via rest api apikey yes yes jokeapi https sv443 net jokeapi v2 programming miscellaneous and dark jokes no yes yes jokes https github com 15dkatz official joke api programming and general jokes no yes unknown jservice http jservice io jeopardy question database no no unknown magic the gathering http magicthegathering io magic the gathering game information no no unknown marvel http developer marvel com marvel comics apikey no unknown mod io https docs mod io cross platform mod api apikey yes unknown open trivia https opentdb com api config php trivia questions no yes unknown pandascore https api pandascore co e sports games and results apikey yes unknown playerunknown s battlegrounds https pubgtracker com site api pubg stats apikey yes unknown pok api https pokeapi co pok mon information no yes unknown pok mon tcg https pokemontcg io pok mon tcg information no yes unknown puns https www punapi rest puns and pun memes no yes yes random facts https fungenerators com api facts random facts from hundreds of categories apikey yes yes rick and morty https rickandmortyapi com all the rick and morty information including images no yes yes riot games https developer riotgames com league of legends game information apikey yes unknown scoresaber https scoresaber com api documentation scoresaber stats no yes unknown scryfall https scryfall com docs api magic the gathering database no yes yes steam https developer valvesoftware com wiki steam web api steam client interaction oauth yes unknown superheroes https superheroapi com all superheroes and villains data from all universes under a single api apikey yes unknown tcgdex https www tcgdex dev a multilanguage pok mon tcg database with cards pictures and most of the informations contained on the cards no yes yes tronald dump https www tronalddump io the dumbest things donald trump has ever said no yes unknown wargaming net https developers wargaming net wargaming net info and stats apikey yes no xkcd https xkcd com json html retrieve xkcd comics as json no yes no back to index index geocoding api description auth https cors administrative divisons db https github com kamikazechaser administrative divisions db get all administrative divisions of a country no yes yes adresse data gouv fr https adresse data gouv fr address database of france geocoding and reverse no yes unknown battuta http battuta medunes net a country region city in cascade location api apikey no unknown bing maps https www microsoft com maps create customize digital maps based on bing maps data apikey yes unknown bng2latlong https www getthedata com bng2latlong convert british osgb36 easting and northing british national grid to wgs84 latitude and longitude no yes yes citysdk http www citysdk eu citysdk toolkit open apis for select european cities no yes unknown country http country is get your visitors country from their ip no yes yes daum maps http apis map daum net daum maps provide multiple apis for korean maps apikey no unknown freegeoip https freegeoip app free geo ip information no registration required 15k hour rate limit no yes yes geoapi https api gouv fr api geoapi html french geographical data no yes unknown geocod io https www geocod io address geocoding reverse geocoding in bulk apikey yes unknown geocode xyz https geocode xyz provides worldwide forward reverse geocoding batch geocoding and geoparsing no yes unknown geocodify com https geocodify com enterprise grade geocoding and geoparsing apikey yes yes geodatasource https www geodatasource com web service geocoding of city name by using latitude and longitude coordinates apikey yes unknown geojs https geojs io ip geolocation with chatops integration no yes yes geokeo https geokeo com forward and reverse geocoding with 2500 free daily limit apikey yes yes geoplugin https www geoplugin com ip geolocation and currency conversion no yes yes google earth engine https developers google com earth engine a cloud based platform for planetary scale environmental data analysis apikey yes unknown google maps https developers google com maps create customize digital maps based on google maps data apikey yes unknown hellosalut https www fourtonfish com hellosalut hello get hello translation following user language no yes unknown here maps https developer here com create customize digital maps based on here maps data apikey yes unknown indian cities https indian cities api nocbegfhqg now sh get all indian cities in a clean json format no yes yes ip 2 country https ip2country info map an ip to a country no yes unknown ip address details https ipinfo io find geolocation with ip address no yes unknown ip labstack http labstack com ip lookup country region city time zone currency and language of an ip apikey yes yes ip location http ip api com find location with ip address no no unknown ip location https ipapi co find ip address location information no yes unknown ip sidekick https ipsidekick com geolocation api that returns extra information about an ip address apikey yes unknown ip vigilante https www ipvigilante com free ip geolocation api no yes unknown ip2location https www ip2location com web service ip2location ip geolocation web service to get more than 55 parameters apikey yes unknown ip2location io https www ip2location io free ip geolocation api to geolocate user s location information apikey yes yes ip2proxy https www ip2location com web service ip2proxy detect proxy and vpn using ip address apikey yes unknown ipgeolocationapi com https ipgeolocationapi com locate your visitors by ip with country details no yes yes ipinfodb https ipinfodb com api free geolocation tools and apis for country region city and time zone lookup by ip address apikey yes unknown ipstack https ipstack com locate and identify website visitors by ip address apikey yes unknown ipgeolocation https ipgeolocation com find geolocation of any ip address with ip geolocation api apikey yes yes isitwater https isitwater com a free api to determine if a lat lon is on water or land rapidapi key yes yes airtel ip https sys airtel lv ip2country 1 1 1 1 full true ip geolocation api collecting data from multiple sources no yes unknown kwelo network https www kwelo com network ip address locate and get detailed information on ip address no yes yes locationiq https locationiq org docs provides forward reverse geocoding and batch geocoding apikey yes yes mapbox https www mapbox com developers create customize beautiful digital maps apikey yes unknown mexico https github com icalialabs sepomex mexico restful zip codes api no yes unknown one map singapore https docs onemap sg singapore land authority rest api services for singapore addresses apikey yes unknown opencage https opencagedata com forward and reverse geocoding using open data apikey yes yes openstreetmap http wiki openstreetmap org wiki api navigation geolocation and geographical data oauth no unknown postcodedata nl http api postcodedata nl v1 postcode postcode 1211ep streetnumber 60 ref domeinnaam nl type json provide geolocation data based on postcode for dutch addresses no no unknown postcodes io https postcodes io postcode lookup geolocation for the uk no yes yes rest countries https restcountries com get information about countries via a restful api no yes yes smartip io https smartip io ip geolocation and threat intelligence api apikey yes yes uebermaps https uebermaps com api v2 discover and share maps with friends apikey yes unknown us zipcode https smartystreets com docs cloud us zipcode api validate and append data for any us zipcode apikey yes yes viacep https viacep com br brazil restful zip codes api no yes unknown zipcodeapi https www zipcodeapi com us zip code distance radius and location api apikey yes unknown zippopotam http www zippopotam us get information about place such as country city state etc no no unknown ziptastic https ziptasticapi com get the country state and city of any us zip code no yes unknown back to index index government api description auth https cors bclaws http www bclaws ca civix template complete api index html access to the laws of british columbia no no unknown businessusa https business usa gov developer authoritative information on u s programs events services and more apikey yes unknown census gov https www census gov data developers data sets html the us census bureau provides various apis and data sets on demographics and businesses no yes unknown city lyon opendata https data beta grandlyon com fr accueil lyon fr city open data apikey yes unknown city nantes opendata https data nantesmetropole fr pages home nantes fr city open data apikey yes unknown city prague opendata http opendata praha eu en prague cz city open data no no unknown code gov https code gov the primary platform for open source and code sharing for the u s federal government apikey yes unknown colorado data engine http codataengine org formatted and geolocated colorado public data no yes unknown colorado information marketplace https data colorado gov colorado state government open data no yes unknown data usa https datausa io about api us public data no yes unknown data gov https api data gov us government data apikey yes unknown district of columbia open data http opendata dc gov pages using apis contains d c government public datasets including crime gis financial data and so on no yes unknown epa https developer epa gov category apis web services and data sets from the us environmental protection agency no yes unknown fec https api open fec gov developers information on campaign donations in federal elections apikey yes unknown federal register https www federalregister gov reader aids developer resources the daily journal of the united states government no yes unknown food standards agency http ratings food gov uk open data en gb uk food hygiene rating data api no no unknown open government australia https www data gov au australian government open data no yes unknown open government belgium https data gov be belgium government open data no yes unknown open government canada http open canada ca en canadian government open data no no unknown open government france https www data gouv fr french government open data apikey yes unknown open government india https data gov in indian government open data apikey yes unknown open government italy https www dati gov it italy government open data no yes unknown open government new zealand https www data govt nz new zealand government open data no yes unknown open government poland https dane gov pl en poland government open data no yes unknown open government romania http data gov ro romania government open data no no unknown open government taiwan https data gov tw taiwan government open data no yes unknown open government usa https www data gov united states government open data no yes unknown represent by open north https represent opennorth ca find canadian government representatives no yes unknown usaspending gov https api usaspending gov us federal spending data no yes unknown back to index index health api description auth https cors diabetes http predictbgl com api logging and retrieving diabetes information no no unknown flutrack http www flutrack org influenza like symptoms with geotracking no no unknown healthcare gov https www healthcare gov developers educational content about the us health insurance marketplace no yes unknown icd 10 codes https clinicaltables nlm nih gov apidoc icd10cm v3 doc html list of all healthcare diagnosis codes no yes unknown lexigram https docs lexigram io v1 welcome nlp that extracts mentions of clinical concepts from text gives access to clinical ontology apikey yes unknown makeup http makeup api herokuapp com makeup information no no unknown medicare https data medicare gov developers access to the data from the cms medicare gov no yes unknown nppes https npiregistry cms hhs gov registry help api national plan provider enumeration system info on healthcare providers registered in us no yes unknown nutritionix https developer nutritionix com worlds largest verified nutrition database apikey yes unknown openfda https open fda gov public fda data about drugs devices and foods no yes unknown usda nutrients https ndb nal usda gov ndb doc index national nutrient database for standard reference no yes unknown back to index index jobs api description auth https cors adzuna https developer adzuna com overview job board aggregator apikey yes unknown careerjet https www careerjet com partners api job search engine apikey no unknown devitjobs https devitjobs us api jobslight jobs for software developers no yes unknown graphql jobs https api graphql jobs jobs with graphql no yes yes indeed https www indeed com publisher job board aggregator apikey yes unknown jobs2careers http api jobs2careers com api spec pdf job aggregator apikey yes unknown jooble https us jooble org api about job search engine apikey yes unknown juju http www juju com publisher spec job search engine apikey no unknown open skills https github com workforce data initiative skills api wiki api overview job titles skills and related jobs data no no unknown reed https www reed co uk developers job board aggregator apikey yes unknown search gov jobs https search gov developer jobs html tap into a list of current jobs openings with the united states government no yes unknown the muse https www themuse com developers api v2 job board and company profiles apikey yes unknown upwork https developers upwork com freelance job board and management system oauth yes unknown usajobs https developer usajobs gov us government job board apikey yes unknown ziprecruiter https www ziprecruiter com publishers job search app and website apikey yes unknown back to index index machine learning api description auth https cors cloudmersive https www cloudmersive com image recognition and processing api image captioning face recognition nsfw classification apikey yes yes deepcode https www deepcode ai docs overview 252foverview ai for code review no yes unknown dialogflow https dialogflow com natural language processing apikey yes unknown keen io https keen io data analytics apikey yes unknown time door https timedoor io a time series analysis api apikey yes yes unplugg https unplu gg test api html forecasting api for timeseries data apikey yes unknown wit ai https wit ai natural language processing oauth yes unknown back to index index music api description auth https cors ai mastering https aimastering com api docs automated music mastering apikey yes yes bandsintown https app swaggerhub com apis bandsintown publicapi 3 0 1 music events no yes unknown deezer https developers deezer com api music oauth yes unknown discogs https www discogs com developers music oauth yes unknown freesound https freesound org docs api music samples apikey yes unknown genius https docs genius com crowdsourced lyrics and music knowledge oauth yes unknown genrenator https binaryjazz us genrenator api music genre generator no yes unknown itunes search https affiliate itunes apple com resources documentation itunes store web service search api software products no yes unknown jamendo https developer jamendo com v3 0 docs music oauth yes unknown kkbox https developer kkbox com get music libraries playlists charts and perform out of kkbox s platform oauth yes unknown lastfm https www last fm api music apikey yes unknown lyrics ovh http docs lyricsovh apiary io simple api to retrieve the lyrics of a song no yes unknown mixcloud https www mixcloud com developers music oauth yes yes musicbrainz https musicbrainz org doc development xml web service version 2 music no yes unknown musixmatch https developer musixmatch com music apikey yes unknown openwhyd https openwhyd github io openwhyd api download curated playlists of streaming tracks youtube soundcloud etc no yes no searchly https www github com albertsuarez searchly similarities search based on song lyrics no yes unknown songkick https www songkick com developer music events oauth yes unknown songsterr https www songsterr com a wa api provides guitar bass and drums tabs and chords no yes unknown soundcloud https developers soundcloud com allow users to upload and share sounds oauth yes unknown spotify https beta developer spotify com documentation web api view spotify music catalog manage users libraries get recommendations and more oauth yes unknown tastedive https tastedive com read api similar artist api also works for movies and tv shows apikey yes unknown theaudiodb https www theaudiodb com api guide php music apikey yes unknown vagalume https api vagalume com br docs crowdsourced lyrics and music knowledge apikey yes unknown back to index index news api description auth https cors associated press https developer ap org search for news and metadata from associated press apikey yes unknown chronicling america http chroniclingamerica loc gov about api provides access to millions of pages of historic us newspapers from the library of congress no no unknown currents https currentsapi services latest news published in various news sources blogs and forums apikey yes yes feedbin https github com feedbin feedbin api rss reader oauth yes unknown new york times https developer nytimes com provides news apikey yes unknown news https newsapi org headlines currently published on a range of news sources and blogs apikey yes unknown npr one http dev npr org api personalized news listening experience from npr oauth yes unknown the guardian http open platform theguardian com access all the content the guardian creates categorised by tags and section apikey yes unknown the old reader https github com theoldreader api rss reader apikey yes unknown back to index index open data api description auth https cors 18f http 18f github io api all the x unofficial us federal government api development no no unknown archive org https archive readme io docs the internet archive no yes unknown callook info https callook info united states ham radio callsigns no yes unknown carto https carto com location information prediction apikey yes unknown civicfeed https developers civicfeed com news articles and public datasets apikey yes unknown enigma public http docs enigma com public public v20 api about broadest collection of public data apikey yes yes fonoapi https fonoapi freshpixl com mobile device description no yes unknown french address search https geo api gouv fr adresse address search via the french government no yes unknown fruits https fruits api netlify app graphql information of fruit trees of the world no yes no linkpreview https www linkpreview net get json formatted summary with title description and preview image for any requested url apikey yes yes marijuana strains http strains evanbusse com marijuana strains races flavors and effects apikey no unknown microlink io https microlink io extract structured data from any website no yes yes opencorporates http api opencorporates com documentation api reference data on corporate entities and directors in many countries apikey yes unknown quandl https www quandl com stock market data no yes unknown recreation information database https ridb recreation gov recreational areas federal lands historic sites museums and other attractions resources us apikey yes unknown scoop it http www scoop it dev content curation service apikey no unknown teleport https developers teleport org quality of life data no yes unknown universities list https github com hipo university domains list university names countries and domains no yes unknown university of oslo https data uio no courses lecture videos detailed information for courses etc for the university of oslo norway no yes unknown upc database https upcdatabase org api more than 1 5 million barcode numbers from all around the world apikey yes unknown wikidata https www wikidata org w api php action help collaboratively edited knowledge base operated by the wikimedia foundation oauth yes unknown wikipedia https www mediawiki org wiki api main page mediawiki encyclopedia no yes unknown yelp https www yelp com developers documentation v3 find local business oauth yes unknown back to index index open source projects api description auth https cors drupal org https www drupal org drupalorg docs api drupal org no yes unknown evil insult generator https evilinsult com api evil insults no yes yes libraries io https libraries io api open source software libraries apikey yes unknown back to index index patent api description auth https cors epo https developers epo org european patent search system api oauth yes unknown tipo https tiponet tipo gov tw gazette opendata od od05 aspx qryds api00 taiwan patent search system api apikey yes unknown uspto https www uspto gov learning and resources open data and mobility usa patent api services no yes unknown back to index index personality api description auth https cors advice slip http api adviceslip com generate random advice slips no yes unknown chucknorris io https api chucknorris io json api for hand curated chuck norris jokes no yes unknown favqs com https favqs com api favqs allows you to collect discover and share your favorite quotes apikey yes unknown foaas http www foaas com fuck off as a service no no unknown forismatic http forismatic com en api inspirational quotes no no unknown hindi quotes https hindi quotes vercel app get random hindi quotes of different categories no yes yes icanhazdadjoke https icanhazdadjoke com api the largest selection of dad jokes on the internet no yes unknown kanye rest https kanye rest rest api for random kanye west quotes no yes yes medium https github com medium medium api docs community of readers and writers offering unique perspectives on ideas oauth yes unknown meme https github com d3vd meme api json api for a random meme scraped from reddit no yes unknown namomemes https github com theiyd namomemes memes on narendra modi no yes unknown programming quotes https programming quotesapi vercel app an api which generates quotes from programmers no yes yes quote garden https pprathameshmore github io quotegarden rest api for more than 5000 famous quotes no yes unknown quotes on design https quotesondesign com api inspirational quotes no yes unknown riddles api https riddles api vercel app get random riddles on every api call no yes yes traitify https app traitify com developer assess collect and analyze personality no yes unknown tronalddump io https www tronalddump io api web archive for the things donald trump has said no yes unknown back to index index photography api description auth https cors flickr https www flickr com services api flickr services oauth yes unknown getty images http developers gettyimages com en build applications using the world s most powerful imagery oauth yes unknown gfycat https developers gfycat com api jiffier gifs oauth yes unknown giphy https developers giphy com docs get all your gifs apikey yes unknown gyazo https gyazo com api docs upload images apikey yes unknown imgur https apidocs imgur com images oauth yes unknown lorem picsum https picsum photos images from unsplash no yes unknown objectcut https objectcut com image background removal apikey yes yes pexels https www pexels com api free stock photos and videos apikey yes yes pixabay https pixabay com sk service about api photography apikey yes unknown placekitten https placekitten com resizable kitten placeholder images no yes unknown screenshotlayer https screenshotlayer com url 2 image no yes unknown unsplash https unsplash com developers photography oauth yes unknown wallhaven https wallhaven cc help api wallpapers apikey yes unknown back to index index science math api description auth https cors arcsecond io https api arcsecond io multiple astronomy data sources no yes unknown core https core ac uk services api access the world s open access research papers apikey yes unknown gbif http api gbif org v1 global biodiversity information facility no yes yes idigbio https github com idigbio idigbio search api wiki access millions of museum specimens from organizations around the world no yes unknown inspirehep net https inspirehep net info hep api ln en high energy physics info system no yes unknown itis https www itis gov ws description html integrated taxonomic information system no yes unknown launch library https launchlibrary net docs 1 3 api html upcoming space launches no yes unknown minor planet center http www asterank com mpc asterank com information no no unknown nasa https api nasa gov nasa data including imagery no yes unknown newton https newton now sh symbolic and arithmetic math calculator no yes unknown numbers https math tools api numbers number of the day random number generation number facts and anything else you want to do with numbers apikey yes yes numbers http numbersapi com facts about numbers no no unknown open notify http open notify org open notify api iss astronauts current location etc no no unknown open science framework https developer osf io repository and archive for study designs research materials data manuscripts etc no yes unknown share https share osf io api v2 a free open dataset about research and scholarly activities no yes unknown spacex https github com r spacex spacex api company vehicle launchpad and launch data no yes unknown sunrise and sunset https sunrise sunset org api sunset and sunrise times for a given latitude and longitude no yes unknown usgs earthquake hazards program https earthquake usgs gov fdsnws event 1 earthquakes data real time no yes unknown usgs water services https waterservices usgs gov water quality and level info for rivers and lakes no yes unknown world bank https datahelpdesk worldbank org knowledgebase topics 125589 world data no no unknown back to index index security api description auth https cors censys io https censys io api search engine for internet connected host and devices apikey yes no crxcavator https crxcavator io apidocs chrome extension risk scoring apikey yes unknown haveibeenpwned https haveibeenpwned com api v3 passwords which have previously been exposed in data breaches apikey yes unknown national vulnerability database https nvd nist gov vuln data feeds json feed changelog u s national vulnerability database no yes unknown securitytrails https securitytrails com corp apidocs domain and ip related information such as current and historical whois and dns records apikey yes unknown shodan https developer shodan io search engine for internet connected devices apikey yes unknown uk police https data police uk docs uk police data no yes unknown back to index index shopping api description auth https cors best buy https bestbuyapis github io api documentation overview products buying options categories recommendations stores and commerce apikey yes unknown bratabase https developers bratabase com database of different types of bra sizes oauth yes unknown ebay https go developer ebay com sell and buy on ebay oauth yes unknown wegmans https dev wegmans io wegmans food markets apikey yes unknown back to index index social api description auth https cors buffer https buffer com developers api access to pending and sent updates in buffer oauth yes unknown cisco spark https developer ciscospark com team collaboration software oauth yes unknown dangerous discord database https discord riverside rocks docs index php database of malicious discord accounts apikey yes unknown discord https discordapp com developers docs intro make bots for discord integrate discord onto an external platform oauth yes unknown disqus https disqus com api docs auth communicate with disqus data oauth yes unknown facebook https developers facebook com facebook login share on fb social plugins analytics and more oauth yes unknown foursquare https developer foursquare com interact with foursquare users and places geolocation based checkins photos tips events etc oauth yes unknown fuck off as a service https www foaas com asks someone to fuck off no yes unknown full contact https www fullcontact com developer docs get social media profiles and contact information oauth yes unknown hackernews https github com hackernews api social news for cs and entrepreneurship no yes unknown instagram https www instagram com developer instagram login share on instagram social plugins and more oauth yes unknown meetup com https www meetup com meetup api data about meetups from meetup com apikey yes unknown mysocialapp https mysocialapp io seamless social networking features api sdk to any app apikey yes unknown open collective https docs opencollective com help developers api get open collective data no yes unknown pinterest https developers pinterest com the world s catalog of ideas oauth yes unknown pwrtelegram bot https pwrtelegram xyz boosted version of the telegram bot api apikey yes unknown reddit https www reddit com dev api homepage of the internet oauth yes unknown slack https api slack com team instant messaging oauth yes unknown telegram bot https core telegram org bots api simplified http version of the mtproto api for bots oauth yes unknown telegram mtproto https core telegram org api getting started read and write telegram data apikey yes unknown trash nothing https trashnothing com developer a freecycling community with thousands of free items posted every day oauth yes yes tumblr https www tumblr com docs en api v2 read and write tumblr data oauth yes unknown twitch https dev twitch tv docs game streaming api oauth yes unknown twitter https developer twitter com en docs read and write twitter data oauth yes no vk https vk com dev sites read and write vk data oauth yes unknown back to index index sports fitness api description auth https cors balldontlie https balldontlie io ballldontlie provides access to stats data from the nba no yes yes bikewise https www bikewise org documentation api v2 bikewise is a place to learn about and report bike crashes hazards and thefts no yes unknown canadian football league cfl http api cfl ca official json api providing real time league team and player statistics about the cfl apikey yes no cartola fc https github com wgenial cartrolandofc the cartola fc api serves to check the partial points of your team no yes unknown city bikes http api citybik es v2 city bikes around the world no no unknown ergast f1 http ergast com mrd f1 data from the beginning of the world championships in 1950 no yes unknown fitbit https dev fitbit com fitbit information oauth yes unknown football soccer videos https www scorebat com video api embed codes for goals and highlights from premier league bundesliga serie a and many more no yes yes football prediction https boggio analytics com fp api predictions for upcoming football matches odds results and stats x mashape key yes unknown football data org http api football data org index football data no no unknown jcdecaux bike https developer jcdecaux com jcdecaux s self service bicycles apikey yes unknown nba stats https any api com nba com nba com docs api description current and historical nba statistics no yes unknown nhl records and stats https gitlab com dword4 nhlapi nhl historical data and statistics no yes unknown sport list data https developers decathlon com products sports list of and ressources related to sports no yes yes strava https strava github io api connect with athletes activities and more oauth yes unknown suredbits https suredbits com api query sports data including teams players games scores and statistics no no no thesportsdb https www thesportsdb com api php crowd sourced sports data and artwork apikey yes yes wger https wger de en software api workout manager data as exercises muscles or equipment apikey yes unknown cricdata https cricketdata org ultimate cricket data api score scorecard players data fantasy api apikey yes unknown back to index index test data api description auth https cors bacon ipsum https baconipsum com json api a meatier lorem ipsum generator no yes unknown dicebear avatars https avatars dicebear com generate random pixel art avatars no yes no fakejson https fakejson com service to generate test and fake data apikey yes yes identicon https www kwelo com media identicon generates abstract avatar image no yes yes jsonplaceholder http jsonplaceholder typicode com fake data for testing and prototyping no no unknown loripsum http loripsum net the lorem ipsum generator that doesn t suck no no unknown pipl https pipl ir free and public api that generates random and fake people s data in json no yes no micro jaymock https micro jaymock now sh tiny api mocking microservice for generating fake json data no yes no randomuser https randomuser me generates random user data no yes unknown robohash https robohash org generate random robot alien avatars no yes unknown this person does not exist https thispersondoesnotexist com generates real life faces of people who do not exist no yes unknown ui names https github com thm uinames generate random fake names no yes unknown uuid generator https www uuidtools com docs generate uuids no yes no yes no https yesno wtf api generate yes or no randomly no yes unknown randommer https randommer io randommer api random data generator apikey yes yes back to index index text analysis api description auth https cors aylien text analysis http docs aylien com a collection of information retrieval and natural language apis apikey yes unknown cloudmersive natural language processing https www cloudmersive com nlp api natural language processing and text analysis apikey yes yes detect language https detectlanguage com detects text language apikey yes unknown google cloud natural https cloud google com natural language docs natural language understanding technology including sentiment entity and syntax analysis apikey yes unknown paralleldots ai apis https apis paralleldots com text docs index html suite of text analysis apis such as sentiment analysis keyword extract and named entity extraction apikey yes yes semantira https semantria readme io docs text analytics with sentiment analysis categorization named entity extraction oauth yes unknown watson natural language understanding https www ibm com watson developercloud natural language understanding api v1 natural language processing for advanced text analysis oauth yes unknown yomi https github com ookii tsuki yomi japanese tokenizer and morphological analysis web api no yes yes back to index index tracking api description auth https cors postmon http postmon com br an api to query brazilian zip codes and orders easily quickly and free no no unknown sweden https developer postnord com docs2 provides information about parcels in transport apikey no unknown ups https www ups com upsdeveloperkit shipment and address information apikey yes unknown whatpulse https whatpulse org pages webapi small application that measures your keyboard mouse usage no yes unknown back to index index transportation api description auth https cors ads b exchange https www adsbexchange com data access real time and historical data of any and all airborne aircraft no yes unknown ais web http www aisweb aer mil br api doc index cfm aeronautical information in digital media produced by the department of airspace control decea apikey no unknown amadeus travel innovation sandbox https sandbox amadeus com travel search limited usage apikey yes unknown bay area rapid transit http api bart gov stations and predicted arrivals for bart apikey no unknown blablacar https dev blablacar com search car sharing trips apikey yes unknown community transit https github com transitland transitland datastore blob master readme md api endpoints transitland api no yes unknown graphhopper https graphhopper com api 1 docs a to b routing with turn by turn instructions apikey yes unknown icelandic apis http docs apis is open apis that deliver services in or regarding iceland no yes unknown izi http api docs izi travel audio guide for travellers apikey yes unknown metro lisboa http app metrolisboa pt status getlinhas php delays in subway lines no no no navitia https api navitia io the open api for building cool stuff with transport data apikey yes unknown open charge map https openchargemap org site develop api global public registry of electric vehicle charging locations no yes unknown refuge restrooms https www refugerestrooms org api docs restrooms provides safe restroom access for transgender intersex and gender nonconforming individuals no yes unknown schiphol airport https developer schiphol nl schiphol apikey yes unknown transitland https transit land documentation datastore api endpoints html transit aggregation no yes unknown transport for atlanta us http www itsmarta com app developer resources aspx marta no no unknown transport for auckland new zealand https api at govt nz auckland transport no yes unknown transport for belgium https hello irail be api belgian transport api no yes unknown transport for bordeaux france https opendata bordeaux metropole fr explore bordeaux m tropole public transport and more france apikey yes unknown transport for boston us https mbta com developers v3 api mbta api no no unknown transport for budapest hungary https bkkfutar docs apiary io budapest public transport api no yes unknown transport for chicago us http www transitchicago com developers cta no no unknown transport for czech republic https www chaps cz eng products idos internet czech transport api no yes unknown transport for denver us http www rtd denver com gtfs developer guide shtml rtd no no unknown transport for finland https digitransit fi en developers finnish transport api no yes unknown transport for germany http data deutschebahn com dataset api fahrplan deutsche bahn db api apikey no unknown transport for grenoble france https data mobilites m fr donnees grenoble public transport no no no transport for honolulu us http hea thebus org api info asp honolulu transportation information apikey no unknown transport for india https data gov in sector transport india public transport api apikey yes unknown transport for lisbon portugal https emel city platform com opendata data about buses routes parking and traffic apikey yes unknown transport for london england https api tfl gov uk tfl api no yes unknown transport for manchester england https developer tfgm com tfgm transport network data apikey yes no transport for ottawa canada http www octranspo com index php developers oc transpo next bus arrival api no no unknown transport for paris france http restratpws azurewebsites net swagger live schedules made simple no no unknown transport for paris france http data ratp fr api v1 console datasets 1 0 search ratp open data api no no unknown transport for philadelphia us http www3 septa org hackathon septa apis no no unknown transport for sao paulo brazil http www sptrans com br desenvolvedores api do olho vivo guia de referencia documentacao api sptrans oauth no unknown transport for sweden https www trafiklab se api public transport consumer oauth yes unknown transport for switzerland https opentransportdata swiss en official swiss public transport open data apikey yes unknown transport for switzerland https transport opendata ch swiss public transport api no yes unknown transport for the netherlands http www ns nl reisinformatie ns api ns only trains apikey no unknown transport for the netherlands https github com skywave kv78turbo ovapi wiki ovapi country wide public transport no yes unknown transport for toronto canada https myttc ca developers ttc no yes unknown transport for united states http www nextbus com xmlfeeddocs nextbusxmlfeed pdf nextbus api no no unknown transport for vancouver canada https developer translink ca translink oauth yes unknown transport for washington us https developer wmata com washington metro transport api oauth yes unknown uber https developer uber com products uber ride requests and price estimation oauth yes yes whereismytransport https developer whereismytransport com platform for public transport data in emerging cities oauth yes unknown back to index index url shorteners api description auth https cors bitly http dev bitly com get started html url shortener and link management oauth yes unknown cleanuri https cleanuri com docs url shortener service no yes yes clickmeter https support clickmeter com hc en us categories 201474986 monitor compare and optimize your marketing links apikey yes unknown rebrandly https developers rebrandly com v1 docs custom url shortener for sharing branded links apikey yes unknown f4nsix https www f4nsix xyz api docs url shortener and track shorten urls apikey yes yes back to index index vehicle api description auth https cors brazilian vehicles and prices https deividfortuna github io fipe vehicles information from funda o instituto de pesquisas econ micas fipe no yes unknown carsxe api https api carsxe com ref public apis github vehicle data and vin decoder specs plates market value ownership cost and images apikey yes unknown kelley blue book http developer kbb com data 1 default vehicle info pricing configuration plus much more apikey yes no mercedes benz https developer mercedes benz com apis telematics data remotely access vehicle functions car configurator locate service dealers apikey yes no nhtsa https vpic nhtsa dot gov api nhtsa product information catalog and vehicle listing no yes unknown smartcar https smartcar com docs lock and unlock vehicles and get data like odometer reading and location works on most new cars oauth yes yes back to index index video api description auth https cors an api of ice and fire https anapioficeandfire com game of thrones api no yes unknown breaking bad https breakingbadapi com documentation breaking bad api no yes unknown breaking bad quotes https github com shevabam breaking bad quotes some breaking bad quotes no yes unknown czech television http www ceskatelevize cz xml tv program tv programme of czech tv no no unknown dailymotion https developer dailymotion com dailymotion developer api oauth yes unknown dune https github com ywalia01 dune api dune api no yes unknown harry potter https www potterapi com harry potter api apikey yes yes open movie database http www omdbapi com movie information apikey yes unknown owen wilson wow https owen wilson wow api onrender com api for actor owen wilson s wow exclamations in movies no yes yes potter db https potterdb com data from the harry potter universe characters movies books spells and potions no yes unknown ron swanson quotes https github com jamesseanwright ron swanson quotes ron swanson quotes api television no yes unknown south park quotes https github com thatskat southpark quotes api a pretty simple api to let you retrieve some of the best quotes from south park mmkay no yes yes stapi https stapi co information on all things star trek no yes yes tmdb https www themoviedb org documentation api community based movie data apikey yes unknown trakt https trakt tv b api docs movie and tv data apikey yes yes tvdb https api thetvdb com swagger television data apikey yes unknown tvmaze http www tvmaze com api tv show data no no unknown vimeo https developer vimeo com vimeo developer api oauth yes unknown youtube https developers google com youtube add youtube functionality to your sites and apps oauth yes unknown shotstack https shotstack io develop video applications with cloud based video editing api apikey yes unknown back to index index weather api description auth https cors 7timer http www 7timer info doc php lang en weather especially for astroweather no no unknown apixu https www apixu com doc request aspx weather apikey yes unknown dark sky https darksky net dev weather apikey yes no metaweather https www metaweather com api weather no yes no metaweather with cors https metaweather with cors now sh weather no yes yes meteorologisk institutt https api met no weatherapi documentation weather and climate data no yes unknown noaa climate data https www ncdc noaa gov cdo web weather and climate data apikey yes unknown odweather http api oceandrivers com static docs html weather and weather webcams no no unknown open meteo https open meteo com global weather forecast api for non commercial use no yes yes openuv https www openuv io real time uv index forecast apikey yes unknown openweathermap http openweathermap org api weather apikey no unknown storm glass https stormglass io global marine weather from multiple sources apikey yes yes weather hacks http weather livedoor com weather hacks weather for japan no no no weatherbit https www weatherbit io api weather apikey yes unknown yahoo weather https developer yahoo com weather weather apikey yes unknown back to index index | public-api | front_end |
Udagram_MonolithToMicroservice | udagram image filtering application udagram is a simple cloud application developed alongside the udacity cloud engineering nanodegree it allows users to register and log into a web client post photos to the feed and process photos using an image filtering microservice the project is split into two parts 1 frontend angular web application built with ionic framework 2 backend restful api node express application solution notes with the solution we can run the application through docker rather than independently installing packages and running the backend api and frontend app refer to the readme in udagram deployment on notes on how to run with docker compose the backend api has been decomposed into two independent microservices for users and for the feed as such there is a bit of duplicate code across the two codebases in real world scenarios this would often be done to get our microservice up and running to clean this up the approach would be to abstract out common code into a library such as an internal npm package that each project would use getting started tip it s recommended that you start with getting the backend api running since the frontend web application depends on the api prerequisite 1 the depends on the node package manager npm you will need to download and install node from https nodejs com en download https nodejs org en download this will allow you to be able to run npm commands 2 environment variables will need to be set these environment variables include database connection details that should not be hard coded into the application code a file named set env sh has been prepared as an optional tool to help you configure these variables on your local development environment database create a postgresql database either locally or on aws rds set the config values for environment variables prefixed with postgres in set env sh s3 create an aws s3 bucket set the config values for environment variables prefixed with aws in set env sh backend api to download all the package dependencies run the command from the directory udagram api bash npm install to run the application locally run bash npm run dev you can visit http localhost 8080 api v0 feed in your web browser to verify that the application is running you should see a json payload feel free to play around with postman to test the api s frontend app to download all the package dependencies run the command from the directory udagram frontend bash npm install install ionic framework s command line tools for us to build and run the application bash npm install g ionic prepare your application by compiling them into static files bash ionic build run the application locally using files created from the ionic build command bash ionic serve you can visit http localhost 8100 in your web browser to verify that the application is running you should see a web interface tips 1 take a look at udagram api does it look like we can divide it into two modules to be deployed as separate microservices 2 the dockerignore file is included for your convenience to not copy node modules copying this over into a docker container might cause issues if your local environment is a different operating system than the docker image ex windows or macos vs linux 3 it s useful to lint your code so that changes in the codebase adhere to a coding standard this helps alleviate issues when developers use different styles of coding eslint has been set up for typescript in the codebase for you to lint your code run the following bash npx eslint ext js ts src to have your code fixed automatically run bash npx eslint ext js ts src fix 4 over time our code will become outdated and inevitably run into security vulnerabilities to address them you can run bash npm audit fix 5 in set env sh environment variables are set with export var value setting it this way is not permanent every time you open a new terminal you will have to run set env sh to reconfigure your environment variables to verify if your environment variable is set you can check the variable with a command like echo postgres username | cloud |
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electron-react-typescript-boilerplate | electron react typescript boilerplate boilerplate for development of an electron app with react amp typescript for frontend for backend webpack babel and lerna this boilerplate is not ready yet | server |
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ARM7 | arm7 embedded system design | os |
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bayesian-machine-learning | bayesian machine learning notebooks doi https zenodo org badge doi 10 5281 zenodo 4318528 svg https doi org 10 5281 zenodo 4318528 this repository is a collection of notebooks about bayesian machine learning the following links display some of the notebooks via nbviewer https nbviewer jupyter org to ensure a proper rendering of formulas dependencies are specified in requirements txt files in subdirectories bayesian regression with linear basis function models https nbviewer jupyter org github krasserm bayesian machine learning blob dev bayesian linear regression bayesian linear regression ipynb introduction to bayesian linear regression implementation with plain numpy and scikit learn see also pymc3 implementation https nbviewer jupyter org github krasserm bayesian machine learning blob dev bayesian linear regression bayesian linear regression pymc3 ipynb open in colab https colab research google com assets colab badge svg https colab research google com github krasserm bayesian machine learning blob dev gaussian processes gaussian processes ipynb gaussian processes https nbviewer jupyter org github krasserm bayesian machine learning blob dev gaussian processes gaussian processes ipynb flush cache true introduction to gaussian processes for regression implementation with plain numpy scipy as well as with scikit learn and gpy open in colab https colab research google com assets colab badge svg https colab research google com github krasserm bayesian machine learning blob dev gaussian processes gaussian processes classification ipynb gaussian processes for classification https nbviewer jupyter org github krasserm bayesian machine learning blob dev gaussian processes gaussian processes classification ipynb introduction to gaussian processes for classification implementation with plain numpy scipy as well as with scikit learn open in colab https colab research google com assets colab badge svg https colab research google com github krasserm bayesian machine learning blob dev gaussian processes gaussian processes sparse ipynb sparse gaussian processes https nbviewer jupyter org github krasserm bayesian machine learning blob dev gaussian processes gaussian processes sparse ipynb introduction to sparse gaussian processes using a variational approach example implementation with jax open in colab https colab research google com assets colab badge svg https colab research google com github krasserm bayesian machine learning blob dev bayesian optimization bayesian optimization ipynb bayesian optimization https nbviewer jupyter org github krasserm bayesian machine learning blob dev bayesian optimization bayesian optimization ipynb introduction to bayesian optimization implementation with plain numpy scipy as well as with libraries scikit optimize and gpyopt hyper parameter tuning as application example variational inference in bayesian neural networks https nbviewer jupyter org github krasserm bayesian machine learning blob dev bayesian neural networks bayesian neural networks ipynb demonstrates how to implement a bayesian neural network and variational inference of weights example implementation with keras reliable uncertainty estimates for neural network predictions https nbviewer jupyter org github krasserm bayesian machine learning blob dev noise contrastive priors ncp ipynb uses noise contrastive priors for bayesian neural networks to get more reliable uncertainty estimates for ood data implemented with tensorflow 2 and tensorflow probability open in colab https colab research google com assets colab badge svg https colab research google com github krasserm bayesian machine learning blob dev latent variable models latent variable models part 1 ipynb latent variable models part 1 gaussian mixture models and the em algorithm https nbviewer jupyter org github krasserm bayesian machine learning blob dev latent variable models latent variable models part 1 ipynb introduction to the expectation maximization em algorithm and its application to gaussian mixture models implementation with plain numpy scipy and scikit learn see also pymc3 implementation https nbviewer jupyter org github krasserm bayesian machine learning blob dev latent variable models latent variable models part 1 pymc3 ipynb open in colab https colab research google com assets colab badge svg https colab research google com github krasserm bayesian machine learning blob dev latent variable models latent variable models part 2 ipynb latent variable models part 2 stochastic variational inference and variational autoencoders https nbviewer jupyter org github krasserm bayesian machine learning blob dev latent variable models latent variable models part 2 ipynb introduction to stochastic variational inference with a variational autoencoder as application example implementation with tensorflow 2 x deep feature consistent variational autoencoder https nbviewer jupyter org github krasserm bayesian machine learning blob dev autoencoder applications variational autoencoder dfc ipynb describes how a perceptual loss can improve the quality of images generated by a variational autoencoder example implementation with keras conditional generation via bayesian optimization in latent space https nbviewer jupyter org github krasserm bayesian machine learning blob dev autoencoder applications variational autoencoder opt ipynb describes an approach for conditionally generating outputs with desired properties by doing bayesian optimization in latent space learned by a variational autoencoder example application implemented with keras and gpyopt | machine-learning bayesian-methods bayesian-machine-learning gaussian-processes bayesian-optimization variational-autoencoder | ai |
texar-pytorch | div align center img src docs static img logo h 035 png br br div pypi https img shields io pypi v texar pytorch svg https pypi python org pypi texar pytorch python build https github com asyml texar pytorch actions workflows main yml badge svg https github com asyml texar pytorch actions workflows main yml codecov https codecov io gh asyml texar pytorch branch master graph badge svg https codecov io gh asyml texar pytorch documentation status https readthedocs org projects texar pytorch badge version latest https texar pytorch readthedocs io en latest badge latest license https img shields io badge license apache 202 0 blue svg https github com asyml texar pytorch blob master license texar pytorch is a toolkit aiming to support a broad set of machine learning especially natural language processing and text generation tasks texar provides a library of easy to use ml modules and functionalities for composing whatever models and algorithms the tool is designed for both researchers and practitioners for fast prototyping and experimentation texar pytorch was originally developed and is actively contributed by petuum https petuum com and cmu https www cmu edu in collaboration with other institutes a mirror of this repository is maintained by petuum open source https github com petuum texar pytorch integrates many of the best features of tensorflow into pytorch delivering highly usable and customizable modules superior to pytorch native ones key features two versions mostly same interfaces texar pytorch this repo and texar tf https github com asyml texar have mostly the same interfaces both further combine the best design of tf and pytorch interfaces and variable sharing in pytorch convention excellent factorization and rich functionalities in tf convention versatile to support broad needs data processing model architectures loss functions training and inference algorithms evaluation encoder s to decoder s sequential and self attentions memory hierarchical models classifiers maximum likelihood learning reinforcement learning adversarial learning probabilistic modeling fully customizable at multiple abstraction level both novice friendly and expert friendly free to plug in whatever external modules since texar is fully compatible with the native pytorch apis modularized for maximal re use and clean apis based on principled decomposition of learning inference model architecture rich pre trained models rich usage with uniform interfaces bert gpt2 xlnet etc for encoding classification generation and composing complex models with other texar components clean detailed documentation https texar pytorch readthedocs io and rich examples examples div align center img src docs static img texar stack png br br div div align center img src docs static img texar modules big png br br div library api example a code example that builds and trains a conditional gpt2 model e g for machine translation and text summarization python import texar torch as tx from texar torch run import 1 modeling class conditionalgpt2model nn module an encoder decoder model with gpt 2 as the decoder def init self vocab size super init use hyperparameter dict for model configuration self embedder tx modules wordembedder vocab size hparams emb hparams self encoder tx modules transformerencoder hparams enc hparams self decoder tx modules gpt2decoder gpt2 small with pre trained weights def get decoder output self batch train true perform model inference i e decoding enc states self encoder inputs self embedder batch source text ids sequence length batch source length if train teacher forcing decoding at training time return self decoder inputs batch target text ids sequence length batch target length 1 memory enc states memory sequence length batch source length else beam search decoding at prediction time start tokens torch full like batch source text ids 0 bos return self decoder beam width 5 start tokens start tokens memory enc states memory sequence length batch source length def forward self batch compute training loss outputs self get decoder output batch loss tx losses sequence sparse softmax cross entropy sequence loss labels batch target text ids 1 logits outputs logits sequence length batch target length 1 automatic masking return loss loss def predict self batch compute model predictions sequence self get decoder output batch train false return gen text ids sequence 2 data create dataset splits using built in data loaders datasets split tx data pairedtextdata hparams data hparams split for split in train valid test model conditionalgpt2model datasets train target vocab size 3 training manage the train eval loop with the executor api executor executor model model datasets datasets optimizer type torch optim adam kwargs lr 5e 4 stop training on cond epoch 20 log every cond iteration 100 validate every cond epoch 1 train metric loss metric runningaverage 10 pred name loss valid metric metric bleu pred name gen text ids label name target text ids save every cond validation better true checkpoint dir outputs saved models executor train executor test datasets test many more examples are available here examples installation texar pytorch requires python 3 6 or 3 7 torch 1 0 0 please follow the official instructions https pytorch org get started locally start locally to install the appropriate version after torch is installed install texar from pypi bash pip install texar pytorch to use cutting edge features or develop locally install from source git clone https github com asyml texar pytorch git cd texar pytorch pip install to use tensorboard support with executor please install tensorboardx with the following command commandline pip install tensorboardx getting started examples examples documentation https texar pytorch readthedocs io reference if you use texar please cite the tech report https arxiv org abs 1809 00794 with the following bibtex entry texar a modularized versatile and extensible toolkit for text generation zhiting hu haoran shi bowen tan wentao wang zichao yang tiancheng zhao junxian he lianhui qin di wang xuezhe ma zhengzhong liu xiaodan liang wanrong zhu devendra sachan and eric xing acl 2019 inproceedings hu2019texar title texar a modularized versatile and extensible toolkit for text generation author hu zhiting and shi haoran and tan bowen and wang wentao and yang zichao and zhao tiancheng and he junxian and qin lianhui and wang di and others booktitle acl 2019 system demonstrations year 2019 license apache license 2 0 license companies and universities supporting texar p float left img src docs static img petuum png width 200 align top nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp nbsp img src https asyml io assets institutions cmu png width 200 align top p | machine-learning natural-language-processing pytorch deep-learning text-generation python machine-translation dialog-systems texar bert gpt-2 xlnet roberta text-data data-processing texar-pytorch casl-project | ai |
real-time-tracking-software | project link project link https developer mbed org users boalinlai notebook real time vehicle tracking system | os |
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dawn | h1 align center img src https img alicdn com tfs tb1ojr6hql0gk0jszfaxxca9pxa 1360 1360 png alt dawn width 200 br dawn br h1 h4 align center lightweight task management and build tool h4 p align center a href https github com alibaba dawn blob main license img src https img shields io npm l dawnjs cli svg alt license a a href https www npmjs com package dawnjs cli img src https img shields io npm v dawnjs cli svg alt npm version a a href https github com alibaba dawn actions workflows main yml img src https github com alibaba dawn actions workflows main yml badge svg alt ci a a href https www npmjs com package dawnjs cli img src https img shields io npm dt dawnjs cli svg alt npm downloads a p pre align center npm i a href https www npmjs com package dawnjs cli dawnjs cli a g pre readme in english readme intl md dawn pipeline middleware pipeline task sh npm install dawnjs cli g sh 1 dn init t front 2 dn dev 3 dn test 4 dn build dawn yml dawn yml dev name dawnjs dn middleware webpack env development entry src js template assets html serveropts port 8001 buid name dawnjs dn middleware webpack env production entry src js template assets html getting started md docs markdowns getting started md pipeline pipeline md docs markdowns pipeline md middleware md docs markdowns middleware md https alibaba github io dawn docs https alibaba github io dawn docs contributing zh md changelog md mit https tldrlegal com license mit license | build-tool build front-end dawn dawn-cli task pack pipeline middleware construction nodejs javascript | front_end |
collie | div align center img src docs assets images banner png div collie collie collaborative tuning of large language models in an efficient way github repo stars https img shields io github stars openlmlab collie style social https github com openlmlab collie stargazers github https img shields io github license openlmlab collie doc https img shields io badge website doc blue https openlmlab collie readthedocs io zh cn latest huggingface badge https img shields io badge f0 9f a4 97huggingface join yellow https huggingface co openlmlab github workflow status with event https img shields io github actions workflow status openlmlab collie python publish yml https pypi org project collie lm github commit activity branch https img shields io github commit activity w openlmlab collie https github com openlmlab collie commits main github issues https img shields io github issues openlmlab collie https github com openlmlab collie issues h4 align center p a href https github com openlmlab collie blob dev readme md a a href https github com openlmlab collie blob dev readme en md english a p h4 2023 07 18 python collie lm https pypi org project collie lm history ul li a href collie collie a li li a href a li li a href collie collie a li li a href a li li a href a li li a href docker docker a li li a href a ul li a href a li li a href a li li a href a li ul li li a href a li li a href a li li a href a li ul collie collie collie collie moss internlm llama chatglm collie collie collie collie deepspeed pytorch div align center img src docs assets images feature list png width 800px div dp pp https arxiv org pdf 1811 06965 pdf tp https arxiv org pdf 2104 04473 pdf zero https arxiv org pdf 1910 02054 pdf lomo https arxiv org pdf 2306 09782 pdf lora https arxiv org pdf 2106 09685 pdf flash attention https arxiv org pdf 2205 14135 pdf details summary summary div align center img src docs assets images features svg width 800px div details collie moss moon https github com openlmlab moss moss moon 003 base https huggingface co fnlp moss moon 003 base moss moon 003 sft https huggingface co fnlp moss moon 003 sft moss moon 003 sft plugin https huggingface co fnlp moss moon 003 sft plugin internlm https github com internlm internlm internlm 7b https huggingface co internlm internlm 7b internlm chat 7b https huggingface co internlm internlm chat 7b internlm chat 7b 8k https huggingface co internlm internlm chat 7b 8k llama https github com facebookresearch llama llama 7b hf https huggingface co decapoda research llama 7b hf llama 13b hf https huggingface co decapoda research llama 13b hf llama 30b hf https huggingface co decapoda research llama 30b hf llama 65b hf https huggingface co decapoda research llama 65b hf llama 2 https github com facebookresearch llama llama 2 7b hf https huggingface co meta llama llama 2 7b hf llama 2 13b hf https huggingface co meta llama llama 2 13b hf llama 2 70b hf https huggingface co meta llama llama 2 70b hf llama 2 7b chat hf https huggingface co meta llama llama 2 7b chat hf llama 2 13b chat hf https huggingface co meta llama llama 2 13b chat hf llama 2 70b chat hf https huggingface co meta llama llama 2 70b chat hf openllama https github com openlm research open llama open llama 3b https huggingface co openlm research open llama 3b open llama 7b https huggingface co openlm research open llama 7b open llama 13b https huggingface co openlm research open llama 13b open llama 7b v2 https huggingface co openlm research open llama 7b v2 chatglm https github com thudm chatglm 6b chatglm 6b https huggingface co thudm chatglm 6b chatglm2 https github com thudm chatglm2 6b chatglm2 6b https huggingface co thudm chatglm2 6b 7b 13b 30b 65b finetune 2 3 6 16 lora 1 1 1 2 lomo 1 1 1 2 adam gpu a100 pytorch 1 13 cuda 11 6 linux os pypi pypi bash pip install collie lm bash git clone https github com openlmlab collie python setup py install docker collie moss lomo zero3 img src docs assets images mario running gif height 50px python from transformers import autotokenizer from collie config import collieconfig from collie data import colliedatasetfortraining from collie data import colliedataloader from collie optim lomo import lomo from collie controller trainer import trainer from collie controller evaluator import evaluatorforperplexity evaluatorforgeneration from collie models moss moon import moss003moonforcausallm from collie utils monitor import steptimemonitor tgsmonitor memorymonitor lossmonitor evalmonitor from collie metrics import decodemetric pplmetric from collie module import gptlmloss from collie utils data provider import gradioprovider moss pretrained model fnlp moss moon 003 sft collie python config collieconfig from pretrained pretrained model trust remote code true config tp size 2 config dp size 1 config pp size 1 epoch config train epochs 1 100 step eval config eval per n steps 100 1 epoch eval config eval per n epochs 1 gpu batch size 16 config train micro batch size 16 eval batch size 1 config eval batch size 1 deepspeed config ds config fp16 fp16 enabled true zero allow untested optimizer true zero force ds cpu optimizer false zero 3 zero optimization stage 3 offload optimizer device cpu pin memory false monitor config enabled true tag adan csv monitor enabled true output path ds logs tokenizer python tokenizer autotokenizer from pretrained fnlp moss moon 003 sft trust remote code true python train dataset input collie is a python package for output finetuning large language models for in range 10000 train dataset colliedatasetfortraining train dataset tokenizer eval dataset train dataset 32 python model moss003moonforcausallm from pretrained pretrained model config config python optimizer lomo model lr 0 001 clip grad norm 5 0 python monitors step steptimemonitor config tgs token tgsmonitor config memorymonitor config loss lossmonitor config eval evalmonitor config evaluator evaluator ppl perplexity decode python evaluator ppl evaluatorforperplexity model model config config dataset eval dataset monitors evalmonitor config metrics ppl pplmetric evaluator decode evaluatorforgeneration model model config config tokenizer tokenizer dataset eval dataset monitors evalmonitor config metrics decode decodemetric trainer python trainer trainer model model config config loss fn gptlmloss 100 optimizer optimizer train dataset train dataset monitors monitors evaluators evaluator ppl evaluator decode trainer train bash command cuda visible devices 0 1 2 3 torchrun rdzv backend c10d rdzv endpoint localhost 29402 nnodes 1 nproc per node 4 finetune moss for training py div align center img src docs assets images progress png div a href https github com openlmlab collie blob dev examples finetune moss for training py examples finetune moss for training py a collie monitor dataprovider monitor collieconfig ds config monitor trainer python monitor config enabled true tag adan csv csv monitor enabled true output path ds logs ds logs div align center img src docs assets images monitor png div dataprovider trainer data provider dataprovider human eval python trainer trainer model model config config loss fn gptlmloss 100 optimizer optimizer train dataset train dataset monitors monitors evaluators evaluator ppl evaluator decode data provider gradioprovider tokenizer div align center img src docs assets images data provider png div collie https openlmlab collie readthedocs io zh cn latest examples a href https github com openlmlab collie graphs contributors img src https contrib rocks image repo openlmlab collie a | deep-learning deepspeed nlp pytorch | ai |
BlockChainExploitation | block chain exploitation labs scripts used in blockchain exploitation blog twitter ficti0n http cclabs io old videos https www youtube com watch v an8lzwlojcw list plcwnlq3toelp0pfnuufuihpeeju5qrkji new course https www youtube com watch v guj3rrexxu0 list plcwnlq3toelpii6gci36pnvrrs8ljbhkq this is a set of smart contracts used in the console cowboys blockchain expoitation blogs | blockchain |
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iOS_Reverse_Engineering | div align center logo images logo jpg github stars https img shields io github stars lauriewired ios reverse engineering https github com lauriewired ios reverse engineering stargazers github forks https img shields io github forks lauriewired ios reverse engineering https github com lauriewired ios reverse engineering network members github contributors https img shields io github contributors lauriewired ios reverse engineering https github com lauriewired ios reverse engineering graphs contributors follow lauriewired on twitter https img shields io twitter follow lauriewired style social https twitter com lauriewired div dive into the world of ios reverse engineering dive deep into the heart of ios with this comprehensive guide to reverse engineering from beginners to experts there s something for everyone explore the wiki the comprehensive ios re wiki https github com lauriewired ios reverse engineering wiki ios re reference contains guides and information for kickstarting your ios reverse engineering example ipa files https github com lauriewired ios reverse engineering tree main obfuscatedappexamples example ios ipa files showcase various obfuscation techniques ghidra scripts https github com lauriewired ios reverse engineering blob main swiftnamedemangler py speed up your ios re journey with custom ghidra scripts join the community and help evolve the landscape of ios reverse engineering i will continue to add information to this repository for more ios reverse engineering assistance | os |
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mote-operation-tasks | mote operation tasks mote operating tasks using tinyos operating system specifically designed for embedded networks stop and wait protocol par positive acknowledgement retransmission final coursework this gave me the opportunity to develop my skills in developing programs for wireless network communication using the nesc programming language specifically i was required to demonstrate my understanding and implement the stop and wait par protocol i have been provided with a new set of files for the blinktoradio application the files include a configuration called amsendreceivec which implements a new interface called amsendreceivei using implementation amsendreceivep this interface provides a send command and receive event to allow radio communication the semantics of send and receive are described in amsendreceivep the blinktoradioc implementation gives an example of how the send command and receive event are used to duplicate the functionality of the last tutorial on mote to pc communication part 1 final coursework protocol par in the absence of errors 1 in the blinktoradio h make sure you set am blinktoradio to 6 so that echo does not drop messages 2 ignore the message sequence number field for now 3 modify event amsendreceivei receive as receiver host to send an acknowledge message each time it receives a data message 4 modify receive as sender host to inform fired when an acknowledge message arrives 5 modify event timer0 fired to wait for the acknowledge message before sending the next data message hint use a boolean variable to communicate between receive and fired 6 test your application using the java listen program to verify that data and acknowledge messages are sent as expected 7 temporarily comment out the sending of acknowledge messages and ensure that timer0 fired is blocked 8 restore the sending of acknowledge messages 9 modify fired to set the sequence number field alternately to 0 1 for each sent data message see lecture notes on the par protocol 10 modify receive as receiver host to set the sequence number field of the ack message appropriately 11 modify receive as sender host to use that sequence number when informing fired 12 test your application listen should show correct field values take a screenshot as you will need it when writing your report protocol par in the presence of errors 1 make sure you set am blinktoradio to 99 so that echo drops messages 2 test your application by using java listen 3 it should deadlock after the first dropped message 4 if it does not then it is not correct 5 do not proceed any further until you have fixed the problem 6 modify fired to save each data message 7 modify fired to resend the saved message if an acknowledge message is not received within t milliseconds of being sent hint you already know how to start timer0 in periodic mode you may create another timer and start in singleshot mode 8 experiment with the value of t 9 test your application listen should show the resending of data after a timeout take a screenshot as you will need it when writing your report part 2 final coursework par at speed in the absence of errors 1 make sure you set am blinktoradio to 6 so that echo does not drop messages 2 to date your application has sent 1 data message per second so it is unlikely that it will still be waiting for an acknowledge message when it wishes to send the next data message 3 you will need to modify your application to send the first 60 data messages as fast as it can however you cannot use the event fired to do this as events all run at the same priority level and your application will never receive any messages hint you may want to create a task instead 4 post your newly created task in the right places inside the code to make sure that the program works as expected 5 test your application by using java listen listen should show that your mote sends the first 60 sequences of 4 messages very fast data to echo data from echo ack to echo ack from echo protocol par at speed in the presence of errors 1 make sure you set am blinktoradio to 99 so that echo drops messages 2 test your application by using java listen and inspect the output | os |
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blockchain | page type sample languages csharp java nodejs python rest solidity products dotnet azure azure blockchain workbench azure blockchain service description this repository contains content and samples for the azure blockchain workbench urlfragment blockchain samples name microsoft azure blockchain workbench samples microsoft azure blockchain workbench https raw githubusercontent com azure samples blockchain master blockchain workbench media logo small png microsoft azure blockchain workbench new version 1 8 0 of workbench has been released please see our release notes https github com azure samples blockchain tree master blockchain workbench changelog md and upgrade instructions https github com azure samples blockchain tree master blockchain workbench scripts upgrade readme md if you re running an older version important notice the azure blockchain development kit has been moved to its own repository https github com azure samples blockchain devkit we are not accepting pull requests for the development kit on this repository anymore this repository contains content and samples in number of areas including applications and smart contracts for azure blockchain workbench https github com azure samples blockchain tree master blockchain workbench application and smart contract samples readme md auth samples https github com azure samples blockchain tree master blockchain workbench auth samples rest api https github com azure samples blockchain tree master blockchain workbench rest api samples readme md including the swagger file and clients for net java and python messaging integration https github com azure samples blockchain tree master blockchain workbench messaging integration samples readme md iot integration https github com azure samples blockchain tree master blockchain workbench iot integration samples readme md office integration https github com azure samples blockchain tree master blockchain workbench office integration samples readme md technology samples https github com azure samples blockchain tree master blockchain workbench technology samples readme md data and reporting https github com azure samples blockchain tree master blockchain workbench data reporting samples readme md including cosmosdb sql db excel and powerbi deployment and management scripts and upgrade scripts https github com azure samples blockchain tree master blockchain workbench scripts readme md frequently asked questions https github com azure samples blockchain tree master blockchain workbench faq readme md to learn more about azure blockchain workbench please visit our product page https aka ms workbenchdocs and documentation http azure microsoft com en us features blockchain workbench how to provide feedback to provide feedback on these samples please see our contribution guidelines https github com azure samples blockchain tree master contributing md for general product feedback please visit our forum https techcommunity microsoft com t5 blockchain bd p azureblockchain to request additional features or samples visit our uservoice site https feedback azure com forums 586780 blockchain | blockchain |
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RISC-V_MYTH_Workshop | risc v myth workshop for students of microprocessor for you in thirty hours three weeks myth workshop https www vlsisystemdesign com riscv based myth offered by redwood eda https www redwoodeda com and training partners vlsi system design vsd https www vlsisystemdesign com and the eeview https theeeview com find here accompanying live info and links for day 3 day 5 which may not correspond to actual days depending on the delivery format about the workshop this workshop has recieved a great deal of attention in the risc v community for enabling students to learn at a pace never before possible through the use of tl verilog and makerchip https makerchip com some links workshop info https www vlsisystemdesign com vsd iat riscv org blogs about 13 year old nicholas sharkey https riscv org blog 2020 11 13 year old nicholas sharkey creates a risc v core and 12 year old niel josiah https riscv org blog 2020 12 risc v microarchitecture for kids steve hoover redwood eda linkedin posts https www linkedin com search results all keywords 23mythworkshop origin global search header riscv org s maintains a list of risc v cores including myth cores https riscv org exchange slack you should have been invited to a slack workspace for collaborative discussions if you have not already been added to the established slack channels request to be added please introduce yourself in the appropriate channel use the provided channels appropriately to ask questions throughout the workshop mentors monitor slack nearly 24 hours day throughout the workshop the search box at the top is your friend others may have encountered similar issues vsd iat for day 1 2 content only if included you should already be up and running with the vlsi design systems intelligent assessment technology platform https vsdiat com if you missed the live tutorial in the first call the recording should have been posted in slack and you can search for it github classroom setup and lab submissions lab submissions begin on day 2 and are done via github classroom you should receive a link to join prior to day 2 for this course all interactions with your github repository can be done from your browser you can add files using the add file dropdown menu you can edit a text file by navigating to it and clicking the pencil icon live training is provided and the recording should have been posted in slack submission process day 2 we just want to see that you have done the work capture a few screenshots and save them in the day2 folder of your repository day 3 5 labs involving the calculator or risc v cpu should be submitted if you miss a few don t sweat it but we want to see your progress for each calculator or risc v lab open your github classroom repository in your web browser navigate into the day3 5 folder and the corresponding calculator solutions tlv or risc v solutions tlv click edit pencil paste your updated solution replacing the existing code within makerchip editor select all ctrl a and copy ctrl c then select all in github editor ctrl a and paste with ctrl v add commit message specifying the slide number or name of the lab and commit changes do not append your changes replace them entirely you prior work is captured in the history or commits day 3 5 slides as you listen to videos and do the lab assignments follow along in the slides comments have been added to address points of confusion day 3 slides https drive google com file d 1zcjlzg 53it4co3jdlofiupzj485jz g view usp sharing day 4 5 slides https drive google com file d 1tqvxmfru31 tezdx30jtnjolcqk308um view usp sharing labs starting point code intro labs all that are not calculator or risc v no special starting point code is required use myth makerchip com https myth makerchip com calculator labs begin with the following starter code https myth makerchip com sandbox code url https 2f 2fraw githubusercontent com 2fstevehoover 2frisc v myth workshop 2fmaster 2fcalculator shell tlv ctrl click risc v labs begin with the following starter code https myth makerchip com sandbox code url https 2f 2fraw githubusercontent com 2fstevehoover 2frisc v myth workshop 2fmaster 2frisc v shell tlv ctrl click note as the complexity of your design increases it might take long time 3 mins to generate the diagrams or they might fail to generate altogether this does not indicate a problem in your code help it s important to take your time with each concept and with each lab rushing ahead will slow you down in the end when you get stuck 1 always check the log keep your log clean of errors both sandpiper errors blue and verilator errors black in some cases we expect warnings logic errors for signals that are used but never assigned where we want makerchip to provide random input values common issues and solutions can be found below 1 check the slide pdfs for any corrections and check below for common issues and solutions 1 review previous lectures 1 follow conversation in slack to see if someone else encountered similar issues 1 explore these reference solutions https myth makerchip com sandbox code url https 2f 2fraw githubusercontent com 2fstevehoover 2frisc v myth workshop 2fmaster 2freference solutions tlv ctrl click no we re not giving away the answers this link will open in makerchip the diagram waveform and visualization for the solution but will not show source code explore these to figure out the issue that s plaguing you and then go back to doing the lab on your own if you are stuck on syntax hover over a signal assignment in the diagram to see an expression note last time we conducted this workshop students relied to heavily on reference solutions this slowed them down furthermore there are intentional bugs in the reference solutions and we can easily tell if you are simply copying them note that you have to comment the line with m4 define m4 calculator 1 to see solutions for risc v labs also we ve pre generated a diagram of the final risc v reference solution at the bottom of this readme 1 share your sandbox url with a mentor via direct message in slack be sure it is saved cloned and clone again before editing 1 we have a zoom plugin in slack feel free to request a meeting with the instructors or meet with others start a meeting with zoom meeting my topic common issues and solutions in some cases the viz logic will make error warning messages a bit more obscure if you have enabled visualization try disabling it sandpiper tm blue log output verilator black log output verilated model didn t dc converge combinational logic loops back on itself so the combinational logic does not stabilize perhaps you missed a 1 errors related to null null disable viz macro and this error will most likely go away debug other sandpiper errors and re enable viz pre generated diagram your generated cpu would look like this after implementing all labs note as noted above in help section refer to this diagram only when stuck reverse engineering this diagram will not help you finish faster and we can tell whether you simply reverse engineer it ctrl click to use your browser s zooming and to hover over assignment statements complete cpu tlv lib fullcore svg after the workshop show off your work github is the new resume show off your work to the world many former students have developed impressive readmes for their repositories and even developed additional features for their cores to showcase what they learned to further explore the technology and to contribute the the community if you have something unique to share about your experience in the workshop and the core you have built we would be happy to showcase it from risc v international s list of risc v cores https riscv org exchange by adding it to this list https github com stevehoover risc v myth workshop blob master student projects md just let us know mailto steve hoover redwoodeda com if you choose to make your workshop repository public follow these steps 1 go to repository risc v myth workshop date yourname on github 2 click on the settings in top ribbon below repo name 3 scroll down to the bottom in danger zone click change visibility 4 in the window that opens select make public type the given text and click i understand 5 done you can use private mode in firefox or incognito in chrome to visit the repo and see how it would look like to the world 6 optionally you can change the repository name if you have any clones of your repository you ll want to push changes and delete them first | os |
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mahotas | mahotas python computer vision library mahotas is a library of fast computer vision algorithms all implemented in c for speed operating over numpy arrays gh actions status https github com luispedro mahotas workflows python 20package 20using 20conda badge svg coverage status https coveralls io repos github luispedro mahotas badge svg branch master https coveralls io github luispedro mahotas branch master license https img shields io badge license mit blue https opensource org licenses mit downloads https pepy tech badge mahotas month https pepy tech project mahotas month install with conda https anaconda org conda forge mahotas badges downloads svg https anaconda org conda forge mahotas install with anaconda https anaconda org conda forge mahotas badges installer conda svg https anaconda org conda forge mahotas python versions 2 7 3 4 are supported notable algorithms watershed https mahotas readthedocs io en latest distance html convex points calculations https mahotas readthedocs io en latest polygon html hit miss thinning zernike haralick lbp and tas features speeded up robust features surf https mahotas readthedocs io en latest surf html a form of local features thresholding https mahotas readthedocs io en latest thresholding html convolution sobel edge detection spline interpolation slic super pixels mahotas currently has over 100 functions for image processing and computer vision and it keeps growing the release schedule is roughly one release a month and each release brings new functionality and improved performance the interface is very stable though and code written using a version of mahotas from years back will work just fine in the current version except it will be faster some interfaces are deprecated and will be removed after a few years but in the meanwhile you only get a warning in a few unfortunate cases there was a bug in the old code and your results will change for the better please cite the mahotas paper https dx doi org 10 5334 jors ac see details below under citation citation if you use it in a publication examples this is a simple example using an example file that is shipped with mahotas of calling watershed using above threshold regions as a seed we use otsu to define threshold python import using mh abbreviation which is common import mahotas as mh load one of the demo images im mh demos load nuclear automatically compute a threshold t otsu mh thresholding otsu im label the thresholded image thresholding is done with numpy operations seeds nr regions mh label im t otsu call seeded watershed to expand the threshold labeled mh cwatershed im max im seeds here is a very simple example of using mahotas distance which computes a distance map python import pylab as p import numpy as np import mahotas as mh f np ones 256 256 bool f 200 240 false f 128 144 32 48 false f is basically true with the exception of two islands one in the lower right corner another middle left dmap mh distance f p imshow dmap p show this is under mahotas demos distance py https github com luispedro mahotas blob master mahotas demos distance py how to invoke thresholding functions python import mahotas as mh import numpy as np from pylab import imshow gray show subplot from os import path load photo of mahotas author in greyscale photo mh demos load luispedro as grey true convert to integer values using numpy operations photo photo astype np uint8 compute otsu threshold t otsu mh otsu photo thresholded otsu photo t otsu compute riddler calvard threshold t rc mh rc photo thresholded rc photo t rc now call pylab functions to display the image gray subplot 2 1 1 imshow thresholded otsu subplot 2 1 2 imshow thresholded rc show as you can see we rely on numpy matplotlib for many operations install if you are using conda https anaconda org you can install mahotas from conda forge https conda forge github io using the following commands bash conda config add channels conda forge conda install mahotas compilation from source you will need python naturally numpy and a c compiler then you should be able to use bash pip install mahotas you can test your installation by running bash python c import mahotas as mh mh test if you run into issues the manual has more extensive documentation on mahotas installation https mahotas readthedocs io en latest install html including how to find pre built for several platforms citation if you use mahotas on a published publication please cite luis pedro coelho mahotas open source software for scriptable computer vision in journal of open research software vol 1 2013 doi https dx doi org 10 5334 jors ac in bibtex format article mahotas author luis pedro coelho title mahotas open source software for scriptable computer vision journal journal of open research software year 2013 doi https dx doi org 10 5334 jors ac month july volume 1 you can access this information using the mahotas citation function development development happens on github https github com luispedro mahotas https github com luispedro mahotas you can set the debug environment variable before compilation to get a debug version bash export debug 1 python setup py test you can set it to the value 2 to get extra checks bash export debug 2 python setup py test be careful not to use this in production unless you are chasing a bug debug level 2 is very slow as it adds many runtime checks the makefile that is shipped with the source of mahotas can be useful too make debug will create a debug build make fast will create a non debug build you need to make clean in between make test will run the test suite links contacts documentation https mahotas readthedocs io https mahotas readthedocs io issue tracker github mahotas issues https github com luispedro mahotas issues mailing list use the pythonvision mailing list https groups google com group pythonvision pli 1 for questions bug submissions etc or ask on stackoverflow tag mahotas https stackoverflow com questions tagged mahotas main author maintainer luis pedro coelho https luispedro org follow on twitter https twitter com luispedrocoelho or github https github com luispedro mahotas also includes code by zachary pincus from scikits image peter j verveer from scipy ndimage and davis king from dlib christoph gohlke as well as others https github com luispedro mahotas graphs contributors presentation about mahotas for bioimage informatics https luispedro org files talks 2013 eubias mahotas html for more general discussion of computer vision in python the pythonvision mailing list https groups google com group pythonvision pli 1 is a much better venue and generates a public discussion log for others in the future you can use it for mahotas or general computer vision in python questions recent changes version 1 4 13 jun 28 2022 fix freeimage testing and make freeimage loading more robust see 129 add gil fixed which triggered crashes in newer numpy versions version 1 4 12 oct 14 2021 update to newer numpy build wheels for python 3 9 3 10 version 1 4 11 aug 16 2020 convert tests to pytest fix testing for pypy version 1 4 10 jun 11 2020 build wheels automatically pr 114 by nathanhillyer https github com nathanhillyer version 1 4 9 nov 12 2019 fix freeimage detection issue 108 version 1 4 8 oct 11 2019 fix co occurrence matrix computation patch by databaaz version 1 4 7 jul 10 2019 fix compilation on windows version 1 4 6 jul 10 2019 make watershed work for 2 voxels issue 102 remove milk from demos improve performance by avoid unnecessary array copies in cwatershed majority filter and color conversions fix bug in interpolation version 1 4 5 oct 20 2018 upgrade code to newer numpy api issue 95 version 1 4 4 nov 5 2017 fix bug in bernsen thresholding issue 84 version 1 4 3 oct 3 2016 fix distribution add missing readme md file version 1 4 2 oct 2 2016 fix resize to return exactly the requested size fix hard crash when computing texture on arrays with negative values issue 72 added distance argument to haralick features pull request 76 by guillaume lemaitre version 1 4 1 dec 20 2015 add filter labeled function fix tests on 32 bit platforms and older versions of numpy version 1 4 0 july 8 2015 added mahotas features py script add short argument to citation function add max iter argument to thin function fixed labeled bbox when there is no background issue 61 reported by daniel haehn bbox now allows dimensions greater than 2 including when using the as slice and border arguments extended croptobbox for dimensions greater than 2 added use x minus y variance option to haralick features add function lbp names version 1 3 0 april 28 2015 improve memory handling in freeimage write multipage fix moments parameter swap add labeled bbox function add return mean and return mean ptp arguments to haralick function add difference of gaussians filter by jianyu wang add laplacian filter by jianyu wang fix crash in median filter when mismatched arguments are passed fix gaussian filter1d for ndim 2 version 1 2 4 december 23 2014 add pil based io version 1 2 3 november 8 2014 export mean filter at top level fix to zernike moments computation reported by sergey demurin fix compilation in platforms without npy float128 patch by gabi davar version 1 2 2 october 19 2014 add minlength argument to labeled sum generalize regmax regmin to work with floating point images allow floating point inputs to cwatershed correctly check for float16 float128 inputs make sobel into a pure function i e do not normalize its input fix sobel filtering version 1 2 1 july 21 2014 explicitly set numpy include dirs in setup py patch by andrew stromnov version 1 2 july 17 2014 export locmax locmin at the mahotas namespace level break away ellipse axes from eccentricity code as it can be useful on its own add find function add mean filter function fix cwatershed overflow possibility make labeled functions more flexible in accepting more types fix crash in close holes with nd images for n 2 remove matplotlibwrap use standard setuptools for building instead of numpy distutils add overlay function version 1 1 1 july 4 2014 fix crash in close holes with nd images for n 2 1 1 0 february 12 2014 better error checking fix interpolation of integer images using order 1 add resize to resize rgb to add coveralls coverage fix slic superpixels connectivity add remove regions where function fix hard crash in convolution fix axis handling in convolve1d add normalization to moments calculation see the changelog https github com luispedro mahotas blob master changelog for older version license fossa status https app fossa io api projects git 2bgithub com 2fluispedro 2fmahotas svg type large https app fossa io projects git 2bgithub com 2fluispedro 2fmahotas ref badge large | computer-vision numpy python c-plus-plus python-2 python-3 | ai |
Deep-Reinforcement-Learning-for-Visual-Object-Tracking-in-Videos | deep reinforcement learning for visual object tracking in videos | ai |
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curriculum | welcome to the mdn front end developer curriculum the mdn front end developer curriculum is intended to provide an up to date industry recommendation for the key fundamental skills and knowledge that a front end web developer should have including the mindset and attitude required for securing a job and for long term success in this field this repository has been created to give the web community an understanding of the curriculum s purpose and target audience an early preview of its content and a chance to give feedback providing feedback on it please let us know what you think rationale the curriculum is an outcome of a research project conducted by the mdn team in early 2023 we asked students new web developers technical hiring managers and web education professionals about their expectations around learning front end web development on mdn and elsewhere the key outcomes of this research were students expect more structured guidance on what topics to learn and when all groups cited a lack of soft skills like teamwork research and critical thinking and knowledge of best practices such as accessibility and privacy in potential new hires as key problems in the industry part of our focus for 2023 is on making mdn a resource where people can learn new skills as well as look up reference material back in 2019 we launched the learning area https developer mozilla org en us docs learn on mdn to help people learn the basics of front end development with a lot of useful content published to date this part of mdn has proven to be successful it averages around 10 of mdn page views however we feel that it needs to be supplemented with stronger guidance on what new front end devs should learn and in what order to be successful in the web industry the curriculum represents a first step towards this goal target audience and purpose we believe the curriculum is useful to several different groups of people from students wanting to learn web development to educators wanting to put together courses to teach it head over to the curriculum introduction curriculum to read more about its target audience and purpose curriculum structure we have structured the curriculum as follows precursor knowledge curriculum 1 precursor topics that are not strictly speaking web development topics but do constitute useful topics for anyone wanting to learn front end web development this includes soft skills and knowledge of a typical development environment core modules curriculum 2 core topics that we feel every web developer should have a good grounding in this includes all the information they need to design and build a basic accessible website app that follows modern best practices and manage and deploy their code using a tool like github optional extension modules curriculum 3 extensions these extension topics constitute useful additional skills to learn as web developers start to expand their knowledge and develop specialisms get started by heading over to the main table of contents toc md to see a granular overview of the included topics and start exploring the content providing feedback we would love to hear your feedback regarding our curriculum to do so please open an issue leave your feedback under general feedback or topic coverage https github com mdn curriculum issues new choose when doing so think about the following questions does our curriculum contain all the fundamental knowledge a front end web developer needs if not what topics are we missing we are interested in high level concerns for example this whole area is missing as well as lower level feedback for example specific css or javascript topic omissions do you think the curriculum is helpful to its key target audiences for example students wanting to learn front end development and teachers wishing to create courses based on it if not why not | front_end |
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deep-nlp | natural language processing with deep learning taking together stanford cs224n course with support of deeppavlov https deeppavlov ai team time 2020 19 00 location 1 9 9235 2 news https t me dlinnlp2020spring chat https t me dlinnlp discuss forum https forum deeppavlov ai c schools hackatons deep learning in nlp 41 course structure weekly quizes quiz 1 https forms gle 2gjgq1ot1dfhqsnz7 quiz 2 https forms gle 1kusvhcmnt7hxsrh7 quiz 3 https forms gle zyxkgxpwli3fane16 up to 5 practical hometasks jupyter notebook to be announced assignment 1 https classroom github com a lu lw 7h assignment 2 https classroom github com a svj6u qk assignment 3 https classroom github com a d89zcsa course project obligatory to be announced spring 2020 syllabus week 1 word vector representations 11 02 2020 word vector representations word2vec https youtu be 8rxd5 xhemo word vectors and word senses https youtu be kemjrjednzm 0 00 38 40 58 00 1 20 00 additional materials cs224n natural language processing with deep learning https youtu be oqq w 63ugq cs224n word vector representations word2vec https youtu be eribwqs9p38 word2vec tutorial the skip gram model http mccormickml com 2016 04 19 word2vec tutorial the skip gram model efficient estimation of word representations in vector space https arxiv org pdf 1301 3781 pdf distributed representations of words and phrases and their compositionality https arxiv org pdf 1310 4546 pdf week 2 neural networks backpropagation 18 02 2020 cs224n word vectors and word senses https youtu be kemjrjednzm 38 40 58 00 enriching word vectors with subword information https www mitpressjournals org doi pdfplus 10 1162 tacl a 00051 cs231n backpropagation neural networks 1 https youtu be i94ovyb6noo additional materials cs224n glove global vectors for word representation https youtu be asn7exxlzws fasttext https youtu be chcexdsdehu week 3 neural networks initialization and normalization cs231n neural networks https www youtube com watch v gypojmlgyxa cs231n lecture notes http cs231n github io neural networks 1 cs231n lecture notes http cs231n github io neural networks 2 week 4 neural networks optimization cs231n neural networks https www youtube com watch v hd kfj5ktuc cs231n lecture notes http cs231n github io neural networks 3 week 5 recurrent neural networks and language models cs224n language models and rnns https youtu be iwea12eau6u cs224n language models and rnns notes http web stanford edu class cs224n readings cs224n 2019 notes05 lm rnn pdf week 6 vanishing gradients fancy rnns cs224n vanishing gradients fancy rnns https www youtube com watch v qew0qea0e50 list ploromvodv4rohcuxmzknm7j3fvwbby42z index 7 paper on the difficulty of training recurrent neural networks http proceedings mlr press v28 pascanu13 pdf article understanding lstm networks https colah github io posts 2015 08 understanding lstms week 7 convolutional networks for nlp cs224n convolutional networks for nlp https youtu be eajora0kx7i paper natural language processing almost from scratch http www jmlr org papers volume12 collobert11a collobert11a pdf paper comparative study of cnn and rnn for natural language processing https arxiv org abs 1702 01923 paper convolutional neural networks for sentence classification https www aclweb org anthology d14 1181 pdf week 8 translation seq2seq attention cs224n translation seq2seq attention https www youtube com watch v xxtpjxzba2c lecture notes http web stanford edu class cs224n readings cs224n 2019 notes06 nmt seq2seq attention pdf paper neural machine translation by jointly learning to align and translate https arxiv org pdf 1409 0473 pdf paper effective approaches to attention based neural machine translation https arxiv org pdf 1508 04025 article attention attention by lilian weng https lilianweng github io lil log 2018 06 24 attention attention html approximate syllabus week 6 deep contextualized word representations comment 1 deep contextualized word representations peters et al 2018 comment 1 universal language model fine tuning for text classification howard and ruder 2018 comment 1 towardsdatascience com elmo helps to further improve your word embeddings c6ed2c9df95f comment 1 nlp fast ai comment 1 jalammar github io illustrated bert comment https youtu be lg6mzw ooli week 7 translation seq2seq attention comment cs224n https youtu be 7m6nov5 l1e comment https clck ru fq8gr comment https clck ru fs497 week 8 contextual word embeddings week 9 question answering week 10 natural language generation project proposals the bert based schema guided state tracking paper https arxiv org pdf 1909 05855 pdf paper https arxiv org pdf 1910 03544 pdf pic https raw githubusercontent com google research datasets dstc8 schema guided dialogue master schema guided overview png the bert cross lingual transferability medium https towardsdatascience com bert based cross lingual question answering with deeppavlov 704242c2ac6f source friends link sk b7aef1c29b8a8f067fe62e3bfbea2292 paper https arxiv org pdf 1906 01502 pdf bert adaptation for new languages and tasks presentation files main huawei pdf how conversational is conversational bert docs http docs deeppavlov ai en master features models bert html grammatical error correction shared task https www cl cam ac uk research nl bea2019st paper for russian https arxiv org pdf 1910 00353 pdf semi supervised morpheme segmentation paper https www aclweb org anthology w19 4218 pdf low resource morphological inflection shared task https sigmorphon github io sharedtasks 2018 to be updated more morphological inflection shared task https sigmorphon github io sharedtasks 2020 automatic data augmentation to be updated https ai journey ru competitions semeval 2020 http alt qcri org semeval2020 index php id tasks semeval 2018 http alt qcri org semeval2018 index php id tasks semeval 2019 http alt qcri org semeval2019 index php id tasks taxonomy enrichment https competitions codalab org competitions 22168 russian aspect based sentiment analysis dialog 2015 http www dialog 21 ru evaluation 2015 sentiment related courses cs224n natural language processing with deep learning course http web stanford edu class cs224n youtube https www youtube com playlist list ploromvodv4rohcuxmzknm7j3fvwbby42z cs231n convolutional neural networks for visual recognition course http cs231n stanford edu youtube https www youtube com playlist list pl3fw7lu3i5jvhm8ljyj zlfqrf3eo8syv machine learning glossary https clck ru ffz2x open machine learning course by yorko http mlcourse ai deep learning http dlcourse ai theoretical deep learning https github com deepmipt tdl4 useful material cs231n python numpy tutorial https clck ru fkkey 100 numpy exercises http github com rougier numpy 100 the matrix calculus you need for deep learning https arxiv org abs 1802 01528 deepmath2019 https www youtube com playlist list plwqvhvmddchzsthhfe4lyaff3pu2m0v2h cmu neural nets for nlp 2020 https www youtube com playlist list pl8pytp1v4i8cj7nmxmc8axv8wqkywj aj | ai |
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arduino-mini-projects-for-embeddedSystems | arduino basics for embedded programming getting started with programming and controlling of tiny computers or microcontrollers in the development and design of embedded systems | os |
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LoyaltyAppIonic | this is a starter template for ionic http ionicframework com docs projects how to use this template this template does not work on its own the shared files for each starter are found in the ionic2 app base repo https github com ionic team ionic2 app base to use this template either create a new ionic project using the ionic node js utility or copy the files from this repository into the starter app base https github com ionic team ionic2 app base with the ionic cli take the name after ionic2 starter and that is the name of the template to be used when using the ionic start command below bash sudo npm install g ionic cordova ionic start myblank blank then to run it cd into myblank and run bash ionic cordova platform add ios ionic cordova run ios substitute ios for android if not on a mac | server |
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GOVINDARAJU | govindaraju today information tomorrow technology | server |
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yaycs | yaycs embedded system game design the legend of the axolotl this project uses a hashtable as a backing structure for the background and map of the game speaker pushbuttons lcd screen and other circuitry make up the backbone of the hardware each hardware piece was imported with a library and pin diagram for the buttons to operate switch statements and methods were used to code the player movement player interaction and speech bubble text for the rpg game | os |
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stellar-design-system | stellar design system components for stellar development foundation s design system usage install as a dependency bash yarn add stellar design system add the main css file to your project for example the main index file of your react project javascript import stellar design system build styles min css import stellar design system components javascript import button input from stellar design system available components can be found here stellar design system src components development this repo has two parts to it stellar design system repo stellar design system everything for the design system stellar design system website repo stellar design system website website for the design system this project is tested with browserstack https email browserstack com c ejwlze1uwyaurthv1doqmf8d1lib7ynbcewasnl lxv8ds6cmnas0imrfwo6abjldubcake2ccpnt1rps8kqlqliq9lcb94w3ivhvs9wgq4pjseksz tph2xd8hesr6sprpwmo3joefjs3yzpl uijvv kb9segvns 38gxzsykzsaikfifdbbiuswfd1nbfzq9ztp7aottahzh yxbql0a8v31g32pjz3v8aqz4rr0 scripts build sds build stellar design system sds project only build sds web build stellar design system website sds web project only build build both projects start sds start sds project for local development start sds web start sds web project for local development clean delete node modules and build directories in the whole repo note you need to run each start command in its own window or tab | os |
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Extracting_ER_From_Relational_Schema | extracting er from relational schema db project | server |
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papers_in_cv | a papers list in computer vision field recent advances in deep learning for object detection https arxiv org pdf 1908 03673v1 pdf reading iccv2019 papers text detection text recognition symmetry constrained rectification network for scene text recognition https arxiv org pdf 1908 01957 pdf end2end towards unconstrained end to end text spotting https arxiv org pdf 1908 09231 pdf others slowfast networks for video recognition https arxiv org pdf 1812 03982 pdf ef bb bf reading cvpr2019 papers papers list https docs google com spreadsheets d 1ru2y iuzwtar hn4v9yz1qpzsielm3iacpuodj vfvq htmlview sle true text detection arbitrary shape scene text detection with adaptive text region representation https arxiv org pdf 1905 05980 pdf learning shape aware embedding for scene text detection http jiaya me papers textdetection cvpr19 pdf text recognition sequence to sequence domain adaptation network for robust text image recognition http openaccess thecvf com content cvpr 2019 papers zhang sequence to sequence domain adaptation network for robust text image recognition cvpr 2019 paper pdf esir end to end scene text recognition via iterative image rectification https arxiv org pdf 1812 05824 pdf aggregation cross entropy for sequence recognition https arxiv org pdf 1904 08364 pdf detection cspnet https arxiv org pdf 1904 02948 pdf keras https github com liuwei16 csp reading aaai2019 papers papers list https aaai org conferences aaai 19 wp content uploads 2018 11 aaai 19 accepted papers pdf text detection text recognition scene text recognition from two dimensional perspective https arxiv org pdf 1809 06508 pdf show attend and read a simple and strong baseline for recognising irregular text https arxiv org pdf 1811 00751v1 pdf scene text recognition aggregation cross entropy for sequence recognition https arxiv org pdf 1904 08364 pdf edit probability fro scene text recognition http openaccess thecvf com content cvpr 2018 papers bai edit probability for cvpr 2018 paper pdf cvpr2018 aon towards arbitrarily oriented text recognition http openaccess thecvf com content cvpr 2018 papers cheng aon towards arbitrarily oriented cvpr 2018 paper pdf cvpr2018 tensorflow code https github com huizhang0110 aon git squeezedtext a real time scene text recognition by binary convolutional encoder decoder network https ren fengbo lab asu edu sites default files 16354 77074 1 pb pdf aaai2018 char net a character aware neural network for distorted scene text recognition http www visionlab cs hku hk publications wliu aaai18 pdf aaai2018 enhancing rnn based ocr by transducative transfer learning from text to images aaai2018 aster an attentional scene text recognizer with flexible rectification http www vlrlab net admin uploads avatars aster an attentional scene text recognizer with flexible rectification pdf pami2018 tensorflow code https github com bgshih aster variable length version https github com huizhang0110 atr git radical analysis network for zero shot learning in printed chinese character recognition https arxiv org pdf 1711 01889 pdf icme2018 scene text recognition from two dimensional perspective https arxiv org pdf 1809 06508 a novel approach to on line handwriting recognition based on bidirectional long short term memory networks https mediatum ub tum de doc 1289961 file pdf icdar2007 similar handwritten chinese characters recognition by critical region selection based on average symmetric uncertainty http www nlpr ia ac cn 2010papers gjhy gh25 pdf icfhr2010 synthetically supervised feature learning for scene text recognition http openaccess thecvf com content eccv 2018 papers yang liu synthetically supervised feature eccv 2018 paper pdf eccv2018 scene text detection detecting text in natural image with connectionist text proposal network https arxiv org pdf 1609 03605 eccv2016 code https github com eragonruan text detection ctpn git east an efficient and accurate scene text detector https arxiv org pdf 1704 03155v2 cvpr2017 tensorflow code https github com argman east detecting oriented text in natural images by linking segments https arxiv org abs 1703 06520 cvpr2017 tensorflow code https github com dengdan seglink git rotation sensitive regression for oriented scene text detection http openaccess thecvf com content cvpr 2018 papers liao rotation sensitive regression for cvpr 2018 paper pdf cvpr2018 learning markov clustering networks for scene text detection http openaccess thecvf com content cvpr 2018 papers liu learning markov clustering cvpr 2018 paper pdf cvpr2018 multi oriented scene text detection via corner localization and region segmentation http openaccess thecvf com content cvpr 2018 papers hong inferring semantic layout cvpr 2018 paper pdf cvpr2018 geometry aware scene text detection with instance transformation network http openaccess thecvf com content cvpr 2018 papers wang geometry aware scene text cvpr 2018 paper pdf cvpr2018 feature enhancement network a refined scene text detector https arxiv org pdf 1711 04249 aaai2018 pixellink detecting scene text via instance segmentation https arxiv org pdf 1801 01315 aaai2018 tensorflow code https github com zjulearning pixel link git inceptext a new inception text module with deformable psroi pooling for multi oriented scene text detection https arxiv org pdf 1805 01167 ijcai2018 textsnake a flexible representation for detecting text of arbitrary shapes http openaccess thecvf com content eccv 2018 papers shangbang long textsnake a flexible eccv 2018 paper pdf eccv2018 accurate scene text detection through border semantics awareness and bootstrapping http openaccess thecvf com content eccv 2018 papers chuhui xue accurate scene text eccv 2018 paper pdf eccv2018 end2end scene text spotting fots fast oriented text spotting with a unified network http openaccess thecvf com content cvpr 2018 papers liu fots fast oriented cvpr 2018 paper pdf cvpr2018 see towards semi supervised end to end scene text recognition https arxiv org pdf 1712 05404 aaai2018 mask textspotter an end to end trainable neural network for spotting text with arbitrary shapes http openaccess thecvf com content eccv 2018 papers pengyuan lyu mask textspotter an eccv 2018 paper pdf eccv2018 using object information for spotting text http openaccess thecvf com content eccv 2018 papers shitala prasad using object information eccv 2018 paper pdf eccv2018 scene text image synthesis verisimilar image synthesis for accurate detection and recognition of texts in scenes http openaccess thecvf com content eccv 2018 papers fangneng zhan verisimilar image synthesis eccv 2018 paper pdf eccv2018 scene text image retrieval image retrieval using textual cues https www di ens fr willow pdfscurrent mishra13 pdf iccv2013 single shot scene text retrieval https arxiv org pdf 1808 09044 eccv2018 tensorflow code https github com lluisgomez single shot str ocr others visual text correction http openaccess thecvf com content eccv 2018 papers amir mazaheri visual text correction eccv 2018 paper pdf eccv2018 docunet document image unwarping via a stacked u net https www juew org publication docunet pdf cvpr2018 object detection general object detection soft nms improving object detection with one line of code http cn arxiv org pdf 1704 04503v2 iccv2017 faster r cnn towards real time object detection with region proposal networks http papers nips cc paper 5638 faster r cnn towards real time object detection with region proposal networks pdf nips2015 tensorflow code https github com endernewton tf faster rcnn git mask r cnn https arxiv org pdf 1703 06870 pdf iccv2017 tensorflow code https github com matterport mask rcnn git sod mtgan small object detection via multi task generative adversarial network http openaccess thecvf com content eccv 2018 papers yongqiang zhang sod mtgan small object eccv 2018 paper pdf eccv2018 reverse attention for salient object detection http openaccess thecvf com content eccv 2018 papers shuhan chen reverse attention for eccv 2018 paper pdf eccv2018 detnet design backbone for object detection http openaccess thecvf com content eccv 2018 papers zeming li detnet design backbone eccv 2018 paper pdf eccv2018 visual inertial object detection and mapping http openaccess thecvf com content eccv 2018 papers xiaohan fei visual inertial object detection eccv 2018 paper pdf eccv2018 zero shot object detection http openaccess thecvf com content eccv 2018 papers ankan bansal zero shot object detection eccv 2018 paper pdf eccv2018 contour knowledge transfer for salient object detection http openaccess thecvf com content eccv 2018 papers xin li contour knowledge transfer eccv 2018 paper pdf eccv2018 fast and accurate intrinsic symmetry detection http openaccess thecvf com content eccv 2018 papers rajendra nagar fast and accurate eccv 2018 paper pdf eccv2018 weakly supervised region proposal network and object detection http openaccess thecvf com content eccv 2018 papers peng tang weakly supervised region eccv 2018 paper pdf eccv2018 zero annotation object detection with web knowledge transfer http openaccess thecvf com content eccv 2018 papers qingyi tao zero annotation object detection eccv 2018 paper pdf eccv2018 fully motion aware network for video object detection http openaccess thecvf com content eccv 2018 papers shiyao wang fully motion aware network eccv 2018 paper pdf eccv2018 geometric constrained joint lane segmentation and lane boundary detection http openaccess thecvf com content eccv 2018 papers jie zhang geometric constrained joint eccv 2018 paper pdf eccv2018 deep feature pyramid reconfiguration for object detection http openaccess thecvf com content eccv 2018 papers tao kong deep feature pyramid eccv 2018 paper pdf eccv2018 receptive field block net for accurate and fast object detection http openaccess thecvf com content eccv 2018 papers songtao liu receptive field block eccv 2018 paper pdf eccv2018 spherenet learning spherical representations for detection and classification in omnidirectional images http openaccess thecvf com content eccv 2018 papers benjamin coors spherenet learning spherical eccv 2018 paper pdf eccv2018 parallel feature pyramid network for object detection http openaccess thecvf com content eccv 2018 papers seung wook kim parallel feature pyramid eccv 2018 paper pdf eccv2018 ts2c tight box mining with surrounding segmentation context for weakly supervised object detection http openaccess thecvf com content eccv 2018 papers yunchao wei ts2c tight box eccv 2018 paper pdf eccv2018 san learning relationship between convolutional features for multi scale object detection http openaccess thecvf com content eccv 2018 papers kim san learning relationship eccv 2018 paper pdf eccv2018 acquisition of localization confidence for accurate object detection http openaccess thecvf com content eccv 2018 papers borui jiang acquisition of localization eccv 2018 paper pdf eccv2018 localization recall precision lrp a new performance metric for object detection http openaccess thecvf com content eccv 2018 papers kemal oksuz localization recall precision eccv 2018 paper pdf eccv2018 deep regionlets for object detection http openaccess thecvf com content eccv 2018 papers hongyu xu deep regionlets for eccv 2018 paper pdf eccv2018 quantization mimic towards very tiny cnn for object detection http openaccess thecvf com content eccv 2018 papers yi wei quantization mimic towards eccv 2018 paper pdf eccv2018 gan unsupervised representation learning with deep convolutional generative adversarial networks https arxiv org pdf 1511 06434 pdf iclr 2016 image to image translation with conditional adversarial networks http openaccess thecvf com content cvpr 2017 papers isola image to image translation with cvpr 2017 paper pdf cvpr 2017 unpaired image to image translation using cycle consistent adversarial networks http openaccess thecvf com content iccv 2017 papers zhu unpaired image to image translation iccv 2017 paper pdf iccv2017 logo detection person re identification alignedreid surpressing human level performance in person re identification https arxiv org pdf 1711 08184 tensorflow code https github com michuanhaohao alignedreid beyond part models person retrieval with refined part pooling improving person re identification by attribute and identity learning bi box regression for pedestrian detection and occlusion estimation http openaccess thecvf com content eccv 2018 papers chunluan zhou bi box regression for eccv 2018 paper pdf eccv2018 cnn base deep residual learning for image recognition https www cv foundation org openaccess content cvpr 2016 papers he deep residual learning cvpr 2016 paper pdf cvpr2016 identity mappings in deep residual networks https arxiv org pdf 1603 05027 pdf eccv2016 aggregated residual transformations for deep neural networks http openaccess thecvf com content cvpr 2017 papers xie aggregated residual transformations cvpr 2017 paper pdf cvpr2017 others spatial transformer networks https arxiv org pdf 1506 02025 nips2015 code https github com kevinzakka spatial transformer network squeeze and excitation networks http openaccess thecvf com content cvpr 2018 cameraready 1287 pdf deep binary representation for efficient image retrieval http downloads hindawi com journals am 2017 8961091 pdf sequential context encoding for duplicate removal https arxiv org pdf 1810 08770 pdf nips2018 training region based object detectors with online hard example mining https www cv foundation org openaccess content cvpr 2016 papers shrivastava training region based object cvpr 2016 paper pdf cvpr2016 | ai |
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ModBus-Topic-FreeRTOS | modbus topic freertos freertos modbus rtu ascii tcp modbus h modbus c define modbus slavetask stack size 512u define modbus slavetask priority num 3u define modbus slavetask buff size 512u modbus 4 modbus modbus modbus c modbus slave typedef modbus createslave modbus slaveinit typedef hslaveinit c typedef struct modbus rxport typedef frxport modbus txport typedef ftxport ticktype t timeouttick modbus type enum msgtype modbus slaveinit typedef modbus rtu modbus slave typedef modbus modbus c int modbus addtopic modbus slave typedef hslave modbus topiclink typedef htopiclink 4 1 5 4 for c modbusrtu hslaveinit frxport jcz gm3rxmodbuspack hslaveinit ftxport jcz gm3tx hslaveinit msgtype modbus type rtu hslaveinit timeouttick jcz modbus net outtime tick hjcz modbusslave modbus createslave hslaveinit if hjcz modbusslave null break htopiclink datamode modbus data mode contain htopiclink datatype modbus data tpye input htopiclink preadfun jcz datapointread htopiclink pwritefun null htopiclink reglen 1 htopiclink slaveid 1 htopiclink ppext null for htopiclink regaddrstart 0 htopiclink regaddrstart 4 htopiclink regaddrstart modbus addtopic hjcz modbusslave htopiclink htopiclink datatype modbus data tpye hold htopiclink pwritefun jcz datapointwrite htopiclink regaddrstart 0 modbus addtopic hjcz modbusslave htopiclink c typedef enum modbus data mode all 0 modbus data mode strict 1 modbus data mode contain 2 modbus datamode enum c int modbus maintxcom modbus slaveinit typedef hslaveinit modbus msg typedef hmodbusmsg modbus slaveinit typedef hmodbusmsg parse c modbus msg typedef hmodbusmsg packdata null packlen null parse 0 modbus slaveinit typedef hinitrs485 frxport rs485 rxpack ftxport rs485 txpack timeouttick jcz modbus rs485 outtime tick msgtype modbus type rtu 02 00 02 hmodbusmsg parse slaveid 0x02u hmodbusmsg parse funcode modbus fun read input hmodbusmsg parse regaddrstart 0x00u hmodbusmsg parse reglen 0x02u rs485 cleanrxdata result modbus maintxcom hinitrs485 hmodbusmsg hmodbusmsg parse pvalue hmodbusmsg modbus maintxcom modbus | os |
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BAC-Swift-project | bac swift project ios application created in software engineering senior projects course in agile environment calculated bac with alcohol database | server |
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ceng3507 | ceng 3507 this is the sharing repo for ceng 3507 web development and programming course offered in msku department of computer engineering http ceng mu edu tr usage install git clone the repository git clone https github com pembeci ceng3507 git for your experiments create a branch and switch to it git checkout b hobara later you can delete the branch with git branch d hobara to get the latest files git checkout b master switch to master branch and then git pull instead of cloning and creating branches you may also fork this repo with your github account and then work on your local copy you need to sync your fork https help github com articles syncing a fork after i added new files visit wiki page https github com pembeci ceng3507 wiki for links and other information | front_end |
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LLM-Sequential-Recommendation | llm sequential recommendations implementation and reproducible experiments behind the research paper leveraging large language models for sequential recommendation published in recsys 23 late breaking results track please cite as follows inproceedings harte2023leveraging author harte jesse and zorgdrager wouter and louridas panos and katsifodimos asterios and jannach dietmar and fragkoulis marios title leveraging large language models for sequential recommendation year 2023 isbn 979 8 4007 0241 9 23 09 publisher association for computing machinery address new york ny usa url https doi org 10 1145 3604915 3610639 doi 10 1145 3604915 3610639 booktitle proceedings of the 17th acm conference on recommender systems numpages 7 location singapore singapore series recsys 23 installation in order to develop code in this repository you ll need poetry in case you don t have poetry installed follow the steps in the steps in the official documentation https python poetry org docs installation activate a shell in the environment this will activate a shell within the current poetry environment if the environment does not exist it will be automatically created bash poetry shell alternatively you can prefix your commands with poetry run which will have the same effect as executing commands within the poetry environment install requirements and local package this command will also install the llm sequential recommendations package in editable mode this means that any changes made in the code will be automatically reflected as long as they reside in directories that existed during the installation if at any point you get errors that do not make sense feel free to run the command again bash poetry install repository organization overview we organized the repository by distinguishing between dataset related code e g beauty implementation code inside main notebooks for analysis and visualization inside notebooks and results in results we explain our dataset related code in a separate readme md in the corresponding directory data related code for now we have included all code we used to process beauty in the beauty directory with a separate readme md regarding the delivery hero dataset we are discussing with our organization to make it available main directory the implementation code in main first of all contains data and eval the former contains the implementation of our sessiondataset which is a convenient object to group together all the code and data related to a single dataset including the train and test set but also the train and test set of the validation folds the latter contains the implementation of each of the metrics and evaluation py which converts the recommendations and ground truths into the format expected by the metrics evaluates the recommendation using the metrics and subsequently provides a view of the evaluation on all metrics furthermore main contains the implementations of each recommendation model in their respective directory inside the main directory the exception here are the llmseqsim and llmseqprompt that are grouped together under openai since both of these models require the utilities in openai utils both models require the product embeddings openai csv gzip file created by create embeddings ipynb this notebook in turn uses uses openai utils py which requires the openai api key to be set in key txt we added the embeddings created by create embeddings ipynb for the beauty dataset in the beauty directory note that llmseqsim is implemented in main openai similarity model and llmseqprompt is implemented in main openai prompt model all models except llmseqsim and llmseqprompt implement the interface of abstract model the train method accepts a pd dataframe containing sessionid and itemid as columns the predict method accepts a dictionary mapping sessionid to a 1 dimensional np ndarray of items sorted according to time the predict method returns an other dictionary mapping sessionid to an other 1 dimensional np ndarray of recommended items sorted descendingly on confidence so the most confidence is on position 0 for more implementation details on the models refer to online material md running experiments running experiments using the models and a given dataset is done in run experiments ipynb you can set dataset filename to the path of the dataset pickle experiments folder is the directory to which the recommendations and configurations are persisted and from which the evaluation retrieves all recommendation pickles to evaluate we hardcoded the optimal configurations returned by our hypersearch in addition we persisted the weights of the neural models with the top performing configurations to ensure reproducilibity | ai |
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embedded | test readme test222 ffff | os |
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Database-Engineering | database engineering my repository on database engineering contains programming and scripting codes and articles on databases like sap hana my sql postgresql and ms sql describing architecture designs enterprise database installation landscapes and methodologies performance fine tuning troubleshooting in real world scenarios monitoring backup and recovery of databases high availability and disaster recovery of database systems | server |
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utrecht | license cc0 1 0 utrecht design system this project is very much work in progress and all components are released as alpha version always define the exact version you want to use and test for breaking changes before upgrading to a newer alpha release applying design elements from this project is strictly prohibited for organisations that are not part of the municipality of utrecht this project is part of a community iniative to use nl design system components for projects that need to adhere to the utrecht design system teams from the central municipality of utrecht as well as those who are contracted by them to develop websites and apps are able to collaborate via this project getting started include the design token css variables html link rel stylesheet type text css href https unpkg com utrecht design tokens dist index css combine it with the latest web components from the nl design system community for example html script src https unpkg com utrecht web component library stencil dist utrecht utrecht esm js type module script then you can go ahead and see the result html utrecht html content class utrecht theme h1 page styled with nl design system h1 utrecht html content avoid automatic upgrades to a new version with breaking changes for all dependencies see what the version is you use while developing and update the url without version to include a version number and ensure your page keeps working even when new versions are released for alpha beta and rc versions text https unpkg com utrecht design tokens dist index css above should become text https unpkg com utrecht design tokens 1 0 0 alpha 10 dist index css for stable versions it would become text https unpkg com utrecht design tokens 1 0 0 dist index css npm packages name version utrecht component library css https www npmjs com package utrecht component library css npm version https img shields io npm v utrecht component library css svg https www npmjs com package utrecht component library css utrecht component library formio https www npmjs com package utrecht component library formio npm version https img shields io npm v utrecht component library formio svg https www npmjs com package utrecht component library formio utrecht components https www npmjs com package utrecht components npm version https img shields io npm v utrecht components svg https www npmjs com package utrecht components utrecht design tokens https www npmjs com package utrecht design tokens npm version https img shields io npm v utrecht design tokens svg https www npmjs com package utrecht design tokens utrecht icon https www npmjs com package utrecht icon npm version https img shields io npm v utrecht icon svg https www npmjs com package utrecht icon utrecht web component library angular https www npmjs com package utrecht web component library angular npm version https img shields io npm v utrecht web component library angular svg https www npmjs com package utrecht web component library angular utrecht web component library react https www npmjs com package utrecht web component library react npm version https img shields io npm v utrecht web component library react svg https www npmjs com package utrecht web component library react utrecht web component library stencil https www npmjs com package utrecht web component library stencil npm version https img shields io npm v utrecht web component library stencil svg https www npmjs com package utrecht web component library stencil contributing install prerequisites you need to have the following tools installed to run storybook locally git node js and npm https nodejs org en pnpm https pnpm js org after installing node js with npm you can install pnpm by running npm install g pnpm open a terminal and run the following commands to check git version a relatively recent version should be installed git 2 28 or later node v should be at least the version defined in the engines section of package json package json npm v should be at least the version defined in the engines section of package json package json pnpm v should be at least version 7 install code editor you can use any editor you d like but in case you use visual studio code https code visualstudio com we recommend the following extensions that are useful for this project editorconfig https marketplace visualstudio com items itemname editorconfig editorconfig eslint https marketplace visualstudio com items itemname dbaeumer vscode eslint markdownlint https marketplace visualstudio com items itemname davidanson vscode markdownlint mdx https marketplace visualstudio com items itemname silvenon mdx prettier code formatter https marketplace visualstudio com items itemname esbenp prettier vscode stylelint https marketplace visualstudio com items itemname stylelint vscode stylelint svg preview https marketplace visualstudio com items itemname simonsiefke svg preview developing locally 1 open terminal 2 select the directory this repository should be cloned into with cd name of directory 3 clone this git repository 4 cd utrecht 5 git checkout main to switch to the main branch if you previously worked in this repository 6 git pull to get to the latest version of the main branch 7 pnpm install to download and install all the dependencies run docusaurus on your computer 1 open terminal 2 ensure your current directory is utrecht 3 run pnpm install to ensure the latest and greatest of all dependencies 4 run pnpm run docs 5 the local version of docusaurus will be running on localhost 3000 utrecht http localhost 3000 utrecht 6 press control c in your terminal to stop docusaurus read the packages docusaurus readme md for docusaurus details run storybook on your computer 1 open terminal 2 ensure your current directory is utrecht 3 run pnpm install to ensure the latest and greatest of all dependencies 4 run pnpm run storybook to start storybook 5 your main browser opens automatically with your local storybook 6 press control c in your terminal to stop storybook debugging storybook first check the logs in the terminal if any error is displayed if something doesn t work as expected secondly check the javascript logs in your browsers developer tools if there are errors or warnings you can run the code checks with pnpm run lint to see if any code errors can be detected you can also check the build logs of the design tokens for errors if you have changed style dictionary json files by building those separately 1 cd proprietary design tokens 2 pnpm run build npm scripts for development web servers script description pnpm run docusaurus start development docusaurus at localhost 3000 http localhost 3000 without access to storybook start pnpm run nx storybook in parallel if you need that pnpm run storybook start html css components storybook localhost 6006 http localhost 6006 pnpm run storybook all start development storybook with composition https storybook js org docs react sharing storybook composition for each framework at localhost 6006 http localhost 6006 pnpm run serve docusaurus start production docusaurus website without access to storybook at localhost 8080 http localhost 8080 pnpm run serve storybook angular start production storybook for angular components at localhost 7009 http localhost 7009 pnpm run serve storybook react start production storybook for react components at localhost 7008 http localhost 7008 pnpm run serve storybook vue start production storybook for vue js components at localhost 7007 http localhost 7007 pnpm run serve start production docusaurus website with access to each storybook at localhost 8080 http localhost 8080 pnpm run storybook angular start development storybook for angular components at localhost 6009 http localhost 6009 pnpm run storybook css start development storybook for css components html components and web components at localhost 6006 http localhost 6006 pnpm run storybook react start development storybook for react components at localhost 6008 http localhost 6008 pnpm run storybook vue start development storybook for vue js components at localhost 6007 http localhost 6007 the scripts above use nx to automatically run all scripts that are a prerequisite for that particular script unfortunately nx often hides the error message when something is wrong in case nx doesn t work use follow these simple steps instead to better see the error message in the terminal project steps design tokens cd proprietary design tokens npm run build storybook for html css cd packages storybook npm run storybook depends on design tokens storybook for react cd packages storybook react npm run storybook depends on design tokens and react components storybook for vue js cd packages storybook vue npm run storybook depends on design tokens and vue js components storybook for angular cd packages storybook angular npm run storybook depends on design tokens and angular components build angular components cd packages component library angular npm run build build vue js components cd packages component library vue npm run build build react components cd packages component library react npm run build build web components cd packages web component library stencil npm run build depends on icons build icons cd components icon npm run build build docusaurus cd packages docusaurus npm run build build css for all components cd components npm run build npm scripts for quality assurance script description pnpm run lint fix check code formatting and automatically fix some types of issues pnpm run lint check code formatting et cetera pnpm run nx lint fix check code formatting and automatically fix some types of issues but faster using cache pnpm run nx lint check code formatting et cetera but faster using cache pnpm run nx test run unit test for each package but faster using cache pnpm run test run unit test for each package npm scripts for the release process script description pnpm run build build each package pnpm run nx build build each package but faster using cache pnpm run publish publish each package to the npm registry pnpm run release determine new version number automatically and update each package json code of conduct we pledge to act and interact in ways that contribute to an open welcoming diverse inclusive and healthy community read our code of conduct code of conduct md if you haven t already license this project is free and open source software licensed under the european union public license eupl v1 2 license md the documentation is licensed as creative commons zero 1 0 universal cc0 1 0 https creativecommons org publicdomain zero 1 0 legalcode for information about proprietary assets in this repository please carefully read the notice file notice md special thanks chromatic https www chromatic com supports us with a free starter plan for open source | nl-design-system | os |
sparkify-redshift-data-warehouse | creating a redshift data warehouse on aws for the music streaming service sparkify table of contents 1 project motivation and description project motivation 2 installation installation 3 file descriptions file descriptions 4 authors and acknowledgements authors acknowledgements project motivation and description a name project motivation a the analytics team of the fictional music streaming service sparkify wants get enabled to understand what songs users are listening to this project aims to support this need by modeling log data which resides in s3 in json format and setting up a redshift data warehouse in the amazon cloud to make the data available for analysis in more detail a cloud based etl pipeline has to be implemented to load data from s3 transform it and load it into the newly designed and created redshift database installation a name installation a aws 0 create iam user dwhadmin in the aws management console files 1 add aws key and secret of the dwhadmin user into dwh example cfg and save it under dwh cfg 2 run create cluster py to create an aws redshift data warehouse 3 add dwh endpoint and dwh role arn into dwh cfg they get logged in step 2 4 run create tables py to create the tables in aws redshift 5 run etl py to load data from staging tables to analytics tables on redshift make sure to delete your redshift cluster afterwards you can use drop cluster py if not needed anymore to prevent unnecessary costs to check if your cluster is still running use check running cluster py file descriptions a name file descriptions a check running cluster py returns a list of running redshift cluster of the attached user create cluster py creates the aws redshift data warehouse and dwh role arn create tables py creates the fact and dimension tables defined in sql queries py in redshift drop cluster py deletes the redshift dwh and iam role created in create cluster py dwh example cfg an example of the configuration file etl py implements the etl pipeline that loads data from s3 into statging tables on redshift processes them and finally loads them into the redshift analytics tables sql queries py contains all sql statements needed within the above files authors and acknowledgements a name authors acknowledgements a this project has been implemented as part of the udacity data engineering nanodegree program the data has been provided by udacity accordingly as well as the project structure file templates | aws redshift cloud etl dwh data-engineering data-warehouse amazon-web-services amazon-s3 | server |
RTOS2_15CO | rtos2 recorda completar la encuestra clase a clase https forms gle rvphazrzdpau6vwg8 material did ctico para la asignatura sistemas operativos en tiempo real 2 de la carrera de especializaci n en sistemas embebidos compatibilidad de los ejercicios los ejecicios son compatibles con el framework firmware v3 https github com epernia firmware v3 trabajo practico el trabajo practico se publicar en la semana 2 para formar los grupos se deber utilizar este https forms gle 4vefonuxjgsgpjfz6 formulario lista de requerimientos https docs google com spreadsheets d 1 vyaqy0edlpg12eqkxe7 bfcb77lkibdfvtndgfbpu0 edit usp sharing scripts script de loopback el test de loopback genera n mensajes aleatorios y los envia validando luego lo recibido la validaci n se realiza comparando y validando que lo enviado sea igual a lo recibido sirve para probar el trabajo pr ctico antes de la implementaci n de c3 configurar config py con los parametros que se desee ejecutar python3 test random loopback py script random el test random genera una serie de mensajes aleatorios y los envia validando luego lo recibido en este caso se cierra el loop en la c3 por lo tanto se puede probar en las etapas en donde 3 ya se encuentra implemnetada y postriormente con el agregado de objectos activos configurar config py con los parametros que se desee ejecutar python3 test random py script test unitarios el test valida las reglas del protocolo sin exigir la ejecucion en terminos temporales ejecutar python3 test unitario py colaboradores diego essaya y santiago abbate script random version inicial lucas orsi script test unitarios version inicial lecturas recomendadas modularidad abstracci n y m ltiples instancias en c para embedded software https embedded exploited blogspot com 2014 04 generalizacion encapsulamiento abstraccion modularidad oop html object oriented programming in c https www state machine com doc an oop in c pdf developing reusable firmware a practical approach to apis hals and drivers https www beningo com store developing reusable firmware a practical approach to apis hals and drivers jacob beningo isbn 13 pbk 978 1 4842 3296 5 gesti n de memoria din mica en sistemas de tiempo real http www gii upv es tlsf files papers mmasmano phdthesis pdf practical design patterns opaque pointers and objects in c https interrupt memfault com blog opaque pointers | os |
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zephyr-inside | old master tree master zephyr os src introduce introduction md hello world src introduce hello world md arduino due src introduce arduino due md zephyr contiki src introduce vs contiki md nanokernel src kernel nanokernel preface md src kernel nanokernel context md task src kernel nanokernel task basic md fiber src kernel nanokernel fiber basic md isr src kernel nanokernel isr basic md src kernel nanokernel thread md nanokernel src kernel nanokernel nanokernel md fiber src kernel nanokernel fiber md isr atomic src kernel nanokernel atomic md dlist src kernel nanokernel dlist md wait q src kernel nanokernel wait q md timeout src kernel nanokernel timeout md timer src kernel nanokernel timer md semaphore src kernel nanokernel sema md fifo src kernel nanokernel fifo md lifo src kernel nanokernel lifo md stack src kernel nanokernel stack md ring buffer src kernel nanokernel ring buf md c swap microkernel kernel microkernel task task fiber k server timer memory map memory pool event semaphore mutex fifo mailbox pipe src driver device driver module md printk gpio i2c spi cc2538 12 31 cc2538 1 2 3 4 7 9 cc2538 18 rf cc2538 23 spi src net introduce md buffer pool buffer src net common simply buf md buffer src net common full buf md yaip mac src net yaip linkaddr md uip contiki src contiki thread md src contiki thread call md src contiki event md src contiki etimer md src contiki example md contiki src net uip loopback md src net uip tcpip def md net context src net uip net context md net context src net uip net context 2 md net core src net uip netcore concept md net core src net uip netcore init md net core src net uip netcore send md net core src net uip netcore recv md l2 buffer src net uip l2 buf md l2 buffer src net uip packet attr md l2 buffer packetbuf src net uip packetbuf md l2 buffer api src net uip l2 buf api md net driver net driver net driver 6lowpan 6lowpan mac mac csma ip buffer | zephyr iot linux rtos | os |
Bitcoin_Dashboard | bitcoin dashboard bitcoin dashboard preview btcimg png bitcoin dashboard is a full stack data engineering project that fetches current and historic bitcoin prices from an external api stores them in a mongodb database computes kpis and displays them in a dashboard this project demonstrates full stack software development data engineering data analysis data visualization and containerization using modern technologies technologies used reactjs with hooks frontend fastapi backend mongodb data storage pandas numpy data processing recharts data visualization apscheduler daily scheduling docker containerization features fetches current bitcoin price from coindesk api and stores it in mongodb bitcoin prices collection once a day using apscheduler fetches and stores historic bitcoin data in mongodb bitcoin historic collection on app start computes and displays various kpis based on historic and current data stored in the database using numpy and pandas including current price overall average price historic maximum price historic minimum price overall volatility visualizes data using recharts line charts to displays historic bitcoin prices from the database with the option to select a year and month visualizes data using recharts bar charts to display the historical maximum and minimum bitcoin prices for a selected year currently set to 2022 dockerized for easy deployment to run the project 1 clone the repository and navigate to the root directory 2 start the docker container docker compose up build 3 open mongodb connection with mongodb compass 4 create emertondb databse and bitcoin prices and bitcoin historic collections 3 start the frontend cd react frontend npm start 4 start the backend cd fastapi backend uvicorn main app reload future improvements implement login and authentication for better security expand data analysis and metrics computation add more data visualization options improve user interface and user experience this project serves as an example of how modern technologies can be used to develop efficient and scalable solutions for full stack software development data engineering analysis and visualization | server |
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BoardGames | boardgames final project presentation of team c eck mates for our software engineering class video is prepared by dylan grandjean https www youtube com watch v clhagunm kc | server |
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AC3.2 | access code 3 2 mobile development with ios in preparation for the midterm on thursday dec 8 we will be reviewing apis this week here s a run down of api projects resources api projects md we ve done schedule link to calendar https calendar google com calendar embed src accesscode 40c4q nyc ctz america new york lesson schedule schedule md curriculum overview curriculum overview pdf | front_end |
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real-time-web-app-dev | apress source code this repository accompanies real time web application development http www apress com 9781484232699 by rami vemula apress 2017 comment cover cover image 9781484232699 jpg download the files as a zip using the green button or clone the repository to your machine using git releases release v1 0 corresponds to the code in the published book without corrections or updates contributions see the file contributing md for more information on how you can contribute to this repository | front_end |
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python-blockchain-sim | python python python http blog hubwiz com 2020 04 01 python sim blockchain git clone https github com ezpod python blockchain sim git python cd python blockchain app pip install r requirements txt export flask app node server py flask run port 8000 8000 python run app py http localhost 5000 web python http blog hubwiz com 2020 04 01 python sim blockchain fig1 png web python http blog hubwiz com 2020 04 01 python sim blockchain fig2 png web python http blog hubwiz com 2020 04 01 python sim blockchain fig3 png | blockchain |
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rf9160-template-project | nrf9160 zephyr project template clone repo sh export project dir pwd myproj change this to suit your needs git clone repo url project dir cd project dir git submodule update init recursive install requirements install python packages sh python mensurepip pip install r ncs zephyr scripts requirements txt pip install r ncs nrf scripts requirements txt install toolchain zephyr sdk currently does not support cortex m33 so we need to install gccv8 instead go to this link https developer arm com open source gnu toolchain gnu rm downloads and download version 8 after download is complete sh cd downloads assuming toolchain downloaded to this directory mkdir p opt gcc arm none eabi mv gcc arm none eabi 8 2018 q4 major linux tar bz2 opt gcc arm none eabi cd opt gcc arm none eabi tar xfv gcc arm none eabi 8 2018 q4 major linux tar bz2 mv gcc arm none eabi 8 2018 q4 major 8 2018 q4 major ln s 8 2018 q4 major latest ensure the toolchain is specified in the path variable install jlink tools go to the segger website https www segger com downloads jlink and download the software and documentation pack for your platform ensure the jlink tools are specified in the path variable building sh cd project dir mkdir p build cd export zephyr toolchain variant gnuarmemb export gnuarmemb toolchain path opt gcc arm none eabi latest source ncs zephyr zephyr env sh cmake gninja dboard nrf9160 pca10090 dcmake export compile commands yes dconf file prj conf app ninja debugging open two terminals in the first terminal run the following try running as root if it does not work sh jlinkgdbserver device nrf9160 if swd in the second terminal sh cd project dir build opt gcc arm none eabi latest bin arm none eabi gdb this will load a gdb shell gdb gdb target remote 2331 gdb load zephyr zephyr elf gdb b main gdb mon reset gdb c this should hopefully trigger a breakpoint in main indicating everyting is working fine after the breakpoint is triggered issue the following command gdb gdb c building secure boot some of the examples provided by nordic semi require secure boot to function properly to help anyone confused by this we have provided a helper script which compiles and flashes the target in a single command sh scripts build and program sh b this helper script also provides a target for the at client example provided by nordic semi see scripts build and program sh help for more details using vscode install vscode https code visualstudio com and opend the project directory this should load a complete development environment complete with build tasks and debug configurations install the following plugins c c for visual studio code https code visualstudio com docs languages cpp cortex debug https marketplace visualstudio com items itemname marus25 cortex debug cmake https marketplace visualstudio com items itemname twxs cmake after all plugins have been installed open the project dir directory the project should be ready to use as is without any additional configurations the following tasks have been configured configure configure project by generating makefiles and running some housekeeping scripts run this task once after opening this project build build project clean clean build files project is still configured no need to run the configure task after running this task pristine delete contents of build directory project is no longer configured run configure before building project after running this task in addition to the tasks above the following launch debug configurations have been supplied cortex debug start jlink gdb server and connect debugger most applications will use this configuration cortex attach attach debugger to running gdb server after starting the debug session the debugger should break at main in some cases the debug session may be unresponsive in this case try clicking restart then continue where to go next the nrf9160 provides of features not explored in this tutorial to help you get started here are some resources nrf9160 examples https github com rallare fw nrfconnect nrf tree nrf9160 samples samples nrf9160 nrf9160 examples provided by github user rallare https github com rallare nrf9160 documentation https www nordicsemi com doclib product nrf9160 20core 20documentation nrf9160 reference manual user guides etc zephyr docs https docs zephyrproject org latest introduction index html zephyr documentation nordic deczone https devzone nordicsemi com developer forum for nordic semi platforms such as nrf9160 nrf52 etc | os |
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Qwen | p align left a href readme cn md a nbsp nbspenglish nbsp nbsp a href readme ja md a nbsp a href readme fr md fran ais a p br br p align center img src https qianwen res oss cn beijing aliyuncs com logo qwen jpg width 400 p br p align center a href https huggingface co qwen hugging face a nbsp nbsp nbsp nbsp a href https modelscope cn organization qwen modelscope a nbsp nbsp nbsp nbsp a href https arxiv org abs 2309 16609 paper a nbsp nbsp nbsp nbsp a href https modelscope cn studios qwen qwen 14b chat demo summary demo a br a href assets wechat png wechat a nbsp nbsp nbsp nbsp dingtalk nbsp nbsp nbsp nbsp a href https discord gg z3gaxxz9ce discord a nbsp nbsp p br br qwen chat qwen chat int4 qwen chat int8 qwen 7b a href https modelscope cn models qwen qwen 7b chat summary a a href https huggingface co qwen qwen 7b chat a a href https modelscope cn models qwen qwen 7b chat int4 summary a a href https huggingface co qwen qwen 7b chat int4 a a href https huggingface co qwen qwen 7b chat int8 a a href https modelscope cn models qwen qwen 7b summary a a href https huggingface co qwen qwen 7b a 14b a href https modelscope cn models qwen qwen 14b chat summary a a href https huggingface co qwen qwen 14b chat a a href https modelscope cn models qwen qwen 14b chat int4 summary a a href https huggingface co qwen qwen 14b chat int4 a a href https huggingface co qwen qwen 14b chat int8 a a href https modelscope cn models qwen qwen 14b summary a a href https huggingface co qwen qwen 14b a we opensource our qwen series now including qwen the base language models namely qwen 7b and qwen 14b as well as qwen chat the chat models namely qwen 7b chat and qwen 14b chat links are on the above table click them and check the model cards also we release the technical report https arxiv org abs 2309 16609 please click the paper link and check it out in brief we have strong base language models which have been stably pretrained for up to 3 trillion tokens of multilingual data with a wide coverage of domains languages with a focus on chinese and english etc they are able to achieve competitive performance on benchmark datasets additionally we have chat models that are aligned with human preference based on sft and rlhf not released yet which are able to chat create content extract information summarize translate code solve math problems and so on and are able to use tools play as agents or even play as code interpreters etc in this repo you can figure out quickstart with qwen and enjoy the simple inference details about the quantization models including gptq and kv cache quantization statistics of inference performance including speed and memory tutorials on finetuning including full parameter tuning lora and q lora instructions on deployment with the example of vllm and fastchat instructions on building demos including webui cli demo etc introduction to dashscope api service as well as the instructions on building an openai style api for your model information about qwen for tool use agent and code interpreter statistics of long context understanding evaluation license agreement also if you meet problems turn to faq faq md for help first still feeling struggled feel free to shoot us issues better in english so that more people can understand you if you would like to help us send us pull requests with no hesitation we are always excited about pr would like to chat with us or date us coffee time welcome to our discord or wechat br br news and updates 2023 10 17 we release the int8 quantized model qwen 7b chat int8 and qwen 14b chat int8 2023 9 25 we release qwen 14b and qwen 14b chat on modelscope and hugging face along with qwen cpp https github com qwenlm qwen cpp and qwen agent https github com qwenlm qwen agent codes and checkpoints of qwen 7b and qwen 7b chat are also updated please pull the latest version compared to qwen 7b original qwen 7b uses more training tokens increasing from 2 2t tokens to 2 4t tokens while the context length extends from 2048 to 8192 the chinese knowledge and coding ability of qwen 7b have been further improved 2023 9 12 we now support finetuning on the qwen 7b models including full parameter finetuning lora and q lora 2023 8 21 we release the int4 quantized model for qwen 7b chat qwen 7b chat int4 which requires low memory costs but achieves improved inference speed besides there is no significant performance degradation on the benchmark evaluation 2023 8 3 we release both qwen 7b and qwen 7b chat on modelscope and hugging face we also provide a technical memo for more details about the model including training details and model performance br performance qwen 14b and qwen 7b this is the new version trained with more tokens and the context length is extended from 2048 to 8192 outperform the baseline models of similar model sizes on a series of benchmark datasets e g mmlu c eval gsm8k math humaneval mbpp bbh etc which evaluate the models capabilities on natural language understanding mathematic problem solving coding etc however even qwen 14b still significantly fall behind gpt 3 5 let alone gpt 4 see the results below p align left img src assets radar 14b jpg width 600 p br model mmlu c eval gsm8k math humaneval mbpp bbh cmmlu 5 shot 5 shot 8 shot 4 shot 0 shot 3 shot 3 shot 5 shot llama2 7b 46 8 32 5 16 7 3 3 12 8 20 8 38 2 31 8 llama2 13b 55 0 41 4 29 6 5 0 18 9 30 3 45 6 38 4 llama2 34b 62 6 42 2 6 2 22 6 33 0 44 1 chatglm2 6b 47 9 51 7 32 4 6 5 33 7 internlm 7b 51 0 53 4 31 2 6 3 10 4 14 0 37 0 51 8 internlm 20b 62 1 58 8 52 6 7 9 25 6 35 6 52 5 59 0 baichuan2 7b 54 7 56 3 24 6 5 6 18 3 24 2 41 6 57 1 baichuan2 13b 59 5 59 0 52 8 10 1 17 1 30 2 49 0 62 0 qwen 7b original 56 7 59 6 51 6 10 4 24 4 31 2 40 6 58 8 qwen 7b 58 2 63 5 51 7 11 6 29 9 31 6 45 0 62 2 qwen 14b 66 3 72 1 61 3 24 8 32 3 40 8 53 4 71 0 for all compared models we report the best scores between their official reported results and opencompass https opencompass org cn leaderboard llm for more experimental results detailed model performance on more benchmark datasets and details please refer to our technical report by clicking here https qianwen res oss cn beijing aliyuncs com qwen technical report pdf br br requirements python 3 8 and above pytorch 1 12 and above 2 0 and above are recommended transformers 4 32 and above cuda 11 4 and above are recommended this is for gpu users flash attention users etc br quickstart below we provide simple examples to show how to use qwen chat with modelscope and transformers before running the code make sure you have setup the environment and installed the required packages make sure you meet the above requirements and then install the dependent libraries bash pip install r requirements txt if your device supports fp16 or bf16 we recommend installing flash attention https github com dao ailab flash attention we support flash attention 2 now for higher efficiency and lower memory usage flash attention is optional and the project can run normally without installing it bash git clone https github com dao ailab flash attention cd flash attention pip install below are optional installing them might be slow pip install csrc layer norm pip install csrc rotary now you can start with modelscope or transformers transformers to use qwen chat for the inference all you need to do is to input a few lines of codes as demonstrated below remember to pass in the correct model names or paths such as qwen qwen 7b chat and qwen qwen 14b chat however please make sure that you are using the latest code python from transformers import automodelforcausallm autotokenizer from transformers generation import generationconfig model names qwen qwen 7b chat qwen qwen 14b chat tokenizer autotokenizer from pretrained qwen qwen 7b chat trust remote code true use bf16 model automodelforcausallm from pretrained qwen qwen 7b chat device map auto trust remote code true bf16 true eval use fp16 model automodelforcausallm from pretrained qwen qwen 7b chat device map auto trust remote code true fp16 true eval use cpu only model automodelforcausallm from pretrained qwen qwen 7b chat device map cpu trust remote code true eval use auto mode automatically select precision based on the device model automodelforcausallm from pretrained qwen qwen 7b chat device map auto trust remote code true eval specify hyperparameters for generation but if you use transformers 4 32 0 there is no need to do this model generation config generationconfig from pretrained qwen qwen 7b chat trust remote code true 1st dialogue turn response history model chat tokenizer history none print response 2nd dialogue turn response history model chat tokenizer history history print response 3rd dialogue turn response history model chat tokenizer history history print response running qwen pretrained base model is also simple details summary running qwen summary python from transformers import automodelforcausallm autotokenizer from transformers generation import generationconfig model names qwen qwen 7b qwen qwen 14b tokenizer autotokenizer from pretrained qwen qwen 7b trust remote code true use bf16 model automodelforcausallm from pretrained qwen qwen 7b device map auto trust remote code true bf16 true eval use fp16 model automodelforcausallm from pretrained qwen qwen 7b device map auto trust remote code true fp16 true eval use cpu only model automodelforcausallm from pretrained qwen qwen 7b device map cpu trust remote code true eval use auto mode automatically select precision based on the device model automodelforcausallm from pretrained qwen qwen 7b device map auto trust remote code true eval specify hyperparameters for generation but if you use transformers 4 32 0 there is no need to do this model generation config generationconfig from pretrained qwen qwen 7b trust remote code true inputs tokenizer ulaanbaatar n reykjavik n return tensors pt inputs inputs to model device pred model generate inputs print tokenizer decode pred cpu 0 skip special tokens true ulaanbaatar n reykjavik n addis ababa details in the event of a network issue while attempting to download model checkpoints and codes from huggingface an alternative approach is to initially fetch the checkpoint from modelscope and then load it from the local directory as outlined below python from modelscope import snapshot download from transformers import automodelforcausallm autotokenizer downloading model checkpoint to a local dir model dir model dir snapshot download qwen qwen 7b revision v1 1 4 model dir snapshot download qwen qwen 7b chat revision v1 1 4 model dir snapshot download qwen qwen 14b revision v1 0 4 model dir snapshot download qwen qwen 14b chat revision v1 0 4 loading local checkpoints trust remote code is still set as true since we still load codes from local dir instead of transformers tokenizer autotokenizer from pretrained model dir trust remote code true model automodelforcausallm from pretrained model dir device map auto trust remote code true eval modelscope modelscope is an opensource platform for model as a service maas which provides flexible and cost effective model service to ai developers similarly you can run the models with modelscope as shown below python from modelscope import automodelforcausallm autotokenizer from modelscope import generationconfig model names qwen qwen 7b chat qwen qwen 14b chat tokenizer autotokenizer from pretrained qwen qwen 7b chat revision v1 0 5 trust remote code true model automodelforcausallm from pretrained qwen qwen 7b chat revision v1 0 5 device map auto trust remote code true fp16 true eval model generation config generationconfig from pretrained qwen qwen 7b chat revision v1 0 5 trust remote code true top p response history model chat tokenizer history none print response response history model chat tokenizer history history print response response history model chat tokenizer history history print response batch inference qwen supports batch inference with flash attention enabled using batch inference can bring a 40 speedup the example code is shown below python import torch from transformers import automodelforcausallm autotokenizer from transformers import generationconfig from qwen generation utils import make context decode tokens get stop words ids tokenizer autotokenizer from pretrained pad token extra 0 eos token endoftext padding side left trust remote code true model automodelforcausallm from pretrained pad token id tokenizer pad token id device map auto trust remote code true eval model generation config generationconfig from pretrained pad token id tokenizer pad token id all raw text batch raw text for q in all raw text raw text make context tokenizer q system you are a helpful assistant max window size model generation config max window size chat format model generation config chat format batch raw text append raw text batch input ids tokenizer batch raw text padding longest batch input ids torch longtensor batch input ids input ids to model device batch out ids model generate batch input ids return dict in generate false generation config model generation config padding lens batch input ids i eq tokenizer pad token id sum item for i in range batch input ids size 0 batch response decode tokens batch out ids i padding lens i tokenizer raw text len len batch raw text i context length batch input ids i size 0 padding lens i chat format chatml verbose false errors replace for i in range len all raw text print batch response response model chat tokenizer history none print response response model chat tokenizer history none print response response model chat tokenizer history none print response cpu to deploy our models on cpu we strongly advise you to use qwen cpp https github com qwenlm qwen cpp which is a pure c implementation of qwen and tiktoken check the repo for more details also it is also simple to directly run the model on cpu which requires your specification of device python model automodelforcausallm from pretrained qwen qwen 7b chat device map cpu trust remote code true eval however it is likely that you suffer from extremely low inference efficiency multiple gpus if you suffer from lack of gpu memory and you would like to run the model on more than 1 gpu you can directly use the default loading method which is now supported by transformers the previous method based on utils py is deprecated however though this method is simple the efficiency of the native pipeline parallelism is low we advise you to use vllm with fastchat and please read the section for deployment br br quantization gptq we provide a solution based on autogptq https github com panqiwei autogptq and release the int4 quantized models which achieve nearly lossless model effects but improved performance on both memory costs and inference speed here we demonstrate how to use our provided quantized models for inference before you start make sure you meet the requirements of auto gptq e g torch 2 0 and above transformers 4 32 0 and above etc and install the required packages bash pip install auto gptq optimum if you meet problems installing auto gptq we advise you to check out the official repo https github com panqiwei autogptq to find a wheel then you can load the quantized model easily and run inference as same as usual python model names qwen qwen 7b chat int4 qwen qwen 14b chat int4 model automodelforcausallm from pretrained qwen qwen 7b chat int4 device map auto trust remote code true eval response history model chat tokenizer hi history none we illustrate the model performance of both bf16 int8 and int4 models on the benchmark and we find that the quantized model does not suffer from significant performance degradation results are shown below quantization mmlu ceval val gsm8k humaneval qwen 7b chat bf16 55 8 59 7 50 3 37 2 qwen 7b chat int8 55 4 59 4 48 3 34 8 qwen 7b chat int4 55 1 59 2 49 7 29 9 qwen 14b chat bf16 64 6 69 8 60 1 43 9 qwen 14b chat int8 63 6 68 6 60 0 48 2 qwen 14b chat int4 63 3 69 0 59 8 45 7 quantization of kv cache attention kv cache can be quantized and compressed for storage to get a higher sample throughput the parameters of use cache quantization and use cache kernel are provided to control kv cache quantization behavior when use cache quantization true and use cache kernel true kv cache quantization will be enabled the specific use method is as follows python model automodelforcausallm from pretrained qwen qwen 7b chat device map auto trust remote code true use cache quantization true use cache kernel true use flash attn false attention currently kv cache quantization and flash attn cannot be turned on at the same time if you enable kv cache quantization and use flash attn at the same time use flash attn true use cache quantization true use cache kernel true use flash attn is disabled by default use flash attn false we have verified that the use of the quantized int8 kvcache model does not suffer from significant performance degradation in downstream evaluation in addition we evaluate its performance focusing on the memory footprint the profiling runs on a single a100 sxm4 80g gpu with pytorch 2 0 1 and cuda 11 4 we use bf16 models and generate 1024 tokens seq length 1024 by default and oom indicates out of memory with kv cache quantization turned on we can run a larger batch size bs use kvcache bs 1 bs 4 bs 16 bs 32 bs 64 bs 100 no 16 3gb 24 1gb 31 7gb 48 7gb oom oom yes 15 5gb 17 2gb 22 3gb 30 2gb 48 2gb 72 4gb with kv cache quantization turned on the model can save more memory when generate longer seq length sl number of tokens generated at infer use kvcache sl 512 sl 1024 sl 2048 sl 4096 sl 8192 no 15 2gb 16 3gb 17 6gb 19 5gb 23 2gb yes 15gb 15 5gb 15 8gb 16 6gb 17 6gb the model which turn on the kv cache quantization will convert the format of layer past from float to int8 meanwhile the quantianted layer past will also store quantiantion parameters of current value specific steps are as follows 1 quantize key value qv scale zero point quantize cache v v 2 store into layer past following is the format of quantized layer past layer past q key key scale key zero point q value value scale value zero point bascial format of layer past layer past key value if you want to use the attention kv which is quantized you can use the dequantization operation to convert the int8 key value back to the float format as following v dequantize cache torch qv scale zero point br inference performance this section provides the statistics of speed and memory of models in different precisions the speed and memory profiling are conducted using this script https qianwen res oss cn beijing aliyuncs com profile py speed we measured the average inference speed tokens s of generating 2048 and 8192 tokens with the models in the precision of bf16 int8 and int4 under the condition of using flash attention v1 v2 or not using it table tr th rowspan 2 model size th th rowspan 2 precision th th rowspan 2 flashattn th th colspan 2 align center sequence length th tr tr th align center 2048 th th align center 8192 th tr tr tr tr th rowspan 9 7b th td align center rowspan 3 bf16 td td align center v2 td td align center 40 93 td td align center 36 14 td tr tr td align center v1 td td align center 40 75 td td align center 35 34 tr tr td align center disabled td td align center 37 55 td td align center 33 56 tr tr td align center rowspan 3 int8 td td align center v2 td td align center 37 47 td td align center 32 54 td tr tr td align center v1 td td align center 37 51 td td align center 32 39 tr tr td align center disabled td td align center 37 84 td td align center 32 65 tr tr td align center rowspan 3 int4 td td align center v2 td td align center 50 09 td td align center 38 61 td tr tr td align center v1 td td align center 45 98 td td align center 36 47 tr tr td align center disabled td td align center 48 12 td td align center 36 70 tr tr th rowspan 9 14b th td align center rowspan 3 bf16 td td align center v2 td td align center 32 88 td td align center 24 87 td tr tr td align center v1 td td align center 32 76 td td align center 28 89 tr tr td align center disabled td td align center 29 32 td td align center 22 91 tr tr td align center rowspan 3 int8 td td align center v2 td td align center 29 28 td td align center 24 22 td tr tr td align center v1 td td align center 28 31 td td align center 23 87 tr tr td align center disabled td td align center 31 12 td td align center 24 60 tr tr td align center rowspan 3 int4 td td align center v2 td td align center 38 72 td td align center 27 33 td tr tr td align center v1 td td align center 37 81 td td align center 26 46 tr tr td align center disabled td td align center 37 65 td td align center 26 00 tr table in detail the setting of profiling is encoding 2048 tokens and generating 8192 new tokens the profiling runs on a single a100 sxm4 80g gpu with pytorch 2 0 1 and cuda 11 8 the inference speed is averaged over the encoded and generated tokens note the generation speed of the int4 int8 models mentioned above is provided by the autogptq library the current speed of the model loaded using automodelforcausallm from pretrained will be approximately 20 slower we have reported this issue to the huggingface team and will update it promptly if a solution is available gpu memory usage we also profile the peak gpu memory usage for encoding 2048 tokens as context and generating single token and generating 8192 tokens with single token as context under bf16 int8 or int4 quantization level respectively the results gb are shown below table tr th rowspan 2 model size th th rowspan 2 precision th th colspan 2 align center sequence length th tr tr th align center 2048 th th align center 8192 th tr tr tr tr th rowspan 3 7b th td align center bf16 td td align center 16 99 td td align center 22 53 td tr tr td align center int8 td td align center 11 20 td td align center 16 62 tr tr td align center int4 td td align center 8 21 td td align center 13 63 td tr tr th rowspan 3 14b th td align center bf16 td td align center 30 15 td td align center 38 94 td tr tr td align center int8 td td align center 18 81 td td align center 27 54 tr tr td align center int4 td td align center 13 01 td td align center 21 79 td tr table br finetuning usage now we provide the official training script finetune py for users to finetune the pretrained model for downstream applications in a simple fashion additionally we provide shell scripts to launch finetuning with no worries this script supports the training with deepspeed https github com microsoft deepspeed and fsdp https engineering fb com 2021 07 15 open source fsdp the shell scripts that we provide use deepspeed note this may have conflicts with the latest version of pydantic and peft you can install them by bash pip install peft deepspeed to prepare your training data you need to put all the samples into a list and save it to a json file each sample is a dictionary consisting of an id and a list for conversation below is a simple example list with 1 sample json id identity 0 conversations from user value from assistant value after data preparation you can use the provided shell scripts to run finetuning remember to specify the path to the data file data the finetuning scripts allow you to perform full parameter finetuning lora q lora full parameter finetuning requires updating all parameters in the whole training process to launch your training run the following script bash distributed training we do not provide single gpu training script as the insufficient gpu memory will break down the training sh finetune finetune ds sh remember to specify the correct model name or path the data path as well as the output directory in the shell scripts another thing to notice is that we use deepspeed zero 3 in this script if you want to make changes just remove the argument deepspeed or make changes in the deepspeed configuration json file based on your requirements additionally this script supports mixed precision training and thus you can use bf16 true or fp16 true remember to use deepspeed when you use fp16 due to mixed precision training empirically we advise you to use bf16 to make your training consistent with our pretraining and alignment if your machine supports bf16 and thus we use it by default similarly to run lora use another script to run as shown below before you start make sure that you have installed peft also you need to specify your paths to your model data and output we advise you to use absolute path for your pretrained model this is because lora only saves the adapter and the absolute path in the adapter configuration json file is used for finding out the pretrained model to load also this script support both bf16 and fp16 bash single gpu training sh finetune finetune lora single gpu sh distributed training sh finetune finetune lora ds sh in comparison with full parameter finetuning lora paper https arxiv org abs 2106 09685 only updates the parameters of adapter layers but keeps the original large language model layers frozen this allows much fewer memory costs and thus fewer computation costs note that if you use lora to finetune the base language model e g qwen 7b instead of chat models e g qwen 7b chat the script automatically switches the embedding and output layer as trainable parameters this is because the base language model has no knowledge of special tokens brought by chatml format thus these layers should be updated for the model to understand and predict the tokens or in another word if your training brings in special tokens in lora you should set the layers to trainable parameters by setting modules to save inside the code also if we have these parameters trainable it is not available to use zero 3 and this is why we use zero 2 in the script by default if you do not have new trainable parameters you can switch to zero 3 by changing the deepspeed configuration file additionally we find that there is a significant gap between the memory footprint of lora with and without these trainable parameters therefore if you have trouble with memory we advise you to lora finetune the chat models check the profile below for more information if you still suffer from insufficient memory you can consider q lora paper https arxiv org abs 2305 14314 which uses the quantized large language model and other techniques such as paged attention to allow even fewer memory costs note to run single gpu q lora training you may need to install mpi4py through pip or conda to run q lora directly run the following script bash single gpu training sh finetune finetune qlora single gpu sh distributed training sh finetune finetune qlora ds sh for q lora we advise you to load our provided quantized model e g qwen 7b chat int4 you should not use the bf16 models different from full parameter finetuning and lora only fp16 is supported for q lora for single gpu training we have to use deepspeed for mixed precision training due to our observation of errors caused by torch amp besides for q lora the troubles with the special tokens in lora still exist however as we only provide the int4 models for chat models which means the language model has learned the special tokens of chatml format you have no worry about the layers note that the layers of the int4 model should not be trainable and thus if you introduce special tokens in your training q lora might not work different from full parameter finetuning the training of both lora and q lora only saves the adapter parameters suppose your training starts from qwen 7b you can load the finetuned model for inference as shown below python from peft import autopeftmodelforcausallm model autopeftmodelforcausallm from pretrained path to adapter path to the output directory device map auto trust remote code true eval if you want to merge the adapters and save the finetuned model as a standalone model you can only do this with lora and you cannot merge the parameters from q lora you can run the following codes python from peft import autopeftmodelforcausallm model autopeftmodelforcausallm from pretrained path to adapter path to the output directory device map auto trust remote code true eval merged model model merge and unload max shard size and safe serialization are not necessary they respectively work for sharding checkpoint and save the model to safetensors merged model save pretrained new model directory max shard size 2048mb safe serialization true note for multi gpu training you need to specify the proper hyperparameters for distributed training based on your machine besides we advise you to specify your maximum sequence length with the argument model max length based on your consideration of data memory footprint and training speed profiling of memory and speed we profile the gpu memory and training speed of both lora lora emb refers to training the embedding and output layer while lora has no trainable embedding and output layer and q lora in the setup of single gpu training in this test we experiment on a single a100 sxm4 80g gpu and we use cuda 11 8 and pytorch 2 0 flash attention 2 is applied we uniformly use a batch size of 1 and gradient accumulation of 8 we profile the memory gb and speed s iter of inputs of different lengths namely 256 512 1024 2048 4096 and 8192 we also report the statistics of full parameter finetuning with qwen 7b on 2 a100 gpus we only report the statistics of 256 512 and 1024 tokens due to the limitation of gpu memory the statistics are listed below table tr th rowspan 2 model size th th rowspan 2 method th th colspan 6 align center sequence length th tr tr th align center 256 th th align center 512 th th align center 1024 th th align center 2048 th th align center 4096 th th align center 8192 th tr tr tr tr th rowspan 4 7b th td lora td td align center 20 1g 1 2s it td td align center 20 4g 1 5s it td td align center 21 5g 2 8s it td td align center 23 8g 5 2s it td td align center 29 7g 10 1s it td td align center 36 6g 21 3s it td tr tr td lora emb td td align center 33 7g 1 4s it td td align center 34 1g 1 6s it td td align center 35 2g 2 9s it td td align center 35 1g 5 3s it td td align center 39 2g 10 3s it td td align center 48 5g 21 7s it td tr tr td q lora td td align center 11 5g 3 0s it td td align center 11 5g 3 0s it td td align center 12 3g 3 5s it td td align center 13 9g 7 0s it td td align center 16 9g 11 6s it td td align center 23 5g 22 3s it td tr tr td full parameter td td align center 139 2g 4 0s it td td align center 148 0g 4 0s it td td align center 162 0g 4 5s it td td align center td td align center td td align center td tr tr th rowspan 3 14b th td lora td td align center 34 6g 1 6s it td td align center 35 1g 2 4s it td td align center 35 3g 4 4s it td td align center 37 4g 8 4s it td td align center 42 5g 17 0s it td td align center 55 2g 36 0s it td tr tr td lora emb td td align center 51 2 1 7s it td td align center 51 1g 2 6s it td td align center 51 5g 4 6s it td td align center 54 1g 8 6s it td td align center 56 8g 17 2s it td td align center 67 7g 36 3s it td tr tr td q lora td td align center 18 7g 5 3s it td td align center 18 4g 6 3s it td td align center 18 9g 8 2s it td td align center 19 9g 11 8s it td td align center 23 0g 20 1s it td td align center 27 9g 38 3s it td tr table br deployment vllm for deployment and fast inference we suggest using vllm with fastchat install the packages first bash pip install vllm pip install fschat model worker webui or you can install them from source by git clone and pip install e we advise you to read their documents if you meet problems in installation to run qwen with vllm and fastchat you need to first launch a controller by bash python m fastchat serve controller then you can launch the model worker which means loading your model for inference for single gpu inference you can directly run bash python m fastchat serve vllm worker model path model path trust remote code however if you hope to run the model on multiple gpus for faster inference or larger memory you can use tensor parallelism supported by vllm suppose you run the model on 4 gpus the command is shown below bash python m fastchat serve vllm worker model path model path trust remote code tensor parallel size 4 after launching your model worker you can launch a web demo or an openai api as you like for web demo run the following command bash python m fastchat serve gradio web server for openai api check the documentation of our openai api for installation first then run the command bash python m fastchat serve openai api server host localhost port 8000 br demo web ui we provide code for users to build a web ui demo thanks to wysaid before you start make sure you install the following packages pip install r requirements web demo txt then run the command below and click on the generated link bash python web demo py p align center br img src assets web demo gif width 600 br p cli demo we provide a cli demo example in cli demo py which supports streaming output for the generation users can interact with qwen 7b chat by inputting prompts and the model returns model outputs in the streaming mode run the command below bash python cli demo py p align center br img src assets cli demo gif width 600 br p br api the most simple way to use qwen through apis is dashscope api service through alibaba cloud we give an introduction to the usage additionally we provide a script for you to deploy an openai style api on your own servers dashscope dashscope is the large language model api service provided by alibaba cloud which now supports qwen note that the models behind dashscope are in house versions temporarily without details provided the services include qwen turbo and qwen plus where the former one runs faster and the latter achieves better performance for more information visit the documentation here https dashscope aliyun com please head to the official website link https help aliyun com zh dashscope developer reference activate dashscope and create an api key spm a2c4g 11186623 0 0 6c2774fahtfxdn to create a dashscope account and obtain the api key ak we recommend setting the ak with an environment variable bash export dashscope api key your dashscope api key then please install the packages and click here https help aliyun com zh dashscope developer reference install dashscope sdk for the documentation if you use python you can install dashscope with pip bash pip install dashscope if you use java sdk you can install it in this way xml https mvnrepository com artifact com alibaba dashscope sdk java dependency groupid com alibaba groupid artifactid dashscope sdk java artifactid version the latest version version dependency the simplest way to use dashscope is the usage with messages which is similar to openai api the example is demonstrated below python import random from http import httpstatus from dashscope import generation def call with messages messages role system content you are a helpful assistant role user content gen generation response gen call generation models qwen turbo messages messages seed random randint 1 10000 set the random seed optional default to 1234 if not set result format message set the result to be message format return response if name main response call with messages if response status code httpstatus ok print response else print request id s status code s error code s error message s response request id response status code response code response message for more usages please visit the official website for more details openai api we provide methods to deploy local api based on openai api thanks to hanpenggit before you start install the required packages bash pip install fastapi uvicorn openai pydantic 2 3 0 sse starlette then run the command to deploy your api bash python openai api py you can change your arguments e g c for checkpoint name or path cpu only for cpu deployment etc if you meet problems launching your api deployment updating the packages to the latest version can probably solve them using the api is also simple see the example below python import openai openai api base http localhost 8000 v1 openai api key none create a request activating streaming response for chunk in openai chatcompletion create model qwen messages role user content stream true specifying stop words in streaming output format is not yet supported and is under development if hasattr chunk choices 0 delta content print chunk choices 0 delta content end flush true create a request not activating streaming response response openai chatcompletion create model qwen messages role user content stream false stop you can add custom stop words here e g stop observation for react prompting print response choices 0 message content p align center br img src assets openai api gif width 600 br p function calling is also supported but only when stream false for the moment see the example usage examples function call examples py here br br tool usage qwen chat has been optimized for tool usage and function calling capabilities users can develop agents langchain applications and even augment qwen with a python code interpreter we provide documentation on how to implement tool calls based on the principle of react prompting please refer to the react example examples react prompt md based on this principle we provide support for function calling in openai api py openai api py we have tested the model s tool calling capabilities on our open source chinese evaluation benchmark and found that qwen chat consistently performs well table tr th colspan 4 align center chinese tool use benchmark th tr tr th align center model th th align center tool selection acc th th align center tool input rouge l th th align center false positive error th tr tr td gpt 4 td td align center 95 td td align center 0 90 td td align center 15 0 td tr tr td gpt 3 5 td td align center 85 td td align center 0 88 td td align center 75 0 td tr tr td qwen 7b chat td td align center 98 td td align center 0 91 td td align center 7 3 td tr tr td qwen 14b chat td td align center 98 td td align center 0 93 td td align center 2 4 td tr table to assess qwen s ability to use the python code interpreter for tasks such as mathematical problem solving data visualization and other general purpose tasks such as file handling and web scraping we have created and open sourced a benchmark specifically designed for evaluating these capabilities you can find the benchmark at this link https github com qwenlm qwen agent tree main benchmark we have observed that qwen performs well in terms of code executability and result accuracy when generating code table tr th colspan 4 align center executable rate of generated code th tr tr th align center model th th align center math th th align center visualization th th align center general th tr tr td gpt 4 td td align center 91 9 td td align center 85 9 td td align center 82 8 td tr tr td gpt 3 5 td td align center 89 2 td td align center 65 0 td td align center 74 1 td tr tr td llama2 7b chat td td align center 41 9 td td align center 33 1 td td align center 24 1 td tr tr td llama2 13b chat td td align center 50 0 td td align center 40 5 td td align center 48 3 td tr tr td codellama 7b instruct td td align center 85 1 td td align center 54 0 td td align center 70 7 td tr tr td codellama 13b instruct td td align center 93 2 td td align center 55 8 td td align center 74 1 td tr tr td internlm 7b chat v1 1 td td align center 78 4 td td align center 44 2 td td align center 62 1 td tr tr td internlm 20b chat td td align center 70 3 td td align center 44 2 td td align center 65 5 td tr tr td qwen 7b chat td td align center 82 4 td td align center 64 4 td td align center 67 2 td tr tr td qwen 14b chat td td align center 89 2 td td align center 84 1 td td align center 65 5 td tr table table tr th colspan 4 align center accuracy of code execution results th tr tr th align center model th th align center math th th align center visualization hard th th align center visualization easy th tr tr td gpt 4 td td align center 82 8 td td align center 66 7 td td align center 60 8 td tr tr td gpt 3 5 td td align center 47 3 td td align center 33 3 td td align center 55 7 td tr tr td llama2 7b chat td td align center 3 9 td td align center 14 3 td td align center 39 2 td tr tr td llama2 13b chat td td align center 8 3 td td align center 8 3 td td align center 40 5 td tr tr td codellama 7b instruct td td align center 14 3 td td align center 26 2 td td align center 60 8 td tr tr td codellama 13b instruct td td align center 28 2 td td align center 27 4 td td align center 62 0 td tr tr td internlm 7b chat v1 1 td td align center 28 5 td td align center 4 8 td td align center 40 5 td tr tr td internlm 20b chat td td align center 34 6 td td align center 21 4 td td align center 45 6 td tr tr td qwen 7b chat td td align center 41 9 td td align center 40 5 td td align center 54 4 td tr tr td qwen 14b chat td td align center 58 4 td td align center 53 6 td td align center 59 5 td tr table p align center br img src assets code interpreter showcase 001 jpg br p in addition we also provide experimental results demonstrating that our model is capable of acting as a huggingface agent for more information please refer to the example documentation examples transformers agent md the model s performance on the evaluation dataset provided by hugging face is as follows table tr th colspan 4 align center huggingface agent benchmark run mode th tr tr th align center model th th align center tool selection th th align center tool used th th align center code th tr tr td gpt 4 td td align center 100 td td align center 100 td td align center 97 4 td tr tr td gpt 3 5 td td align center 95 4 td td align center 96 3 td td align center 87 0 td tr tr td starcoder base 15b td td align center 86 1 td td align center 87 0 td td align center 68 9 td tr tr td starcoder 15b td td align center 87 0 td td align center 88 0 td td align center 68 9 td tr tr td qwen 7b chat td td align center 87 0 td td align center 87 0 td td align center 71 5 td tr tr td qwen 14b chat td td align center 93 5 td td align center 94 4 td td align center 87 0 td tr table table tr th colspan 4 align center huggingface agent benchmark chat mode th tr tr th align center model th th align center tool selection th th align center tool used th th align center code th tr tr td gpt 4 td td align center 97 9 td td align center 97 9 td td align center 98 5 td tr tr td gpt 3 5 td td align center 97 3 td td align center 96 8 td td align center 89 6 td tr tr td starcoder base 15b td td align center 97 9 td td align center 97 9 td td align center 91 1 td tr tr td starcoder 15b td td align center 97 9 td td align center 97 9 td td align center 89 6 td tr tr td qwen 7b chat td td align center 94 7 td td align center 94 7 td td align center 85 1 td tr tr td qwen 14b chat td td align center 97 9 td td align center 97 9 td td align center 95 5 td tr table br long context understanding to extend the context length and break the bottleneck of training sequence length we introduce several techniques including ntk aware interpolation window attention and logn attention scaling to extend the context length of qwen 7b 14b from 2k to over 8k tokens and qwen 7b from 8k to 32k tokens we conduct language modeling experiments on the arxiv dataset with the ppl evaluation and find that qwen can reach outstanding performance in the scenario of long context results are demonstrated below table tr th rowspan 2 model th th colspan 6 align center sequence length th tr tr th align center 1024 th th align center 2048 th th align center 4096 th th align center 8192 th th align center 16384 th th align center 32768 th tr tr td qwen 7b original td td align center 4 23 td td align center 3 78 td td align center 39 35 td td align center 469 81 td td align center 2645 09 td td align center td tr tr td dynamic ntk td td align center 4 23 td td align center 3 78 td td align center 3 59 td td align center 3 66 td td align center 5 71 td td align center td tr tr td dynamic ntk logn td td align center 4 23 td td align center 3 78 td td align center 3 58 td td align center 3 56 td td align center 4 62 td td align center td tr tr td dynamic ntk logn window attn td td align center 4 23 td td align center 3 78 td td align center 3 58 td td align center 3 49 td td align center 4 32 td td align center td tr tr tr td qwen 7b td td align center b 4 23 b td td align center b 3 81 b td td align center b 3 52 b td td align center b 3 31 b td td align center 7 27 td td align center 181 49 td tr tr td dynamic ntk logn window attn td td align center b 4 23 b td td align center b 3 81 b td td align center b 3 52 b td td align center b 3 33 b td td align center b 3 22 b td td align center b 3 17 b td tr tr td qwen 14b td td align center b b td td align center b 3 46 b td td align center 22 79 td td align center 334 65 td td align center 3168 35 td td align center td tr tr td dynamic ntk logn window attn td td align center b b td td align center b 3 46 b td td align center b 3 29 b td td align center b 3 18 b td td align center 3 42 td td align center td tr table tokenizer our tokenizer based on tiktoken is different from other tokenizers e g sentencepiece tokenizer you need to pay attention to special tokens especially in finetuning for more detailed information on the tokenizer and related use in fine tuning please refer to the documentation tokenization note md br br reproduction for your reproduction of the model performance on benchmark datasets we provide scripts for you to reproduce the results check eval evaluation md eval evaluation md for more information note that the reproduction may lead to slight differences from our reported results br br faq if you meet problems please refer to faq faq md and the issues first to search a solution before you launch a new issue br br citation if you find our work helpful feel free to give us a cite article qwen title qwen technical report author jinze bai and shuai bai and yunfei chu and zeyu cui and kai dang and xiaodong deng and yang fan and wenbin ge and yu han and fei huang and binyuan hui and luo ji and mei li and junyang lin and runji lin and dayiheng liu and gao liu and chengqiang lu and keming lu and jianxin ma and rui men and xingzhang ren and xuancheng ren and chuanqi tan and sinan tan and jianhong tu and peng wang and shijie wang and wei wang and shengguang wu and benfeng xu and jin xu and an yang and hao yang and jian yang and shusheng yang and yang yao and bowen yu and hongyi yuan and zheng yuan and jianwei zhang and xingxuan zhang and yichang zhang and zhenru zhang and chang zhou and jingren zhou and xiaohuan zhou and tianhang zhu journal arxiv preprint arxiv 2309 16609 year 2023 br license agreement researchers and developers are free to use the codes and model weights of both qwen and qwen chat we also allow their commercial use check our license at license license for more details if you have requirements for commercial use please fill out the form 7b https dashscope console aliyun com openmodelapply qianwen 14b https dashscope console aliyun com openmodelapply qwen 14b chat to apply br br contact us if you are interested to leave a message to either our research team or product team join our discord or wechat groups also feel free to send an email to qianwen opensource alibabacloud com | chinese large-language-models natural-language-processing flash-attention llm pretrained-models | ai |
Malayalam-NLP | malayalam nlp natural language processing for malayalam | ai |
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NLP | introduction to nlp course version https img shields io badge version pro blue release https img shields io badge release 1 1 0 blue language https img shields io badge language python 3 7 7c3 8 brightgreen last update https img shields io badge last update 10 19 2023 orange last update https img shields io badge license mit orange free hands on course with the implementation in python and description of several natural language processing nlp algorithms and techniques on several modern platforms and libraries although it is not intended to have the formal rigor of a book we tried to be as faithful as possible to the original algorithms and methods only adding variants when these were necessary for didactic purposes quick start the best way to get the most out of this course is to carefully read each selected problem try to think of a possible solution language independent and then look at the proposed python code and try to reproduce it in your favorite ide if you already have knowledge of the python language then you can go directly to programming your solution and then compare it with the one proposed in the course if you want to play with these notebooks online without having to install any library or configure hardware you can use the following service a href https colab research google com github ansegura7 nlp blob main target blank img src https colab research google com assets colab badge svg alt open in colab a what is nlp natural language processing project with python frameworks nlp is a discipline where computer science artificial intelligence and cognitive logic are intercepted with the objective that machines can read and understand our language for decision making nlp header https raw githubusercontent com ansegura7 nlp master image header jpg contents details open summary 1 a href https ansegura7 github io nlp pages nlp spacy html nlp with spacy a summary ul li read natural text of a book in spanish li li create a nlp model with spacy li li working with pos ner and sentences li ul details details open summary 2 a href https ansegura7 github io nlp pages nlp semanticenrich html semantic enrichment of entities a summary ul li semantic enrichment li li sparql li li dbpedia li ul details details open summary 3 a href https ansegura7 github io nlp pages nlp spellchecker html spell checker corrector a summary ul li spell checker from scratch li li spell checker using pyspellchecker class li ul details details open summary 4 a href https ansegura7 github io nlp pages nlp word2vec html word embedding with gensim a summary ul li read natural text of a book in english li li tokenize and remove stopwords li li create a word2vec model cbow li li plot similars words li li export similarity between the words li ul details details open summary 5 a href https ansegura7 github io nlp pages nlp network html relationship between words a summary ul li networks and force system li li d3 js li ul details details open summary 6 a href https ansegura7 github io nlp pages nlp stanza html introduction to stanza stanford corenlp a summary ul li stanza text processing li li stanford corenlp interface li ul details data books in plain text both in english and spanish the enrichment of the entities is done from dbpedia python dependencies console conda install c conda forge spacy python m spacy download en core web sm python m spacy download es core news sm conda install c conda forge sparqlwrapper pip install pyspellchecker conda install c anaconda gensim conda install c conda forge wordcloud conda install c conda forge stanza software version python 3 8 5 spacy 3 0 5 gensim 4 0 1 stanza 1 2 3 contributing and feedback any kind of feedback suggestions would be greatly appreciated algorithm design documentation improvement ideas spelling mistakes etc if you want to make a contribution to the course you can do it through a pr documentation please read the contributing https github com ansegura7 nlp blob main docs contributing md and code of conduct https github com ansegura7 nlp blob main docs code of conduct md documentation author created by andr s segura tinoco created on june 04 2019 license this project is licensed under the terms of the a href https github com ansegura7 nlp blob main license mit license a acknowledgments i would like to thank a href http www gutenberg org target blank project gutenberg a for sharing the books in english and a href https norvig com target blank peter norvig a for the spell checker algorithm | nlp python spacy gensim word2vec wordcloud spellchecker text-processing stanza stanford-corenlp | ai |
llm-code-eval | large language models are state of the art evaluators of code generation news 28 04 2023 preprint is online 24 04 2023 drafting the preprint 22 04 2023 experiments started paper arxiv https arxiv org abs 2304 14317 and original version assets paper pdf abstract recent advancements in the field of natural language generation have facilitated the use of large language models to assess the quality of generated text although these models have shown promising results in tasks such as machine translation and summarization their applicability in code generation tasks remains limited without human involvement the complexity of programming concepts required for such tasks makes it difficult to develop evaluation metrics that align with human judgment token matching based metrics such as bleu have demonstrated weak correlations with human practitioners in code generation tasks moreover the utilization of human written test suites to evaluate functional correctness can be challenging in domains with low resources to overcome these obstacles we propose a new evaluation framework based on the gpt 3 5 gpt 3 5 turbo for code generation assessments our framework addresses the limitations of existing approaches by achieving superior correlations with functional correctness and human preferences without the need for test oracles or references we evaluate the efficacy of our framework on two different tasks and four programming languages comparing its performance with the state of the art codebertscore metric which relies on a pre trained model our results demonstrate that our framework surpasses codebertscore delivering high levels of accuracy and consistency across various programming languages and tasks we encourage further research in the evaluation of code generation overview framework assets framework png our framework assesses code generation from two aspects human based usefulness usefulness of the code snippet based on the problem description execution based functional correctness execution based quality of the code snippet combined with the problem environment setup our experiment is mainly built on the codegen metrics https github com jetbrains research codegen metrics and code bert score https github com neulab code bert score repositories to replicate all experiments please follow their instructions to set up the environment to run compute results ipynb and modules in llm code eval folder use the following command to install all dependencies bash pip install r requirements txt folder description data contains all processed data used in the paper data conala contains the conala dataset with all automatic evaluation results data humaneval contains the humaneval dataset with all automatic evaluation results data humaneval humaneval java grade json java split data humaneval humaneval cpp grade json c split data humaneval humaneval python grade json python split data humaneval humaneval js grade json javascript split experiment source contains the scripts to collect all automatic evaluation results they require specific modifications to run on your machine note that for any of these scripts using metrics evaluation metrics you need to use the implementations in metrics evaluation folder from codegen metrics https github com jetbrains research codegen metrics llm code eval contains the implementation of a minimum viable product mvp of this project you are able to use it to evaluation any generated code snippet please refer to the use large language models to downstream tasks of source code or more details use large language models to evaluate downstream tasks of source code we implement a minimum viable product mvp of this project to install the project please use the following command bash pip install e you can use it to evaluate any generated code snippet with the inputs of problem output task aspect and model like the following example python from llm code eval import evaluate score evaluate problem given a list of integers return the sum of all the integers output sum 0 nfor i in range len list n tsum list i nreturn sum task code gen aspect usefulness model gpt 3 5 turbo print score if you want to evaluate with reference code you can use the option of reference in the following example python from llm code eval import evaluate score evaluate problem given a list of integers return the sum of all the integers output sum 0 nfor i in range len list n tsum list i nreturn sum reference sum 0 nfor i in range len list n tsum list i nreturn sum task code gen aspect usefulness model gpt 3 5 turbo print score you can also use the option of cot true to enable the zero shot chain of thought evaluation in the following example python from llm code eval import evaluate score eval step evaluate problem given a list of integers return the sum of all the integers output sum 0 nfor i in range len list n tsum list i nreturn sum task code gen aspect usefulness model gpt 3 5 turbo cot true print score print eval step call for contributions the mvp requires a lot of improvements in terms of the design and the diversity of the evaluation tasks with the proper prompts we welcome any contributions to this project please feel free to open an issue or submit a pull request to do evaluate more large language models extend the evaluation aspects and tasks acknowledgement we thank jetbrains research https research jetbrains org and neulab http www cs cmu edu neulab for their open source code and data citation article zhuo2023large title large language models are state of the art evaluators of code generation author zhuo terry yue journal arxiv preprint arxiv 2304 14317 year 2023 | automatic-evaluation code-generation code-quality evaluation gpt-35-turbo large-language-models llm | ai |
balena-engine | balenaengine docs static balena engine svg moby based container engine for iot highlights small footprint 3 5x smaller than docker ce packaged as a single binary multi arch support available for a wide variety of chipset architectures supporting everything from tiny iot devices to large industrial gateways true container deltas bandwidth efficient updates with binary diffs 10 70x smaller than pulling layers minimal wear and tear extract layers as they arrive to prevent excessive writing to disk protecting your storage from eventual corruption failure resistant pulls atomic and durable image pulls defend against partial container pulls in the event of power failure conservative memory use prevents page cache thrashing during image pull so your application runs undisturbed in low memory situations motivation balenaengine is a container engine purpose built for embedded and iot use cases and compatible with docker containers based on docker s moby project balenaengine supports container deltas for 10 70x more efficient bandwidth usage has 3x smaller binaries uses ram and storage more conservatively and focuses on atomicity and durability of container pulling since 2013 when we first ported docker to armv6 and the raspberry pi https www balena io blog docker on raspberry pi the balena team has been working in and around the docker codebase meanwhile having seen iot devices used in production for tens of millions of hours we ve become intimately acquainted with the unique needs of the embedded world so we built a container engine that runs docker containers just as well shares the docker components that are needed for our use case and is augmented with the iot specific features that we ve built out over time transitioning from docker ce we left out docker features that we saw as most needed in cloud deployments and therefore not warranting inclusion in a lightweight iot focused container engine specifically we ve excluded docker swarm cloud logging drivers plugin support overlay networking drivers non boltdb discovery backends consul zookeeper etcd etc buildkit although support can be enabled using a build tag unless you depend on one of the features in docker that balenaengine omits using balenaengine should be a drop in replacement license balenaengine is licensed under the apache license version 2 0 see license https github com balena os balena engine blob master license for the full license text | server |
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Natural-Language-Processing-with-Python-Cookbook | natural language processing with python cookbook natural language processing with python cookbook published by packt | ai |
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language-resources | language resources and tools build status https travis ci org google language resources svg branch master https travis ci org google language resources datasets and scripts for basic natural language and speech processing this is not an official google product natural languages directory language available af afrikaans bn bengali bangla hi ur hindi urdu is icelandic jv javanese km khmer lo lao my burmese myanmar ne nepali si sinhala su sundanese xh xhosa zu zulu tools we are including a few tools for working with the natural language datasets these tools are written in c and python and are built with bazel http bazel io to compile and use these tools install a recent version of bazel http bazel io docs install html minimally bazel release 0 4 5 is required opensourced audio data resource link sinhala tts recordings 3k https www openslr org 30 https www openslr org 30 tts recordings for four south african languages af st tn xh https www openslr org 32 https www openslr org 32 large javanese asr training data set 185k https www openslr org 35 https www openslr org 35 large sundanese asr training data set 220k https www openslr org 36 https www openslr org 36 high quality tts data for bengali languages https www openslr org 37 https www openslr org 37 high quality tts data for javanese https www openslr org 41 https www openslr org 41 high quality tts data for khmer https www openslr org 42 https www openslr org 42 high quality tts data for nepali https www openslr org 43 https www openslr org 43 high quality tts data for sundanese https www openslr org 44 https www openslr org 44 large sinhala asr training data set https www openslr org 52 https www openslr org 52 large bengali asr training data set https www openslr org 53 http www openslr org 53 large nepali asr training data set https www openslr org 54 https www openslr org 54 crowdsourced high quality argentinian spanish speech data set https www openslr org 61 https www openslr org 61 crowdsourced high quality malayalam multi speaker speech data set https www openslr org 63 https www openslr org 63 crowdsourced high quality marathi multi speaker speech data set https www openslr org 64 https www openslr org 64 crowdsourced high quality tamil multi speaker speech data set https www openslr org 65 https www openslr org 65 crowdsourced high quality telugu multi speaker speech data set https www openslr org 66 https www openslr org 66 data set which contains recordings of catalan https www openslr org 69 https www openslr org 69 crowdsourced high quality nigerian english speech data set https www openslr org 70 https www openslr org 70 crowdsourced high quality chilean spanish speech data set https www openslr org 71 https www openslr org 71 crowdsourced high quality colombian spanish speech data set https www openslr org 72 https www openslr org 72 crowdsourced high quality peruvian spanish speech data set https www openslr org 73 https www openslr org 73 crowdsourced high quality puerto rico spanish speech data set https www openslr org 74 https www openslr org 74 crowdsourced high quality venezuelan spanish speech data set https www openslr org 75 https www openslr org 75 crowdsourced high quality basque speech data set https www openslr org 76 https www openslr org 76 crowdsourced high quality galician speech data set https www openslr org 77 https www openslr org 77 crowdsourced high quality gujarati multi speaker speech data set https www openslr org 78 https www openslr org 78 crowdsourced high quality kannada multi speaker speech data set https www openslr org 79 https www openslr org 79 crowdsourced high quality burmese speech data set https www openslr org 80 https www openslr org 80 data set which contains male and female recordings of english from various dialects of the uk and ireland https www openslr org 83 https www openslr org 83 crowdsourced high quality yoruba speech data set https www openslr org 86 https www openslr org 86 other reading resources sltu 2016 tutorial https sites google com site sltututorial overview https sites google com site sltututorial overview publications burmese speech corpus finite state text normalization and pronunciation grammars with an application to text to speech https research google pubs pub49146 crowdsourcing latin american spanish for low resource text to speech https research google pubs pub49150 open source multi speaker corpora of the english accents in the british isles https research google pubs pub49149 open source multi speaker speech corpora for building gujarati kannada malayalam marathi tamil and telugu speech synthesis systems https research google pubs pub49148 google crowdsourced speech corpora and related open source resources for low resource languages and dialects an overview https research google pubs pub48928 open source high quality speech datasets for basque catalan and galician https research google pubs pub49136 text normalization for bangla khmer nepali javanese sinhala and sundanese tts systems https ai google research pubs pub47344 a step by step process for building tts voices using open source data and framework for bangla javanese khmer nepali sinhala and sundanese https ai google research pubs pub47347 fonbund a library for combining cross lingual phonological segment data https research google pubs pub46930 building open javanese and sundanese corpora for multilingual text to speech https ai google research pubs pub46929 rapid development of tts corpora for four south african languages https research google pubs pub47393 building statistical parametric multi speaker synthesis for bangladeshi bangla https ai google research pubs pub45301 tts for low resource languages a bangla synthesizer https ai google research pubs pub45300 license unless otherwise noted all original files are licensed under an apache license version 2 0 license where specifically noted some datasets are licensed under a creative commons attribution 4 0 international license cc by 4 0 http creativecommons org licenses by 4 0 the directory third party third party contains third party works which we are including under the respective licenses of the upstream projects see third party readme md third party readme md for further details | natural-language | ai |
KickStart | this project is discontinued it is superseded by w2ui starter es6 https github com vitmalina w2ui starter es6 w2ui starter react https github com vitmalina w2ui starter react w2ui starter vue https github com vitmalina w2ui starter vue kickstart is a boiler plate for enterprise web applications it is primarily a front end solution and can be used with back end written in any language the project includes few server side solutions located in api folder all front end related source files are in web folder installation before using project tools you need to install npm and nodejs to install dependencies run npm install production for production npm install for development to run development tasks configured in gulpfile js run gulp dev this will auto compile all your less files as well as generated icon font json back end since data is read from static json files and cannot be modified json back end is purely for demo purposes it is enabled by default you can change it in web app home config js file by modifying context property if you use nginx as web server and you have problem with post request 405 not allowed there is small hack http stackoverflow com questions 24415376 post request not allowed 405 not allowed nginx even with headers included nginx conf server listen 80 error page 403 403 html to allow post on static pages error page 405 200 uri nodejs back end to use nodejs back end you need to do a series of steps install and start mongodb server used for session storage install and start postgresql db server run sql scripts in setup folder change db configuration in api node conf js file change context property to http localhost 3000 to run nodejs locally nodemon api node start js to start mongodb used for sessions mongod dbpath users vitali library mongodbdata folder structure api server side code json sample json back end node sample nodejs back end admin admin module shared shared components mod 1 module 1 mod n module n w2ui server side w2ui library conf js db and other config security js security file start js server side starting point build build folder log logs node modules nodejs modules setup db setup scripts web front end code app application mod 1 module 1 mod n module n icons icon font icons modules js description of all modules start js client side starting point libs 3rd party libraries index html index page login html login page gruntfile js build tool config package json npm dependencies readme md this read me file | front_end |
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sql-challenge | sql challenge designed tables to hold data in csvs import the csvs into a sql database and answer questions about the data by performing data engineering and data analysis data modeling inspected the csvs and sketched out an erd of the tables using http www quickdatabasediagrams com data engineering used the information to create a table schema for each of the six csv files specifying data types primary keys foreign keys and other constraints data analysis 1 listed the following details of each employee employee number last name first name sex and salary 2 listed first name last name and hire date for employees who were hired in 1986 3 listed the manager of each department with the following information department number department name the manager s employee number last name first name 4 listed the department of each employee with the following information employee number last name first name and department name 5 listed first name last name and sex for employees whose first name is hercules and last names begin with b 6 listed all employees in the sales department including their employee number last name first name and department name 7 listed all employees in the sales and development departments including their employee number last name first name and department name 8 in descending order listed the frequency count of employee last names i e how many employees share each last name additional review sqlalchemy 1 imported the sql database into pandas using sqlalchemy 2 created a histogram to visualize the most common salary ranges for employees 3 created a bar chart of average salary by title | server |
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SER517-P6-Street-Card | ser517 streetcard project info the streetcard is a carefully reasoned comprehensive social program that seeks to connect homeless persons with information technology to optimize the process of providing benefits and housing motivation this is a humanitarian project the intent being to remedy the needless suffering of our poorest citizens homeless persons are a vulnerable and essentially un represented population the services that are available are often underfunded and inadequate by making use of information technology to streamline the provision of benefits we hope to eliminate the cracks in the system through which taxpayer dollars are profligately wasted money saved might be then reinvested in improving services building shelters and low cost housing and improving pay and benefits for service providers technology stack backend django rest framework rabbitmq message broker celery distributed task queue memcached frontend react database postgres hosted on amazon rds installation guide 3 1 quick installation guide using docker on your local system clone the git repository git clone https github com ansamant ser517 p6 street card git install docker on your local system https docs docker com get docker install docker compose https docs docker com compose install run sudo service docker start create env files note we need environment variables set up to run the web application we need to have a env file in frontend and backend folders which has a set of environment variables go to frontend with in the project root folder create a env file with keys as shown below react app key your google map api key react app ip http localhost 8000 go to backend with in the project root folder create a env file with keys as shown below secret key your secret key db user your db user db password your db password db host your db host db port your db port db name you db name django email usr your user email django email pwd your email password switch the celery broker url in backend api settings py to localhost as directed for development environment amqp localhost for production environment amqp guest guest rabbit 5672 celery broker url amqp localhost edit backend dockerfile and add the following command before line 6 python3 manage py makemigrations python3 manage py migrate run the following docker command this is where the magic happens docker compose up d go to http localhost 3000 3 2 quick installation guide using docker on aws ec2 instance follow the steps to create an ec2 instance https docs aws amazon com awsec2 latest userguide get set up for amazon ec2 html after creating the instance save the key pair pem file run the command sudo chmod 400 your key pair pem note this command assigns appropriate permission to the key pair to ssh into the aws ec2 instance scp i your key pair pem ec2 user your public dns install git using command sudo yum install git install docker using command sudo yum install docker install docker compose https docs docker com compose install run sudo service docker start create env files note we need environment variables set up to run the web application we need to have a env file in frontend and backend folders which has a set of environment variables go to frontend with in the project root folder create a env file with keys as shown below react app key your google map api key react app ip http aws ip instance 3000 go to backend with in the project root folder create a env file with keys as shown below secret key your secret key db user your db user db password your db password db host your db host db port your db port db name you db name django email usr your user email django email pwd your email password switch the celery broker url in backend api settings py as directed for development environment amqp localhost for production environment amqp guest guest rabbit 5672 celery broker url amqp guest guest rabbit 5672 edit backend dockerfile and add the following command before line 6 python3 manage py makemigrations python3 manage py migrate run the docker command this is where the magic happens docker compose up d go to your aws instance ip 3000 in a web browser useful commands to run independent services database setup br setup your own database by modifying this piece of code within the settings py file databases default engine your database engine user your db username password your db password host your db host port your db port name your db name br migrations files within the app folder would help you setup the database br to set up the tables in your database python manage py makemigrations python manage py migrate to install pre requisite backend libraries pip install r requirements txt to install pre requisite frontend libraries npm install to start the backend server python manage py runserver to start the front end app npm start rabbitmq unix homebrew see https brew sh brew update brew install rabbitmq other commands brew services start rabbitmq starts rabbitmq server brew services stop rabbitmq stops rabbitmq server brew services restart rabbitmq restarts rabbitmq server brew services list lists all services present on computer along with their status information for more info https www rabbitmq com install homebrew html windows installer see https www rabbitmq com install windows html others see https www rabbitmq com download html celery go to top level folder of the project to run celery use command celery a api l worker info info is optional but recommended as it demonstrates what is working references for daemonizing celery after deployment https docs celeryproject org en stable userguide daemonizing html for celery integration in django https docs celeryproject org en stable django index html important tips for email configuration information this project currently utilizes google smtp server configurations the email host user and email host password configurations are set in host environment for security reason if you choose to link with the project through a different account make sure that your email account is configured to accept emails from less secure sources see https support google com accounts answer 6010255 hl en if your account uses two step verification it is better to use a specially generated password for the app see https support google com mail answer 185833 caching to avoid redundant get call to the database we have implemented caching we are using django s cache framework https docs djangoproject com en 3 0 topics cache the cache system we are using is memcached installing memcached step 1 install memcached brew install memcached step 2 start the memcached service brew services start memcached step 3 verify installation memcached v installing memcached binding use the following command to install python memcached client python memcached pip install python memcached to use memcached with django set backend to django core cache backends memcached memcachedcache set location to ip port values where ip is the ip address of the memcached daemon and port is the port on which memcached is running add the below code snippet to api settings py caches default backend django core cache backends memcached memcachedcache location 127 0 0 1 11211 | server |
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rs-natural | rs natural build status https travis ci org christophertrml rs natural svg branch master https travis ci org christophertrml rs natural natural language processing library written in rust still very much a work in progress basically an experiment but hey maybe something cool will come out of it currently working jaro winkler distance levenshtein distance tokenizing ngrams with and without padding phonetics soundex naive bayes classification serialization via serde term frequency inverse document frequency tf idf serialization via serde near sight goals logistic regression classification optimize naive bayes currently pretty slow plural singular inflector how to use use at your own risk some functionality is missing some other functionality is slow as molasses because it isn t optomized yet i m targeting master and don t offer backward compatibility setup it s a crate with a cargo toml add this to your cargo toml dependencies natural 0 3 0 or enable serde support natural version 0 4 0 features serde support serde 1 0 distance rust extern crate natural use natural distance jaro winkler distance use natural distance levenshtein distance assert eq levenshtein distance kitten sitting 3 assert eq jaro winkler distance dixon dicksonx 0 767 note don t actually assert eq on jwd since it returns an f64 to test i actually use rust fn f64 eq a f32 b f32 assert a b abs 0 01 phonetics there are two ways to gain access to the soundex algorithm in this library either through a simple soundex function that accepts two str parameters and returns a boolean or through the soundexword struct i will show both here rust use natural phonetics soundex use natural phonetics soundexword assert soundex rupert robert let s1 soundexword new rupert let s2 soundexword new robert assert s1 sounds like s2 assert s1 sounds like str robert tokenization rust extern crate natural use natural tokenize tokenize assert eq tokenize hello world vec hello world assert eq tokenize my dog has fleas vec my dog has fleas ngrams you can create an ngram with and without padding e g rust extern crate natural use natural ngram get ngram use natural ngram get ngram with padding assert eq get ngram hello my darling 2 vec vec hello my vec my darling assert eq get ngram with padding my fleas 2 vec vec my vec my fleas vec fleas classification rust extern crate natural use natural classifier naivebayesclassifier let mut nbc naivebayesclassifier new nbc train string to train label nbc train string to train label nbc train string to train label nbc train string to train label nbc guess string to guess returns a label with the highest probability tf idf rust extern crate natural use natural tf idf tfidf tf idf add this document is about rust tf idf add this document is about erlang tf idf add this document is about erlang and rust tf idf add this document is about rust it has rust examples println tf idf get rust 0 2993708f32 println tf idf get erlang 0 13782766f32 average of multiple terms println tf idf get rust erlang 0 21859923 | ai |
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PACMAN | pacman replica of the classic pacman game designed and built with hardware software interaction to showcase my embedded systems oriented skills at the end of ucr s cs120b course | os |
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gorpc | gorpc a golang rpc framework designed for large scale distributed system run a gorpc test go test v github com johntech o gorpc command above will show performance info at the end of test output client conn status result 127 0 0 1 6668 creating 0 idle 0 readamount 907910 working 30 errno 0 client conn qps result 73011 errno 0 heapobjects 2981940 21634 2960306 totalalloc 789211080 50114848 739096232 pausetotalms 1 0 1 numgc 12 1 11 server call status result call s 71929 callamount 980847 err s 0 erroramount 0 readbytes 50014893 readbytes s 3668379 writebytes 30399247 writebytes s 2229799 errno 0 client conn status result 127 0 0 1 6668 creating 0 idle 30 readamount 1000008 working 0 errno 0 client conn qps result 19164 errno 0 10 calls consume less than 12 ms 20 calls consume less than 13 ms 30 calls consume less than 13 ms 40 calls consume less than 14 ms 50 calls consume less than 14 ms 60 calls consume less than 14 ms 70 calls consume less than 14 ms 80 calls consume less than 15 ms 90 calls consume less than 15 ms 100 calls consume less than 76 ms request amount 1000000 cost times 14 second average qps 69428 max client qps 73011 pass testechostruct 15 44s gorpc test go 240 testechostruct result hello echo struct count 1000000 pass ok github com johntech o gorpc 15 481s example run command on terminal to start the rpc server go run example serverdemo main go run command on another terminal to see result go run example clientdemo main go rpc service and method define in data data go service testrpcabc has a method echostruct type testrpcabc struct a b c string func r testrpcabc echostruct arg testrpcabc res string error res echocontent return nil service testrpcint has a method update type testrpcint struct i int func r testrpcint update n int res int error r i n res r i 100 return nil client example example client go var client gorpc client var a string func init netoptions gorpc newnetoptions time second 10 time second 20 time second 20 client gorpc newclient netoptions flag stringvar a a 127 0 0 1 6668 remote address port flag parse func main var strreturn string call remote service testrpcabc and method echostruct err client callwithaddress a testrpcabc echostruct data testrpcabc aaa bbb ccc strreturn if err nil println err error err errno println strreturn var intreturn int call remote service testrpcint and method update client callwithaddress a testrpcint update 5 intreturn if err nil println err error err errno println intreturn server example example server go func main s gorpc newserver l register the services testrpcint and testrpcabc s register new data testrpcint s register new data testrpcabc s serve panic server fail | os |
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OSGA | osga open source generative agents welcome to open source generative agents osga a community driven initiative that builds upon the foundation of generative agents our ethos revolves around the following pillars compatibility ensuring seamless integration with open source large language models innovation continuously enhancing performance methodologies and adaptability expansion rolling out novel optional features to enrich user experience centralisation offering a consolidated repository for all osga associated endeavours citation if you re leveraging this code or data please cite the original paper inproceedings park2023generativeagents author park joon sung and o brien joseph c and cai carrie j and morris meredith ringel and liang percy and bernstein michael s title generative agents interactive simulacra of human behavior year 2023 publisher association for computing machinery address new york ny usa booktitle in the 36th annual acm symposium on user interface software and technology uist 23 keywords human ai interaction agents generative ai large language models location san francisco ca usa series uist 23 environment compatibility the system has been rigorously tested on python 3 11 however if you re using an older version do ensure backward compatibility configuration to start you need to configure utils py inside the reverie directory python base api url your http call here replace with your specific http call for instance base api url http localhost 8000 v1 if the model resides on the same system initiating the communication usage guide 1 setup execute the install requirements bat file this will handle all necessary python dependencies 2 launching activate the servers both frontend and backend by running start bat 3 simulation selection when prompted input the name of a pre existing simulation corresponding to a folder name located at osga main environment frontend server storage this becomes the genesis point for your simulation 4 fork naming specify a unique name for your fork this name will represent your simulation in the storage directory 5 simulation duration to dictate the duration of your simulation use commands like run 6000 here the integer represents steps where 1 in game hour equates to 360 steps 6 continuation if you wish to extend a paused simulation initiate a new fork from your previous state this requires terminating the prior frontend and backend processes and then launching fresh ones important notes upon executing grant the frontend sufficient time to load initial actions by the personas might cause minor delays refrain from refreshing the frontend page as it could potentially halt the backend simulation the root cause of this issue is under investigation navigate the simulator gui using your keyboard s arrow keys access points simulator home http 127 0 0 1 8000 simulator home http 127 0 0 1 8000 simulator home past simulations ensure frontend is active http localhost 8000 replay simulation name starting time step http localhost 8000 replay simulation name starting time step facing issues raise them on our github ensuring you tag them appropriately we endeavour to address them promptly please bear with us during peak times as our response rate might be slightly delayed engage with our community we believe in the power of collaboration engage in vibrant discussions share ideas and explore potential partnerships on our discord platform https discord gg gefgyx4qt6 contribute to osga your insights are invaluable here s why you should consider contributing collective growth hosting projects within our ecosystem fosters shared learning and collective enhancement knowledge hub our vision is to establish a nexus of derivative projects propelling innovation in generative agent systems if our mission resonates with you and you re keen to contribute or host your project please connect with us on discord sharing your github username post a review we ll onboard you as a contributor and showcase your work on a test branch ensuring it aligns with our standards potential applications embarking on a project and seeking inspiration here are some avenues to explore benchmarking large language models llms character representation gauge the proficiency of llms in crafting character portrayals based on detailed character sheets social dynamics analyse the prowess of llms in orchestrating authentic and profound social dialogues world engagement investigate the logical reasoning abilities of llms when they interface with diverse world constituents role playing games rpgs bespoke engines integrate generative agents within proprietary game engines infusing characters with dynamism and reactivity popular engines craft plugins for renowned game engines integrating the capabilities of generative agents the horizon is limitless our platform is a canvas and we eagerly anticipate the masterpieces you ll conceive authored by elliott dyson | ai |
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SQL-Legacy-Employee-Database-Analysis | legacy database insights sql illustration https github com anushdecosta sql legacy employee database analysis assets 67308030 89d42106 3315 454d b17b 4129416a3804 summary the legacy database insights project aims to provide an in depth understanding of a legacy database through three crucial components establishing data model engineering data and analyzing data the analysis involves constructing an entity relationship diagram erd formulating table schemas importing data into an sql database and utilizing sql queries to gain insights from the data introduction this analysis is designed to delve into three crucial components all aiming to provide an in depth understanding of a legacy database establishing data model engineering data analysing data initially we dive into the csv files and construct an entity relationship diagram erd to outline the interrelations of the tables following this we formulate a table schema for the six csv files and infuse the data from these files into the sql database finally we use sql queries to respond to specific questions about the data establishing data model in this part we examine the csv files and forge a graphical illustration of the relationships among the tables to achieve this we employ quickdbd a tool enabling the sketching of a diagram showcasing the connections between tables encompassing primary keys foreign keys and other constraints erd diagram https github com anushdecosta sql legacy employee database analysis assets 67308030 167afa20 1c41 4d8d 822b 3badd0102245 engineering data upon completion of the erd we move forward to formulating the table schemas for each of the six csv files this involves specifying the data types primary keys foreign keys and other constraints for every column ensuring the uniqueness of primary keys is vital here it s paramount to establish the tables in the appropriate order to reference the foreign keys accurately the data from the csv files are then imported into the corresponding sql tables to prevent errors it s crucial to import the data following the order of table creation and consider the headers during the import analysing data the final segment of the analysis involves addressing certain questions about the data the queries include listing the employee number last name first name gender and salary of each employee providing the first name last name and hire date for employees who joined in 1986 enlisting the manager of every department along with their department number department name employee number last name and first name documenting the department number for every employee along with their employee number last name first name and department name presenting the first name last name and gender of each employee bearing the first name hercules and whose last name begins with b listing all employees in the sales department including their employee number last name and first name enlisting each employee in the sales and development departments inclusive of their employee number last name first name and department name recording the frequency counts in descending order of all employee last names indicating how many employees share each last name to address these questions we resort to sql queries that fetch the necessary data from the sql database tools quick dbd postgresql pgadmin4 microsoft excel vscode files data modeling erd diagram employee sql erd diagram png erd schema employee sql erd schema txt data engineering sql schema employee sql schema sql data analysis sql queries employee sql queries sql | data-analysis database postgresql sql | server |
harmony-dashboard | harmony dashboard the dashboard for harmony blockchain displaying interesting metrics build setup bash install dependencies npm install serve with hot reload at localhost 8080 npm run dev build for production with minification npm run build build for production and view the bundle analyzer report npm run build report | front_end |
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dlib | dlib c library github actions c status https github com davisking dlib actions workflows build cpp yml badge svg https github com davisking dlib actions workflows build cpp yml github actions python status https github com davisking dlib actions workflows build python yml badge svg https github com davisking dlib actions workflows build python yml dlib is a modern c toolkit containing machine learning algorithms and tools for creating complex software in c to solve real world problems see http dlib net http dlib net for the main project documentation and api reference compiling dlib c example programs go into the examples folder and type bash mkdir build cd build cmake cmake build that will build all the examples if you have a cpu that supports avx instructions then turn them on like this bash mkdir build cd build cmake duse avx instructions 1 cmake build doing so will make some things run faster finally visual studio users should usually do everything in 64bit mode by default visual studio is 32bit both in its outputs and its own execution so you have to explicitly tell it to use 64bits since it s not the 1990s anymore you probably want to use 64bits do that with a cmake invocation like this bash cmake g visual studio 14 2015 win64 t host x64 compiling your own c programs that use dlib the examples folder has a cmake tutorial https github com davisking dlib blob master examples cmakelists txt that tells you what to do there are also additional instructions on the dlib web site http dlib net compile html alternatively if you are using the vcpkg https github com microsoft vcpkg dependency manager you can download and install dlib with cmake integration in a single command bash vcpkg install dlib compiling dlib python api before you can run the python example programs you must install the build requirement bash python m venv venv pip install build then you must compile dlib and install it in your environment type bash python m build wheel pip install dist dlib version whl or download dlib using pypi bash pip install dlib running the unit test suite type the following to compile and run the dlib unit test suite bash cd dlib test mkdir build cd build cmake cmake build config release dtest runall note that on windows your compiler might put the test executable in a subfolder called release if that s the case then you have to go to that folder before running the test this library is licensed under the boost software license which can be found in dlib license txt https github com davisking dlib blob master dlib license txt the long and short of the license is that you can use dlib however you like even in closed source commercial software dlib sponsors this research is based in part upon work supported by the office of the director of national intelligence odni intelligence advanced research projects activity iarpa under contract number 2014 14071600010 the views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements either expressed or implied of odni iarpa or the u s government | machine-learning deep-learning c-plus-plus python computer-vision machine-learning-library dlib | ai |
stm32f3_Modbus_Slave_UART-DMA-FreeRTOS | stm32f3 modbus slave uart dma freertos light modbus rtu slave without spl and hal this lib doesn t need timers only uart and 2 channels of dma how to use 1 you need cmsis library for stm32f3 2 you need freertos library 3 start thread c include freertos h include task h include modbus rtu h int main void systeminit modbusslavestarttread vtaskstartscheduler return 0 | os |
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Cloud_Engineering-DevOps-Tech-Challenge | cloud engineering devops tech challenge challenge 1 3 tier environment using aws the script i provided is intended to be used in a shell environment with the aws command line interface cli installed and properly configured with your aws credentials here s how you can use the script prerequisites make sure you have the aws cli installed on your local machine or an aws ec2 instance configure the aws cli with your aws credentials using aws configure modify script replace all placeholders e g your vpc id your web ami id your key pair name etc with your actual values each placeholder represents a specific aws resource or configuration run the script save the script in a file for example create 3tier environment sh make the script executable with the command chmod x create 3tier environment sh execute the script with create 3tier environment sh the script will create a 3 tier environment in your aws account including vpc subnets security groups ec2 instances for web and application tiers and an rds database instance for the data tier it will also configure security groups and other essential settings please be cautious when running scripts that create aws resources and ensure that you understand the implications and costs associated with the resources being created it s recommended to test such scripts in a sandbox or non production environment first challenge 2 code that will query the meta data of an instance within aws environment and provide a json formatted output to query the metadata of an ec2 instance within aws and provide a json formatted output you can use the instance metadata service provided by aws the metadata service is accessible from within an ec2 instance and it provides information about the instance itself save the script get instance metadata sh to a file make it executable and run it with the desired key as an argument it will retrieve the specified key and format it as a json object that s it you now have a way to query the metadata of an aws ec2 instance and format the output as json with the bonus ability to retrieve specific data keys individually challenge 3 nested object value retrieval save the script to a file extract value sh make it executable using chmod x extract value sh and then run it it will parse the nested objects and return the values associated with the specified keys | cloud |
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Hacktoberfest-2022 | bmi calculator a new flutter project getting started this project is a starting point for a flutter application a few resources to get you started if this is your first flutter project lab write your first flutter app https flutter dev docs get started codelab cookbook useful flutter samples https flutter dev docs cookbook for help getting started with flutter view our online documentation https flutter dev docs which offers tutorials samples guidance on mobile development and a full api reference | hacktoberfest hacktoberfest-accepted hacktoberfest2022 projects pythonprojects enhancement good-first-issue upforgrabs | front_end |
autoprognosis | autoprognosis a system for automating the design of predictive modeling pipelines tailored for clinical prognosis div align center test in colab https img shields io badge tutorial model 20search orange https colab research google com drive 1sfvnnxjrmcnvin ikc ja44u0ll4joy usp sharing test in colab https img shields io badge tutorial build 20a 20demonstrator orange https colab research google com drive 1zwjd9rkosctboyblh4c8sqv1dugy1h2x usp sharing arxiv https img shields io badge arxiv 2210 12090 b31b1b svg https arxiv org abs 2210 12090 tests https github com vanderschaarlab autoprognosis actions workflows test pr yml badge svg https github com vanderschaarlab autoprognosis actions workflows test pr yml tests https github com vanderschaarlab autoprognosis actions workflows test full yml badge svg https github com vanderschaarlab autoprognosis actions workflows test full yml tests r https github com vanderschaarlab autoprognosis actions workflows test r yml badge svg https github com vanderschaarlab autoprognosis actions workflows test r yml tutorials https github com vanderschaarlab autoprognosis actions workflows test tutorials yml badge svg https github com vanderschaarlab autoprognosis actions workflows test tutorials yml documentation status https readthedocs org projects autoprognosis badge version latest https autoprognosis readthedocs io en latest badge latest https pepy tech badge autoprognosis https pypi org project autoprognosis license https img shields io badge license apache 2 0 blue svg https github com vanderschaarlab autoprognosis blob main license about https img shields io badge about the 20van 20der 20schaar 20lab blue https www vanderschaar lab com slack https img shields io badge chat on 20slack purple logo slack https join slack com t vanderschaarlab shared invite zt 1pzy8z7ti zvsuphaktgcd1uoy8xttew div image https github com vanderschaarlab autoprognosis raw main docs arch png autoprognosis key features rocket automatically learns ensembles of pipelines for classification regression or survival analysis tasks cyclone easy to extend pluginable architecture fire interpretability and uncertainty quantification tools adhesive bandage data imputation using hyperimpute https github com vanderschaarlab hyperimpute zap build demonstrators using streamlit https streamlit io notebook python high brightness tutorials and r tutorials https github com vanderschaarlab autoprognosis tree main tutorials bindings r available book read the docs https autoprognosis readthedocs io note the r bindings have been tested using r version 4 2 and python 3 8 rocket installation using pip the library can be installed from pypi using bash pip install autoprognosis or from source using bash pip install redis optional but recommended autoprognosis can use redis as a backend to improve the performance and quality of the searches for that install the redis server package following the steps described on the official site https redis io topics quickstart environment variables the library can be configured from a set of environment variables variable description n opt jobs number of cores to use for hyperparameter search default 1 n learner jobs number of cores to use by inidividual learners default all cpus redis host ip address for the redis database default 127 0 0 1 redis port redis port default 6379 example export n opt jobs 2 to use 2 cores for hyperparam search boom sample usage advanced python tutorials can be found in the python tutorials section high brightness tutorials r examples can be found in the r tutorials section https github com vanderschaarlab autoprognosis tree main tutorials bindings r list the available classifiers python from autoprognosis plugins prediction classifiers import classifiers print classifiers list available create a study for classifiers python from sklearn datasets import load breast cancer from autoprognosis studies classifiers import classifierstudy from autoprognosis utils serialization import load model from file from autoprognosis utils tester import evaluate estimator x y load breast cancer return x y true as frame true df x copy df target y study name example study classifierstudy study name study name dataset df pandas dataframe target target the label column in the dataset model study fit predict the probabilities of each class using the model model predict proba x advanced customize the study for classifiers python from pathlib import path from sklearn datasets import load breast cancer from autoprognosis studies classifiers import classifierstudy from autoprognosis utils serialization import load model from file from autoprognosis utils tester import evaluate estimator x y load breast cancer return x y true as frame true df x copy df target y workspace path workspace study name example study classifierstudy study name study name dataset df pandas dataframe target target the label column in the dataset num iter 100 how many trials to do for each candidate timeout 60 seconds classifiers logistic regression lda qda workspace workspace study run output workspace study name model p model load model from file output model contains the optimal architecture but the model is not trained yet you need to call fit to use it this way we can further benchmark the selected model on the training set metrics evaluate estimator model x y print f model model name metrics str train the model model fit x y predict the probabilities of each class using the model model predict proba x list the available regressors python from autoprognosis plugins prediction regression import regression print regression list available create a regression study python third party import pandas as pd autoprognosis absolute from autoprognosis utils serialization import load model from file from autoprognosis utils tester import evaluate regression from autoprognosis studies regression import regressionstudy load dataset df pd read csv https archive ics uci edu ml machine learning databases 00291 airfoil self noise dat header none sep t last col df columns 1 y df last col x df drop columns last col df x copy df target y search the model study name regression example study regressionstudy study name study name dataset df pandas dataframe target target the label column in the dataset model study fit predict using the model model predict x advanced customize the regression study python stdlib from pathlib import path third party import pandas as pd autoprognosis absolute from autoprognosis utils serialization import load model from file from autoprognosis utils tester import evaluate regression from autoprognosis studies regression import regressionstudy load dataset df pd read csv https archive ics uci edu ml machine learning databases 00291 airfoil self noise dat header none sep t last col df columns 1 y df last col x df drop columns last col df x copy df target y search the model workspace path workspace workspace mkdir parents true exist ok true study name regression example study regressionstudy study name study name dataset df pandas dataframe target target the label column in the dataset num iter 10 how many trials to do for each candidate default 50 num study iter 2 how many outer iterations to do default 5 timeout 50 timeout for optimization for each classfier default 600 seconds regressors linear regression xgboost regressor workspace workspace study run test the model output workspace study name model p model load model from file output model contains the optimal architecture but the model is not trained yet you need to call fit to use it this way we can further benchmark the selected model on the training set metrics evaluate regression model x y print f model model name score metrics str train the model model fit x y predict using the model model predict x list available survival analysis estimators python from autoprognosis plugins prediction risk estimation import riskestimation print riskestimation list available create a survival analysis study python third party import numpy as np from pycox import datasets autoprognosis absolute from autoprognosis studies risk estimation import riskestimationstudy from autoprognosis utils serialization import load model from file from autoprognosis utils tester import evaluate survival estimator df datasets gbsg read df df df df duration 0 x df drop columns duration t df duration y df event eval time horizons np linspace t min t max 5 1 1 study name example risks study riskestimationstudy study name study name dataset df target event time to event duration time horizons eval time horizons model study fit predict using the model model predict x eval time horizons advanced customize the survival analysis study python stdlib import os from pathlib import path third party import numpy as np from pycox import datasets autoprognosis absolute from autoprognosis studies risk estimation import riskestimationstudy from autoprognosis utils serialization import load model from file from autoprognosis utils tester import evaluate survival estimator df datasets gbsg read df df df df duration 0 x df drop columns duration t df duration y df event eval time horizons np linspace t min t max 5 1 1 workspace path workspace study name example risks study riskestimationstudy study name study name dataset df target event time to event duration time horizons eval time horizons num iter 10 num study iter 1 timeout 10 risk estimators cox ph survival xgboost score threshold 0 5 workspace workspace study run output workspace study name model p model load model from file output model contains the optimal architecture but the model is not trained yet you need to call fit to use it this way we can further benchmark the selected model on the training set metrics evaluate survival estimator model x t y eval time horizons print f model model name score metrics str train the model model fit x t y predict using the model model predict x eval time horizons high brightness tutorials plugins test in colab https colab research google com assets colab badge svg https colab research google com drive 1qo7k3jqw8l4pgvslxjveztu5ifd9yhb usp sharing imputation tutorials plugins tutorial 00 imputation plugins ipynb test in colab https colab research google com assets colab badge svg https colab research google com drive 1wqgzxqkqs0wg5stb9fk rvyey35adizu usp sharing preprocessing tutorials plugins tutorial 01 preprocessing plugins ipynb test in colab https colab research google com assets colab badge svg https colab research google com drive 1wtzo 2hqaeovvathpsicw220xc1wajlc usp sharing classification tutorials plugins tutorial 02 classification plugins ipynb test in colab https colab research google com assets colab badge svg https colab research google com drive 17bltkujn8ilhw4cm7 53kic0vcjo pvb usp sharing pipelines tutorials plugins tutorial 03 pipelines ipynb test in colab https colab research google com assets colab badge svg https colab research google com drive 1k0yvwm4jqrxrbmkj em7ttyghxwtok5c usp sharing interpretability tutorials plugins tutorial 04 interpretability ipynb test in colab https colab research google com assets colab badge svg https colab research google com drive 1by4cbiqme2uoqeuu2d49aidyrbtp156x usp sharing survival analysis tutorials plugins tutorial 05 survival analysis plugins ipynb test in colab https colab research google com assets colab badge svg https colab research google com drive 1uk6wbsvit5noq bahsfyijhpktwppnuu usp sharing regression tutorials plugins tutorial 06 regression plugins ipynb automl test in colab https colab research google com assets colab badge svg https colab research google com drive 1 lpuqatjhesl32ahfqysfl8ujandwxej usp sharing classification tasks tutorials automl tutorial 00 classification study ipynb test in colab https colab research google com assets colab badge svg https colab research google com drive 16udaa3f5jgw yvy8xlyqwjfxcuv1ohjo usp sharing classification tasks with imputation tutorials automl tutorial 01 automl classification with imputation ipynb test in colab https colab research google com assets colab badge svg https colab research google com drive 1dtzcqebhaydkb3ci5dr3ht0kvzpatuoi usp sharing survival analysis tasks tutorials automl tutorial 02 survival analysis study ipynb test in colab https colab research google com assets colab badge svg https colab research google com drive 1sfvnnxjrmcnvin ikc ja44u0ll4joy usp sharing survival analysis tasks with imputation tutorials automl tutorial 03 automl survival analysis with imputation ipynb test in colab https colab research google com assets colab badge svg https colab research google com drive 1hlhwi trzn4e9ijq6ieiuppudszgwkcc usp sharing regression tasks tutorials automl tutorial 04 regression ipynb test in colab https colab research google com assets colab badge svg https colab research google com drive 1ehw1l79 m3vq9y 0wpllcmbsd7dqajwo usp sharing classifiers with explainers tutorials automl tutorial 05 classification with explainers ipynb test in colab https colab research google com assets colab badge svg https colab research google com drive 1dm3crmo jd6x7v5wepcidpcauqtut6ls usp sharing multiple imputation example tutorials automl tutorial 06 automl multiple imputation example ipynb building a demonstrator test in colab https colab research google com assets colab badge svg https colab research google com drive 1lqbelevja2q0jdsxpgb8k qutdczvuqq usp sharing classification demonstrator tutorials demonstrators tutorial 00 build a demonstrator classification ipynb test in colab https colab research google com assets colab badge svg https colab research google com drive 1zwjd9rkosctboyblh4c8sqv1dugy1h2x usp sharing survival analysis demonstrator tutorials demonstrators tutorial 01 build a demonstrator survival analysis ipynb zap plugins imputation methods python from autoprognosis plugins imputers import imputers imputer imputers get name name description hyperimpute iterative imputer using both regression and classification methods based on linear models trees xgboost catboost and neural nets mean replace the missing values using the mean along each column with simpleimputer https scikit learn org stable modules generated sklearn impute simpleimputer html median replace the missing values using the median along each column with simpleimputer https scikit learn org stable modules generated sklearn impute simpleimputer html most frequent replace the missing values using the most frequent value along each column with simpleimputer https scikit learn org stable modules generated sklearn impute simpleimputer html missforest iterative imputation method based on random forests using iterativeimputer https scikit learn org stable modules generated sklearn impute iterativeimputer html sklearn impute iterativeimputer and extratreesregressor https scikit learn org stable modules generated sklearn ensemble extratreesregressor html ice iterative imputation method based on regularized linear regression using iterativeimputer https scikit learn org stable modules generated sklearn impute iterativeimputer html sklearn impute iterativeimputer and bayesianridge https scikit learn org stable modules generated sklearn linear model bayesianridge html mice multiple imputations based on ice using iterativeimputer https scikit learn org stable modules generated sklearn impute iterativeimputer html sklearn impute iterativeimputer and bayesianridge https scikit learn org stable modules generated sklearn linear model bayesianridge html softimpute low rank matrix approximation via nuclear norm regularization https jmlr org papers volume16 hastie15a hastie15a pdf plugin softimpute py src hyperimpute plugins imputers plugin softimpute py em iterative procedure which uses other variables to impute a value expectation then checks whether that is the value most likely maximization em imputation algorithm https joon3216 github io research materials 2019 em imputation html gain gain missing data imputation using generative adversarial nets https arxiv org abs 1806 02920 preprocessing methods python from autoprognosis plugins preprocessors import preprocessors preprocessor preprocessors get name name description maxabs scaler scale each feature by its maximum absolute value maxabsscaler https scikit learn org stable modules generated sklearn preprocessing maxabsscaler html scaler standardize features by removing the mean and scaling to unit variance standardscaler https scikit learn org stable modules generated sklearn preprocessing standardscaler html sklearn preprocessing standardscaler feature normalizer normalize samples individually to unit norm normalizer https scikit learn org stable modules generated sklearn preprocessing normalizer html sklearn preprocessing normalizer normal transform transform features using quantiles information quantiletransformer https scikit learn org stable modules generated sklearn preprocessing quantiletransformer html sklearn preprocessing quantiletransformer uniform transform transform features using quantiles information quantiletransformer https scikit learn org stable modules generated sklearn preprocessing quantiletransformer html sklearn preprocessing quantiletransformer minmax scaler transform features by scaling each feature to a given range minmaxscaler https scikit learn org stable modules generated sklearn preprocessing minmaxscaler html sklearn preprocessing minmaxscaler classification python from autoprognosis plugins prediction classifiers import classifiers classifier classifiers get name name description neural nets pytorch based neural net classifier logistic regression logisticregression https scikit learn org stable modules generated sklearn linear model logisticregression html catboost gradient boosting on decision trees catboost https catboost ai random forest a random forest classifier randomforestclassifier https scikit learn org stable modules generated sklearn ensemble randomforestclassifier html tabnet tabnet attentive interpretable tabular learning https github com dreamquark ai tabnet xgboost xgboostclassifier https xgboost readthedocs io en stable survival analysis python from autoprognosis plugins prediction risk estimation import riskestimation predictor riskestimation get name name description survival xgboost xgboost survival embeddings https github com loft br xgboost survival embeddings loglogistic aft log logistic aft model https lifelines readthedocs io en latest fitters regression loglogisticaftfitter html deephit deephit a deep learning approach to survival analysis with competing risks https github com chl8856 deephit cox ph cox s proportional hazard model https lifelines readthedocs io en latest fitters regression coxphfitter html weibull aft weibull aft model https lifelines readthedocs io en latest fitters regression weibullaftfitter html lognormal aft log normal aft model https lifelines readthedocs io en latest fitters regression lognormalaftfitter html coxnet coxnet is a cox proportional hazards model also referred to as deepsurv https github com havakv pycox regression python from autoprognosis plugins prediction regression import regression regressor regression get name name description tabnet regressor tabnet attentive interpretable tabular learning https github com dreamquark ai tabnet catboost regressor gradient boosting on decision trees catboost https catboost ai random forest regressor randomforestregressor https scikit learn org stable modules generated sklearn ensemble randomforestregressor html xgboost regressor xgboostclassifier https xgboost readthedocs io en stable neural nets regression pytorch based neural net regressor linear regression linearregression https scikit learn org stable modules generated sklearn linear model linearregression html explainers python from autoprognosis plugins explainers import explainers explainer explainers get name name description risk effect size feature importance using cohen s distance between probabilities lime lime explaining the predictions of any machine learning classifier https github com marcotcr lime symbolic pursuit symbolic pursuit learning outside the black box at the pursuit of interpretable models shap permutation sampler shap permutation sampler https shap readthedocs io en latest generated shap explainers permutation html kernel shap shap kernelexplainer https shap lrjball readthedocs io en latest generated shap kernelexplainer html invase invase instance wise variable selection https github com vanderschaarlab invase uncertainty python from autoprognosis plugins uncertainty import uncertaintyquantification model uncertaintyquantification get name name description cohort explainer conformal prediction jackknife hammer test after installing the library the tests can be executed using pytest bash pip install testing pytest vxs m not slow citing if you use this code please cite the associated paper misc https doi org 10 48550 arxiv 2210 12090 doi 10 48550 arxiv 2210 12090 url https arxiv org abs 2210 12090 author imrie fergus and cebere bogdan and mckinney eoin f and van der schaar mihaela keywords machine learning cs lg artificial intelligence cs ai fos computer and information sciences fos computer and information sciences title autoprognosis 2 0 democratizing diagnostic and prognostic modeling in healthcare with automated machine learning publisher arxiv year 2022 copyright creative commons attribution 4 0 international references 1 autoprognosis automated clinical prognostic modeling via bayesian optimization with structured kernel learning https arxiv org abs 1802 07207 2 prognostication and risk factors for cystic fibrosis via automated machine learning https www nature com articles s41598 018 29523 2 3 cardiovascular disease risk prediction using automated machine learning a prospective study of 423 604 uk biobank participants https www ncbi nlm nih gov pubmed 31091238 | automl survival-analysis healthcare interpretability | os |
ITIT | itit information technology invention theory | server |
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db-reverse-laravel | db reverse script 8 a light weight php cli tool for reverse engineering existing databases to laravel migrations models seeders usage clone this repo run composer install run the script via terminal type help on the terminal to display the full command list and thier functions start cli php index php create migration for users table create migrations users create all migrations create migrations create seeders for users table create seeders users expected output creating 2020 09 21 61920 create migrations table php created 2020 09 21 61920 create migrations table php creating 2020 09 21 61921 create test table php created 2020 09 21 61921 create test table php display help help output commands description connect connects to a new database create seeders table name create database seeder for table name table create models table name create database models for table name table create migrations table name create database migrations for table name table create migrations create migrations file for all tables create seeders create seeders for all tables create models create models for all tables generate crud controllers create controllers with read updated delete functions status show connection status help show this message connection status check status output variable status connected connected database name lumen username username port 80 driver mysql host localhost requirement php 7 3 | server |
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Semester_Registration | semester registration a simple semester registration application for colleges or schools this app uses various android application development concepts such as intents threading database implementation ui ux graphics fragments action bar and many more the app was originally built with firebase as backend but for demonstration the given apk has sqlite as backend | server |
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yarn | yarn this repo contains the code and data for the yarn context window extension method preprint preprint arxiv yarn efficient context window extension of large language models https arxiv org abs 2309 00071 a list of mistakes caught by our readers are listed here thank you errata md errata md v2 of the preprint will be published on arxiv with all the corrections models we publish 7b and 13b variants of llama 2 https about fb com news 2023 07 llama 2 fine tuned with yarn at 64k and 128k context window length they are available under the llama 2 license on hugging face size context link 7b 64k nousresearch yarn llama 2 7b 64k https huggingface co nousresearch yarn llama 2 7b 64k 7b 128k nousresearch yarn llama 2 7b 128k https huggingface co nousresearch yarn llama 2 7b 128k 13b 64k nousresearch yarn llama 2 13b 64k https huggingface co nousresearch yarn llama 2 13b 64k 13b 128k nousresearch yarn llama 2 13b 128k https huggingface co nousresearch yarn llama 2 13b 128k in addition we also publish 8k context window versions of llama 2 7b fine tuned with ntk aware https huggingface co emozilla ntk llama 2 7b 8k and yarn https huggingface co emozilla yarn llama 2 7b 8k table 1 in the conference paper reproduction we strongly believe in open science and thus publish all code and data to reproduce the results in our paper to reproduce clone the repository and perform a local installation python git clone https github com jquesnelle yarn cd yarn pip install e training to train the models run accelerate config and enable deepspeed acceleration deepspeed zero3 json was the configuration file used for training sh train sh the tokenized training data is available on hugging face https huggingface co datasets emozilla pg books tokenized bos eos chunked 65536 and was derived from the pg19 https huggingface co datasets emozilla pg19 dataset evaluation to reproduce the evaluations install lm evaluation harness https github com eleutherai lm evaluation harness with pip install git https github com eleutherai lm evaluation harness and then run the two provided scripts sh eval sh eval harness sh citation misc peng2023yarn title yarn efficient context window extension of large language models author bowen peng and jeffrey quesnelle and honglu fan and enrico shippole year 2023 eprint 2309 00071 archiveprefix arxiv primaryclass cs cl | ai |
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figma-plugin-ds | figma plugin ds a lightweight ui library for creating figma plugins contents contents contents intro intro getting started getting started roadmap roadmap components components button button checkbox checkbox disclosure disclosure icon icon icon button icon button input input labels and sections labels and sections onboarding tip onboarding tip radio button radio button select menu select menu switch switch textarea textarea type type intro this package contains css and javascript to closely match the look feel and function of those found in figma it has been created without any frameworks like react vue etc and only leverages native javascript for components not possible without getting started you can install this package as a dependecy on your own project npm install figma plugin ds styles to use the styles you can use them via a link tag or import them like a module using a css loader html standard link tag link rel stylesheet href node modules figma plugin ds dist figma plugin ds css javascript you could also inport the css via a css loader in your js environment refer to syntax of whatever loader you are using import styles from figma plugin ds dist figma plugin ds css to use the select menu or disclosure components you will need to import the javascript files as well this package supports both standard iife immediately invoked function expressions and es6 modules there are a number of ways to get started scripts iife hosted on cdn quick and easy i don t want to mess with npm packages html script src https cdn jsdelivr net gh thomas lowry figma plugin ds dist iife figma plugin ds js script script selectmenu init initiates the select menu component disclosure init initiates the disclosure component script scripts iife html standard link tag script src node modules figma plugin ds dist iife figma plugin ds js script scripts es6 modules javascript import selectmenu disclosure from figma plugin ds roadmap new documentation website improved support for keyboard nav in the select menu slider component components button to use the button use the following html markup each button has a destructive option teritary buttons are styled like hyperlinks html primary button class button button primary label button button class button button primary destructive label button button class button button primary disabled label button secondary button class button button secondary label button button class button button secondary destructive label button button class button button secondary disabled label button tertirary hyperlink style button button class button button tertiary label button button class button button tertiary destructive label button button class button button tertiary disabled label button modifiers modifier class description button primary primary button button primary destructive primary button with red destructive variant for actions such as deleting something button secondary secondary button with outline style button secondary destructive secondary button with red destructive variant for actions such as deleting something button tertiary tertiary button with hyperlink style button tertiary destructive tertiary button with red destructive variant for actions such as deleting something checkbox to use the checkbox use the following html markup remember each checkbox should get a unique id that is referenced in the label this ensures the checkbox and the entire label are clickable html checkbox unchecked div class checkbox input id uniqueid type checkbox class checkbox box label for uniqueid class checkbox label label label div checkbox checked div class checkbox input id uniqueid type checkbox class checkbox box checked label for uniqueid class checkbox label label label div checkbox disabled div class checkbox input id uniqueid type checkbox class checkbox box disabled label for uniqueid class checkbox label label label div disclosure to use a disclosure panel you must use the following markup and also make sure you initialize the javascript for the disclosure to work html disclosure ul class disclosure li class disclosure item disclosure expanded div class disclosure label disclosure section disclosure heading div this item is styled as a section div class disclosure content panel content here div li li class disclosure item disclosure expanded this item is expanded on load div class disclosure label disclosure heading div div class disclosure content panel content here div li li class disclosure item div class disclosure label disclosure heading div div class disclosure content panel content here div li ul to initialize with javascript javascript initialize all disclosure panels disclosure init uninitialize all disclosure panels disclosure destroy modifiers modifier class description disclosure section add this class to the disclosure label to style it like a heading disclosure expanded add this class to the disclosure item to have it expanded on load icon to use the icon component use the following markup apply the appropriate modifier class to select the item you wish to use you can also add additional modifiers to change the color or even spin the icon you can also specify no icon name to use a text character as an icon for example like found in the width height icon inputs in figma html icon div class icon icon theme div icon with blue modifier class to change color div class icon icon theme icon blue div spinner icon with spinning animation div class icon icon spinner icon spin div text icon div class icon w div modifiers modifier class description icon iconname sepcify which icon to use ex icon adjust or icon settings icon spin causes the icon to spin in an endless loop for example a loader used with icon spinner icon colorname pass the name of any figma color var to this prop ex icon blue or icon black3 colors accepted icon blue icon purple icon purple4 icon hot pink icon green icon red icon yellow icon black icon black8 icon black3 icon white icon white8 icon white4 icons to use an icon add one of the following modifier classes see markup example above icon modifier class iconadjust src icons adjust svg icon icon adjust iconalert src icons alert svg icon icon alert iconangle src icons angle svg icon icon angle iconarrowleftright src icons arrow left right svg icon icon arrow left right iconupdown src icons arrow up down svg icon icon updown iconautolayouthorizontal src icons auto layout horizontal svg icon icon auto layout horizontal iconautolayoutvertical src icons auto layout vertical svg icon icon auto layout vertical iconback src icons back svg icon icon back iconblendempty src icons blend empty svg icon icon blend empty iconblend src icons blend svg icon icon blend iconbreak src icons break svg icon icon break iconcaretdown src icons caret down svg icon icon caret down iconcaretleft src icons caret left svg icon icon caret left iconcaretright src icons caret right svg icon icon caret right iconcaretup src icons caret up svg icon icon caret up iconcheck src icons check svg icon icon check iconclose src icons close svg icon icon close iconcomponent src icons component svg icon icon component iconcornerradius src icons corner radius svg icon icon corner radius iconcorners src icons corners svg icon icon corners icondistributehorizontalspacing src icons distribute horizontal spacing svg icon icon distribute horizontal spacing icondistributeverticalspacing src icons distribute vertical spacing svg icon icon distribute vertical spacing icondraft src icons draft svg icon icon draft iconeffects src icons effects svg icon icon effects iconellipses src icons ellipses svg icon icon ellipses iconeyedropper src icons eyedropper svg icon icon eyedropper iconforward src icons forward svg icon icon forward iconframe src icons frame svg icon icon frame icongroup src icons group svg icon icon group iconhidden src icons hidden svg icon icon hidden iconhorizontalpadding src icons horizontal padding svg icon icon horizontal padding iconhyperlink src icons hyperlink svg icon icon hyperlink iconimage src icons image svg icon icon image iconinstance src icons instance svg icon icon instance iconkey src icons key svg icon icon key iconlayoutalignbottom src icons layout align bottom svg icon icon layout align bottom iconalignhorizontalcenters src icons layout align horizontal centers svg icon icon align horizontal centers iconalignleft src icons layout align left svg icon icon align left iconalignright src icons layout align right svg icon icon align right iconaligntop src icons layout align top svg icon icon align top iconalignverticalcenters src icons layout align vertical centers svg icon icon align vertical centers iconlayoutgridcolumns src icons layout grid columns svg icon icon layout grid columns iconlayoutgridrows src icons layout grid rows svg icon icon layout grid rows iconlayoutgriduniform src icons layout grid uniform svg icon icon layout grid uniform iconlibrary src icons library svg icon icon library iconlinkbroken src icons link broken svg icon icon link broken iconlinkconnected src icons link connected svg icon icon link connected iconlistdetailed src icons list detailed svg icon icon list detailed iconlisttile src icons list tile svg icon icon list tile iconlist src icons list svg icon icon list iconlockoff src icons lock off svg icon icon lock off iconlockon src icons lock on svg icon icon lock on iconminus src icons minus svg icon icon minus iconplay src icons play svg icon icon play iconplus src icons plus svg icon icon plus iconrandom src icons random svg icon icon random iconrecent src icons recent svg icon icon recent iconresizetofit src icons resize to fit svg icon icon resize to fit iconresolvefilled src icons resolve filled svg icon icon resolve filled iconresolve src icons resolve svg icon icon resolve iconreverse src icons reverse svg icon icon reverse iconsearchlarge src icons search large svg icon icon search large iconsearch src icons search svg icon icon search iconsettings src icons settings svg icon icon settings iconshare src icons share svg icon icon share iconsmiley src icons smiley svg icon icon smiley iconsortalphaasc src icons sort alpha asc svg icon icon sort alpha asc iconsortalphadsc src icons sort alpha dsc svg icon icon sort alpha dsc iconsorttopbottom src icons sort top bottom svg icon icon sort top bottom iconspacing src icons spacing svg icon icon spacing iconspinner src icons spinner svg icon icon spinner iconstaroff src icons star off svg icon icon star off iconstaron src icons star on svg icon icon star on iconstrokeweight src icons stroke weight svg icon icon stroke weight iconstyles src icons styles svg icon icon styles iconswap src icons swap svg icon icon swap icontheme src icons theme svg icon icon theme icontidyupgrid src icons tidy up grid svg icon icon tidy up grid icontidyuplisthorizontal src icons tidy up list horizontal svg icon icon tidy up list horizontal icontidyuplistvertical src icons tidy up list vertical svg icon icon tidy up list vertical icontimer src icons timer svg icon icon timer icontrash src icons trash svg icon icon trash iconverticalpadding src icons vertical padding svg icon icon vertical padding iconvisible src icons visible svg icon icon visible iconwarninglarge src icons warning large svg icon icon warning large iconwarning src icons warning svg icon icon warning icon button the icon button is essentially a wrapper for the icon component refer to the icon component above for its usage html icon button with a blend icon div class icon button div class icon icon blend div div icon button with selected modifier div class icon button icon button selected div class icon icon blend div div input to use the input use the following markup you can also insert an icon into the input see icon component for usage html input with placeholder div class input input type input class input field placeholder placeholder div input with initial value div class input input type input class input field value initial value div disabled input div class input input type input class input field value initial value disabled div input with icon div class input input with icon div class icon icon angle div input type input class input field value value div modifiers modifier class description input with icon add this modifier class if you plan to include the icon component within the input labels and sections to use a label or section use the following markup html label div class label label div section title div class section title section title div onboarding tip to create an onboarding tip use the following markup the tip also makes use of the icon component see icon component for usage html div class onboarding tip div class icon icon styles div div class onboarding tip msg onboarding tip goes here div div radio button to create an radio button use the following markup remember each group of radio buttons must share the same name so that they are related to one another each button should have a unique id so that its label is associated with it and remains part of the clickable hit area html radio button div class radio input id radiobutton1 type radio class radio button value value name radiogroup label for radiobutton1 class radio label radio button label div radio button checked div class radio input id radiobutton2 type radio class radio button value value name radiogroup checked label for radiobutton2 class radio label radio button label div radio button disabled div class radio input id radiobutton3 type radio class radio button value value name radiogroup disabled label for radiobutton3 class radio label radio button label div select menu to create an select menu use the following markup the select menu also requires you to initalize it with javascript if your plugin requires you to add or remove items in the select menu simply use javascript to modify the select menu and the select will reinitialize the select menu will open and position the menu to the selected object if there is no vertical room inside your plugin s iframe the position of the menu will be moved to ensure it fits inside the iframe if you have a select menu with too many options to fit within the iframe the menu will scroll vertically html select menu default behavior is for the initial item to get selected select id uniqueid class select menu option value 1 item 1 option option value 2 item 2 option option value 3 item 3 option select select menu provide an initial item with no value to have no items selected select id uniqueid class select menu option please make a selection option option value 2 item 2 option option value 3 item 3 option select disabled select menu select id uniqueid class select menu disabled option value 1 item 1 option option value 2 item 2 option option value 3 item 3 option select to initialize with javascript javascript initialize all select menus selectmenu init uninitialize all select menus selectmenu destroy switch to use the switch use the following html markup remember each switch should get a unique id that is referenced in the label this ensures the switch and the entire label are clickable html switch div class switch input class switch toggle type checkbox id uniqueid label class switch label for uniqueida label label div switch checked div class switch input class switch toggle type checkbox id uniqueid checked label class switch label for uniqueid label label div disabled switch div class switch input class switch toggle type checkbox id uniqueid disabled label class switch label for uniqueid label label div textarea to use the textarea use the following html markup html textarea textarea class textarea rows 2 initial value textarea disabled textarea textarea class textarea rows 2 disabled initial value textarea type to use the typography that is styled like it is in the figma ui use the following markup plus additional modifier classes to modify the size weight and letterspacing that is optimized for positive dark text on light background and negative light text on dark background applications html div class type ui11 size xsmall default weight normal positive div div class type type large type bold ui13 size large weight bold positive div div class type type small type medium type inverse ui12 size large weight medium negative div modifiers modifier class description type small font size 12px type large font size 13px type xlarge font size 14px type medium font weight medium type bold font weight bold type inverse inversed negative application where light text is on dark background with increased letterspacing defaults font size 11px normal weight positive application | figma-plugins | os |
Employee-Database-SQL-mystery | sql challenge background it is a beautiful spring day and it is two weeks since you have been hired as a new data engineer at pewlett hackard your first major task is a research project on employees of the corporation from the 1980s and 1990s all that remain of the database of employees from that period are six csv files in this assignment you will design the tables to hold data in the csvs import the csvs into a sql database and answer questions about the data in other words you will perform 1 data engineering 2 data analysis note you may hear the term data modeling in place of data engineering but they are the same terms data engineering is the more modern wording instead of data modeling before you begin 1 create a new repository for this project called sql challenge do not add this homework to an existing repository 2 clone the new repository to your computer 3 inside your local git repository create a directory for the sql challenge use a folder name to correspond to the challenge employeesql 4 add your files to this folder 5 push the above changes to github instructions data modeling inspect the csvs and sketch out an erd of the tables feel free to use a tool like http www quickdatabasediagrams com http www quickdatabasediagrams com data engineering use the information you have to create a table schema for each of the six csv files remember to specify data types primary keys foreign keys and other constraints for the primary keys check to see if the column is unique otherwise create a composite key https en wikipedia org wiki compound key which takes to primary keys in order to uniquely identify a row be sure to create tables in the correct order to handle foreign keys import each csv file into the corresponding sql table note be sure to import the data in the same order that the tables were created and account for the headers when importing to avoid errors data analysis once you have a complete database do the following 1 list the following details of each employee employee number last name first name sex and salary 2 list first name last name and hire date for employees who were hired in 1986 3 list the manager of each department with the following information department number department name the manager s employee number last name first name 4 list the department of each employee with the following information employee number last name first name and department name 5 list first name last name and sex for employees whose first name is hercules and last names begin with b 6 list all employees in the sales department including their employee number last name first name and department name 7 list all employees in the sales and development departments including their employee number last name first name and department name 8 in descending order list the frequency count of employee last names i e how many employees share each last name bonus optional as you examine the data you are overcome with a creeping suspicion that the dataset is fake you surmise that your boss handed you spurious data in order to test the data engineering skills of a new employee to confirm your hunch you decide to take the following steps to generate a visualization of the data with which you will confront your boss 1 import the sql database into pandas yes you could read the csvs directly in pandas but you are after all trying to prove your technical mettle this step may require some research feel free to use the code below to get started be sure to make any necessary modifications for your username password host port and database name sql from sqlalchemy import create engine engine create engine postgresql localhost 5432 your db name connection engine connect consult sqlalchemy documentation https docs sqlalchemy org en latest core engines html postgresql for more information if using a password do not upload your password to your github repository see https www youtube com watch v 2uatpmnvh0i https www youtube com watch v 2uatpmnvh0i and https help github com en github using git ignoring files https help github com en github using git ignoring files for more information 2 create a histogram to visualize the most common salary ranges for employees 3 create a bar chart of average salary by title epilogue evidence in hand you march into your boss s office and present the visualization with a sly grin your boss thanks you for your work on your way out of the office you hear the words search your id number you look down at your badge to see that your employee id number is 499942 submission create an image file of your erd create a sql file of your table schemata create a sql file of your queries optional create a jupyter notebook of the bonus analysis create and upload a repository with the above files to github and post a link on bootcamp spot | data-analysis data-engineering sql-database employee-database foreign-keys | server |
blockchain-oracle | ethereum oracle this project intends to create a trustable oracle for the ethereum blockchain requirements yarn https yarnpkg com ou npm https www npmjs com truffle https www trufflesuite com truffle ganache cli https github com trufflesuite ganache cli local development the following setup will launch three terminals and requires the use of the tmux https github com tmux tmux wiki terminal and teamocile http www teamocil com for managing windows however it can be ran with a normal terminal opening three windows and running the commands identified here scripts oracle yml https github com pedroduartecosta blockchain oracle blob master scripts oracle yml tmux teamocil oracle local development setup install the teamocil ruby gem gem install teamocil create your layout directory mkdir teamocil copy the scripts oracle yml https github com pedroduartecosta blockchain oracle blob master scripts oracle yml configuration following to teamocil cp scripts oracle yml teamocil launch tmux tmux run your oracle layout teamocil oracle | blockchain ethereum | blockchain |
NLP | nlp nlp pku msr 1 2 3 n gram 4 5 6 7 w wcrf msr f1 95 7 nlp wcrf 8 | bayes ngram mm memm crf | ai |
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