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web-development | web development p align center b the submissions for this repository are now closed no more pull requests will be accepted br you can still fork this repository and complete these tasks for yourself b p this repository is for users gettings started in web developments there are some tasks listed in issues https github com cc bhu web development issues q is 3aissue is 3aopen sort 3acreated asc view the task complete them and try to submit a pull request to this repository the pull request will be reviewed by a member and merged after review view the listed tasks here https github com cc bhu web development issues q is 3aissue is 3aopen sort 3acreated asc and start coding the solutions to the tasks are present in the solutions solutions folder of this repository leaderboard no user name completed portfolio tasks 1 omkeshwargupta https github com omkeshwargupta omkeshwar gupta 11 11 portfolio https omkeshwargupta github io link omkeshwargupta 2 atiwari2000 https github com atiwari2000 abhishek tiwari 11 11 link atiwari2000 3 rajkumar562 https github com rajkumar562 rajkumar choudhury 11 11 link rajkumar562 4 uditagrawal08 https github com uditagrawal08 udit kumar agrawal 11 11 link uditagrawal08 5 pawankrgithub https github com pawankrgithub pawan kumar 10 11 link pawankrgithub 6 animeshkumarrai https github com animeshkumarrai animesh kumar rai 9 11 link animeshkumarrai 7 rvmis https github com rvmis ram vikas mishra 9 11 link rvmis 8 sanskar2807 https github com sanskar2807 sanskar sharma 8 11 link sanskar2807 9 varshasaroj https github com varshasaroj varsha saroj 8 11 link varshasaroj 10 ghost git24 https github com ghost git24 ashish anand 7 11 link ghost git24 11 preeteshspec https github com preeteshspec preetesh bera 7 11 link preeteshspec 12 anukulsahu https github com anukulsahu anukul sahu 6 11 link anukulsahu 13 ketanjamnal https github com ketanjamnal ketan jamnal 6 11 link ketanjamnal 14 msagastya https github com msagastya suyash mishra 5 11 link msagastya 15 joshiharsh920 https github com joshiharsh920 harsh joshi 2 11 link joshiharsh920 16 nainsi04 https github com nainsi04 nainsi singh 2 11 link nainsi04 17 anchalsingh20 https github com anchalsingh20 anchal singh 1 11 link anchalsingh20 submission guideline note all submission should made under a folder with your username i e cc bhu task html to submit your pull request to thi repository follow the steps 1 fork this repository using the fork button fork creates a copy of the repository under your profile that will contain your version of the project you can make any changes to the repository img src https github com cc bhu github demo raw main assets fork png alt fork button style width 150px 2 clone your forked repository this step wll create a local copy of the repository in your system bash git clone repository url 3 add and commit your changes make your changes and save those changes by making a commit bash git add filenames git commit m commit description 4 push your local changes your commit will be pushed and will reflect in your github repository bash git push 5 create a pull request submit your pull request containg changes using github | front_end |
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blockchain-node | blockchain node build status https badge buildkite com 8f80e5ba2dd64290fb11c5126477a023b0ea75d35f08783085 svg branch master https buildkite com helium blockchain node this is an erlang application that is a helium blockchain node it follows the blockchain and exposes functionality using a jsonrpc 2 0 api documentation see api endpoint documentation https helium github io blockchain node blockchain node reference html in markdown format docs blockchain node reference md developer usage clone this repository run make make release in the top level folder run make start to start the application logs will be at build dev rel blockchain node log once started the application will start syncing the blockchain and loading blocks if this is done from scratch it can take a number of days to download all blocks from the network and aobsorb them in the local ledger file descriptors the application uses a lot of file descriptors for network communication and local storage if you see errors related to too many open files or nofile stop the application and increase the file descriptor limit macos you may see an error similar to the following error io error while open a file for appending data blockchain db 020311 sst too many open files check this https superuser com a 443168 superuser answer for a workaround linux update your etc security limits conf to increase your file limits an example of what to add can be seen here https github com helium blockchain node blob master buildkite config blockchain limits conf installing ubuntu required packages if running on ubuntu you will need the following packages installed before running make release bash wget https packages erlang solutions com erlang solutions 2 0 all deb sudo dpkg i erlang solutions 2 0 all deb sudo apt get update sudo apt install esl erlang 1 24 3 3 1 cmake libsodium dev libssl dev build essential curl proto https tlsv1 2 ssf https sh rustup rs sh if you already have rust installed please ensure it is at least at verion 1 51 or upgrade to the latest stable using rustup update stable using docker building the docker image make docker build running the docker container make docker start updating docker navigate to your copy of the blockchain node repository cd path to blockchain node stop the node make docker stop update the repository git pull remove the existing docker container make docker clean rebuild the docker image make docker build run the updated docker container make docker start log the node output tail f home node data log console log | helium blockchain erlang jsonrpc2 rocksdb | blockchain |
awesome | list of knowledge categories development tech articles connected with soft development star development basics advance git code review etc https github com nbakaev awesome blob master docs development md qa https github com nbakaev awesome blob master docs qa md backend https github com nbakaev awesome blob master docs backend md devops docker apm logging clustering https github com nbakaev awesome blob master docs devops md frontend ui ux https github com nbakaev awesome blob master docs frontend frontend md nodejs https github com nbakaev awesome blob master docs nodejs md infrastructure ci https github com nbakaev awesome blob master docs infrastructure md mongodb nosql database https github com nbakaev awesome blob master docs mongodb md web servers nginx load balancing https github com nbakaev awesome blob master docs web server md mobile development https github com nbakaev awesome blob master docs mobile desktop md databases https github com nbakaev awesome blob master docs databases md utils and terminal cli https github com nbakaev awesome blob master docs utils md rest api documentation https github com nbakaev awesome blob master docs backend rest api documentation md gamedev https github com nbakaev awesome blob master docs gamedev md information security https github com nbakaev awesome blob master docs security md messengers bots calls https github com nbakaev awesome blob master docs messengers call md c https github com nbakaev awesome blob master docs cpp md ops snippets https github com nbakaev awesome blob master docs ops md business articles connected with analytic internet marketing product development team workflow etc analytic web analytic https github com nbakaev awesome blob master docs analytic md business intelligence data science https github com nbakaev awesome blob master docs data processing bi md crm erp task management business soft https github com nbakaev awesome blob master docs crm erp business soft md email marketing https github com nbakaev awesome blob master docs email md marketing https github com nbakaev awesome blob master docs marketing md business https github com nbakaev awesome blob master docs business md workflow https github com nbakaev awesome blob master docs workflow md common for all star headphones https github com nbakaev awesome blob master docs podcasts md soft https github com nbakaev awesome blob master docs soft md star services saas https github com nbakaev awesome blob master docs web services md awesome star body of knowledge babok pmbok swebok https en wikipedia org wiki body of knowledge 115 https habrahabr ru company pechkin blog 274667 300 http habrahabr ru post 250621 awesome collective twitters https github com iamstarkov awesome collective twitters video camera online courses education course dicsounts http udemycoupon discountsglobal com s salesforce technical skillsmatter https skillsmatter com skillscasts coursera https ru coursera org star udemy com https www udemy com star pluralsight https www pluralsight com lektorium tv https www lektorium tv mail ru https park mail ru materials video geekbrains 2014 2015 pcrec h 264 1 https nnm club me forum viewtopic php t 929895 udacity free courses https www udacity com courses all ted ideas worth spreading https www ted com it ebooks it ebooks info business http netology ru | front_end |
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codespace-devops | codespace devops this is a python for devops codespace repo taken from the course cloud data engineering coursera duke university what it does this is a basic cookbook for building command line tools for the modern era click cli tool with full continuous integration docker cli | cloud |
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DeepPyramid | deeppyramid deeppyramid is a simple toolkit for building feature pyramids from deep convolutional networks the deeppyramid data structure is nearly identical to the hog feature pyramid created by the featpyramid m function in the voc dpm https github com rbgirshick voc dpm code references this code was used in our tech report http arxiv org pdf 1409 5403v2 pdf about the relationship between deformable part models and convolutional networks article girshick14dpdpm author ross girshick and forrest iandola and trevor darrell and jitendra malik title deformable part models are convolutional neural networks journal corr year 2014 volume abs 1409 5403 url http arxiv org abs 1409 5403 year 2014 installation 0 prerequisites 0 matlab tested with 2014a on 64 bit linux 0 caffe s prerequisites http caffe berkeleyvision org installation html prequequisites 0 install caffe this is the most complicated part 0 follow the caffe installation instructions http caffe berkeleyvision org installation html 0 let s call the place where you installed caffe caffe root you can run export caffe root pwd 0 important make sure to compile the caffe matlab wrapper which is not built by default make matcaffe 0 important make sure to run cd caffe root data ilsvrc12 get ilsvrc aux sh to download the imagenet image mean 0 deeppyramid has been tested with master and dev at the time of this writing 0 get deeppyramid 0 git clone https github com rbgirshick deeppyramid git 0 if you haven t installed r cnn you ll need to download its models https dl dropboxusercontent com s og7ghmiken2olzh r cnn release1 data tgz dl 0 1 copy r cnn s non finetuned imagenet network rcnnpath data caffe nets ilsvrc 2012 train iter 310k to deeppyramidpath data caffe nets ilsvrc 2012 train iter 310k or just create a symlink usage 1 run matlab from inside the deeppyramid code directory 2 add the matcaffe mex function to your path addpath path to caffe matlab caffe 3 run the demo demo deep pyramid uses deeppyramid can be used for implementing dpms on deep convolutional network features rather than hog features it can also be used whenever you need a dense multiscale pyramid of image features caveats the implementation is designed to be simple and as a result is very inefficient there are a variety of ways to speed it up and they will be done in the future for now it takes about 0 5 to 0 6 seconds to compute a feature pyramid on an nvidia titan gpu which is acceptable | ai |
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CMLM-ZhongJing | cmlm zhongjing https github com pariskang cmlm zhongjing blob main readme md english https github com pariskang cmlm zhongjing blob main readme en md p align center img src https raw githubusercontent com pariskang cmlm zhongjing main logo png alt logo title logo width 50 p p align center b 1 bing cmlm zhongjing logo b p 1 alpaca belle self instruct self instruct openai api p align center img src https raw githubusercontent com pariskang cmlm zhongjing main logo image strategy jpeg alt strategy title strategy width 100 p p align center b 2 b p 1 1 instruction prompt 15 instruction input output 1 2 8 tokens instruction input output tokens patient therapeutic story data1 json 62722 208 208 208 diagnostic analysis json 1492105 6592 6592 6592 formula funtion data json 100533 2115 2115 2115 diagnosis treatment expected result formatted 33822 153 153 153 chinese medicine dictionary json 2188672 20376 20376 20376 antonyms json 272 9 9 9 interactive story instructed data json 55262 219 219 219 patient therapeutic story data3 json 50785 660 660 660 syndrome noun explanation json 67443 976 976 976 narrative medicine formatted data json 61336 213 213 213 chinese medicine symptom synonyms json 1515796 27650 27650 27650 synonyms2 json 111186 2217 2217 2217 ancient books content json 15971297 31395 31395 31395 tongue palse json 328597 3723 3723 3723 therapeutic template making json 335602 4929 4929 4929 patient therapeutic story data2 json 50785 660 660 660 critical thinking data json 31502 229 229 229 follow up data json 504717 5990 5990 5990 prescription data json 107694 2898 2898 2898 herb dosage json 564394 5973 5973 5973 case study data json 58319 243 243 243 gynecology synonyms json 29740 543 543 543 real world problem json 1493551 7990 7990 7990 disease mechanism json 997377 8024 8024 8024 treatment noun explanation cleaned data json 81211 1123 1123 1123 total 26294720 135108 135108 135108 2 300 384 tokens 512 tokens 1 27 1 2 100 200 3 4 3 5 2 62 3 3 22 1 100 200 3 4 4 4 4 4 4 4 4 1 4 4 4 4 4 4 4 1 4 12 3 1 8 5 29 6 30 2 2 30 2 30 2 7 45 10g 10g 10g 10g 10g 10g 10g 20g 20g 20g 20g 3g 8 8 8 15 39 5 10g 10g 10g 10g 10g 10g 20g 10g 10g 10g 10g 9 18 10g 10g 10g 10g 10g 10g 10g 10g 10g 10g 10g 1 1 1 1 1 1 1 1 3 1 7 2 1 1 1 1 1 1 1 1 3 1 7 2 7b instruct llama2 7b p align center img src https raw githubusercontent com pariskang cmlm zhongjing main logo image evaluation jpeg alt evaluation title evaluation width 100 p p align center b 3 b p lora alpaca lora chinese vicuna 21110860035 m fudan edu cn roi lab https www fudanroilab com https github com techlead krischang citation if you find this work useful in your research please cite our repository misc cmlm zhongjing author kang yanlan and chang yang and fu jiyuan and wang haofen and zhang wenqiang title cmlm zhongjing large language model is good story listener year 2023 publisher github journal github repository howpublished url https github com pariskang cmlm zhongjing | ai |
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CupCarbon | cupcarbon iot and wsn simulation platform installation http cupcarbon com src download html | cupcabon simulator iot internet-of-things senscript java javafx connected-objects | server |
segger-jlink-rtos-plugin-sdk | license https img shields io github license micro os plus segger jlink rtos plugin sdk https github com micro os plus segger jlink rtos plugin sdk blob xpack license github issues https img shields io github issues micro os plus segger jlink rtos plugin sdk svg https github com micro os plus segger jlink rtos plugin sdk issues github pulls https img shields io github issues pr micro os plus segger jlink rtos plugin sdk svg https github com micro os plus segger jlink rtos plugin sdk pulls segger j link gdb rtos plug in sdk a source xpack with the segger j link gdb rtos plug in sdk sources this sdk is the base for thread aware debugging plug ins for segger j link gdb server easy install npm install ilg segger jlink rtos plugin sdk original files this xpack does not include any original segger files license the original content is released under the mit license https opensource org licenses mit with all rights reserved to liviu ionescu https github com ilg ul the plug in client api definitions the rtos functions and the gdb server api definitions are compatible with the segger gdbserver rtos plug in sdk api definitions all ip rights title and interest in the gdbserver rtos plug in sdk are and shall at all times remain with segger copyright c 2004 2016 segger microcontroller gmbh co kg internet www segger com support support segger com | os |
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design-system | storybook design system img src src design system png width 100 npm https img shields io npm v storybook design system svg https www npmjs com package storybook design system javascript style guide https img shields io badge code style standard brightgreen svg https standardjs com used by storybook homepage https storybook js org learnstorybook com https www learnstorybook com chromatic com https www chromatic com note this design system is not used in storybook s ui the stack is different and theming requirements of storybook add complexity beyond the scope of this project however storybook s visual design is identical to what s here tech stack building components storybook https storybook js org for ui component development and auto generated docs https medium com storybookjs storybook docs sneak peak 5be78445094a storybook theming https emotion sh for component scoped styling react https reactjs org declarative component centric ui maintaining the system npm https www npmjs com for packaging and distribution https blog hichroma com how packaging makes it dead simple to share ui components 29912593539d chromatic https www chromatic com to prevent ui bugs in components by storybook maintainers circleci https circleci com continuous integration why the storybook design system codifies existing ui components into a central well maintained repository it is built to address having to paste the same components into multiple projects again and again this simplifies building ui s with storybook s design patterns what we re doing build and maintain a design system in the open share ui components between multiple apps dogfood upcoming storybook features welcome contributors of all levels and backgrounds what we re not doing rewrite all new components from scratch overhaul the visual design of components typescript the consumer apps don t use it compete with more general design systems like ant or material install bash npm install save storybook design system global styles components within the design system assume that a set of global styles have been configured depending upon the needs of the application this can be done several ways option 1 render the globalstyle component useful when you don t need any custom body styling in the application typically this would be placed in a layout component that wraps all pages or a top level app component javascript import global from storybook design system const globalstyle global javascript render the global styles once per page globalstyle option 2 use the bodystyles to apply styling useful when you want build upon the shared global styling javascript import global css from storybook theming import global from storybook design system const bodystyles global const customglobalstyle css body bodystyles custom body styling for the app const customglobalstyle global styles customglobalstyle javascript render the global styles once per page customglobalstyle font loading rather than import fonts in the globalstyle component the design system s font url is exported with the intention of using it in a link tag as the href different frameworks and environments handle component re renders in their own way a re render would cause the font to be re fetched so this approach allows the design system consumers to choose the font loading method that is most appropriate for their environment option 1 build the link tag manually javascript import global from storybook design system const fontlink document createelement link fontlink href global fonturl fontlink rel stylesheet document head appendchild fontlink option 2 render the link tag in a component jsx import react from react import global from storybook design system const layout children html head link href global fonturl rel stylesheet head body children body html export default layout development scripts yarn release bump the version push a release to github and npm push a changelog to github notes requires authentication with npm adduser https docs npmjs com cli adduser html auto https github com intuit auto is used to generate a changelog and push it to github in order for this to work correctly environment variables called gh token and npm token are needed that reference a github personal access token https help github com en articles creating a personal access token for the command line and a npm publish token https docs npmjs com creating and viewing access tokens with the appropriate permissions to update the repo license mit shilman https github com shilman | os |
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ethereumex | ethereumex circleci https circleci com gh mana ethereum ethereumex svg style svg https circleci com gh exthereum ethereumex module version https img shields io hexpm v ethereumex svg https hex pm packages ethereumex hex docs https img shields io badge hex docs lightgreen svg https hexdocs pm ethereumex total download https img shields io hexpm dt ethereumex svg https hex pm packages ethereumex license https img shields io hexpm l ethereumex svg https github com mana ethereum ethereumex blob master license md last updated https img shields io github last commit mana ethereum ethereumex svg https github com mana ethereum ethereumex commits master mdoc elixir json rpc client for the ethereum blockchain check out the documentation here https hexdocs pm ethereumex ethereumex html content installation add ethereumex to your list of dependencies in mix exs elixir def deps do ethereumex 0 10 end ensure ethereumex is started before your application elixir def application do applications ethereumex end configuration in config config exs add ethereum protocol host params to your config file elixir config ethereumex url http localhost 8545 you can also configure the http request timeout for requests sent to the ethereum json rpc you can also overwrite this configuration in opts used when calling the client elixir config ethereumex http options pool timeout 5000 receive timeout 15 000 http headers content type application json pool timeout this timeout is applied when we check out a connection from the pool default value is 5 000 receive timeout the maximum time to wait for a response before returning an error default value is 15 000 if you want to use ipc you will need to set a few things in your config first specify the client type elixir config ethereumex client type ipc this will resolve to http by default second specify the ipc path elixir config ethereumex ipc path path to ipc if you want to count the number of rpc calls per rpc method or overall you can attach yourself to executed telemetry events there are two events you can attach yourself to ethereumex has rpc method name in metadata emitted event event ethereumex counter 1 method name method name or more granular ethereumex rpc method metadata emitted event event ethereumex method name as atom counter 1 each event caries a single ticker that you can pass into your counters like statix increment 2 be sure to add telemetry as project dependency the ipc client type mode opens a pool of connection workers default is 5 and 2 respectively you can configure the pool size elixir config ethereumex ipc worker size 5 ipc max worker overflow 2 ipc request timeout 60 000 test download parity and initialize the password file make setup run parity make run run tests make test usage available methods web3 clientversion https eth wiki json rpc api web3 clientversion web3 sha3 https eth wiki json rpc api web3 sha3 net version https eth wiki json rpc api net version net peercount https eth wiki json rpc api net peercount net listening https eth wiki json rpc api net listening eth protocolversion https eth wiki json rpc api eth protocolversion eth syncing https eth wiki json rpc api eth syncing eth coinbase https eth wiki json rpc api eth coinbase eth mining https eth wiki json rpc api eth mining eth hashrate https eth wiki json rpc api eth hashrate eth gasprice https eth wiki json rpc api eth gasprice eth accounts https eth wiki json rpc api eth accounts eth blocknumber https eth wiki json rpc api eth blocknumber eth getbalance https eth wiki json rpc api eth getbalance eth getstorageat https eth wiki json rpc api eth getstorageat eth gettransactioncount https eth wiki json rpc api eth gettransactioncount eth getblocktransactioncountbyhash https eth wiki json rpc api eth getblocktransactioncountbyhash eth getblocktransactioncountbynumber https eth wiki json rpc api eth getblocktransactioncountbynumber eth getunclecountbyblockhash https eth wiki json rpc api eth getunclecountbyblockhash eth getunclecountbyblocknumber https eth wiki json rpc api eth getunclecountbyblocknumber eth getcode https eth wiki json rpc api eth getcode eth sign https eth wiki json rpc api eth sign eth sendtransaction https eth wiki json rpc api eth sendtransaction eth sendrawtransaction https eth wiki json rpc api eth sendrawtransaction eth call https eth wiki json rpc api eth call eth estimategas https eth wiki json rpc api eth estimategas eth getblockbyhash https eth wiki json rpc api eth getblockbyhash eth getblockbynumber https eth wiki json rpc api eth getblockbynumber eth gettransactionbyhash https eth wiki json rpc api eth gettransactionbyhash eth gettransactionbyblockhashandindex https eth wiki json rpc api eth gettransactionbyblockhashandindex eth gettransactionbyblocknumberandindex https eth wiki json rpc api eth gettransactionbyblocknumberandindex eth gettransactionreceipt https eth wiki json rpc api eth gettransactionreceipt eth getunclebyblockhashandindex https eth wiki json rpc api eth getunclebyblockhashandindex eth getunclebyblocknumberandindex https eth wiki json rpc api eth getunclebyblocknumberandindex eth getcompilers https eth wiki json rpc api eth getcompilers eth compilelll https eth wiki json rpc api eth compilelll eth compilesolidity https eth wiki json rpc api eth compilesolidity eth compileserpent https eth wiki json rpc api eth compileserpent eth newfilter https eth wiki json rpc api eth newfilter eth newblockfilter https eth wiki json rpc api eth newblockfilter eth newpendingtransactionfilter https eth wiki json rpc api eth newpendingtransactionfilter eth uninstallfilter https eth wiki json rpc api eth uninstallfilter eth getfilterchanges https eth wiki json rpc api eth getfilterchanges eth getfilterlogs https eth wiki json rpc api eth getfilterlogs eth getlogs https eth wiki json rpc api eth getlogs eth getproof eth getwork https eth wiki json rpc api eth getwork eth submitwork https eth wiki json rpc api eth submitwork eth submithashrate https eth wiki json rpc api eth submithashrate db putstring https eth wiki json rpc api db putstring db getstring https eth wiki json rpc api db getstring db puthex https eth wiki json rpc api db puthex db gethex https eth wiki json rpc api db gethex shh post https eth wiki json rpc api shh post shh version https eth wiki json rpc api shh version shh newidentity https eth wiki json rpc api shh newidentity shh hasidentity https eth wiki json rpc api shh hasidentity shh newgroup https eth wiki json rpc api shh newgroup shh addtogroup https eth wiki json rpc api shh addtogroup shh newfilter https eth wiki json rpc api shh newfilter shh uninstallfilter https eth wiki json rpc api shh uninstallfilter shh getfilterchanges https eth wiki json rpc api shh getfilterchanges shh getmessages https eth wiki json rpc api shh getmessages ipcclient you can follow along with any of these examples using ipc by replacing httpclient with ipcclient examples elixir iex ethereumex httpclient web3 client version ok parity v1 7 2 beta 9f47909 20170918 x86 64 macos rustc1 19 0 using the url option will overwrite the configuration iex ethereumex httpclient web3 client version url http localhost 8545 ok parity v1 7 2 beta 9f47909 20170918 x86 64 macos rustc1 19 0 iex ethereumex httpclient web3 sha3 wrong param error code 32602 message invalid params invalid format iex ethereumex httpclient eth get balance 0x407d73d8a49eeb85d32cf465507dd71d507100c1 ok 0x0 note that all method names are snakecases so for example shh getmessages method has corresponding ethereumex httpclient shh get messages 1 method signatures can be found in ethereumex client behaviour there are more examples in tests eth call example read only smart contract calls in order to call a smart contract using the json rpc interface you need to properly hash the data attribute this will need to include the contract method signature along with arguments if any you can do this manually or use a hex package like abi https hex pm packages ex abi to parse your smart contract interface or encode individual calls elixir defp deps do ethereumex 0 9 ex abi 0 5 end now load the abi and pass the method signature note that the address needs to be converted to bytes elixir address 0xf742d4ce7713c54dd701aa9e92101ac42d63f895 string slice 2 1 base decode16 case mixed contract address 0xc28980830dd8b9c68a45384f5489ccdaf19d53cc abi encoded data abi encode balanceof address address base encode16 case lower now you can use eth call to execute this smart contract command elixir balance bytes ethereumex httpclient eth call data 0x abi encoded data to contract address to convert the balance into an integer elixir balance bytes string slice 2 1 base decode16 case lower typedecoder decode raw uint 256 list first custom requests many ethereum protocol implementations support additional json rpc api methods to use them you should call ethereumex httpclient request 3 method for example let s call parity s personal listaccounts method elixir iex ethereumex httpclient request personal listaccounts ok 0x71cf0b576a95c347078ec2339303d13024a26910 0x7c12323a4fff6df1a25d38319d5692982f48ec2e batch requests to send batch requests use ethereumex httpclient batch request 1 or ethereumex httpclient batch request 2 method elixir requests web3 client version net version web3 sha3 0x68656c6c6f20776f726c64 ethereumex httpclient batch request requests ok ok parity v1 7 2 beta 9f47909 20170918 x86 64 macos rustc1 19 0 ok 42 ok 0x47173285a8d7341e5e972fc677286384f802f8ef42a5ec5f03bbfa254cb01fad mdoc built on ethereumex if you are curious what others are building with ethereumex you might want to take a look at these projects exw3 https github com hswick exw3 a high level contract abstraction and other goodies similar to web3 js eth https github com izelnakri eth ethereum utilities for elixir eth contract https github com agilealpha eth contract a set of helper methods for calling eth smart contracts via json rpc ethers https github com alisinabh elixir ethers interacting with evm contracts like first class elixir functions similar to ethers js contributing 1 fork it http github com ayrat555 ethereumex fork 2 create your feature branch git checkout b my new feature 3 commit your changes git commit am add some feature 4 push to the branch git push origin my new feature 5 create new pull request copyright and license copyright c 2018 ayrat badykov released under the mit license which can be found in the repository in license md license md | elixir ethereum-client ethereum json-rpc | blockchain |
capstone | capstone udacity cloud devops engineering nanodegree finall project this is project being part of become a cloud dev ops engineer https www udacity com course cloud dev ops nanodegree nd9991 what was done here implemented demo flask jokesapi returning random jokes docker image was defined for application dockerfile created aws ec2 machine and set up jenkins instance on top of it created cloudformation scripts and deployed to aws cloud 1 aws virtual private cloud vpc network infrastructure 2 aws elastic kubernetes sercice eks cluster attached node groups to eks to handle cluster workload deployed jokesapi application to kubernetes cluster with rolling update strategy and 4 replicas deployed elastic load balancer to vpc and attached it to cluster as kubernetes service to expose application to world configured jenkins credentials to allow push to dockerhub images registry configured jenkins credentials to allow utilize aws cli deploy step configured jenkins pipeline for ci cd consisting of steps 1 linting python web api flask as well dockerfile 2 building application to docker image and pushing following versions to dockerhub registry 3 deploy to aws elastic kubernetes service | cloud |
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web-developer-roadmap | h1 align center web developer roadmap h1 web roadmap https github com canopas web developer roadmap blob master images title image png web developer roadmap is a learning path to understand web development including frontend backend and cloud aws how to learn web development a web development can be divided into four different parts 1 database 2 backend 3 frontend 4 cloud server this roadmap consists of widely used technologies frameworks for frontend and backend it also includes overview about cloud aws and some information about server table of contents sprint 1 basic linux commands version control web technologies and coding conventions https github com canopas web developer roadmap sprint 1 version control basic web technologies and coding conventions sprint 2 docker databases and php https github com canopas web developer roadmap sprint 2 docker databases and php sprint 3 golang https github com canopas web developer roadmap sprint 3 golang sprint 4 node js https github com canopas web developer roadmap sprint 4 nodejs sprint 5 vue js https github com canopas web developer roadmap sprint 5 vuejs sprint 6 useful concepts https github com canopas web developer roadmap sprint 6 useful concepts sprint 1 basic linux commands version control web technologies and coding conventions practical 1 1 basic commands and version control write a commands for following operations in terminal list all files with details in directory give only read permission to any file give all read and write permissions to any file get ip address of own pc observe disk space usage view previously executed commands history linux command to install uninstall php linux command to start stop mysql service write and save any file from terminal perform following operations in gitlab create a repository to gitlab check git status of repository commit new updated files into a git repository push in a git repository pull new changes from repository checkout new branch merge branch into another rebase and squash create merge request write a command to clone this https github com canopas web developer roadmap repo references basic linux commands https www digitalocean com community tutorials linux commands what is version control https about gitlab com topics version control how to use git version control with git https www udacity com course version control with git ud123 git the beginner s guide to understanding core version control concepts https www freecodecamp org news git the laymans guide to understanding the core concepts git commands https dzone com articles top 20 git commands with examples practical 1 2 basic web technologies with coding conventions ui design with coding standards design static ui given in the link https github com canopas web developer roadmap blob master images static png design responsive ui given in the link https www w3schools com w3css tryw3css templates food blog htm references html5 web development bootcamp course on udemy https www udemy com course the complete web development bootcamp learn lecture 12299746 start 0 overview section 2 and 3 basic html and html5 from free code camp https www freecodecamp org css3 web development bootcamp course on udemy https www udemy com course the complete web development bootcamp learn lecture 12299746 start 0 overview section 4 and 5 bootstrap from web development bootcamp course on udemy https www udemy com course the complete web development bootcamp learn lecture 12299746 start 0 overview section 6 and 8 what is flexbox https css tricks com snippets css a guide to flexbox bootstrap from frontend libraries in free code camp https www freecodecamp org finish responsive web design certification course from free code camp https www freecodecamp org code formatting and best practices 25 most used vs code shortcuts https www crio do blog vs code shortcuts code formatting in vs code https mkyong com vscode how to format source code in visual studio code vscode web development best practices https code tutsplus com tutorials 30 css best practices for beginners net 6741 practical 1 3 unit converter create a unit converter that should take input from users and output the value in the asked unit conversion units can be centimeters meters and kilometers references javascript jquery basic javascript from web development bootcamp course on udemy https www udemy com course the complete web development bootcamp learn lecture 12299746 start 0 overview section 9 and 10 basic javascript es6 regular expressions and debugging from free code camp https www freecodecamp org web development bootcamp course on udemy https www udemy com course the complete web development bootcamp learn lecture 12299746 start 0 overview section 11 and 13 json apis and ajax from free code camp https www freecodecamp org finish javascript algorithms and data structures from free code camp https www freecodecamp org web development bootcamp course on udemy https www udemy com course the complete web development bootcamp learn lecture 12299746 start 0 overview section 14 to 16 finish frontend libraries projects from free code camp https www freecodecamp org difference between and basics of jquery https learn jquery com using jquery core coding conventions coding standards 1 https medium com luqman qureshi think twice code once c49faa8cd18 coding standards 2 https google github io styleguide htmlcssguide html text use 20only 20lowercase with 20the 20exception 20of 20strings sprint 2 docker databases and php practical 2 1 install mysql using docker install mongodb using docker references what is docker https opensource com resources what docker getting started with docker https docs docker com get started overview install mysql using docker https hub docker com mysql install mongodb using docker https hub docker com mongo practical 2 2 perform following queries in mysql create a table named students with fields id first name last name standard percentage interest etc and insert data into it create table student attendances with fields id created at presence absence fields and insert data into it create a table named teachers with fields id first name last name subject interests etc and insert data into it create table teachers attendances with fields id created at presence absence fields and insert data into it prepare queries to find student s presence absence on a particular day find total absence presence of every student find absent students with a percentage lower than 70 find a student who has higest presence get all student s and teacher s first name last name full name interest standard subject and total absence perform following queries in mongodb can use mongodbplayground https mongoplayground net to peform queries create a collection named students with fields id first name last name standard percentage interest etc and insert data into it form a query to find students with a percentage lower than 70 and interest in sport count the total students with a percentage above 70 references web development bootcamp course udemy https www udemy com course the complete web development bootcamp learn lecture 12299746 start 0 overview section 24 and 25 mysql subquery https www mysqltutorial org mysql subquery and union query https www mysqltutorial org sql union mysql aspx practical 2 3 create a blog application with following requirements using php it should have two sides 1 admin 2 user implement register and login functionality for both user and admin admin can add posts post fields title description created date author category image admin can decide which user can see the post added posts will be visible on user side latest first references php language reference https www php net manual en langref php php introduction https www w3schools com php coding standard https flowframework readthedocs io en stable thedefinitiveguide partv codingguidelines php html basic concepts installation wamp mamp xampp how it exactly works basic syntax variables constants operators control structures conditions loops switch cases arrays string and various string functions functions includes required classes objects constructors namespaces extensions exceptions json encode decode sessions cookies file manipulation indexed array vs associative array object vs stdobject timeout of php script predefined variables http php net manual en reserved variables php global server get post files request session cookie advanced options interfaces traits crons composer php ini tweaks keywords private public static sprint 3 golang practical 3 1 implement music app https github com canopas web developer roadmap blob master music app md with given requirements basic concepts why go https yourbasic org golang advantages over java python run the hello world program in go https golang org doc tutorial getting started go modules https golang org doc tutorial create module gin framework https www educative io edpresso what is gin in golang constants variable types functions multi return functions init packages and import conditional statements and loops arrays and slices pointers structs and methods error handling gofmt https go dev blog gofmt best practicies https go dev talks 2013 bestpractices slide 20 others jwt https jwt io introduction json https www w3schools com whatis whatis json asp rest api https www edureka co blog what is rest api advance concepts maps in go concurrency and goroutine defer error handling panic and recover reflection type conversion file manipulations logging authentication with jwt json web token packages gin https github com gin gonic gin mysql package https github com jmoiron sqlx net http https golang org pkg net http file compressor https github com gin contrib gzip logging https github com sirupsen logrus mongodb https godoc org go mongodb org mongo driver mongo ioutil https golang org pkg io ioutil os https golang org pkg os strings https golang org pkg strings m all parse static file to binary https github com markbates pkger null value handler https github com guregu null tree v3 5 0 jwt https github com dgrijalva jwt go socket io https github com googollee go socket io sentry https github com evalphobia logrus sentry sprint 4 node js practical 4 1 implement one one real time chat application practical 4 2 implement an ecommerce app https github com canopas web developer roadmap blob master ecommerce web app backend md with given requirements references yarn https classic yarnpkg com en docs getting started npm https nodejs org en knowledge getting started npm what is npm yarn vs npm https www knowledgehut com blog web development yarn vs npm node https nodejs org en about why node js https www toptal com nodejs why the hell would i use node js basic concepts commonjs and esmodule https www freecodecamp org news modules in javascript console https nodejs org api console html scope https scotch io tutorials understanding scope in javascript this keyword part 1 https github com getify you dont know js blob 1st ed this 20 26 20object 20prototypes ch1 md this keyword part 2 https github com getify you dont know js blob 1st ed this 20 26 20object 20prototypes ch2 md understanding package json import export require callbacks async await async waterfall ref https webapplog com node js fundamentals a concise overview of the main concepts event loop http blog mixu net 2011 02 01 understanding the node js event loop node mailer to send emails best practices https code tutsplus com tutorials 24 javascript best practices for beginners net 5399 express js installation https expressjs com en starter installing html express generator https expressjs com en starter generator html routing https expressjs com en starter basic routing html host static files https expressjs com en starter static files html template engine ejs https expressjs com en guide using template engines html body parser sprint 5 vue js practical 5 1 implement frontend of ecommerce app using vue js https github com canopas web developer roadmap blob master ecommerce web app frontend md with given requirements references get started with vue js https v3 vuejs org tailwind css for beginners https www youtube com watch v bxmdnn7lrnk list pl4cuxegkcc9gpxorlehjc5bgnii5heghw tailwind css tutorial https tsh io blog tailwind css tutorial pinia https pinia vuejs org introduction html concepts what is vue js getting started installation application and component instances template syntax data properties and methods computed properties and watchers class and style bindings conditional rendering conditional style binding list rendering event handling form input bindings component basics props component registration slots template refs state management sprint 6 useful concepts concepts http protocols https www tutorialspoint com http asynchronous vs synchronous behaviour https www geekinsta com difference between synchronous and asynchronous programming caching understanding of redis testing unit and feature testing overview docker in detail https docker curriculum com nginx vs apache server server login with password ssh keys ip tables php fpm and httpd ssh ssl certificates and keys cloud what is aws https searchaws techtarget com definition amazon web services s3 ec2 rds elasticache route 53 ses cloudwatch vpc aws lamda https www guru99 com aws lambda function html aws api gateway https docs aws amazon com apigateway latest developerguide welcome html microservices https www educative io blog microservices architecture tutorial all you need to get started affiliate id 5082902844932096 utm source google utm medium cpc utm campaign platform2 utm content ad 1 dynamic gclid cj0kcqjwk8b7brcaarisaarrtl6juttppjrgo hrvsuym pvm kenavrrrsxnxt1zyfp8r2mqn5 zhwaataiealw wcb what are microservices how aws implement it https relevant software blog microservices on aws microservices implementation using go https blog canopas com golang serverless microservices with gin f3c2a4943a6d additional golang roadmap https github com alikhll golang developer roadmap nodejs roadmap https github com aliyr nodejs developer roadmap | developer-roadmap development-roadmap web web-development backend backend-development web-application backend-application web-development-roadmap backend-development-roadmap javascript php mysql html | front_end |
blockparser | blockparser who wrote it author znort987 yahoo com tip here if you find it useful 1znortsostc1zstxbw6cutkvqew8czmmg i ve also been cherry picking changes i found useful from various github forks credits for these git log grep author grep iv znort canonical source code repo git clone github com znort987 blockparser git license code is in the public domain what is it a barebone c block parser that parses the entire block chain from scratch to extract various types of information from it the code chews linearly through the block chain and calls user defined callbacks when it hits on certain events in the chain here events essentially means that the parser is starting to assemble a new blockchain data structure a block a tx an input etc or that the parser has just completed a data structure in which case it will usually run the callback with the completed data structure the blockchain data structure level of granularity at which these events happen is somewhat arbitrary for example you won t get called every time a new byte is seen user defined means that if you want to extract new types of information from the chain you have to add your own c piece of code to those already in directory cb your c code will get called by the main parser at events of your choosing linearly is a bit of an abuse because the parser code often has to jump back to previously seen parts of the blockchain to provide user callbacks with fully complete data structures the parser code also has to walk the blockchain a few times to compute the longest valid chain but the user callbacks get a fairly linear view of it all blockparser was designed for bitcoin but works on most altcoins that were derived from the bitcoin code base what it is not blockparser is not a verifier it assumes a clean blockchain as typically constructed and verified by the bitcoin core client blockparser does not perform any kind of verification and will likely crash if applied to an unclean chain blockparser is not very efficient if you want to perform repetitive tasks on thr block chain the basic idea premise of blockparser is that it s going to chew through the entire block chain every time given the size of the blockchain these days that s not something you want to do very 5 minutes blockparser is not lean and mean it used to be when the blockchain was still relatively small now that we are inching towards the 100 s of gigabytes the very proposition that it has to chew through entire chain by design implies that it s going to take quite a while whichever way you slice it also the entire index is built on the fly and kept in ram at current sizes this is not a very smart choice this might get addressed in the near future why write this it started as an exercise for me to get a close to the metal understanding of how bitcoin works the quality and state of the original bitcoin codebase made this damn near impossible it s clear to me satosh albeit clearly a genius was not a professional software engineer also things have vastly improved since then it then grew into a fun hobby project the parser code is minimal and very easy to follow if someone wants to quickly understand for real how the block chain is structured it is a good place to start it has also slowly grown into an altcoin zoo it is very far from being a compendium there s so many of the darn things these days but adding your fave alt is very easy talking about zoo i ve also started to track and document weird txo s in the chain comments p2sh multi sigs bugs etc not a complete compendium yet but getting there a side goal was also to build something that can independently as in the codebase is very different from that of bitcoin core verify some of the conclusions of other bitcoin implementations such as how many coins are attached to an address another thing that blockparser is really nice for is to easily reconstruct snapshots of the state of the blockchain from a specific time e g the a option of the allbalances command how do i build it you ll need a 64 bit unix box because of ram consumption blockparser won t work inside a 32bit address space if you are unfortunate enough to still have to use windows there is a port floating somehwere on github i also have heard rumors of it working on osx you ll need a block chain somewhere on your hard drive this is typically created by a statoshi bitcoin client such as this one https github com bitcoin bitcoin git install dependencies sudo apt get install libssl dev build essential g libboost all dev libsparsehash dev git core perl get the source git clone git github com znort987 blockparser git build it cd blockparser make it crashes at this point blockparser uses a lot of memory 20 gig is typical this can cause all sorts of woes on an under dimensioned box chief amongst which box goes into heavy swapping and parser takes for ever to complete task parser eats up all ram and all swap and crashes here s a possible remedy http askubuntu com questions 178712 how to increase swap space how does blockparser deal with multi sig transactions afaik there are two types of multi sig transactions 1 pay to script which is in fact more general than multisig this one is easy because it pays to a hash which can readily be converted to an address that starts with the character 3 instead of 1 2 naked multi sig transactions these are harder because the output of the transactions does not neatly map to a specific bitcoin address i think i have found a neat work around i compute hash160 m n sortedlistofaddresses which can now be properly mapped to a bitcoin address to mark the fact that this addres is neither a pay to script type 3 nor a pay to pubkey or pubkeyhash type 1 i prefix them with 4 note this may be worthy of an actual bip if someone writes one i ll happily adjust the code note this trick is only a blockparser thing this means that these new address types starting with a 4 won t be recognized by other bitcoin implementations such as blockchain info examples show all supported commands parser help show help for a specific command parser allbalances help compute simple blockchain stats parser simple extract all transactions for a very popular address 1dice6wbxymyi3t94heuag6mpg5ecelg1 parser transactions 06f1b66fa14429389cbffa656966993eab656f37 compute the closure of an address that is the list of addresses that very probably belong to the same person parser closure 06f1b66fa14429389cbffa656966993eab656f37 compute and print the balance for all keys ever used since the beginning of time parser all all txt see how much of the btc 10k pizza tainted all the subsequent tx in the chain chances are you have some dust coming from that famous tx lingering on one of your addresses parser taint pizzataint txt see all the block rewards and fees parser rewards rewards txt see a greatly detailed dump of the famous pizza transaction parser show track all mined blocks with unspent reward parser pristine show the first valid pay to script hash p2sh transaction in the chain parser showtx 9c08a4d78931342b37fd5f72900fb9983087e6f46c4a097d8a1f52c74e28eaf6 show the first valid naked multi sig transaction in the chain it s a 1 of 2 multi sig parser showtx 60a20bd93aa49ab4b28d514ec10b06e1829ce6818ec06cd3aabd013ebcdc4bb1 note the general syntax is parser command option option arg arg note use parser help command or parser command help to get detailed help for a specific command note command may have multiple aliases and can also be abbreviated for example parser tx parser tr and parser transactions are equivalent note whenever specifying a list of things e g a list of addresses you can instead enter file list txt and the list will be read from the file note whenever specifying a list file you can use file and blockparser will read the list directly from stdin caveats you will need an x86 84 ubuntu box and a recent version of gcc 4 4 a recent version of boost and openssl dev you may be able to compile on other platforms but the code wasn t really designed for those as of this writing it needs a log of ram to work typically upwards of 25gigs i will switch to an on disk hash table at some point but for now you ll just need that if you ever hope to see it finish in a reasonable amount of time or at all if your swap space is too small the code could be cleaner and better architected it was just a quick and dirty way for me to learn about bitcoin there really isn t much in the way of comments either d otoh it is fairly simple short if you want to understand how the blockchain data structures work the code in parser cpp is a solid way to start hacking the code parser cpp contains the generic parser that reads the blockchain parses it and calls user defined callbacks as it hits interesting bits of information it typically calls out when it begins reading finishes assembling a data structure util cpp contains a grab bag of useful bitcoin related routines interesting examples include showscript getbasereward solveoutputscript decompresspublickey blockparser comes with a bunch of interesting user callbacks cb allbalances cpp code to all balance of all addresses cb closure cpp code to compute the transitive closure of an address cb dumptx cpp code to display a transaction in very great detail cb help cpp code to dump detailed help for all other commands cb pristine cpp code to show all pristine i e unspent blocks cb rewards cpp code to show all block rewards including fees cb simplestats cpp code to compute simple stats cb sql cpp code to product an sql dump of the blockchain cb taint cpp code to compute the taint from a given tx to all txs cb transactions cpp code to extract all transactions pertaining to an address you can very easily add your own custom command you can use the existing callbacks in directory cb as a template to build your own cp cb allbalances cpp cb myextractor cpp add to makefile hack away recompile run you can also read the file callback h the base class from which you derive to implement your own new commands it has been heavily commented and should provide a good basis to pick what to overload to achieve your goal the code makes heavy use of the google dense hash maps you can switch it to use sparse hash maps see makefile search for dense undef it sparse hash maps are slower but save quite a bit of ram | blockchain |
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esp-cjson | esp cjson cjson https github com davegamble cjson library packaged for esp open rtos https github com superhouse esp open rtos | os |
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silverstripe-skeleton-theme | silverstripe skeleton theme based on skeleton http www getskeleton com a beautiful boilerplate for responsive mobile friendly development featuring navigataur a pure css responsive navigation menu https github com micjamking navigataur silverstripe foundation theme https raw github com ryanwachtl silverstripe skeleton theme master images screenshot png installation head over to the releases https github com ryanwachtl silverstripe skeleton theme releases and download the theme extract the skeleton folder to your themes directory optionally rename the folder and use as your starting point for your own custom theme enable theme ssviewer theme skeleton in mysite config config yml requirements silverstripe 3 https github com silverstripe silverstripe framework | front_end |
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Mosaic | p align center a href https mosaicjs netlify app target blank rel noopener noreferrer img width 100 height 100 src mosaiclogo png alt mosaic logo a p p align center a href https www npmjs com package mosaic framework img alt npm src https img shields io npm v mosaic framework svg color 41bb18 a a href https npmcharts com compare mosaic framework minimal true img src https img shields io npm dm mosaic framework svg color seagreen alt downloads a img alt npm src https img shields io npm l mosaic framework svg color blue p a target blank rel noopener noreferrer href https mosaicjs netlify app mosaic a mosaic is a declarative front end javascript library for building user interfaces web component based mosaic uses the custom elements api to create web components that can keep track of their own data actions and more and can be included in other components to create front end web applications observable data mosaic uses observables to keep track of changes to a component s data this means that there is no need to call setstate or anything like that to update a component instead just change the data directly smart dom updates in mosaic work by remembering which nodes are dynamic i e subject to change and traveling directly to those nodes to make changes rather than traversing the tree again built in router comes with a basic client side router which allows mosaic components to be used as separate pages state manager comes with a built in global state manager called portfolio lightweight the minified mosaic library comes in at a size of 28kb tagged template literals views are written using tagged template literals which means there is no need for a compiler javascript const name mosaic html h1 welcome to name h1 demo here is an example of a simple mosaic application all you need is an index html file and an index js file for a more detailed example run the project inside the example folder index html html html head title my mosaic app title head div id root div script src https unpkg com mosaic framework latest dist index js script script type text javascript src index js script html index js js import mosaic import mosaic html from mosaic framework create components mosaic name my label data text view self return html h2 self data text h2 p this is a custom label component p self descendants const app mosaic name my app element root data title mosaic app sayhello function console log hello world console log this component is this view function return html h1 this is a this data title h1 p click below to print a message p button onclick this sayhello click me button my label text welcome to mosaic awesome right my label paint the mosaic onto the page app paint installation the easiest way to use mosaic is to first install the npm package by using shell npm install save mosaic framework shell yarn add mosaic framework save or with a script tag html script src https unpkg com mosaic framework latest dist index js script optional for fast builds and hot reloading install the build tool parcel this is not required though as mosaic uses built in javascript features this means that no build tool is required but any may be used if it helps the overall project structure shell npm install save dev parcel bundler now you are ready to use mosaic author year 2019 programmer adeola uthman languages tools javascript typescript parcel | front-end ui components javascript webcomponents | front_end |
LocalHomePage | a local home page for osx web development this is a small and simple local home page that automatically lists and provide links to your local sites it s a companion project for a blog post http mallinson ca post osx web development i wrote about setting up your mac for web development img icon gear png from icons db http www iconsdb com black icons gear 2 icon html | front_end |
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FreeVersion | free version a featured limited version of the http sdr4 space embedded software originally designed for embedded gpu systems releases x86 the x86 64 version available as realease runs under linux systems and doest not support gpu jetson nano the nanosdr version has been tested on jetson nanao devkit and supports gpu acceleration it has been tested on our custom image available for download here https github com sdr technologies nanosdr documentation the doc is available here http sdr4 space doc english only basic examples based on the documentation are available here https github com sdr4space examples tutorial thanks to aaron from dragonos team for this youtube tutorial on sdr4space lite basics https www youtube com watch v bxok9gzz7hg installation debian package deb install soapysdr 0 8 and device sub modules from sources https github com pothosware soapysdr releases tag soapy sdr 0 8 0 download the sdr4space lite deb file and run dpkg installer wget https github com sdr4space freeversion raw main sdr4space lite deb sudo dpkg i sdr4space lite deb the binary sdr4space light executable file is then installed in opt vmbase sdr4space light help sdr4 space version b1d5b5e3571afd9f5cee649b609507d0ccf0c18e build 20210605 c sdr technologies sas http sdr4 space creating radio device factory disk free space 21 0 javascript sdr sat dsp and more usage sdrvm option t timing enable timing for each running task h help print usage f file arg script file name d workdir arg working directory default is program location v verbose verbose mode default true appimage file download latest appimage file from our releases https github com sdr4space freeversion releases make it executable chmod x sdr4space lite appimage and run it notes all needed dependencies are in the appimage file including soapysdr v0 8 first version v0 1 supports rtlsdr and plutosdr devices only test notice the 0 disk free space we are in a read only environment home user downloads sdr4space lite sdr4space lite appimage help sdr4 space version b1d5b5e3571afd9f5cee649b609507d0ccf0c18e build 20210716 c sdr technologies sas http sdr4 space creating radio device factory disk free space 0 0 javascript sdr sat dsp and more usage sdrvm option t timing enable timing for each running task h help print usage f file arg script file name d workdir arg working directory default is program location v verbose verbose mode default true running testing create a test file for example hello js print hello world then run it sdr4space light f test js sdr4 space version b1d5b5e3571afd9f5cee649b609507d0ccf0c18e build 20210605 c sdr technologies sas http sdr4 space creating radio device factory disk free space 21 0 vm starting loading test js boot 0 hello world no running task ending the virtual machine stops when no task is running dependencies the dpkg installer should be able to install needed dependencies by itself in case of trouble or for manual installation the following packages are required soapysdr version 0 8 fftw3 3 liquid sdr libliquid1d libsndfile1 libliquid1d mosquitto mosquitto clients note for liquid sdr this library can be downloaded and installed from source following instructions from https github com jgaeddert liquid dsp for ubuntu 18 04 or 20 04 a package is available here https ubuntu pkgs org 18 04 ubuntu universe amd64 libliquid1d 1 3 1 1 amd64 deb html support bug reports while full support is provided to our commercial customers free version users should use the github issue tracking | os |
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FALBUHIRY | hi i m falbuhiry i m interested in i m currently learning i m looking to collaborate on how to reach me https github com falbuhiry falbuhiry git falbuhiry falbuhiry is a special repository because its readme md this file appears on your github profile you can click the preview link to take a look at your changes technology information erp system for full managment your company | server |
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HackerBooksPro | pr ctica programaci n ios avanzada keepcoding startup engineering master iii xcode 8 swift 3 | os |
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poco | alt text logo poco ci https github com pocoproject poco actions workflows ci yml badge svg branch master https github com pocoproject poco actions workflows ci yml cii best practices https bestpractices coreinfrastructure org projects 370 badge https bestpractices coreinfrastructure org projects 370 poco portable components c libraries are a collection of c class libraries conceptually similar to the java class library or the net framework focused on solutions to frequently encountered practical problems focused on internet age network centric applications written in efficient modern 100 ansi iso standard c based on and complementing the c standard library stl highly portable and available on many different platforms from embedded to server open source licensed under the boost software license https spdx org licenses bsl 1 0 alt text overview to start using poco see the guided tour https pocoproject org docs 00100 guidedtour html and getting started https pocoproject org docs 00200 gettingstarted html documents quick start with cmake prerequisites cmake 3 5 or newer a c 14 compiler visual c 2015 gcc 5 0 clang 3 4 or newer openssl headers and libraries optional but recommended mysql postgresql and odbc client libraries optional most unix linux systems already have openssl preinstalled if your system does not have openssl please get it from http www openssl org or another source you do not have to build openssl yourself a binary distribution is fine for example via debian apt apt get install openssl libssl dev on macos the easiest way to install openssl is via homebrew https brew sh brew install openssl the easiest way to install openssl on windows is to use a binary prebuild release for example the one from shining light productions that comes with a windows installer https www slproweb com products win32openssl html on windows poco can also use the native windows tls apis schannel installing all dependencies linux and macos all dependencies can be installed with the following commands debian linux including ubuntu and raspbian sudo apt get y update sudo apt get y install git g make cmake libssl dev redhat linux sudo yum install y git gcc c make cmake3 openssl devel macos with homebrew brew install cmake openssl building with cmake linux macos windows cmake https cmake org version 3 5 or newer is the recommended build system for building the poco c libraries git clone b master https github com pocoproject poco git cd poco mkdir cmake build cd cmake build cmake cmake build config release on macos it s necessary to tell cmake where to find the openssl headers and libraries by setting the openssl root dir cmake variable for example if openssl has been installed with homebrew the cmake invocation becomes cmake dopenssl root dir usr local opt openssl other common ways of building with cmake e g cmake gui will also work there are also a number of project specific cmake variables that can be changed cross compiling with a proper cmake toolchain file specified via the cmake toolchain file cmake variable the poco c libraries can be cross compiled for embedded linux systems cmake dcmake toolchain file path to mytoolchain cmake dcmake install prefix path to target installing the poco c libraries headers and libraries can be optionally be installed by building the install target sudo cmake build target install the default install location is usr local on linux and macos and c program files x64 on windows and can be overridden by setting the cmake install prefix cmake variable building and installing using vcpkg you can download and install poco using the vcpkg https github com microsoft vcpkg dependency manager git clone https github com microsoft vcpkg git cd vcpkg bootstrap vcpkg sh vcpkg integrate install vcpkg install poco the poco port in vcpkg is kept up to date by microsoft team members and community contributors if the version is out of date please create an issue or pull request https github com microsoft vcpkg on the vcpkg repository building and installing using conan you can download and install poco using the conan https github com conan io conan package manager it needed to be installed first https conan io downloads html you can install poco libraries from conan center https conan io center html conan install r conancenter poco 1 12 0 or you can download poco recipe and build locally conan install r conancenter poco 1 12 0 build poco the poco recipe and packages in conan center are kept up to date by conan team members and community contributors if the version is out of date or you detect any wrong behavior please create an issue or pull request https github com conan io conan center index on the conan center index repository building without cmake if you do not want to or cannot use cmake poco can also be built with visual studio project and solution files included or gnu make linux macos and other supported unix platforms please refer to the documentation https pocoproject org docs for more information getting poco via a package manager poco can also be obtained via different package managers https pocoproject org download html community and contributing poco has an active user and contributing community please visit our website https pocoproject org and blog https pocoproject org blog answers to poco related questions can also be found on stack overflow https stackoverflow com questions tagged poco libraries please see contributing contributing md for submitting contributions bugs reports feature requests or security issues poco vs boost in regards to boost in spite of some functional overlapping poco is best thought of as a boost complement rather than replacement side by side use of boost and poco is a very common occurrence overview doc images overview png poco overview logo doc images logo png poco logo | c-plus-plus xml json networking logging http-client http-server configuration cross-platform sql database-access poco | server |
langchain-book-examples | example code for my book langchain project lab book hooking large language models up to the real world you can purchase this book on leanpub and get free updates as new versions are released this book can be purchased or read free online at https leanpub com langchain https leanpub com langchain i would like to thank readers who have purchased this book i very much appreciate your support | ai |
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moko-graphics | moko graphics img logo png github license https img shields io badge license apache 20license 202 0 blue svg style flat http www apache org licenses license 2 0 download https img shields io maven central v dev icerock moko graphics https repo1 maven org maven2 dev icerock moko graphics kotlin version https kotlin version aws icerock dev kotlin version group dev icerock moko name graphics mobile kotlin graphics this is a kotlin multiplatform library that provides graphics primitives to common code table of contents features features requirements requirements installation installation usage usage samples samples set up locally set up locally contributing contributing license license features color converting according to the platform side requirements argb rgba all kotlin multiplatform targets support requirements gradle version 6 8 android api 16 ios version 11 0 installation root build gradle groovy allprojects repositories mavencentral project build gradle groovy dependencies commonmainapi dev icerock moko graphics 0 9 0 usage color kotlin val red color red 0xff green 0x00 blue 0x00 alpha 0xff val rgba long red rgba val argb long red argb android compatible samples please see more examples in the sample directory sample set up locally the graphics directory graphics contains the graphics library the sample directory sample contains sample apps for android and ios plus the mpp library connected to the apps contributing all development both new features and bug fixes is performed in the develop branch this way master always contains the sources of the most recently released version please send prs with bug fixes to the develop branch documentation fixes in the markdown files are an exception to this rule they are updated directly in master the develop branch is pushed to master on release for more details on contributing please see the contributing guide contributing md license copyright 2019 icerock mag inc 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 | android ios moko kotlin kotlin-multiplatform kotlin-native graphics | front_end |
dnlp | dnlp natural language processing api docker dnlp 1 fasttext https fasttext cc 176 2 nltk https www nltk org 3 html trafilatura https trafilatura readthedocs io 4 levenshtein https maxbachmann github io levenshtein docker docker engine https docs docker com engine install dnlp shell git clone https github com lord alfred dnlp git https github com lord alfred dnlp archive refs heads main zip zip shell cd dnlp docker compose up build d dnlp shell docker compose stop curl shell curl v xpost d text some useful info http 127 0 0 1 9090 detect curl v xpost d text test sent 3f don 27t or ms not 21 yes 2c of course maybe mr jeck and band lang en http 127 0 0 1 9090 tokenize html curl v xpost d html 3chtml 3e 3cbody 3e 3ch1 3etest 3c 2fh1 3e 3cp 3ethis 20is 20test 3c 2fp 3e 3c 2fbody 3e 3c 2fhtml 3e http 127 0 0 1 9090 extract curl v xpost d sentences 1 sentence 2 sentence another sentence threshold 0 8 http 127 0 0 1 9090 deduplicate ip 9090 api http post api endpoint detect text count 3 json json confidence 0 5937589406967163 code en name english family indo european endonym english iso639 1 en iso639 2 t eng iso639 2 b eng iso639 3 eng api endpoint tokenize text lang en json en english ru russian cs czech da danish nl dutch et estonian fi finnish fr french de german el greek it italian ml malayalam no norwegian pl polish pt portuguese sl slovene es spanish sv swedish tr turkish json json test sent don t or ms not yes of course maybe mr jeck and band html api endpoint extract html html urlencode url encoding html api endpoint deduplicate json json sentences threshold 0 0 1 0 0 8 json json 2 sentence another sentence lord alfred https t me lord alfred https t me lord alfred | fasttext nltk language-detection language-recognition sentence-tokenizer article-extracting article-extractor readability text-processing nlp nlp-parsing | ai |
Fitness-platinium-database | fitness platinium database fitness platinium database project for data engineering course theoretical computer science at jagiellonian university | server |
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air-flights | air flights unam s school of engineering final database project | server |
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hubot-natural | hubot natural build status https travis ci org rocketchat hubot natural svg branch master https travis ci org rocketchat hubot natural natural language chatbot hubot is one of the most famous bot creating framework on the web that s because github made it easy to create if you can define your commands in a regexp param basically you can do anything with hubot that s a great contribution to chatops culture inspired by that we wanted to provide the same simplicity to our community to develop chatbots that can actually process natural language and execute tasks as easy as building regexp oriented bots so we ve found a really charming project to initiate from the digital ocean s heartbot https github com digitalocean heartbot a shot of love to for your favorite chat client based on heartbot we introduced some nlp power from naturalnode https github com naturalnode natural team an impressive collections of natural language processing libs made to be used in nodejs and so the magic happens welcome to hubotnatural a new an exciting chatbot framework based in hubot and naturalnode libs with an simple and extensible architecture designed by digital ocean s heartbot team made with love and care by rocket chat team we hope you enjoy the project and find some time to contribute how it works hubotnatural is made to be easy to train and extend so what you have to understand basically is that it has an yaml corpus where you can design your chatbot interactions using nothing but yaml s notation all yaml interactions designed in corpus can have it s own parameters which will be processed by an event class event classes give the possibility to extend hubotnatural by writing your own event classes you can give your chatbot the skills to interact with any services you need yaml corpus the yaml file is loaded in scripts index js parsed and passed to chatbot bind which will be found in scripts bot index js the cortex of the bot where all information flux and control are programmed the yaml corpus is located in training data corpus yml and it s basic structure looks like this yaml trust 85 interactions name salutation expect hi there hello everyone what s up bot good morning answer hello there user how are you glad to be here hey there nice to see you event respond what this syntax means trust the minimum level of certain that must be returned by the classifier in order to run this interaction value is 0 to 1 0 to 100 if a classifier returns a value of certainty minor than trust the bots responds with and error interaction node interactions an vector with lots of interaction nodes that will be parsed every interaction designed to your chatbot must be under an interaction node object structure name that s the unique name of the interaction by which it will be identified do not create more than one interaction with the same node name attribute expect those are the sentences that will be given to the bots training they can be strings or keywords vectors like consume use answer the messages that will be sent to the user if the classifiers get classified above the trust level the node message will be parsed and sent by event class in order to use multiline strings inside your yaml you must follow the yaml multiline strings http yaml multiline info syntax you can specify variables in message by default hubotnatural comes with user bot and room variables event is the name of the coffeescript or javascript class inside scripts events without the file extension event coffee classes event classes can be written to extend the chatbot skills they receives the interaction object and parse the message like this yaml class respond constructor interaction process msg sendmessages stringelserandomkey interaction answer msg module exports respond it s base constructor is the interaction node so you can have access to all attributes inside an interaction just using interaction attribute here you can parse texts call apis read files access databases and everything else you need you may want to use the function stringelserandomkey to get a random element of a list if it s parameter is a list and use the function sendmessages to send messages to an user logistic regression classifier the naturalnode library comes with two kinds of classifiers the naive bayes classifier known as the bayesclassifier and the logisticregressionclassifier functions by default hubotnatural uses the logisticregressionclassifier it just came with better results in our tests porterstemmer there is also more than one kind of stemmer you should set the stemmer to define your language by default we use the porterstemmerpt for portuguese but you can find english russian italian french spanish and other stemmers in naturalnode libs or even write your own based on those just check inside node modules natural lib natural stemmers to change the stemmers language just set the environment variable hubot lang as pt en es and any other language termination that corresponds to a stemmer file inside the above directory deploy with docker we have a dockerfile that builds a lightweight image based in linux alpine with all the repository content so you can upload that image to a docker registry and deploy your chatbot from there it is located on docker folder you also can use docker compose yml file to load a local instance of rocket chat mongodb and hubotnatural services where you can change the parameters if you must the docker compose file looks like this yaml version 2 services rocketchat image rocketchat rocket chat latest restart unless stopped volumes uploads app uploads environment port 3000 root url http localhost 3000 mongo url mongodb mongo 27017 rocketchat mongo oplog url mongodb mongo 27017 local mail url smtp smtp email http proxy http proxy domain com https proxy http proxy domain com depends on mongo ports 3000 3000 mongo image mongo 3 2 restart unless stopped volumes data db data db data dump dump command mongod smallfiles oplogsize 128 replset rs0 mongo init replica image mongo 3 2 command mongo mongo rocketchat eval rs initiate id rs0 members id 0 host localhost 27017 depends on mongo hubot natural build restart unless stopped environment hubot adapter rocketchat hubot name hubot natural hubot owner rocketchat hubot description hubot natural language processing hubot log level debug hubot corpus corpus yml hubot lang pt respond to dm true respond to livechat true respond to edited true listen on all public false rocketchat auth password rocketchat url rocketchat 3000 rocketchat room general rocketchat user botnat rocketchat password botnatpass hubot natural debug mode true volumes scripts home hubotnat bot scripts training data home hubotnat bot training data depends on rocketchat ports 3001 8080 you can change the attributes of variables and volumes to your specific needs and run docker compose up in terminal to start the rocketchat service at http localhost 3000 attention you must remember that hubot must have a real rocketchat user created to login with so by the first time you run this you must first go into rocketchat and create a new user for hubot change the rocketchat user and rocketchat password variables in the docker compose yml file and then reload the services using docker compose stop docker compose up to start it all over again if you want to run only the hubot natural service to connect an already running instance of rocket chat you just need to remember to set the rocketchat url to a correct value like https open rocket chat bot configuration in order to correctly use hubot natural after running docker compose up command it is necessary to do some configuration steps to do that there are two main options the first one is to do manually the steps described at bot config documentation docs config bot md the second option is to execute the script bot config py located at root directory on project that will automatically configure bot based on following variables defined on script admin name admin password bot name and bot password it is important to remember of properly set the values of this variables according to the context the values used on bot name and bot password must be the same defined on docker compose yml on the variables rocketchat user and rocketchat password respectively and the values defined at admin name and admin password variables must be the credentials of an pre existent user on rocketchat that has admin permissions to create an admin user automatically before executing the services just define the variables admin username and admin pass for rocketchat service on docker compose yml deploy with hubot to deploy hubotnatural first you have to install yo hubot generator shell npm install g yo generator hubot then you will clone hubotnatural repository shell git clone https github com rocketchat hubot natural git mybot change mybot in the git clone command above to whatever your bot s name will be and install hubot binaries without overwitting any of the files inside the folder shell cd mybot npm install yo hubot extracting input for self replication process hubot xx owner diego diego dorgam rocket chat bot name mybot description a simple helpful chatbot for your company bot adapter rocketchat create bin hubot create bin hubot cmd conflict procfile overwrite procfile do not overwrite skip procfile conflict readme md overwrite readme md do not overwrite skip readme md create external scripts json create hubot scripts json conflict gitignore overwrite gitignore do not overwrite skip gitignore conflict package json overwrite package json do not overwrite skip package json create scripts example coffee create editorconfig now to run your chatbot in shell you should run shell bin hubot wait a minute for the loading process and then you can talk to mybot take a look to adapters to run your bot in other platafforms configure live transfer it s possible to configure hubot natural to redirect conversation to a real person in moments when the bot can not help users as much as needed to activate and configure live transfer feature follow the steps described on live transfer config documentation docs config live transfer md env variables in your terminal window run shell export hubot adapter rocketchat export hubot owner rocketchat export hubot name bot name export hubot description description of your bot export rocketchat url https open rocket chat export rocketchat room general export listen on all public false export respond to dm true export respond to livechat true export rocketchat user catbot export rocketchat password bot password export rocketchat auth password export hubot log level debug export hubot corpus corpus yml export hubot lang en bin hubot a rocketchat name hubot name you can check hubot rocketchat https github com rocketchat hubot rocketchat adapter project for more details pm2 json file as nodejs developers we learned to love process manager pm2 http pm2 keymetrics io and we really encourage you to use it shell npm install pm2 g create a mybot json file and jut set it s content as json apps name mybot interpreter bin bash watch true ignore watch client img script bin hubot args a rocketchat port 3001 env rocketchat url https localhost 3000 rocketchat room general respond to dm true rocketchat user mybot rocketchat password 12345 rocketchat auth password hubot log level debug you can also instantiate more than one process with pm2 if you want for example to run more than one instance of your bot json apps name mybot 0 interpreter bin bash watch true ignore watch client img script bin hubot args a rocketchat port 3001 env rocketchat url https localhost 3000 rocketchat room general respond to dm true rocketchat user mybot rocketchat password 12345 rocketchat auth password hubot log level debug name mybot 1 interpreter bin bash watch true ignore watch client img script bin hubot args a rocketchat port 3002 env rocketchat url https mycompany rocket chat rocketchat room general respond to dm true rocketchat user mybot rocketchat password 12345 rocketchat auth password hubot log level debug and of course you can go nuts setting configs for different plataforms like facebook mensenger twitter or telegram p hubot adapters hubot comes with at least 38 adapters including rocket chat addapter of course to connect to your rocket chat instance you can set env variables our config pm2 json file checkout other hubot adapters https github com github hubot blob master docs adapters md for more info thanks to in rocket chat we are so in love by what we do that we couldn t forget to thanks everyone that made it possible github hubot team thanks guys for this amazing framework hubots lives in the heart of rocket chat and we recommend everyone to checkout https hubot github com and find much much more about hubot natural node project to the naturalnode team our most sincere thak you very much we loved your project and we are excited to contribute checkout https github com naturalnode natural and let your mind blow digital ocean s heartbot we can not thanks digital ocean enough not only for this beautifull heartbot project https github com digitalocean heartbot but also for all the great tutorials and all the contributions to opensource moviment thanks to our community and for last but not least thanks to our big community of contributors testers users partners and everybody who loves rocket chat and made all this possible | hubot chatbot rocketchat rocketchat-hubot nlp nodejs coffeescript hubot-natural | ai |
MooSQL | moosql a toolkit for applying software engineering techniques on databases install metacello new baseline moosql repository github juliendelplanque moosql repository load moosql | server |
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validation-analysis | validation analysis analyze the files that fail to meet our validation standards also will include reverse engineering of aws ui database | server |
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Stacks | stacks ci status gh action badge gh action url npm version npm badge npm url stacks is stack overflow s design system it includes the resources needed to create consistent predictable interfaces and workflows that conform to stack overflow s principles design language and best practices our documentation is built with stacks itself using its immutable atomic classes http johnpolacek com rethinking and components the stacks website documents product semantic and accessible component markup cross browser compatible less css an icon library https github com stackexchange stacks icons email email templates components stacks documentation can be found at https stackoverflow design table of contents using stacks using stacks migrating from v1 to v2 migrating from v1 to v2 building stacks building stacks format stacks format stacks linting stacks linting stacks testing stacks testing stacks releasing stacks releasing a new version of stacks bugs and feature requests bugs and feature requests contributing contributing license license using stacks using stacks is outlined in our usage guidelines https stackoverflow design product guidelines using stacks migrating from v1 to v2 to migrate from stacks v1 to v2 see our migration guide migration guide md building stacks to contribute to stacks documentation or its css library you ll need to build stacks locally view our building guidelines https stackoverflow design product guidelines building having trouble getting these steps to work open an issue https github com stackexchange stacks issues new with a setup label format stacks format the source code with prettier via running sh npm run format linting stacks run all lint suites by running sh npm run lint lint the styles stylelint by running sh npm run lint css lint the typescript source code eslint via running sh npm run lint ts lint the source code format prettier via running sh npm run lint format testing stacks run all test suites by running sh npm test unit component tests unit component tests are written with dom testing library https testing library com docs dom testing library intro please follow the library s principles and documentation to write tests stacks uses web test runner https modern web dev docs test runner overview and playwright https modern web dev docs test runner browser launchers playwright to run tests in a real browser context execute the unit component tests suite by running sh npm run test unit or if you prefer watch mode run sh npm run test unit watch visual regression tests prerequisites git lfs installation docs https docs github com en repositories working with files managing large files installing git large file storage docker installation docs https docs docker com engine install pwsh installation docs https learn microsoft com en us powershell scripting install installing powershell view powershell 7 3 this web test runner plugin https www npmjs com package web test runner visual regression is used to run visual regression tests dom testing library https testing library com docs dom testing library intro visual regression tests end with this suffix visual test ts execute the visual regression tests suite by running sh npm run test visual update the visual baseline via sh npm run test visual update failing tests including diffs can be found under screenshots browser failed folders less tests this is an experimental suite to test the generation of css from less files less tests end with this suffix less test ts execute the less tests suite by running sh npm run test less update the css snapshots via sh npm run test less update releasing a new version of stacks stacks uses semantic versioning https semver org is distributed via npm https www npmjs com package stackoverflow stacks and publishes release notes on github https github com stackexchange stacks releases follow the steps below to release a new version of stacks bump the version number sh npm version major minor patch push the new tag sh git push git push tags create release notes on github https github com stackexchange stacks releases new 1 visit https github com stackexchange stacks releases new 1 choose your new version from the choose a tag dropdown 1 click generate release notes 1 cleanup and complete the release notes prominently mention any breaking changes if applicable include a what s changed section in the release notes mention significant bug fixes mention new features mention significant under the hood changes that could impact consumers ship your newly created version to npm https www npmjs com package stackoverflow stacks sh npm publish merge develop into production and push sh git checkout production git merge develop git push push the updated docs site head to netlify https app netlify com navigate to the stacks overview click on production deploys and select deploy site from the trigger deploy dropdown bugs and feature requests have a bug or feature request first search existing or closed issues to make sure the issue hasn t been noted yet if not review our issue guidelines contributing md open an issue for submitting a bug report contributing md reporting bugs or feature request contributing md feature requests contributing if you d like to contribute to stacks please read through our contribution guidelines contributing md included are directions for opening issues coding standards and notes on development license code and documentation copyright 2017 2022 stack exchange inc and released under the mit license license md gh action url https github com stackexchange stacks actions workflows main yml gh action badge https github com stackexchange stacks actions workflows main yml badge svg branch develop npm url https npmjs org package stackoverflow stacks npm badge https img shields io npm v stackoverflow stacks svg | os |
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gh-pipeline | github archive pipeline in development table of contents 1 description 2 technologies 3 workflow and architecture 4 how to reproduce the project 5 what to improve description tba technologies i have learned about technologies used in this project from data engineering zoomcamp provided by datatalks club goole cloud storage as the data lake google bigquery as the data warehouse prefect as the scheduler terraform as the iaac tool docker as the containerization tool dbt cloud as the transformation tool looker as the dashboard workflow and architecture infrastructure workflow https github com entroopie gh pipeline assets 115804976 1f79657b 84af 4af2 b011 65976619fa3e more tba how to reproduce the project tba what to improve implement daily schedule for dbt transformation important use a vm for example google cloud engine comprehensive tests improve project reproducibility add ci cd more sophisticated analysis more problems to analyze better visualizations | cloud |
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blacksmith | blacksmith blacksmith is a low code platform offering a complete and consistent approach for self hosted data engineering solutions blacksmith allows software engineers to write low code etl using the go language it also allows data engineers to write templated sql for tlt and database migrations on top of one or multiple databases any team that is building or think about building such a platform knows the tremendous amount of work needed to properly accomplish this mission think of blacksmith as the central piece of your data engineering workflow leading you to save months of customized and professional work data engineering with blacksmith https nunchi studio images blacksmith overview png by leveraging blacksmith organizations benefit a single source of truth for all their data with a unique developer experience production ready modules blacksmith modules https nunchi studio images blacksmith modules png product offerings blacksmith is not an open source software this repository only holds the public go apis allowing organizations to build reliable data engineering solutions on top of blacksmith using go and sql blacksmith itself is built and distributed as a cli and as a docker image https github com nunchistudio blacksmith docker blacksmith is available in two editions https nunchi studio blacksmith pricing blacksmith standard edition addresses the technical complexity of data engineering it is and will always be free this edition focuses on building etlt platforms with go and sql for better data quality and stronger data reliability across systems blacksmith enterprise edition addresses the complexity of collaboration and governance across multi team and multi scope data solutions this edition adds advanced features for the enterprises such as migrations management distributed semaphore and a built in dashboard blacksmith dashboard https nunchi studio images blacksmith dashboard 002 png professional services along consulting and training we provide different product offerings as well as different levels of support request a demo https nunchi studio blacksmith forms demo discover our services https nunchi studio support license repository licensed under the apache license version 2 0 license by downloading installing and using blacksmith you agree to the blacksmith terms and conditions https nunchi studio legal terms | server |
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calculate-flops.pytorch | markdownlint disable first line h1 markdownlint disable html div align center h1 calflops a flops and params calculate tool for neural networks h1 div pypi python version https img shields io pypi pyversions calflops pypi version https img shields io pypi v calflops svg https pypi org project calflops pypi license https img shields io pypi l calflops https github com mryxj calculate flops pytorch blob main license h4 align center p b english b a href https github com mryxj calculate flops pytorch blob main readme cn md a p h4 introduction this tool calflops is designed to compute the theoretical amount of flops floating point operations macs multiply add operations and parameters in all various neural networks such as linear cnn rnn gcn transformer bert llama etc large language model even including any custom models via torch nn function as long as based on the pytorch implementation meanwhile this tool supports the printing of flops parameter calculation value and proportion of each submodule of the model it is convient for users to understand the performance consumption of each part of the model latest news calflops has launched a tool on huggingface space which is more convenient for computing flops in the model of huggingface platform welcome to use it https huggingface co spaces mryxj calculate model flops img width 1480 alt 2023 09 13 23 25 05 src https github com mryxj calculate flops pytorch assets 21152077 75b77665 9c72 49a9 a86c 0114da1945fd for llm this is probably the easiest tool to calculate flops and it is very convenient for huggingface platform models you can use calflops calculate flops hf model name by model name which in huggingface models https huggingface co models to calculate model flops without downloading entire model weights locally notice this method requires the model to support the empty model being created for model inference in meta device screenshot huggingface model names png python from calflops import calculate flops hf model name meta llama llama 2 7b access token your application token for using llama2 flops macs params calculate flops hf model name model name access token access token default input shape 1 128 print s flops s macs s params s n model name flops macs params if model can t inference in meta device you just need assign llm corresponding tokenizer to the parameter transformers tokenizer to pass in funcional of calflops calculate flops and it will automatically help you build the model input data whose size is input shape alternatively you also can pass in the input data of models which need multi data as input that you have constructed in addition the implementation process of this package inspired by ptflops https github com sovrasov flops counter pytorch deepspeed https github com microsoft deepspeed tree master deepspeed hf accelerate https github com huggingface accelerate libraries thanks for their great efforts they are both very good work meanwhile this package also improves some aspects to calculate flops based on them how to install install the latest version from pypi python pip install calflops and you also can download latest calflops py3 none any whl files from https pypi org project calflops python pip install calflops py3 none any whl how to use calflops example cnn model if model has only one input you just need set the model input size by parameter input shape it can automatically generate random model input to complete the calculation python from calflops import calculate flops from torchvision import models model models alexnet batch size 1 input shape batch size 3 224 224 flops macs params calculate flops model model input shape input shape output as string true output precision 4 print alexnet flops s macs s params s n flops macs params alexnet flops 4 2892 gflops macs 2 1426 gmacs params 61 1008 m if the model has multiple inputs use the parameters args or kargs as shown in the transfomer model below calculate huggingface model by model name online no need to download the entire parameter weight of the model to the local just by the model name can test any open source large model on the huggingface platform screenshot huggingface model name png python from calflops import calculate flops hf batch size max seq length 1 128 model name baichuan inc baichuan 13b chat flops macs params calculate flops hf model name model name input shape batch size max seq length print s flops s macs s params s n model name flops macs params you can also use this model urls of huggingface platform to calculate it flops screenshot huggingface model name2 png python from calflops import calculate flops hf batch size max seq length 1 128 model name https huggingface co thudm chatglm2 6b thudm chatglm2 6b flops macs params calculate flops hf model name model name input shape batch size max seq length print s flops s macs s params s n model name flops macs params there are some model uses that require an application first and you only need to pass the application in through the access token to calculate its flops screenshot huggingface model name3 png python from calflops import calculate flops hf batch size max seq length 1 128 model name meta llama llama 2 7b access token your application for using llama2 flops macs params calculate flops hf model name model name access token access token input shape batch size max seq length print s flops s macs s params s n model name flops macs params transformer model local compared to the cnn model transformer model if you want to use the parameter input shape to make calflops automatically generating the input data you should pass its corresponding tokenizer through the parameter transformer tokenizer python transformers model such as bert from calflops import calculate flops from transformers import automodel from transformers import autotokenizer batch size max seq length 1 128 model name hfl chinese roberta wwm ext model save pretrain models model name model automodel from pretrained model save tokenizer autotokenizer from pretrained model save flops macs params calculate flops model model input shape batch size max seq length transformer tokenizer tokenizer print bert hfl chinese roberta wwm ext flops s macs s params s n flops macs params bert hfl chinese roberta wwm ext flops 67 1 gflops macs 33 52 gmacs params 102 27 m if you want to use your own generated specific data to calculate flops you can use parameter args or kwargs and parameter input shape can no longer be assigned to pass in this case here is an example that can be seen is inconvenient comparedt to use parameter transformer tokenizer python transformers model such as bert from calflops import calculate flops from transformers import automodel from transformers import autotokenizer batch size max seq length 1 128 model name hfl chinese roberta wwm ext model save code yexiaoju generate tags models pretrain models model name model automodel from pretrained model save tokenizer autotokenizer from pretrained model save text inputs tokenizer text add special tokens true return attention mask true padding true truncation longest first max length max seq length if len inputs input ids max seq length apply num max seq length len inputs input ids inputs input ids extend 0 apply num inputs token type ids extend 0 apply num inputs attention mask extend 0 apply num inputs input ids torch tensor inputs input ids inputs token type ids torch tensor inputs token type ids inputs attention mask torch tensor inputs attention mask flops macs params calculate flops model model kwargs inputs print results false print bert hfl chinese roberta wwm ext flops s macs s params s n flops macs params bert hfl chinese roberta wwm ext flops 22 36 gflops macs 11 17 gmacs params 102 27 m large language model online python from calflops import calculate flops hf batch size max seq length 1 128 model name meta llama llama 2 7b access token your application for using llama flops macs params calculate flops hf model name model name access token access token input shape batch size max seq length print s flops s macs s params s n model name flops macs params local note here that the tokenizer must correspond to the llm model because llm tokenizer processes maybe are different python large languase model such as llama2 7b from calflops import calculate flops from transformers import llamatokenizer from transformers import llamaforcausallm batch size max seq length 1 128 model name llama2 hf 7b model save model model name model llamaforcausallm from pretrained model save tokenizer llamatokenizer from pretrained model save flops macs params calculate flops model model input shape batch size max seq length transformer tokenizer tokenizer print llama2 7b flops s macs s params s n flops macs params llama2 7b flops 1 7 tflops macs 850 00 gmacs params 6 74 b show each submodule result of flops macs params the calflops provides a more detailed display of model flops calculation information by setting the parameter print result true which defaults to true flops of the model will be printed in the terminal or jupyter interface print results https github com mryxj calculate flops pytorch blob main screenshot alxnet print result png raw true meanwhile by setting the parameter print detailed true which default to true the calflops supports the display of the calculation results and proportion of flops macs and parameter in each submodule of the entire model so that it is convenient to see the largest part of the calculation consumption in the entire model print detailed https github com mryxj calculate flops pytorch blob main screenshot alxnet print detailed png raw true more use introduction details summary how to make output format more elegant summary you can use parameter output as string output precision output unit to determine the format of output data is value or string if it is string how many bits of precision to retain and the unit of value such as flops the unit of result is tflops or gflops mflops details details summary how do deal with model has multiple inputs summary the calflops support multiple inputs of model just use parameter args or kwargs to construct multiple inputs can be passed in as model inference details details summary how to calculate the results of flops include forward and backward pass of the model summary you can use the parameter include backpropagation to select whether the calculation of flops results includes the process of model backpropagation the default is false that is result of flops only include forward pass in addition the parameter compute bp factor to determine how many times backward as much computation as forward pass the defaults that is 2 0 according to https epochai org blog backward forward flop ratio details details summary how to calculate flops for only part of the model module summary you can use the parameter ignore modules to select which modules of model are ignored during flops calculation the default is that is all modules of model would be calculated in results details details summary how to calculate flops of the generate function in llm summary you just need to assign generate to parameter forward mode details api of the calflops details summary calflops calculate flops summary python from calflops import calculate flops def calculate flops model input shape none transformer tokenizer none args kwargs forward mode forward include backpropagation false compute bp factor 2 0 print results true print detailed true output as string true output precision 2 output unit none ignore modules none returns the total floating point operations macs and parameters of a model args model torch nn module the model of input must be a pytorch model input shape tuple optional input shape to the model if args and kwargs is empty the model takes a tensor with this shape as the only positional argument default to transformers tokenizer none optional transforemrs toekenizer must be special if model type is transformers and args kwargs is empty default to none args list optinal list of positional arguments to the model such as bert input args is input ids token type ids attention mask default to kwargs dict optional dictionary of keyword arguments to the model such as bert input kwargs is input ids token type ids attention mask default to forward mode str optional to determine the mode of model inference default to forward and use generate if model inference uses model generate include backpropagation bool optional decides whether the final return flops computation includes the computation for backpropagation compute bp factor float optional the model backpropagation is a multiple of the forward propagation computation default to 2 print results bool optional whether to print the model profile defaults to true print detailed bool optional whether to print the detailed model profile defaults to true output as string bool optional whether to print the output as string defaults to true output precision int optional output holds the number of decimal places if output as string is true default to 2 output unit str optional the unit used to output the result value such as t g m and k default is none that is the unit of the output decide on value ignore modules type optional the list of modules to ignore during profiling defaults to none details details summary calflops calculate flops hf summary python def calculate flops hf model name input shape none library name transformers trust remote code true access token forward mode forward include backpropagation false compute bp factor 2 0 print results true print detailed true output as string true output precision 2 output unit none ignore modules none returns the total floating point operations macs and parameters of a model args model name str the model name in huggingface platform https huggingface co models such as meta llama llama 2 7b baichuan inc baichuan 13b chat etc input shape tuple optional input shape to the model if args and kwargs is empty the model takes a tensor with this shape as the only positional argument default to trust remote code bool optional trust the code in the remote library for the model structure access token str optional some models need to apply for a access token such as meta llama2 etc forward mode str optional to determine the mode of model inference default to forward and use generate if model inference uses model generate include backpropagation bool optional decides whether the final return flops computation includes the computation for backpropagation compute bp factor float optional the model backpropagation is a multiple of the forward propagation computation default to 2 print results bool optional whether to print the model profile defaults to true print detailed bool optional whether to print the detailed model profile defaults to true output as string bool optional whether to print the output as string defaults to true output precision int optional output holds the number of decimal places if output as string is true default to 2 output unit str optional the unit used to output the result value such as t g m and k default is none that is the unit of the output decide on value ignore modules type optional the list of modules to ignore during profiling defaults to none example code block python from calflops import calculate flops hf batch size 1 max seq length 128 model name baichuan inc baichuan 13b chat flops macs params calculate flops hf model name model name input shape batch size max seq length print s flops s macs s params s n model name flops macs params returns the number of floating point operations multiply accumulate operations macs and parameters in the model details details summary calflops generate transformer input summary python def generate transformer input model tokenizer input shape device automatically generates data in the form of transformes model input format args input shape tuple transformers model input shape batch size seq len tokenizer transformer model tokenization transformers model tokenization tokenizer returns dict data format of transformers model input it is a dict which contain input ids attention mask token type ids etc details details citation if calflops was useful for your paper or tech report please cite me online calflops author xiaoju ye title calflops a flops and params calculate tool for neural networks in pytorch framework year 2023 url https github com mryxj calculate flops pytorch common model calculate flops large language model input data format batch size 1 seq len 128 fwd flops the flops of the model forward propagation bwd fwd flops the flops of model forward and backward propagation in addition note that fwd bwd does not include calculation losses for model parameter activation recomputation if the results wants to include activation recomputation that is only necessary to multiply the result of fwd flops by 4 according to the paper https arxiv org pdf 2205 05198 pdf and in calflops you can easily set parameter computer bp factor 3 to make the result of including the activate the recalculation model input shape params b params total fwd flops g fwd macs g fwd bwd flops g fwd bwd macs g bloom 1b7 1 128 1 72b 1722408960 310 92 155 42 932 76 466 27 bloom 7b1 1 128 7 07b 7069016064 1550 39 775 11 4651 18 2325 32 bloomz 1b7 1 128 1 72b 1722408960 310 92 155 42 932 76 466 27 baichuan 7b 1 128 7b 7000559616 1733 62 866 78 5200 85 2600 33 chatglm 6b 1 128 6 17b 6173286400 1587 66 793 75 4762 97 2381 24 chatglm2 6b 1 128 6 24b 6243584000 1537 68 768 8 4613 03 2306 4 qwen 7b 1 128 7 72b 7721324544 1825 83 912 88 5477 48 2738 65 llama 7b 1 128 6 74b 6738415616 1700 06 850 5100 19 2550 llama2 7b 1 128 6 74b 6738415616 1700 06 850 5100 19 2550 llama2 7b chat 1 128 6 74b 6738415616 1700 06 850 5100 19 2550 chinese llama 7b 1 128 6 89b 6885486592 1718 89 859 41 5156 67 2578 24 chinese llama plus 7b 1 128 6 89b 6885486592 1718 89 859 41 5156 67 2578 24 eleutherai pythia 1 4b 1 128 1 31b 1311625216 312 54 156 23 937 61 468 69 eleutherai pythia 12b 1 128 11 59b 11586549760 2911 47 1455 59 8734 41 4366 77 moss moon 003 sft 1 128 16 72b 16717980160 4124 93 2062 39 12374 8 6187 17 moss moon 003 sft plugin 1 128 16 06b 16060416000 3956 62 1978 24 11869 9 5934 71 we can draw some simple and interesting conclusions from the table above the chatglm2 6b in the model of the same scale the model parameters are smaller and flops is also smaller which has certain advantages in speed performance the parameters of the llama1 7b llama2 7b and llama2 7b chat models did not change at all and flops remained consistent the structure of the model that conforms to the 7b described by meta in its llama2 report https ai meta com research publications llama 2 open foundation and fine tuned chat models has not changed the main difference is the increase of training data tokens similarly it can be seen from the table that the chinese llama 7b and chinese llama plus 7b data are also in line with cui s report https arxiv org pdf 2304 08177v1 pdf just more chinese data tokens are added for training and the model structure and parameters do not change more model flops would be updated successively see github calculate flops pytorch https github com mryxj calculate flops pytorch bert input data format batch size 1 seq len 128 model input shape params m params total fwd flops g fwd macs g fwd bwd flops g fwd bwd macs g hfl chinese roberta wwm ext 1 128 102 27m 102267648 22 363 11 174 67 089 33 523 you can use calflops to calculate the more different transformer models based bert look forward to updating in this form benchmark torchvision https pytorch org docs 1 0 0 torchvision models html input data format batch size 1 actually input shape 1 3 224 224 note the flops in the table only takes into account the computation of forward propagation of the model total refers to the total numerical representation without unit abbreviations model input resolution params m params total flops g flops total macs g macs total alexnet 224x224 61 10 61100840 1 43 1429740000 741 19 7418800000 vgg11 224x224 132 86 132863000 15 24 15239200000 7 61 7609090000 vgg13 224x224 133 05 133048000 22 65 22647600000 11 31 11308500000 vgg16 224x224 138 36 138358000 30 97 30973800000 15 47 15470300000 vgg19 224x224 143 67 143667000 39 30 39300000000 19 63 19632100000 vgg11 bn 224x224 132 87 132869000 15 25 15254000000 7 61 7609090000 vgg13 bn 224x224 133 05 133054000 22 67 22672100000 11 31 11308500000 vgg16 bn 224x224 138 37 138366000 31 00 31000900000 15 47 15470300000 vgg19 bn 224x224 143 68 143678000 39 33 39329700000 19 63 19632100000 resnet18 224x224 11 69 11689500 3 64 3636250000 1 81 1814070000 resnet34 224x224 21 80 21797700 7 34 7339390000 3 66 3663760000 resnet50 224x224 25 56 25557000 8 21 8211110000 4 09 4089180000 resnet101 224x224 44 55 44549200 15 65 15690900000 7 80 7801410000 resnet152 224x224 60 19 60192800 23 09 23094300000 11 51 11513600000 squeezenet1 0 224x224 1 25 1248420 1 65 1648970000 0 82 818925000 squeezenet1 1 224x224 1 24 1235500 0 71 705014000 0 35 349152000 densenet121 224x224 7 98 7978860 5 72 5716880000 2 83 2834160000 densenet169 224x224 14 15 14195000 6 78 6778370000 3 36 3359840000 densenet201 224x224 20 01 20013900 8 66 8658520000 4 29 4291370000 densenet161 224x224 28 68 28681000 15 55 1554650000 7 73 7727900000 inception v3 224x224 27 16 27161300 5 29 5692390000 2 84 2837920000 thanks to zigangzhao ai https github com zigangzhao ai use calflops to static torchvision form you also can compare torchvision results of calculate flops with anthoer good tool ptflops readme md https github com sovrasov flops counter pytorch concact author author mryxj https github com mryxj mail yxj2017 gmail com | flops-counter large-language-models pytorch calflops | ai |
ML-For-Beginners | github license https img shields io github license microsoft ml for beginners svg https github com microsoft ml for beginners blob master license github contributors https img shields io github contributors microsoft ml for beginners svg https github com microsoft ml for beginners graphs contributors github issues https img shields io github issues microsoft ml for beginners svg https github com microsoft ml for beginners issues github pull requests https img shields io github issues pr microsoft ml for beginners svg https github com microsoft ml for beginners pulls prs welcome https img shields io badge prs welcome brightgreen svg style flat square http makeapullrequest com github watchers https img shields io github watchers microsoft ml for beginners svg style social label watch https github com microsoft ml for beginners watchers github forks https img shields io github forks microsoft ml for beginners svg style social label fork https github com microsoft ml for beginners network github stars https img shields io github stars microsoft ml for beginners svg style social label star https github com microsoft ml for beginners stargazers machine learning for beginners a curriculum travel around the world as we explore machine learning by means of world cultures azure cloud advocates at microsoft are pleased to offer a 12 week 26 lesson curriculum all about machine learning in this curriculum you will learn about what is sometimes called classic machine learning using primarily scikit learn as a library and avoiding deep learning which is covered in our forthcoming ai for beginners curriculum pair these lessons with our data science for beginners curriculum https aka ms datascience beginners as well travel with us around the world as we apply these classic techniques to data from many areas of the world each lesson includes pre and post lesson quizzes written instructions to complete the lesson a solution an assignment and more our project based pedagogy allows you to learn while building a proven way for new skills to stick hearty thanks to our authors jen looper stephen howell francesca lazzeri tomomi imura cassie breviu dmitry soshnikov chris noring anirban mukherjee ornella altunyan ruth yakubu and amy boyd thanks as well to our illustrators tomomi imura dasani madipalli and jen looper special thanks to our microsoft student ambassador authors reviewers and content contributors notably rishit dagli muhammad sakib khan inan rohan raj alexandru petrescu abhishek jaiswal nawrin tabassum ioan samuila and snigdha agarwal extra gratitude to microsoft student ambassadors eric wanjau jasleen sondhi and vidushi gupta for our r lessons getting started students https aka ms student page to use this curriculum fork the entire repo to your own github account and complete the exercises on your own or with a group start with a pre lecture quiz read the lecture and complete the activities pausing and reflecting at each knowledge check try to create the projects by comprehending the lessons rather than running the solution code however that code is available in the solution folders in each project oriented lesson take the post lecture quiz complete the challenge complete the assignment after completing a lesson group visit the discussion board https github com microsoft ml for beginners discussions and learn out loud by filling out the appropriate pat rubric a pat is a progress assessment tool that is a rubric you fill out to further your learning you can also react to other pats so we can learn together for further study we recommend following these microsoft learn https docs microsoft com en us users jenlooper 2911 collections k7o7tg1gp306q4 wt mc id academic 77952 leestott modules and learning paths teachers we have included some suggestions for teachers md on how to use this curriculum video walkthroughs some of the lessons are available as short form video you can find all these in line in the lessons or on the ml for beginners playlist on the microsoft developer youtube channel https aka ms ml beginners videos by clicking the image below ml for beginners banner ml for beginners video banner png https aka ms ml beginners videos meet the team promo video ml gif https youtu be tj1xwrdsyju promo video gif by mohit jaisal https linkedin com in mohitjaisal click the image above for a video about the project and the folks who created it pedagogy we have chosen two pedagogical tenets while building this curriculum ensuring that it is hands on project based and that it includes frequent quizzes in addition this curriculum has a common theme to give it cohesion by ensuring that the content aligns with projects the process is made more engaging for students and retention of concepts will be augmented in addition a low stakes quiz before a class sets the intention of the student towards learning a topic while a second quiz after class ensures further retention this curriculum was designed to be flexible and fun and can be taken in whole or in part the projects start small and become increasingly complex by the end of the 12 week cycle this curriculum also includes a postscript on real world applications of ml which can be used as extra credit or as a basis for discussion find our code of conduct code of conduct md contributing contributing md and translation translations md guidelines we welcome your constructive feedback each lesson includes optional sketchnote optional supplemental video video walkthrough some lessons only pre lecture warmup quiz written lesson for project based lessons step by step guides on how to build the project knowledge checks a challenge supplemental reading assignment post lecture quiz a note about languages these lessons are primarily written in python but many are also available in r to complete an r lesson go to the solution folder and look for r lessons they include an rmd extension that represents an r markdown file which can be simply defined as an embedding of code chunks of r or other languages and a yaml header that guides how to format outputs such as pdf in a markdown document as such it serves as an exemplary authoring framework for data science since it allows you to combine your code its output and your thoughts by allowing you to write them down in markdown moreover r markdown documents can be rendered to output formats such as pdf html or word a note about quizzes all quizzes are contained in this app https gray sand 07a10f403 1 azurestaticapps net for 52 total quizzes of three questions each they are linked from within the lessons but the quiz app can be run locally follow the instruction in the quiz app folder lesson number topic lesson grouping learning objectives linked lesson author 01 introduction to machine learning introduction 1 introduction readme md learn the basic concepts behind machine learning lesson 1 introduction 1 intro to ml readme md muhammad 02 the history of machine learning introduction 1 introduction readme md learn the history underlying this field lesson 1 introduction 2 history of ml readme md jen and amy 03 fairness and machine learning introduction 1 introduction readme md what are the important philosophical issues around fairness that students should consider when building and applying ml models lesson 1 introduction 3 fairness readme md tomomi 04 techniques for machine learning introduction 1 introduction readme md what techniques do ml researchers use to build ml models lesson 1 introduction 4 techniques of ml readme md chris and jen 05 introduction to regression regression 2 regression readme md get started with python and scikit learn for regression models ul li python 2 regression 1 tools readme md li li r 2 regression 1 tools solution r lesson 1 html li ul ul li jen li li eric wanjau li ul 06 north american pumpkin prices regression 2 regression readme md visualize and clean data in preparation for ml ul li python 2 regression 2 data readme md li li r 2 regression 2 data solution r lesson 2 html li ul ul li jen li li eric wanjau li ul 07 north american pumpkin prices regression 2 regression readme md build linear and polynomial regression models ul li python 2 regression 3 linear readme md li li r 2 regression 3 linear solution r lesson 3 html li ul ul li jen and dmitry li li eric wanjau li ul 08 north american pumpkin prices regression 2 regression readme md build a logistic regression model ul li python 2 regression 4 logistic readme md li li r 2 regression 4 logistic solution r lesson 4 html li ul ul li jen li li eric wanjau li ul 09 a web app web app 3 web app readme md build a web app to use your trained model python 3 web app 1 web app readme md jen 10 introduction to classification classification 4 classification readme md clean prep and visualize your data introduction to classification ul li python 4 classification 1 introduction readme md li li r 4 classification 1 introduction solution r lesson 10 html ul li jen and cassie li li eric wanjau li ul 11 delicious asian and indian cuisines classification 4 classification readme md introduction to classifiers ul li python 4 classification 2 classifiers 1 readme md li li r 4 classification 2 classifiers 1 solution r lesson 11 html ul li jen and cassie li li eric wanjau li ul 12 delicious asian and indian cuisines classification 4 classification readme md more classifiers ul li python 4 classification 3 classifiers 2 readme md li li r 4 classification 3 classifiers 2 solution r lesson 12 html ul li jen and cassie li li eric wanjau li ul 13 delicious asian and indian cuisines classification 4 classification readme md build a recommender web app using your model python 4 classification 4 applied readme md jen 14 introduction to clustering clustering 5 clustering readme md clean prep and visualize your data introduction to clustering ul li python 5 clustering 1 visualize readme md li li r 5 clustering 1 visualize solution r lesson 14 html ul li jen li li eric wanjau li ul 15 exploring nigerian musical tastes clustering 5 clustering readme md explore the k means clustering method ul li python 5 clustering 2 k means readme md li li r 5 clustering 2 k means solution r lesson 15 html ul li jen li li eric wanjau li ul 16 introduction to natural language processing natural language processing 6 nlp readme md learn the basics about nlp by building a simple bot python 6 nlp 1 introduction to nlp readme md stephen 17 common nlp tasks natural language processing 6 nlp readme md deepen your nlp knowledge by understanding common tasks required when dealing with language structures python 6 nlp 2 tasks readme md stephen 18 translation and sentiment analysis natural language processing 6 nlp readme md translation and sentiment analysis with jane austen python 6 nlp 3 translation sentiment readme md stephen 19 romantic hotels of europe natural language processing 6 nlp readme md sentiment analysis with hotel reviews 1 python 6 nlp 4 hotel reviews 1 readme md stephen 20 romantic hotels of europe natural language processing 6 nlp readme md sentiment analysis with hotel reviews 2 python 6 nlp 5 hotel reviews 2 readme md stephen 21 introduction to time series forecasting time series 7 timeseries readme md introduction to time series forecasting python 7 timeseries 1 introduction readme md francesca 22 world power usage time series forecasting with arima time series 7 timeseries readme md time series forecasting with arima python 7 timeseries 2 arima readme md francesca 23 world power usage time series forecasting with svr time series 7 timeseries readme md time series forecasting with support vector regressor python 7 timeseries 3 svr readme md anirban 24 introduction to reinforcement learning reinforcement learning 8 reinforcement readme md introduction to reinforcement learning with q learning python 8 reinforcement 1 qlearning readme md dmitry 25 help peter avoid the wolf reinforcement learning 8 reinforcement readme md reinforcement learning gym python 8 reinforcement 2 gym readme md dmitry postscript real world ml scenarios and applications ml in the wild 9 real world readme md interesting and revealing real world applications of classical ml lesson 9 real world 1 applications readme md team postscript model debugging in ml using rai dashboard ml in the wild 9 real world readme md model debugging in machine learning using responsible ai dashboard components lesson 9 real world 2 debugging ml models readme md ruth yakubu offline access you can run this documentation offline by using docsify https docsify js org fork this repo install docsify https docsify js org quickstart on your local machine and then in the root folder of this repo type docsify serve the website will be served on port 3000 on your localhost localhost 3000 pdfs find a pdf of the curriculum with links here https microsoft github io ml for beginners pdf readme pdf help wanted would you like to contribute a translation please read our translation guidelines translations md and add a templated issue to manage the workload here https github com microsoft ml for beginners issues other curricula our team produces other curricula check out web dev for beginners https aka ms webdev beginners iot for beginners https aka ms iot beginners data science for beginners https aka ms datascience beginners ai for beginners https aka ms ai beginners | ml data-science machine-learning machine-learning-algorithms machinelearning python machinelearning-python scikit-learn scikit-learn-python r education | ai |
cv-aimbot | description this computer vision aimbot provides character detection and targeting for video games that use humanoid character models this aimbot approach is targeted at cloud hosted games as they are immune to tradisional cheats tradisional cheats require access to client side process memory object code and network traffic with cloud hosted games the user has no way to tamper with the internals of the game this project is mainly a proof of concept as the computational requirements and level of performance renders this approach infeasible for an overview of how this aimbot functions see the methodology md https github com ryansawchuk cv aimbot blob main methodology md file built with python3 https www anaconda com products individual yolov5 https pytorch org hub ultralytics yolov5 getting started prerequisites python3 https www anaconda com products individual cuda toolkit v11 if using an nvidia gpu conda install pytorch torchvision torchaudio cudatoolkit 11 1 c pytorch c conda forge pytorch installation https pytorch org get started locally installation 1 clone the repo sh git clone https github com ryansawchuk cv aimbot git 2 install python packages sh python m pip install r requirements txt usage python3 python aimbot py exit key toggle firing key road map cli argument for mirror screen size record filtered screen capture known issues finiky toggle firing key some applications dont allow mouse movements acknowledgments yolov5 https pytorch org hub ultralytics yolov5 | computer-vision aimbot destiny2 poc fps-game opencv pyautogui python3 torch yolov5 battlefield callofduty csgo | ai |
POCS | welcome to pocs documentation p align center img src https 1730110767 files gitbook io files v0 b gitbook x prod appspot com o spaces 2fdwxhux4dyp5m2iepanyp 2fuploads 2fgqcffci1isqlxhwqxspc 2fpan001 png alt media token 3b6a7fc1 efc1 416d 863b d23304a6c28b alt pan001 p br gha status https img shields io endpoint svg url https 3a 2f 2factions badge atrox dev 2fpanoptes 2fpocs 2fbadge 3fref 3ddevelop style flat https actions badge atrox dev panoptes pocs goto ref develop codecov https codecov io gh panoptes pocs branch develop graph badge svg https codecov io gh panoptes pocs documentation status https readthedocs org projects pocs badge version latest https pocs readthedocs io en latest badge latest pypi version https badge fury io py panoptes pocs svg https badge fury io py panoptes pocs project panoptes panoptes https www projectpanoptes org is an open source citizen science project designed to find transiting exoplanets https spaceplace nasa gov transits en with digital cameras the goal of panoptes is to establish a global network of of robotic cameras run by amateur astronomers and schools or anyone in order to monitor as continuously as possible a very large number of stars for more general information about the project including the science case and resources for interested individuals see the project overview https projectpanoptes org articles pocs pocs panoptes observatory control system is the main software driver for a panoptes unit responsible for high level control of the unit for more information see the full documentation at https pocs readthedocs io install pocs environment if you are running a panoptes unit then you will most likely want an entire panoptes environment which includes the necessary tools for operation of a complete unit there is a bash shell script that will install an entire working pocs system on your computer some folks even report that it works on a mac to install pocs via the script open a terminal and enter you may be prompted for your sudo password bash curl fssl https install projectpanoptes org install pocs sh bash install pocs sh or using wget bash wget qo https install projectpanoptes org install pocs sh bash install pocs sh the install script will ask a few questions at the beginning of the process if you are unsure of the answer the default is probably okay in addition to installing pocs the install script will create the config server and power monitor services which will automatically be restarted upon reboot of the computer pocs module if you want just the pocs module for instance if you want to override it in your own ocs see huntsman pocs https github com astrohuntsman huntsman pocs for an example then install via pip bash pip install panoptes pocs if you want the extra features such as google cloud platform connectivity then use the extras options bash pip install panoptes pocs google focuser testing running pocs pocs requires a few things to properly run 1 a panoptes utils https github com panoptes panoptes utils git config server running to provide dynamic configuration 2 an observatory instance that has details about the location of a pocs unit real or simulated which hardware is available etc a minimal working example with a simulated observatory would be python import os from panoptes utils config server import config server from panoptes pocs core import pocs os environ pandir var panoptes conf server config server conf files pocs yaml i 01 20 01 01 10 886 starting panoptes config server with config file conf files pocs yaml s 01 20 01 01 10 926 config server loaded 17 top level items i 01 20 01 01 10 928 config items saved to flask config server i 01 20 01 01 10 934 starting panoptes config server with localhost 6563 pocs pocs from config simulators all i 01 20 01 01 20 408 initializing panoptes unit generic panoptes unit mauna loa observatory i 01 20 01 01 20 419 making a pocs state machine from panoptes i 01 20 01 01 20 420 loading state table panoptes s 01 20 01 01 20 485 unit says hi there w 01 20 01 01 20 494 scheduler not present w 01 20 01 01 20 495 cameras not present w 01 20 01 01 20 496 mount not present i 01 20 01 01 20 497 scheduler not present cannot get current observation pocs initialize w 01 20 01 01 28 386 scheduler not present w 01 20 01 01 28 388 cameras not present w 01 20 01 01 28 389 mount not present s 01 20 01 01 28 390 unit says looks like we re missing some required hardware out 10 false for a more realistic usage see the full documentation at https pocs readthedocs io https pocs readthedocs io for actually deploying a panoptes unit refer to the operating guider https projectpanoptes gitbook io pocs user guide operation operating guides developing pocs see coding in panoptes https github com panoptes pocs wiki coding in panoptes testing to test the software you can use the standard pytest tool from the root of the directory by default all tests will be run if you want to run one specific test give the specific filename as an argument to pytest bash pytest tests test mount py links panoptes homepage https www projectpanoptes org forum https forum projectpanoptes org documentation https pocs readthedocs io source code https github com panoptes pocs testing testing | exoplantes astronomy telescopes python panoptes citizen-science | os |
Pothole_Detection | future developments alt minimum viable product https s3 amazonaws com wildrydes yash chakka images architecture new 1 png genral architecture alt minimum viable product https s3 amazonaws com wildrydes yash chakka architecture object detection png general flow general flow https s3 amazonaws com wildrydes yash chakka general flow png pothole deployments deployment environment for pothole detection system sagemaker deployment notes deploying a custom machine learning package to sagemaker there are quite a few moving parts lets go through the standard workflow docker image the key component is to get your setup to compile correctly with docker install docker and test hello world example using docker run hello world you should see hello from docker this message shows that your installation appears to be working correctly run the following standard commands to validate the setup locally docker build t trial1 it will build the environment and download the large models docker run p 80 8080 trial1 serve this will start the docker container locally the serve program command is run which starts the wsgi and predictor py flask handler note if you get an error python r you may have a bad hard return in the serve file run dos2unix serve to repair it cd local test and run predict sh this will past a standard test request to the docker container the input s3 filename will be downloaded and processed by the container with the above validated you re ready to deploy to sagemaker push docker to ecr setup your aws cli environment aws configure using your key and secret ensure you have access to the ecr service where you push docker login to ecr aws ecr get login then run the response to create a login session run build and push sh pothole docker image o this does the same docker build as the local test above and then pushes the image to ecr o pothole docker image can be any name of the container log into ecr and note the id of the docker repository ie 588698724959 dkr ecr us east 1 amazonaws com pothole docker image create model log into sagemaker and create endpoint o create a new model o you ll need to configure the endpoint configuration and the endpoint o once the endpoint is created it will say creating after about 10 minutes the model should be active you can see progress by going to the cloudwatch logs https console aws amazon com cloudwatch home region us east 1 logeventviewer group aws sagemaker endpoints create lambda create a lambda function ie callpothole it should run when a new s3 object is place in the bucket you may need to give it access to access full control of sagemaker iams policy this allows it to call the sagemaker endpoint copy and paste the lambda handler py script into the lambda editor test event the lambda handler py has an s3 test event in the source code you can copy this into a test event for lambda it simulates an s3 file being placed note the lambda will timeout after 3 seconds but sagemaker will continue to run and will complete normally o if wanted you can add to lambda to send an alert email once an output mp4 file appears in the bucket or have a more sophisticated monitoring tool that s it you can validate it s running by placing a video file in the s3 bucket directory the lambda should see it call sagemaker which then processes the file and outputs it as input filename output mp4 when ready to fully activate the system change the lambda variable is live from false to true false only processes 0 01s of video true processes the entire video but can potentially take day potential issues or exceptions note server file controls how many server workers there are this can typically be the number of cpus for now it s hard coded at 2 meaning it can process two concurrent videos at a time though the exact performance requirements would need to be evaluated the multithreaded system causes some trouble with the rcnn model for this reason it was necessary to secure the model session before use see backend get session in video markup py note the rcnn model py file had a concurrency issue as well when creating a directory that may already exist this was the only changed needed to the ml library parts cognito ses of the above listed architecture hasn t been implemented as there weren t super critical but for full security we definatelt need to implement them | ai |
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itp | information technology project installation 1 copy env example to env and update the configs 2 install dependencies bash composer install choose one prefer yarn yarn npm install 3 update config admin php 4 install laravel admin package it will migrate admin tables bash php artisan admin install 5 migrate application tables bash php artisan migrate 6 create dummy data bash php artisan db seed 7 good to go | server |
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Altschool-Third-Semester-Exams | altschool third semester exams this repository is for altschool cloud engineering third semester exams | cloud |
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Kodluyoruz-MGD-Bootcamp-Homework1 | kodluyoruz mgd homework1 kodluyoruz mobile game development bootcamp 1st homework br a simple guess the number game the player picks a number between 1 and 1000 the computer guesses until it finds the number the player picked and when it finds the number the game is over when the computer guesses wrong the user informs the computer with the up or down arrow keys the computer s guesses are displayed to the user on the ui screen when the computer makes the correct guess player presses the p key and the game is over when the game is over no input is expected from the player | csharp unity unity3d unity-3d unity3d-games | front_end |
seed-design | cover cover png foundations seed design design token https github com daangn seed design tree main packages design token foundations seed design stylesheet https github com daangn seed design tree main packages stylesheet stylesheet seed design icon https github com daangn seed icon convenience libraries seed design react theming https github com daangn seed design tree main packages react theming react seed design gatsby plugin seed design https github com daangn seed design tree main packages gatsby plugin seed design gatsby seed design document seed design docs https github com daangn seed design tree main docs web document contributing guide https github com daangn seed design tree main docs contributing md | design-system | os |
blockchain-cli | h1 align center br a href https github com seanseany blockchain js img src https raw githubusercontent com seanseany blockchain cli master img logo png width 200 a br blockchain cli br h1 h4 align center a minimal blockchain command line interface h4 p align center a href https badge fury io js blockchain cli img src https badge fury io js blockchain cli svg alt gitter a a href https www npmjs com package blockchain cli img src https img shields io npm dt blockchain cli svg alt gitter a p br screenshot https raw githubusercontent com seanseany blockchain cli master img demo gif features blocks with index hash data and timestamp proof of work system in memory javascript array to store the blockchain block integrity validation decentralized and distributed peer to peer communication merkle tree implementation installation to install this application you ll need node js https nodejs org en download which comes with npm http npmjs com installed on your computer from your command line source you ll need git https git scm com to run the project from source from your command line bash clone this repository git clone https github com seanseany blockchain cli go into the repository cd blockchain cli install dependencies npm install run the app npm start built with vorpal https github com dthree vorpal interactive node cli peer exchange https github com mappum peer exchange peer to peer communication crypto js https github com brix crypto js crypto library for hashing blocks license this project is licensed under the apache 2 0 license see the license license file for details acknowledgments this article https medium com lhartikk a blockchain in 200 lines of code 963cc1cc0e54 written by lauri hartikka original repo https github com lhartikk naivechain by lauri hartikka antony jone s fork https github com antony naivechain for refactoring nick fallon fork https github com nickfallon naivechain for pow implementation logo designed by muammark freepik faq by u sheepiroth https www reddit com r javascript comments 6ohc9h a blockchain commandline interface built with dkiahix faq when or why i would use this you should use this if you want to build a bitcoin wallet payment processor or bitcoin merchant portal in javascript you might also be interested in why decentralized networks or p2p applications are useful or what advantages they have this project seems like a good way to learn about that what is the block chain actually for the blockchain is for authorizing payments of a cryptocurrency between two peers without the need for a centralized 3rd party approving of the transaction there are other uses of the blockchain which are more in line with the second point digital signatures but they are secondary to the main purpose of peer to peer transfer of value bitcoin is blockchain s killer app why the hell should i care about the blockchain blockchain facilitates trade over a network imagine a metal as scarce as gold with a magical property of can be transported over a communications channel this has implications with respect to individual rights the world economy and the way we monetize and transfer value at a level higher than bartering directly for goods lately people are distancing themselves from the proof of work concept and are using blockchain to describe only the mechanism of signing a transaction as verification of sending an amount change sending an amount to almost anything else authorizing a change in a ruleset casting a vote for a politician verifying a point of iot data is authentic now add in the concept of a peer to peer network to this and you ve eliminated a middleman that once existed thereby improving the efficiency and reducing cost in these cases blockchain refers to the structuring of a program or database in such a way that it has no central point of failure while still providing all of the features expected for example augur and gnosis are decentralized prediction markets ethereum has implemented smart contracts which enable decentralized release of funds based on a gambling outcome | blockchain nodejs blockchain-technology blockchain-demos cryptocurrency cryptocurrencies javascript | blockchain |
EE445L | ee445l ee445l embedded systems design lab | os |
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PMIT-JU | pmit ju jahangirnagar university institute of information technology professional masters in information technology pmit 6111 software testing quality assurance pmit 6113 mobile application development pmit 6217 wireless networks pmit 6307 data mining knowledge discovery pmit 6104 database security pmit 6107 artificial intelligence neural network pmit 6201 cloud mobile computing pmit 6223 iot fog computing pmit 6204 cryptography steganography pmit 6311 human computer interaction pmit 6316 special topics related to ict pmit 6000 research project pmit 6001 computer programming environment pmit 6002 information system analysis design pmit 6003 data structures algorithms pmit 6004 operating system pmit 6005 discrete mathematics numerical analysis pmit 6006 database management system pmit 6007 web technology pmit 6008 computer networks data communication pmit 6009 microprocessor interfacing pmit 6010 software engineering pmit 6011 computer architecture pmit 6012 digital logic design pmit 6013 telecommunication system pmit 6014 signal systems pmit 6101 distributed computing pmit 6102 distributed database pmit 6103 advanced database management system pmit 6105 advanced operating system pmit 6106 advanced data structure algorithm pmit 6108 management information system pmit 6109 e commerce web security pmit 6110 advanced software engineering pmit 6112 software project management pmit 6114 advanced web engineering pmit 6115 simulation modeling pmit 6116 information system ethics cyber law pmit 6117 it forensic pmit 6118 computer graphics pmit 6119 graph theory application pmit 6120 embedded system pmit 6202 multimedia asset management system pmit 6203 network security pmit 6205 cellular network planning pmit 6206 fiber optic communication pmit 6207 radio frequency technology pmit 6208 vlsi layout algorithms pmit 6209 modeling of data networks pmit 6210 telecommunication network management pmit 6211 digital signal processing pmit 6212 digital image processing pmit 6213 speech recognition pmit 6214 information coding pmit 6215 tele traffic engineering pmit 6216 telecommunication traffic network planning pmit 6218 advanced networking internet technology pmit 6219 advanced digital communication pmit 6220 advanced optical communication pmit 6221 advanced wireless communication pmit 6222 advanced satellite communication pmit 6301 computational biology pmit 6302 computational geometry pmit 6303 advanced neuroinformatics pmit 6304 health informatics pmit 6305 bio informatics pmit 6306 advanced data mining for biological data pmit 6308 neuronal information discovery pmit 6309 modeling of biological systems pmit 6310 computer vision pmit 6312 robotics and automation pmit 6313 big data analysis pmit 6314 information retrieval pmit 6315 natural language processing | server |
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video2frame | video2frame video2frame is also an easy to use tool to extract frames from video why this tool forwchen s vid2frame tool https github com forwchen vid2frame is great but i am always confused by their parameters at the same time i also want to add something i need to the tool so i re wrote the code and now it is a new wheel it is hard to make a pr since i changed the code style how to use 1 establish the environment we recommend using conda https conda io to establish the environment just using sh conda env create f install conda environment yml you can also do it manually this project relays on the following packages python ffmpeg python packages can be installed using pip install r install pip requirements txt h5py lmdb numpy easydict tqdm 1 make the annotation json file the json file should like json meta class num 2 class name class 1 class 2 annotation label1 abcdefg path path to the video file 1 mp4 class 1 label2 asdfghj path path to the video file 2 mp4 class 2 1 extract frames using video2frame py examples using the default options sh python video2frame py dataset json specify the output file name sh python video2frame py dataset json db name my dataset using lmdb rather than hdf5 sh python video2frame py dataset json db type lmdb or sh python video2frame py dataset json db name my dataset lmdb random clip 5 seconds sh python video2frame py dataset json duration 5 0 get 3 video clips with a length of 5 seconds sh python video2frame py dataset json clips 3 duration 5 0 resize the frames to 320x240 sh python video2frame py dataset json resize mode 1 resize 320x240 keep the aspect ration and resize the shorter side to 320 sh python video2frame py dataset json resize mode 2 resize s320 keep the aspect ration and resize the longer side to 240 sh python video2frame py dataset json resize mode 2 resize l240 extract 5 frames per second sh python video2frame py dataset json fps 5 uniformly sample 16 frames per video sh python video2frame py dataset json sample mode 1 sample 16 randomly sample 16 frames per video sh python video2frame py dataset json sample mode 2 sample 16 use 16 threads to speed up sh python video2frame py dataset json threads 16 resize the frames to 320x240 extract one frame every two seconds uniformly sample 32 frames per video and using 20 threads sh python video2frame py dataset json resize mode 1 resize 320x240 fps 0 5 sample mode 1 sample 32 threads 20 all parameters text usage video2frame py h db name db name db type lmdb hdf5 file pkl tmp dir tmp dir clips clips duration duration resize mode 0 1 2 resize resize fps fps sample mode 0 1 2 3 sample sample threads threads keep annotation file positional arguments annotation file the annotation file in json format optional arguments h help show this help message and exit db name db name the database to store extracted frames default none db type lmdb hdf5 file pkl type of the database default hdf5 tmp dir tmp dir temporary folder default tmp clips clips num of clips per video default 1 duration duration length of each clip default 1 resize mode 0 1 2 resize mode 0 do not resize 1 800x600 resize to wxh 2 l600 or s600 keep the aspect ration and scale the longer shorter side to s default 0 resize resize parameter of resize mode default none fps fps sample the video at x fps default 1 sample mode 0 1 2 3 frame sampling options 0 keep all frames 1 uniformly sample n frames 2 randomly sample n continuous frames 3 randomly sample n frames 4 sample 1 frame every n frames default 0 sample sample how many frames default none threads threads number of threads default 0 keep do not delete temporary files at last default false tools 1 video folder to json py a json generator where the videos are arranged in this way text root swimming xxx mp4 root swimming xxy avi root swimming xxz flv root dancing 123 mkv root dancing nsdf3 webm root dancing asd932 mov 1 something to json py a json generator that converts the something something dataset 1 ucf101 to json py a json generator that converts the ucf101 dataset examples 1 pytorch skvideo dataset py get frames using skvideo package when training and evaluating it is okay when your batch size is small and your cpus are powerful enough 1 pytorch lmdb video dataset py a pytorch dataset example to read lmdb dataset 1 pytorch hdf5 video dataset py a pytorch dataset example to read hdf5 dataset always ensure num workers 0 or num workers 1 of your data loader 1 pytorch pkl video dataset py a pytorch dataset example to read pickle dataset 1 pytorch file video dataset py a pytorch dataset example to read image files dataset | ai |
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low-level-design | low level design resources work tech machine coding section https workat tech machine coding refactoringguru design patterns https refactoring guru design patterns o reilly head first design patterns book https www oreilly com library view head first design 0596007124 educative io grokking the low level design interview https www educative io courses grokking the low level design interview using ood principles youtube christopher design patterns in oops https www youtube com playlist list plrhzvicii6gnjpardno4uetuavr9embpc youtube the code mate low level design series https www youtube com playlist list plac2am9o1c5kioumeh9qijbav rmmx8rd youtube sudocode low level design primer course https www youtube com playlist list pltcru9sgybupcpy20eked6blbhi4zz55k youtube derek banas oops playlist https www youtube com playlist list plglfvvz lvvs5p7khyr4xdp7t9lck9pge youtube udit agarwal low level system design https www youtube com playlist list pl564gox0bclqtolrihisr2jpv11w8lesw youtube packt design patterns and solid principles with java https www youtube com playlist list pltgrmocmrb3mvu6oexrp0nasuk9kcnnfc youtube steven chin google s clean code talks https www youtube com playlist list plx5t1p9lqyue 3lrwrrjn5agu5jcd 3bg youtube success in tech coding and system design interview questions https www youtube com playlist list pla8lyuzflbqay6dkzhj5vxuaaqr4vwrka youtube thriver ashish low level system design https www youtube com playlist list plx2rzgha5vylgw7pxsylmrnc3 f2ihsbm youtube soumyajit bhattacharyay low level design https www youtube com playlist list pl12bcqe lp650cg6fzw7sozwn8rw1wji7 youtube sde skills object oriented design https www youtube com playlist list plbtmh4xfa9fgzqi8qpugwi1j9wddb6rfo roadmap sh design patterns for humans in php https roadmap sh guides design patterns for humans java design patterns website https java design patterns com medium how to approach object oriented design questions step by step https medium com nrkapri how to approach object oriented design questions step by step 67ed6a5a30e5 rooftop slushie how to approach an lld question for sde1 https www rooftopslushie com request how 20to 20approach 20ood 20questions 20for 20sde 1 9654 hashnode blog series on software design by maxi contieri https hashnode com series object oriented design ckh0jxdzs07qw20s16jmh8og7 hashnode blog series on code smells by maxi contieri https hashnode com series code smells ckh0jrbfm07pu20s1bc0yaae1 github design patterns code examples in java https github com braindevoiler design patterns patterns dev design patterns in javascript https www patterns dev posts design patterns credits this repo is just a collection of low level design resources available on the internet and these resources are copyright to their respective owners license mit license | low-level-design-problems low-level-design lld system-design machine-coding low-level-programming design-patterns interview-preparation interview-preparation-resources interview-preparation-kit object-oriented-programming oops machine-coding-interview design-principles designpatterns flipkart-interview software-design | os |
ml_cheat_sheet | supervised learning superstitions cheat sheet this notebook contains my notes and beliefs about several commonly used supervised learning algorithms my dream is that it will be useful as a quick reference or for people who are studying for machine learning interviews quizzes etc after some setup code the methods discussed are logistic regression decision trees support vector machines k nearest neighbors naive bayes to better understand each classifier we train on various versions of the two moons dataset and plot empirical decision boundaries each plot shows the training data on top of a few thousand randomly chosen points which have been colored by the output of the learned model superstition 1 the plots suggest that linear classifiers are often out performed on high quality training sets but still produce sane results on noisy small datasets note not all the plots have the same xy dimensions other resources http blog echen me 2011 04 27 choosing a machine learning classifier good blog about choosing a classifier http hunch net p 22 about overfitting http www dataschool io comparing supervised learning algorithms table of superstitions https github com soulmachine machine learning cheat sheet more like cheat 100 sheets http scott fortmann roe com docs biasvariance html blog about the bias variance problem | ai |
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plo | phoenix rtos loader this repository contains the source for the phoenix rtos loader to learn more refer to the phoenix rtos documentation https github com phoenix rtos phoenix rtos doc license this work is licensed under a bsd license see the license file for details | os |
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MOOC-Coursera-Advanced-Machine-Learning | advanced machine learning coursera mooc specialization national research university higher school of economics yandex img src logo png width 800 height 200 coursera webpage https www coursera org specializations aml syllabus this specialization gives an introduction to deep learning reinforcement learning natural language understanding computer vision and bayesian methods top kaggle machine learning practitioners and cern scientists will share their experience of solving real world problems and help you to fill the gaps between theory and practice upon completion of 7 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real world data and settings you will master your skills by solving a wide variety of real world problems like image captioning and automatic game playing throughout the course projects you will gain the hands on experience of applying advanced machine learning techniques that provide the foundation to the current state of the art in ai br table of contents 1 introduction to deep learning certified completion x week 1 optimization x week 2 multilayer perceptron and introduction to tensorflow keras x week 3 convolutional neural networks x week 4 autoencoders and generative adversarial networks x week 5 recurrent neural networks x final project image captioning 2 how to win a data science competition learn from top kagglers certified completion x week 1 feature preprocessing and engineering x week 2 exploratory data analysis validation strategies and data leakages x week 3 metric optimization and advanced feature engineering i x week 4 hyperparameter optimization advanced feature engineering ii and ensembling x final project kaggle competition predict future sales 3 bayesian methods for machine learning certified completion x week 1 refresher on bayesian probability theory x week 2 expectation maximization algorithm and gaussian mixture models x week 3 variational inference and latent dirichlet allocation x week 4 markov chain monte carlo x week 5 bayesian neural networks and variational autoencoders x week 6 gaussian processes and bayesian optimization x final project forensics to generate images of suspects 4 natural language processing on hold x week 1 text classification with linear models x week 2 language modelling with probabilistic graphical models and neural networks x week 3 word embeddings and topic models x week 4 machine translation and sequence to sequence models final project stackoverflow task oriented chatbot 5 practical reinforcement learning certified completion x week 1 introduction to reinforcement learning x week 2 model based reinforcement learning dynamic programming x week 3 model free reinforcement learning sarsa monte carlo q learning x week 4 approximate and deep reinforcement learning deep q learning x week 5 policy gradient reinforcement learning x week 6 advanced topics on exploration and planning br future courses 6 addressing large hadron collider challenges by machine learning on hold 7 deep learning in computer vision on hold | ai |
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Roads | roads roads is a webcomponents based mvc javascript framework getting started what you will need to start a new roads app is get all lib dependencies at lib folder plus roads js and roads css at dist folder lib hammer js lib iscroll min js lib ol js only if you will use ro map tag lib ol deps js only if you will use ro map tag lib x tag js dist roads min js dist roads css roads app anatomy views roads uses individual files to define each screen layout in master detail example at examples folder wee have three diferent html files at views filder categories html tasks html tasksdetail html a minimal view layout ro view id tasks ro controller tasks ro header ro topbar ro back button text categories icon coisa ro back button ro title tasks ro title ro topbar ro header ro stage ro stage ro view ro view this is the screen layout base the attribute id is to be referenced when you need navigate from one screen to another the attribute ro controller is which acontroller will control this view ro header is a placement where you can add everything that should be fixed at top of screen ro topbar render a top bar component ro back button if the app is running in a ios or firefoxos display a backbutton to back to previous screen if is running in a android or windows phone this button will be automatically hidden ro title where screen title goes ro stage here is the body of your app controllers by default each view will have a controller but controllers can control how many views are needed todo controllers tasks new ro controller tasks init function show function init this function will run when a new controller is initialized usually this runs once and when the app is initialized show runs every time that the views controlled by this controller become visible you will use this method to update view content or change the backbutton callback function main js here how a basic main js file will look like this callback function will run when all views are loaded ro init function this code will calculate all view positions show the main view and hide all others roapp putviewsinfirstposition you will need initialize the controllers this is the moment when init method will run todo categories new todo controllers categories todo tasks new todo controllers tasks todo taskdetail new todo controllers taskdetail a typical roads app should have a index html file doctype html html head all javascripts and csss related files link rel import href views tasks html head body ro loader ro loader ro app id roapp ro app body html link rel import this tag is how you will relate your view files in the app all these layouts will be loaded and only after that ro ini callback will run ro load is a loader component roads will use when is loading all your files ro app this is the main app tag all other tags will be appended here roads tags ro app gotoview from to is how you can navigate between views backtoview from to is the same as gotoview but the animation is in inverse direction both gotoview and backtoview will trigger show method from related view controller ro list ro list ro item action dosomething description ro item ro list ro item is the item layout will be used as a template ro uses the template engine to render ro item content replacing all by related data ro item action this is the function that will be trigged when user tap a ro item also is possible use dynamic parameters using template syntax dosomething id will be replaced by current item id setdata is how to add a list of items to a ro list every time that a setdata is called ro list parse the content again var list document queryselector ro list var data desciption a desciption b list setdata data ro input ro layout ro map ro tabs ro tabs ro float menu alt text https cdn1 vox cdn com thumbor chlsepyrnkp0jzeqro4rnda7bey cdn0 vox cdn com uploads chorus asset file 3329476 inbox 3up 0 png float menu reproduce a very similar behavior to android s material design ro float menu icon more iconactive close state hideitems ro item icon newdocument text i18n text i18nkey menus newdocument i18n action roapp gotoview docs newedit ro item ro item icon editdocument text i18n text i18nkey menus editdocument i18n action roapp gotoview docs docsedit ro item ro item icon deletedocument text i18n text i18nkey menus deletedocument i18n action myapp documents delete ro item ro float menu each ro item will be rendered as a menu item ro float menu icon icon attribute is the main menu icon ro float menu iconactive iconative is what will showed after you click in a menu ro item text is a item text if you don t need internationalization just add the description here if so use i18n and i18nkey to set up the translation info ro item action is the function that will be triggered when user tap a menu item roads functions templateengine roads template engine combine any string with pattern and the data passed as argument var template div desctiption div ro templateengine template description hello you salso can use more complex data var template div user info ssn div var user name jonh smith job front end developer info ssn 433 433 111 ro templateengine template user stylegenerator sometimes developers need to change a large number of styles in a same dom element talking about performance this may be slow if this element is already rendered style generator will create a css text and you can use it to change all styles at the same time var e document createelement div e style csstext ro stylegenerator border 1px solid red color red font size 12px ro filter filters are functions that will be used to evaluate the value when templateengine is replacing data in a template string roads come with some basic filters but is also easy create and use new ones date is used to format date values var template date date var renderedtemplate ro templateengine template date new date 2015 06 25t01 05 55 973z 06 25 2015 roads will get ro i18n defaults date as default if you want to use another pattern you can change ro i18n defaults date and you will have a new default value for your app but you also can pass a new date format as a parameter var template date date dd mm yyyy var renderedtemplate ro templateengine template date new date 2015 06 25t01 05 55 973z 25 06 2015 ro i18n i18n is a internationalization and localization layer you can use to change your app translation and to format your data between different patterns i18n is nothing more than a specific templateengine filter you should storage all your translations keys at ro i18n translations object ro i18n translations hello world ol mundo var template h1 hello world i18n h1 var renderedtemplate ro templateengine template h1 ol mundo h1 also is possible translate tag attributes ro title i18n i18nkey views headers myview i18n ro title when you use a attribute i18n roads will get the attribute i18nkey tranlated and will add as a innerhtml of this tag ro input type text i18n placeholder i18nkey views form username i18n when you need translate a specific attribute set the value of i18n attribute with the attribute name you want translate in previus example roads will get i18nkey translate it and after that will the a new attribute placeholder with translated text | front_end |
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AntiSougou | anti sougou filter the telegram sogou social engineering information database users use your telegram service we plan to support php golang node js c python access | server |
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homlr | hands on machine learning with r by brad boehmke brandon greenwell br welcome to the supplementary repository for hands on machine learning with r http bit ly homl with r this project aims to teach you the fundamentals of machine learning with the r machine learning tech stack and this website is designed to provide you with additional content and resources that we could not include in the hard copy book such as an environment to run code from the book chapter exercises direct access to the data sets slides from relevant workshops errata you can find these supplementary resources on the book s companion website under heavy construction https koalaverse github io homlr with the exception of the book all this content is cc by 4 0 licensed license md | r machine-learning data-science supervised-learning unsupervised-learning | ai |
Fall-2015-CV | readme computer vision ntu fall 2015 | ai |
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Anthias | anthias digital signage for the raspberry pi run unit tests https github com screenly anthias actions workflows docker test yaml badge svg https github com screenly anthias actions workflows docker test yaml codeql https github com screenly anthias actions workflows codeql analysis yaml badge svg https github com screenly anthias actions workflows codeql analysis yaml lint code base https github com screenly anthias actions workflows linter yml badge svg https github com screenly anthias actions workflows linter yml anthias logo https github com screenly anthias blob master static img dark svg raw true anthias logo about anthias anthias is a digital signage platform for raspberry pi formerly known as screenly ose it was rebranded to clear up the confusion between screenly the paid version and anthias more details can be found in this blog post https www screenly io blog 2022 12 06 screenly ose now called anthias anthias works on all raspberry pi versions including raspberry pi zero raspberry pi 3 model b and raspberry pi 4 model b want to help anthias thrive support us using github sponsor https github com sponsors screenly star history star history chart https api star history com svg repos screenly anthias type date https star history com screenly anthias date balena disk images the quickest way to get started is to use raspberry pi imager https www screenly io blog 2022 12 13 anthias and screenly now in rpi imager where you can find anthias under other specific purpose os alternatively you can find our pre built disk images powered by balena hub https hub balena io here https github com screenly anthias releases latest do however note that we are still in the process of knocking out some bugs you can track the known issues here https github com screenly anthias issues you can also check the discussions in the anthias forums https forums screenly io if you d like more control over your digital signage instance try installing it on raspberry pi os lite installing on raspberry pi os lite installing on raspberry pi os lite the tl dr for on raspberry pi os https www raspberrypi com software bullseye lite is bash curl sl https install anthias srly io if you ve selected n when prompted for an upgrade ndash i e would you like to perform a full system upgrade as well y n ndash you ll get the following message when the installer is almost done executing please reboot and run home user screenly bin upgrade containers sh to complete the installation would you like to reboot now y n you have the option to reboot now or later on the next boot make sure to run upgrade containers sh as mentioned above otherwise if you ve selected y for the system upgrade then you don t need to do a reboot for the containers to be started however it s still recommended to do a reboot this installation will take 15 minutes to several hours depending on variables such as the raspberry pi hardware version the sd card the internet connection during ideal conditions raspberry pi 3 model b class 10 sd card and fast internet connection the installation normally takes 15 30 minutes on a raspberry pi zero or raspberry pi model b with a class 4 sd card the installation will take hours installing with balena go through the steps in this documentation docs balena fleet deployment md to deploy anthias on your balena fleet quick links forum https forums screenly io website https anthias screenly io hosted on github and the source is available here https github com screenly anthias tree master website general documentation https github com screenly anthias blob master docs readme md developer documentation https github com screenly anthias blob master docs developer documentation md | digital-signage raspberry-pi python iot | server |
explorer | helium blockchain explorer code that powers the official helium blockchain explorer https explorer helium com development and contribution any and all contributions from the community are encouraged guidelines for how to contribute to this repository are here https github com helium explorer blob master contributing md discussion about the development and usage of the helium blockchain explorer takes place in the official helium discord server https discord gg helium specifically in the explorer channel join us for a list of issues and prioritization please go to our project page https github com orgs helium projects 9 getting started 1 first clone the repository to your local machine and navigate into the folder for example bash git clone https github com helium explorer git cd explorer 2 second install all the dependencies bash yarn 3 edit your environment variables open the sample env file located at the root of the project create a mapbox account https account mapbox com auth signup and copy your public access token https account mapbox com access tokens paste it in place of 123 for the next public mapbox key variable that line should now look like this next public mapbox key pk ey the rest of your access token rename the file env delete sample from the file name 4 then run the development server bash yarn dev or npm run dev and open http localhost 3000 http localhost 3000 with your browser to see the result you can start editing the page by modifying pages index js the page auto updates as you edit the file and save your changes 5 create a new logically named branch for example bash git checkout b witness list enhancements 6 push your changes to github and create a pr against the master branch linking the pr to any relevant issues styling right now the explorer is styled using a combination of the ant design system https ant design inline styles https www w3schools com react react css asp and classes found in the styles explorer css file ant design is a really powerful way to quickly implement various complex ui elements that would take a long time to build manually e g tables with pagination date pickers search bars as well as common ui elements that have a lot of nuance in functionality e g checkboxes buttons tooltips etc but sometimes systems like ant can be very limiting and opinionated when it comes to customizing those ui elements and systems like ant design can also become hard to work with when something like their column and row system doesn t quite align with the way you think a layout should look or you want to remove or add extra padding somewhere for example and on the other hand inline styles are a nice option for keeping the styles attached to the jsx code and for conditionally styling things based on some logic but inline styles don t allow for media queries which are increasingly necessary to create a good mobile first experience as the explorer website becomes more mature and the broader helium product family adopts a more cohesive design customizing ui elements in a consistent way across the explorer will become more necessary especially considering that around 50 of the explorer s visitors are visiting from mobile devices because everything helium makes is intended to become open source over time ensuring that the design stays consistent across the entire family of helium products the hotspot app console helium com etc can become difficult the more one off solutions are used to fix specific ui problems so in order to help us minimize the following things in explorer hardcoded hex colors font sizes font weights padding and margin values long css files with confusing media queries inconsistent class and id names one off solutions to override or fight against design systems like ant we are trying out tailwindcss https tailwindcss com there is a tailwind config js file at the root of the project which defines things like the website s color palette breakpoints sm md lg xl and in the future things like font sizes font weights spacing tokens and whatever else makes sense to include moving the explorer s styling code to tailwind will be a gradual transition and it might turn out that tailwind isn t the best fit for explorer in the end in which case this section of the readme will be updated accordingly how to use tailwind the tailwind docs are a really great resource for getting up to speed quickly on the syntax but here is a brief example to show the power of tailwind something like this is common to see in the explorer codebase right now jsx div style display flex flexdirection row backgroundcolor 222e46 alignitems center justifycontent center maxwidth 1200 margin 0 auto padding 16px 32px p style color ffffff fontsize 16 example text p p style color eeeeee fontsize 16 example text p div in tailwind the same code could be written like this jsx div classname flex flex row bg navy 500 items center justify center max w xl mx auto py 4 px 8 p classname text white example text p div but the real power of tailwind shows when you want to change the styling between mobile and desktop screen sizes in the first code snippet above to make the styling different between screen sizes you d need to do something like this jsx div classname container come up with a logical class name and add it style display flex flexdirection row delete this row backgroundcolor 222e46 alignitems center justifycontent center maxwidth 1200 margin 0 auto padding 16px 32px and then add some css code like this css container flex direction row media screen and max width 576px container flex direction column in tailwind you could accomplish the same thing without having to come up with a new class name or adding any css by simply changing the first few utility classes in the above div to read like this jsx div classname flex flex col sm flex row that might seem confusing at first glance but another reason tailwind is powerful is because it encourages us as frontend developers to take a mobile first approach when styling things the above set of utility classes in plain english translates to flex direction is column but if the screen size is bigger than small mobile flex direction is row another nice use case for tailwind is styling things conditionally while keeping the design system tokens intact for example jsx p classname font bold text xl status online text green 400 text red 400 status p the syntax takes some getting used to and sometimes the class names can start to get unwieldy but the utility class model fits really nicely with components so any time you find yourself repeating a string of tailwind utility classes is probably a good time to encapsulate that jsx as a component questions if you run into any issues or you have any questions about how to get started contributing feel free to reach out on the explorer channel in the official helium community discord server http discord gg helium learn more this is a next js https nextjs org project to learn more about next js take a look at the following resources next js documentation https nextjs org docs learn about next js features and api learn next js https nextjs org learn an interactive next js tutorial | blockchain |
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Python-Machine-Learning | python machine learning tutorials on machine learning and deep learning with python | ai |
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TideSDK | tidesdk current build status build status https travis ci org tidesdk tidesdk png https travis ci org tidesdk tidesdk realtime build only tests linux create multi platform desktop apps with html5 css3 and javascript tidesdk is the best way to create beautiful unique desktop apps using your web development skills tidesdk is open source and driven by a collaborative world wide effort involving a skilled team together with variety of open source contributors tidesdk supports the use of python php and ruby this provides capabilities beyond the realm of any competitive framework our plans include the expansion and extension tidesdk s api s for even better more complex and reliable user experiences on the desktop as we move forward tidesdk is a significant and substantial software project and became an affiliate project of spi software in the public interest http spi inc org projects as of september 2012 as an affiliate project tidesdk in the company of other important open source projects such as postgres debian archlinux drupal and jenkins ci that are also non profit beyond recogition and credibility our affiliation also allows us to raise funds through donations for the viability and long term future of the project we currently have a strong core team of talented individuals dedicated to this solution as an open solution we welcome code and documention contributions other displays of support for the project such tweeting or blogging about tidesdk is also greatly appreciated if you like or use tidesdk in your development or your business your financial support can help our development efforts the tidesdk codebase is more complex and requires dedicated time of programmers with deeper programming knowledge to ensure it remains available donations are completely tax deductible in the usa cloud servers for our ci continuous integration system are also a great way to say thank you please get in touch with us if this is something you can offer a minimum 1gb ram machine or virtual machine using one of the supported operating systems is sincerely appreciated tide microkernel tidesdk has a compact microkernel written in c for running pluggable modules the microkernel supports a cross language cross platform binding and invocation framework simply put the provides developers with the capability to pass objects between languages ie pass a javascript object to a python function and so on tide currently supports the following languages c c python ruby php javascript that said any c c exposed language that supports embedding can be supported without much effort possibilities for the future include lua falcon c maybe through mono java the supported languages may be embedded in the dom or included by reference in your application code the module api the module api is simple but powerful modules to extend the api may be written in c or even in the supported languages like python once the module is loaded you can can do anything you wish with its functionality the binding api a module communicates to other modules through binding binding is the process of either adding values primitive objects functions etc to the sdk runtime or retrieving them a number of other utility functions including logging are exposed through the api module the binding implementation for each language knows how to exchange values between the sdk runtime and the native language runtime this is the some of the magic of tidesdk getting and using tidesdk tidesdk is available for download at http tidesdk org http tidesdk org enhanced documentation efforts are a work in progress and we are always looking to improve the link below with take your to the most current docs additional support is available though our wiki we are also focused on the development of guides on our documentation site each guide serves as a tutorial on a development topic please consider contributing time to enhance our documentation or to contribute guide building tidesdk developers note if you want to develop applications with tidesdk just download and install tidesdk to use it for sdk developers please refer to https github com tidesdk tidesdk wiki building tidesdk for up to date build instructions support documentation http tidesdk multipart net docs user dev generated http tidesdk multipart net docs user dev generated source code https github com tidesdk tidesdk https github com tidesdk tidesdk tutorials get started easily http tidesdk multipart net docs user dev generated guide http tidesdk multipart net docs user dev generated guide q amp a on stack overflow get help http stackoverflow com questions tagged tidesdk http stackoverflow com questions tagged tidesdk report a bug help us improve https github com tidesdk tidesdk issues https github com tidesdk tidesdk issues irc chat with us on tidesdk on freenode net twitter follow tidesdk tidesdk http twitter com tidesdk blog read our blog at http tidesdk org blog http tidesdk org blog knowledge base read the wiki https github com tidesdk tidesdk wiki https github com tidesdk tidesdk wiki google groups join our mailing list https groups google com forum forum tidesdk https groups google com forum forum tidesdk legal tidesdk is licensed under the apache 2 0 license see license for more details | front_end |
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fsa-style | deprecation notice fsa style will no longer be updated on this repo the usda fpac repository is the new location for this design resource fsa style a css framework implementation of the fpac design system usda fpac s visual language and html css framework as documented by the fpac design system http usda fsa github io fsa design system this digital resouce is made available across the department and to other federal agencies it supports the requirements of omb memorandum m 16 21 federal source code policy achieving efficiency transparency and innovation through reusable and open source software this policy requires agencies to develop plans to release at least 20 percent of new custom developed source code as open source software oss when commissioning new custom software the design system comprises of two core repositories 1 fsa design system https github com usda fsa fsa style source code for the design system web site http usda fsa github io fsa design system documenting the fsa style and accompanying guidelines 1 fsa style https github com usda fsa fsa style style assets html css images available for download or install as documented by the design system web site http usda fsa github io fsa design system background the components and style guide of the design system follow industry standard web accessibility guidelines and use the best practices of existing style libraries and modern web design they are designed for use by fpac product teams who want to create beautiful easy to use online experiences that are consistent to the fsa style it was created and maintained within fpac s issdob fbcss division and was initially influenced by the v1 x x version u s web design system https designsystem digital gov created and maintained by the wonderful folks at 18f https 18f gsa gov recent updates information about the most recent release can always be found in the release history https github com usda fsa fsa style releases we include details about significant updates and any backwards incompatible changes along with a list of all changes using fsa style assets two options are available for usage of fsa style html css images and web fonts npm install install using npm download zip download zip install using npm if you have node installed on your machine you can use npm to install the standards add fsa style to your project s package json as a dependency shell npm install save fsa style the package will be installed in node modules fsa style you can either use the un compiled files found in the src or the compiled files in the dist directory for example if you re interested in using the sass files scss you would use the src directory otherwise dist is what you want node modules fsa style dist css fonts img js boilerplate html index html src fonts img js stylesheets boilerplate html index html the main sass scss source file is here node modules fsa style src stylesheets fsa style scss the compiled and minified css files location node modules fsa style dist css fsa style css node modules fsa style dist css fsa style min css lastly refer to using the boilerplate using the boilerplate for basic guidance on html structure download zip if you don t have node or the ability to incorporate source files into a build process grunt gulp webpack etc follow these steps to manually use the fsa style download the latest assets https github com usda fsa fsa style releases download 2 7 7 fsa style 2 7 7 zip 1 visual index index html is a visual index of this css framework s visual language including basic html elements expressed in that style viewable at http usda fsa github io fsa style index html 2 boilerplate boilerplate html provides a non designed starting point it serves as general guidance for the html structure most typically required of an fpac digital product viewable at http usda fsa github io fsa style boilerplate html 3 manually adding to your project add the downloaded zip s assets to a relevant place in your code base likely a directory where you keep third party libraries sh fsa style x x x css fsa style css fsa style css map fsa style min css fsa style min css map fonts img js vendor note that fonts and img must be alongside css as the css files reference them at a specific relative path e g img file png refer to using the boilerplate using the boilerplate for further steps using the boilerplate http usda fsa github io fsa style boilerplate html reference this basic list for the general requirements for your typical html structure reviewing this list is perhaps best done while viewing its html source https github com usda fsa fsa style blob master src boilerplate html 1 html5 doctype doctype html 1 wrap html start element in ie conditional comment 1 enable responsive web design via meta name viewport content width device width initial scale 1 1 reference css file s via link tag 1 reference ie conditional commented js files to polyfill features below ie9 1 include ie conditional commented browser upgrade message 1 include skipnav anchor link with href attribute pointing to main element 1 wrap primary contents with main id main content main 1 build your thing contributing for complete instructions on how to contribute code please read contributing md contributing md if you have questions or concerns about our contributing workflow please contact us by filing a github issue https github com usda fsa fsa style issues reuse of open source style guides this design system was initially based on the draft u s web design standards https playbook cio gov designstandards created and maintained by the u s digital service https www whitehouse gov digital united states digital service and 18f https 18f gsa gov designers and developers the draft u s web design standards are designed for use by government product teams who want to create beautiful easy to use online experiences for the public to learn more about the project check out their blog post https 18f gsa gov 2015 09 28 web design standards other inspiration further design system sources of inspiration some government oriented some not uk s government digital service s ui elements http govuk elements herokuapp com consumer financial protection bureau s design manual https cfpb github io design manual u s patent and trademark office s design patterns http uspto github io designpatterns healthcare gov style guide http styleguide healthcare gov vets gov playbook design https www vets gov playbook design salesforce lightning design system https www lightningdesignsystem com mailchimp patterns http ux mailchimp com patterns code for america website style guide https style codeforamerica org google material design https material google com | design-system style-guide css-framework usda farm-service-agency ux government design-systems fsa fpac farm-production-and-conservation | os |
Blockchain-Application | blockchain application based on the autonomous information asset of clumsiest information inc the blockchain technology is used to develop the daily application | server |
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Criterion-Database | criterion database criterion database of water drainaging and supplying in municipal engineering about essues | server |
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ViewUIPlus | p align center a href https www iviewui com img width 200 src https file iviewui com view ui logo new svg a p h1 view ui plus h3 an enterprise level ui component library and front end solution based on vue js 3 h3 h1 view ui plus https img shields io npm v view ui plus svg style flat square https www npmjs org package view ui plus npm downloads http img shields io npm dm view ui plus svg style flat square https npmjs org package view ui plus npm downloads https img shields io npm dt view ui plus svg style flat square https npmjs org package view ui plus js gzip size http img badgesize io https unpkg com view ui plus dist viewuiplus min js compression gzip label gzip 20size 20js style flat square css gzip size http img badgesize io https unpkg com view ui plus dist styles viewuiplus css compression gzip label gzip 20size 20css style flat square join the chat at https gitter im iview iview https img shields io badge chat on gitter 30b392 svg style flat square https gitter im iview iview utm source badge utm medium badge utm campaign pr badge utm content badge docs https www iviewui com https www iviewui com start on cloud ide https idegithub com view design viewuiplus https idegithub com view design viewuiplus features dozens of useful and beautiful components friendly api it s made for people with any skills level extensive documentations and demos it is quite awesome install we provide starter kit for you view ui plus project based on vue cli https github com view design view ui project vuecli view ui plus project based on vite https github com view design view ui project vite view ui plus project based on typescript https github com view design view ui project ts view ui plus project based on nuxt https github com view design view ui project nuxt install view ui plus using npm npm install view ui plus save using a script tag for global use html script type text javascript src viewuiplus min js script link rel stylesheet href dist styles viewuiplus css you can find more info on the website https www iviewui com view ui plus guide install usage vue template slider v model value range template script setup import ref from vue const value ref 20 50 script using css via import js import view ui plus dist styles viewuiplus css community if you want to contribute us or in case you are haiving any doubt questions find other users at the gitter chat https gitter im iview iview or post on stackoverflow using iview ui tag https stackoverflow com questions tagged iview ui bugs file a issue here https www iviewui com new issue please provide a example so we can help you better contribute contact us in gitter chat https gitter im iview iview wechat or via mail to admin aresn com prs welcome major contributors name avatar name avatar name avatar aresn https github com icarusion https avatars3 githubusercontent com u 5370542 v 3 s 60 jingsam https github com jingsam https avatars3 githubusercontent com u 1522494 v 3 s 60 rijn https github com rijn https avatars2 githubusercontent com u 6976367 v 3 s 60 lcx960324 https github com lcx960324 https avatars3 githubusercontent com u 9768245 v 3 s 60 gitleonine1989 https github com gitleonine1989 https avatars1 githubusercontent com u 7582490 v 3 s 60 huixisheng https github com huixisheng https avatars1 githubusercontent com u 1518967 v 3 s 60 sergio crisostomo https github com sergiocrisostomo https avatars3 githubusercontent com u 5614559 v 3 s 60 lison16 https github com lison16 https avatars3 githubusercontent com u 20942571 v 3 s 60 xotic750 https github com xotic750 https avatars3 githubusercontent com u 216041 v 3 s 60 huanghong1125 https github com huanghong1125 https avatars3 githubusercontent com u 12794817 v 3 s 60 yangdan8 https github com yangdan8 https avatars2 githubusercontent com u 16515026 v 3 s 60 likuner https github com likuner https avatars3 githubusercontent com u 18632318 v 3 s 60 license mit http opensource org licenses mit copyright c 2016 present viewdesign | component-library iview vue viewdesign | front_end |
desafio_pediufarma | desafio pediu farma p align center a img alt javascript src https img shields io github languages top lcsdiniz desafio pediufarma a a href https www linkedin com in lucas diniz santos henrique 3aa825157 img alt created by lucas diniz src https img shields io badge created 20by lucas 20diniz blueviolet a p the desafio pediu farma is a challenge where participants must create a script that will access a specified database and return some data from it the script can be created with any programming language and must return the data in a file with json format technologies python 3 8 2 programming language mysql connector python https dev mysql com downloads connector python and json libraries mysql database authors lucas diniz github https github com lcsdiniz linkedin https www linkedin com in lucas diniz santos henrique 3aa825157 | server |
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WebFramework | scavix software web development framework the scavix software web development framework has been build to assist developers in creating rich web applications it s the foundation of all functionalities we need in our daily work so that we don t need to reinvent the wheel for each new customer project it provides you with everything you need from the database access layer to the ui development so that you can focus on the business logic of the application without loosing yourself in the thousands of baby steps that need to be implemented for every project to give you a quick start here s a nice article over at codeproject describing how to use this framework ultra rapid php application development http www codeproject com articles 553018 ultra rapid php application development folders netbeans contains a netbeans project for the contents of the web folder tools currently only contains phptracer which is a tool to monitor logfiles written in c web documentor an app we use this to create the api reference documentation https github com scavixsoftware webframework wiki web sample blog a sample blog application using the webframework web sample shop a sample shop application using the webframework web sample chart a sample on how to show charts in your application based on the webframework web system the framework code as submodule installation just clone the scavix wdf code from https github com scavixsoftware scavix wdf or directly add it as submodule to your git repo resources api reference documentation https github com scavixsoftware webframework wiki basic usage ultra rapid php application development http www codeproject com articles 553018 ultra rapid php application development a real world sample easily implementing your own online shop http www codeproject com articles 586703 easily implementing your own online shop upgrading projects to php namespaces an easy solution http www codeproject com articles 643091 upgrading projects to php namespaces an easy solut | front_end |
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wasted | wasted w eb a pplication st ack for e xtreme d evelopment wasted logo https filedump mayflower de wasted gif this is a generic vagrant box ubuntu with php mysql composer and nginx and preconfigured vhosts for symfony2 zend framework 1 sections how to get wasted how to get wasted usage usage notes notes recommended plugins recommended plugins lxc lxc contributing contributing how to get wasted wasted is designed to be used with an existing git repository containing your application check out the setup setup section of this document for instructions if you want to get wasted without an existing project see the notes notes section setup adding wasted to your project switch to the root of your existing git repository and execute the following commands git subtree add prefix vagrant https github com mayflower wasted git master squash vagrant bootstrap sh this requires that there is no existing vagrant directory note on git subtree git subtree was first added to git with 1 7 11 in may 2012 if it isn t available on your machine see the installation instructions https github com git git blob master contrib subtree install all git subtree commands accept a squash flag to squash the subtree commits into one commit configuration all configuration happens in the devstack yaml which gets created when running the bootstrap above you may add a local devstack yaml in which you can overwrite configuration in devstack yaml e g when using different ip or box name todo document devstack yaml possibilities updating to update the devstack use from the root of your git repository git subtree pull prefix vagrant https github com mayflower wasted git master squash usage once you completed the steps described in the setup setup section just do a vagrant up r10k will first bootstrap your local puppet modules and after that the provisioning process will be started this might not work if you are using non virtualbox providers additional information for windows users for all users of windows nt based systems windows 2000 vista 7 and 8 with using ntfs filesystem is the default you can use the vagrant bootstrap cmd to work in the same way that is described in the adding wasted to your project section all other window users you have to run vagrant up from the vagrant subdirectory you have to edit your c windows system32 drivers etc hosts file manually notes recommended plugins the config makes use of but does not require vagrant cachier package file caching vagrant hostmanager etc hosts management to update vbox guest extensions automatically you can use vagrant vbguest lxc if you are using lxc instead of virtualbox obviously you should have lxc and a recent kernel 3 5 installed wasted without an existing project if you do not yet have a project to use with wasted the easiest way to test wasted is to create a dummy repository create directory switch to new directory and initialize git repository mkdir dummy project cd dummy project git init create a file and commit it to ensure there is a head for the subtree command to work with touch dummy file git add dummy file git commit dummy file m initial commit your dummy repository is now ready for use with wasted just follow the instruction from the setup setup section requirements for vagrant boxes wasted requires puppet 3 7 0 to work correctly which is included in the default boxes otherwise it will fail with a cryptic error due to a bug in contain on fully qualified class names error undefined method ref for nil nilclass in case you stab your toe on this using the mayflower trusty64 puppet3 box run vagrant box update contributing for development of wasted create a staging directory and clone the wasted repository directly into the vagrant directory mkdir wasted development cd wasted development git clone git github com mayflower wasted vagrant now you can hack on wasted code as you would usually do while still retaining the ability to bootstrap and use it like described in the setup setup section merge changes back from within your project if wasted was subtree merged into your project and changes were made inside the vagrant directory that you would like to contribute you need to use git subtree push if you have push access you may create a new branch directly and then submit a pull request git subtree push prefix vagrant git github com mayflower wasted branch name otherwise please fork this repository and then create a pull request from your fork git subtree push prefix vagrant git github com your user wasted branch name | front_end |
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Binary-Brains | binary brains it2010 mobile application development group project art zone | front_end |
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Data-Modeling-with-Apache-Cassandra | project 1b data modeling with apache cassandra images data modeling with cassandra sparkify png this project was provided as part of udacity s data engineering nanodegree program you can see all the nano degree projects from here https github com mohamedbakhet data engineering udacity nano degree program introduction 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 analysis team is particularly interested in understanding what songs users are listening to currently there is no easy way to query the data to generate the results since the data reside in a directory of csv files on user activity on the app they d like a data engineer to create an apache cassandra database which can create queries on song play data to answer the questions and wish to bring you on the project your role is to create a database for this analysis you ll be able to test your database by running queries given to you by the analytics team from sparkify to create the results project description the goal from project is build data modeling using apache cassandra and build etl pipeline througth build and create apache cassandra database and deal with csv files to preprossecing them and insert them into cassandra database it created in previous step and build cassandra database to optimize this there queries after merge csv files to large csv file build cassandra table to optimize the next queries and in next figure show the attributes needed on each query images capture png query 1 images carbon png to optimize this query build song info by session cassandra table table name song info by session column 1 artist text column 2 song text column 3 length decimal column 4 sessionid int column 5 itemlnsession int primary key sessionid itemlnsession sessionid is a partition key and itemlnsession is cluster key query 2 images carbon2 png to optimize this query build song playing history by user cassandra table table name song playing history by user column 1 artist text column 2 song text column 3 first name text column 4 last name text column 5 sessionid int column 6 itemlnsession int column 7 userid int primary key userid sessionid itemlnsession userid and sessionid are composed partition key and itemlnsession is cluster key it used as cluster key to order the song order descending with itemlnsession query 3 images carbon3 png to optimize this query build who listen to song cassandra table table name who listen to song column 1 userid int column 2 first name text column 3 last name text column 4 song text primary key song user id song is partition key and userid is cluster key dataset working with event data dataset it contaion 30 file contains the history of music streaming app the directory of csv files partitioned by date here are examples of filepaths to two files in the dataset images image event datafile new jpg usage 1 prepare environment install python 2 install apache cassandra you can view cassandra documentation https cassandra apache org doc latest cassandra getting started installing html to install it 3 run project 1b project template ipynb using jupyter notebook or any notebook editor 4 don t forget to close any connection opening files folder even data this folder contains a collection of csv files each file is contain information about history of music streaming app in day folder images this folder contains some images they were used in this repository to illustrate some thang project 1b project template ipynb a python jupyter notebook that was used to reads and processes a data and collect them on one file same eda and detailed instructions on the etl and create and deal with cassandra database libreries and tools tools juputer notebook python apache cassandra text editor python package 1 panads 2 cassandra drive 3 os 4 csv about me i m mohamed bekheet you con browser other repository on my github profile https github com mohamedbakhet and view my linkedin page https www linkedin com in mohamedbekheet and kaggle profile https www kaggle com mohamedbakhet and you can contect with me throgth mohamedbekheet33 gmail com | server |
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dl4nlp | dl4nlp deep learning for natural language processing introduction dl4nlp is a python package inspired by a course at stanford university by richard socher http cs224d stanford edu requirements the recommended environment is anaconda with python 3 4 and pycharm for development https www continuum io https www jetbrains com pycharm for matrix computation it uses only numpy i e you don t need theano or other framework word2vec dl4nlp implements word2vec model skip gram proposed by thomas mikolov at 2013 https code google com archive p word2vec in order to train word vectors use this command bin word2vec py input txt output txt vector size context size nplm neural probabilistic language model bengio 2003 in order to train neural probabilistic language model use this command bin train nplm py input txt output txt vocabulary size context size feature size hidden size iterations in order to predict next word using trained nplm use this command bin predict nplm py output txt here output txt is the output of train nplm py then type context words to standard input unit testing run the following command on the top of project directory python m unittest discover tests | ai |
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RoboCop | robocop discussion forum https img shields io badge discussion googlegroups blue svg https groups google com forum forum robocopai gitter https badges gitter im gitterhq gitter svg https gitter im robocop project robocop version https img shields io badge version 0 0 7 orange svg https github com g10dras robocop tree master build status https travis ci org g10dras robocop svg branch master https travis ci org g10dras robocop codacy badge https api codacy com project badge grade 5d0c9488c3fc444a9e71678ac4ffc8f2 https www codacy com app g10dras robocop utm source github com amp utm medium referral amp utm content g10dras robocop amp utm campaign badge grade coverage status https coveralls io repos github g10dras robocop badge svg branch master https coveralls io github g10dras robocop branch master codecov https codecov io gh g10dras robocop branch master graph badge svg https codecov io gh g10dras robocop requirements status https requires io github g10dras robocop requirements svg branch master https requires io github g10dras robocop requirements branch master license https img shields io aur license yaourt svg style flat square https github com g10dras robocop blob master license python versions https img shields io badge python 2 7 203 4 blue svg https github com g10dras robocop github issues https img shields io badge issues open orange svg https github com g10dras robocop issues q is 3aopen is 3aissue github pull requests https img shields io badge pr open orange svg https github com g10dras robocop pulls q is 3aopen waffle io columns and their card count https badge waffle io g10dras robocop svg columns all https waffle io g10dras robocop say thanks https img shields io badge say 20thanks 1eaedb svg https saythanks io to g10dras artificially intelligent personal assistant with computer vision natural language processing continuos learning sense and feelings no internet required offline speech to text hacking proof man in middle attack proof 100 privacy and zero inconvenience custom voices donate your voice to robocop and robocop will talk in your voice img src images robocop jpg width 1200 height 300 facebook twitter mpd gmail calendar maps weather rss slack time wiki hue img src images facebook png width 40 height 40 img src images twitter png width 40 height 40 img src images mpd png width 40 height 40 img src images gmail png width 40 height 40 img src images gcalendar png width 40 height 40 img src images gmaps png width 40 height 40 img src images openweathermap png width 40 height 40 img src images rss png width 40 height 40 img src images slack png width 40 height 40 img src images time png width 40 height 40 img src images wikipedia png width 40 height 40 img src images hue png width 38 height 38 classroom dictionary sudoku math test exchange marksheet openhab spelling sportnews img src images classroom png width 40 height 40 img src images dictionary png width 40 height 40 img src images sudoku png width 40 height 40 img src images mathtest png width 40 height 40 img src images exchange png width 40 height 40 img src images marksheet png width 40 height 40 img src images openhab png width 40 height 40 img src images spellingtest png width 40 height 40 img src images sportnews png width 40 height 40 imdb jokes stockexch technews telegram tictactoe wolframalfa tankbattle selfie nfc img src images imdb png width 40 height 40 img src images jokes png width 40 height 40 img src images stockexchange png width 40 height 40 img src images technews png width 40 height 40 img src images telegram png width 40 height 40 img src images tictactoe png width 40 height 40 img src images wolfram png width 40 height 40 img src images tankbattle png width 40 height 40 img src images selfie png width 40 height 40 img src images nfc png width 40 height 40 rhymes gesture stocknews prodinfo facialexpressions kali translate jukebox bluetooth img src images rhymes png width 40 height 40 img src images gesture jpg width 40 height 40 img src images stocknews png width 40 height 40 img src images productinfo png width 40 height 40 img src images faceexpression jpg width 40 height 40 img src images kali png width 40 height 40 img src images translate png width 40 height 40 img src images jukebox png width 40 height 40 img src images bluetooth png width 40 height 40 internet radio home assistant horoscope cleverbot santabot aiml alicebot uber speaker img src images internetradio png width 40 height 40 img src images homeassistant png width 40 height 40 img src images horoscope png width 40 height 40 img src images cleverbot png width 40 height 40 img src images santabot png width 40 height 40 img src images aiml png width 40 height 40 img src images alice png width 40 height 40 img src images uber png width 40 height 40 img src images speaker png width 40 height 40 arduino space station forum nasdaq shutdown quote slide game inkspill game glyph control img src images arduino png width 40 height 40 img src images iss png width 40 height 40 img src images forum png width 40 height 40 img src images nasdaq png width 40 height 40 img src images shutdown png width 40 height 40 img src images qotd png width 40 height 40 img src images slide png width 40 height 40 img src images ink png width 40 height 40 img src images glyph jpg width 40 height 40 libraries requirements status https requires io github g10dras robocop requirements svg branch master https requires io github g10dras robocop requirements branch master pocket sphinx festvox opencv matlab tensor flow pulse pyaudio dejavu julius espeak bluez img src images cmusphinx png width 40 height 38 img src images festvox png width 40 height 38 img src images opencv png width 40 height 38 img src images matlab png width 40 height 40 img src images tensorflow png width 40 height 40 img src images pulseaudio png width 40 height 40 img src images pyaudio png width 40 height 40 img src images dejavu png width 40 height 40 img src images julius png width 40 height 40 img src images espeak png width 40 height 40 img src images bluez jpg width 40 height 40 hardware raspberrypi 2b 3 stealth wifi adapter bluetooth adapter bluetooth receiver nodemcu esp8266 img src images raspberrypi png width 80 height 80 img src images wifis jpg width 80 height 80 img src images bluetooth jpg width 80 height 80 img src images bluetoothr jpg width 80 height 80 img src images nodemcu esp8266 jpg width 80 height 80 arduino uno lcd 20x4 3g 4g usb modem sound card power bank wifi adapter img src images arduinouno png width 80 height 80 img src images lcd 20x4 jpeg width 80 height 80 img src images 3gmodem jpg width 80 height 80 img src images soundcard2 jpg width 80 height 80 img src images powerbank jpg width 80 height 80 img src images wifi jpg width 80 height 80 voice text sensors interfaces to robocop high quality microphone bluetooth headset support telegram bot interface voice text facebook messenger bot interface text android app voice img src images mic jpg width 150 height 120 img src images jabrastealth jpg width 150 height 120 img src images telegrambot jpg width 150 height 120 img src images facebookbot png width 150 height 120 img src images robocopapp jpg width 150 height 120 cross plateform desktop browsers voice firefox chrome magic hand with sensors webcam interface voice image video gesture security ip camera remote surveillance img src images firefoxdesktop jpg width 150 height 120 img src images magichand jpg width 150 height 120 img src images sonyps3eye jpg width 150 height 120 img src images ipcamera jpg width 150 height 120 super market helper mode show it a product it will take a snapshot of product and try to locate it in the store for you awesome super market helper img src images beerfinder jpg width 1200 height 220 the singer show it a music sheet of a song it will read it convert sheet music in to the machine readable format and then sing that song like a singer if you donate your voice to robocop it will sing that song in your voice super awesome robocop the singer sample song img src images robosinger jpg width 1000 height 220 img src images wav jpg width 200 height 220 images baabaablacksheep wav sudoku solver solve a sudoku puzzle by looking into paper cutting img src images sudoku jpg width 1200 height 220 img src images sudokusolver jpg width 1200 height 220 speech recognition gramophone play your favorite song by saying song title or play internet radio jazz blue rock pop supported voice commands play pause stop next prev shuffle playall resume playlist speech recognition gramophone gramophone live in action img src images gramophone jpg width 600 height 220 img src images gramophone2 jpg width 600 height 220 speech recognition jukebox srjuke play your favorite song by saying song title or play internet radio jazz blue rock pop supported voice commands play pause stop next prev shuffle playall resume playlist speech recognition jukebox srjuke srjuke live in action youtube img src images bluespeaker jpg width 600 height 220 img src images jukebox jpg width 600 height 220 https www youtube com watch v czvc4g5zuk jukebox control with glyphs jukebox control with glyphs glyphs cards img src images glyphcontrols jpg width 600 height 220 img src images glyphcards jpg width 600 height 220 home automation openhab home automation home automation live in action youtube img src images homeautomation jpg width 600 height 220 img src images hayoutube jpg width 600 height 220 https www youtube com watch v qr8iqsu vlo voice controlled games destroy enemy tank by controlling your furry with your voice tanks battle game furry tanks battle live in action youtube img src images tankgame jpg width 600 height 220 img src images tbyoutube jpg width 600 height 220 https www youtube com watch v xd4wh6suxja ink spill game ink spill game ink spill live in action youtube img src images inkspill jpg width 600 height 220 img src images isyoutube jpg width 600 height 220 https www youtube com watch v hvcnnoyei2c slide puzzle game with magic hand slide puzzle game with magic hand slide puzzle live in action youtube img src images magichand jpg width 600 height 220 img src images spyoutube jpg width 600 height 220 https www youtube com watch v cya6bxto1fc tic tac toe tic tac toe game img src images tictactoe jpg width 1200 height 220 send twitts to twitter sh what would you like to tweet happy new year friends send twitts twitter trends img src images twitts jpg width 1200 height 220 spelling test for kids sh tell me the spelling of elephant e l e p h a n t e l e p h a n t is elephant correct answer keep it up baby spelling test spelling test live in action youtube img src images spellbee jpg width 600 height 220 img src images spellingtest jpg width 600 height 220 https www youtube com watch v 7qgtpxcurja storyteller sh robocop tell me a story robocop the storyteller img src images storyteller jpg width 1200 height 220 animal game for kids sh what you want to see show me an eagle this is an eagle animal picture game img src images eagle jpg width 1200 height 220 live face recognization img src images facerecognition jpg width 300 height 300 draw sketch and poster draw sketch and poster img src images sketchandposter jpg width 1200 height 220 browser based configuration interface hotword configuration openhab home automation img src images hotwordstt jpg width 600 height 300 img src images openhab jpg width 600 height 300 security features live face recognition speaker diarization master s voice in built hotword detection complete offline speech recognition offline stt combination of hand gesture as passkey functionalities party mode load dj mix playlist in automix and connect system to 7 1 channel music system set home lights to party mode load friends faces database and greet them on gate by recognizaing their faces change the song on demand voice operated coffee beer machine hand gesture jukebox control play next stop control jukebox with various glyphs next prev stop play home security mode intrusion detection with infrared sensores motion detection with night vision cameras controlled pnumatic gun loaded with baseballs dial 100 facility dial ambulance if intruder looks injured kids engagement mode robo nanny mother tell the story by scanning pages from paper book or read e book in mother or grandma s orignal voice play rhymes of your choice play wheel on the bus detects kid s facial expression and play rhymes accordingly experimental recognise kids activity playing sleeping crowling seating play picture game play math puzzles and check the answers play spelling bee game and check the spelling play tank game voice commands play tic tc toe voice commands play ink spill game voice commands play slide puzzle game voice commands japanese game janken take picture and print sketch and posters for kids fun activities spy stealth mode rocker wi fi network scanning find today s wifi password etc bluetooth mac finder who is around is she around rfid scanning nfc nfc automation evil twin wireless access point kali linux metasploit payloads automation home automation mode openhab home assistance domoticz phillips hue pi controlled relay search all running services in lan appletv smarttv hue routers general mode facebook birthday notification twitter notification treands send tweets telegram bot telegram updates slack bot wolfram knowledge engine check horoscope for a day internet radio jazz blue pop rock check product info by reading barcodes google calendar search events meetings etc english german spanish jokes world news local new sport news technology news translate one language to another play local music library search song name album random choice currency exchange rate weather forecast today tommorow weekly take selfie photo and send it to your email address as an attachment check emails your portfolio performance on nasdaq news about stocks in your portfolio solve a sudoku puzzle by looking into paper cutting get movie info from imdb database wikipedia search dictionary search take your photo and draw poster out of it and then print it voice driven shopping kart database queries classroom mode scan the student faces and record attendance scan exam omr sheets and create marksheet for student run a knowledge engine for students check the noise level in classroom and students provide assistance to teacher chat conversation mode with custom voices chat with santa chat with alice in wonderland chat with cleverbot chat with drumphbot | speechrecognition natural-language-processing voice-control artificial-intelligence | ai |
itelios-frontend-challenge | itelios http www itelios com br images logo itelios orange 2x png itelios frontend challenge bem vindo ao desafio de admiss o de front end da itelios o que preparamos para este desafio um pedacinho do que voc ir fazer aqui na itelios caso seja admitido o objetivo do desafio simples consumir uma json via xhttp e com o resultado dele montar um widget de prateleira de cross sell na resposta deste request voc receber uma lista de produtos esta lista servir para montar um pequeno carrossel de produtos design a prateleira apresentada deve seguir o seguinte design desafio front end itelios jpg baseado neste layout fa a uma adapta o responsiva para celulares como realizar o teste fa a um fork deste reposit rio em seu github adicione ao readme uma descri o de como executar seu projeto descreva as funcionalidades do seu desafio por exemplo se fez a prateleira com javascript puro por m utilizou uma biblioteca para o carrossel deixe isso no readme se usou es6 com um transpiler conte isso pra gente venda o seu peixe n o h um limite de tempo por m consideramos ideal n o gastar mais que 8h neste desafio fa a commits parciais para que possamos acompanhar o seu desenvolvimento dicas o cone do bot o o add shopping plus do material design https material io icons a fontes utilizadas s o roboto condensed bold t tulos roboto regular texto corrido e roboto bold pre o e pre o parcelado we 3 vanilla javascript tente executar este teste usando javascript puro s utilize bibliotecas como ltimo recurso we 3 css responsivo organizado modular e feito com pr processadores sinta se livre para usar a arquitetura css que achar mais adequada trabalhamos com clientes perfeccionistas portanto tenha aten o com espa amentos tamanhos e estilos de fonte crit rios de avalia o alcan ar os objetivos propostos qualidade de c digo commits parciais mostrando a linha de desenvolvimento boa descri o das funcionalidades do desafio n o utiliza o de bibliotecas ou frameworks fidelidade ao design proposto adapta o mobile | front_end |
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c_steady_term | c steady term steady terminal with frame buffer designed for embedded systems send your frame buffer over serial port and use vt100 compatible terminal as simple display | os |
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MT2TA4-Class-Labs | mt2ta4 class labs this repository preserves and displays all our lab work from our second year embedded systems design course all written in c and used to program stm32f4 microcontrollers completed labs l1 l2 l3 l4 l5 | os |
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astralprotocol | astraldao https github com astraldao overview blob master brand identity transparent logo only astral png raw true twitter https img shields io twitter follow astralprotocol style social https twitter com astralprotocol astral protocol monorepo containing the implementation of the astral protocol project status ropsten testnet hosted powergate build status https www travis ci com astralprotocol astralprotocol svg branch master https www travis ci com astralprotocol astralprotocol project structure this monorepo is made up of several different packages package current version coverage description astralprotocol core npm https img shields io npm v astralprotocol core core astral protocol implementation astralprotocol contracts npm https img shields io npm v astralprotocol contracts coverage status https coveralls io repos github astralprotocol astralprotocol badge svg branch master https coveralls io github astralprotocol astralprotocol branch master astral smart contracts geodid registry astralprotocol subgraph npm https img shields io npm v astralprotocol subgraph subgraph for astral protocol contracts astralprotocol stac validator js npm https img shields io npm v astralprotocol stac validator js node validator for stac item files astralprotocol ipld encoded geotiff npm https img shields io npm v astralprotocol ipld encoded geotiff ipld encoded geotiffs development project setup this project uses yarn and lerna to manage packages and dependencies to install dependencies for all packages in this repo go to root dir and run yarn install then build all packages yarn run build | ethereum typescript stac subgraph powergate filecoin geodid did spatial-data | server |
XVERSE-13B | div align center h1 xverse 13b h1 div p align center a href https huggingface co xverse xverse 13b xverse 13b a nbsp nbsp a href https huggingface co xverse xverse 13b chat xverse 13b chat a nbsp nbsp a href https modelscope cn organization xverse rel nofollow img src resources modelscope png width 20px style max width 100 modelscope a nbsp nbsp a href resources wechat png a p h4 align left p b b a href readme en md english a a href readme ja md a p h4 2023 09 26 7b xverse 7b https github com xverse ai xverse 7b xverse 7b chat https github com xverse ai xverse 7b 2023 08 22 xverse 13b chat xverse 13b large language model xverse 13b decoder only transformer 8k context length 1 4 token 40 bpe byte pair encoding gb 100 278 58 5 mmlu https arxiv org abs 2009 03300 c eval https cevalbenchmark com agieval https arxiv org abs 2304 06364 gaokao bench https github com openlmlab gaokao bench gaokao english https github com expressai ai gaokao mmlu c eval agieval sup 1 sup gaokao bench sup 1 sup gaokao english sup 1 sup baichuan 13b 51 6 sup 2 sup 53 6 sup 3 sup 40 5 45 9 56 9 baichuan 13b chat 52 1 sup 2 sup 51 5 sup 2 sup 34 6 46 7 63 8 chinese alpaca 2 13b 53 2 41 3 36 6 38 4 65 1 llama 1 13b 46 9 sup 4 sup 28 8 27 3 26 4 38 1 llama 2 13b 54 8 sup 4 sup 35 6 33 4 35 4 60 6 moss moon 003 base 16b 24 7 33 1 sup 3 sup 26 8 28 5 34 7 moss moon 003 sft 16b 25 5 33 6 27 6 28 8 29 2 openllama 13b 42 4 24 7 24 0 25 6 33 3 opt 13b 25 2 25 0 24 2 24 4 31 1 pythia 12b 25 1 26 2 25 3 25 3 26 8 vicuna 13b v1 5 53 5 27 9 29 7 31 6 52 9 ziya llama 13b pretrain v1 43 9 30 2 27 2 26 4 37 6 ziya llama 13b v1 1 50 6 29 3 23 6 26 7 27 3 xverse 13b 55 1 54 7 41 4 53 9 66 5 xverse 13b chat 60 2 53 1 48 3 50 7 80 6 sup 1 sup sup 2 baichuan 13b https github com baichuan inc baichuan 13b sup sup 3 c eval https cevalbenchmark com sup sup 4 llama 2 https arxiv org abs 2307 09288 sup mmlu https github com hendrycks test c eval agieval gaokao bench gaokao english mmlu 5 shot mmlu average stem social science humanities others baichuan 13b 51 6 41 6 60 9 47 4 58 5 baichuan 13b chat 52 1 40 9 60 9 48 8 59 0 chinese alpaca 2 13b 53 2 41 8 61 2 51 3 59 2 llama 1 13b 46 9 35 8 53 8 45 0 53 3 llama 2 13b 54 8 44 1 62 6 52 8 61 1 moss moon 003 base 16b 24 7 23 0 24 0 25 2 26 3 moss moon 003 sft 16b 25 5 25 9 23 8 27 1 24 4 openllama 13b 42 4 34 7 48 6 40 0 47 1 opt 13b 25 2 23 9 24 1 25 9 26 3 pythia 12b 25 1 24 8 23 0 26 1 26 0 vicuna 13b v1 5 53 5 42 3 61 3 50 3 60 9 ziya llama 13b pretrain v1 43 9 36 3 48 8 41 1 50 3 ziya llama 13b v1 1 50 6 40 7 57 8 48 1 56 7 xverse 13b 55 1 44 5 64 4 50 5 62 9 xverse 13b chat 60 2 48 1 67 7 56 4 68 0 c eval average stem social science humanities others baichuan 13b 53 6 47 0 66 8 57 3 49 8 baichuan 13b chat 51 5 43 7 64 6 56 2 49 2 chinese alpaca 2 13b 41 3 37 8 51 1 42 4 37 8 llama 1 13b 28 8 27 5 33 9 27 7 27 7 llama 2 13b 35 6 34 5 39 8 36 2 33 2 moss moon 003 base 16b 33 1 31 6 37 0 33 4 32 1 moss moon 003 sft 16b 33 6 31 4 38 6 33 8 32 9 openllama 13b 24 7 25 5 23 5 24 2 24 7 opt 13b 25 0 24 4 24 6 25 9 25 4 pythia 12b 26 2 26 8 25 1 26 7 25 4 vicuna 13b v1 5 27 9 25 4 33 2 29 3 26 2 ziya llama 13b pretrain v1 30 2 27 8 34 3 32 0 29 0 ziya llama 13b v1 1 29 3 27 5 32 8 29 7 29 0 xverse 13b 54 7 45 6 66 2 58 3 56 9 xverse 13b chat 53 1 44 5 65 3 56 5 54 3 1 shell git clone https github com xverse ai xverse 13b cd xverse 13b 2 pip shell pip install r requirements txt transformers xverse 13b chat python import torch from transformers import autotokenizer automodelforcausallm from transformers generation utils import generationconfig model path xverse xverse 13b chat tokenizer autotokenizer from pretrained model path model automodelforcausallm from pretrained model path trust remote code true torch dtype torch bfloat16 device map auto model generation config generationconfig from pretrained model path model model eval history role user content 1955 response model chat tokenizer history print response 1955 d history append role assistant content response history append role user content response model chat tokenizer history print response d 1953 1961 8 demo web server xverse 13b chat shell python chat demo py port port model path path to model tokenizer path path to tokenizer xverse 13b chat chat demo py xverse 13b chat details summary b b summary xverse 13b chat xverse 13b chat xverse 13b chat xverse 13b chat xverse 13b chat xchat transformer 2023 7 details details summary b b summary xverse 13b chat 70 7 1 500 400 details details summary b b summary 5 xverse 13b chat 100 details details summary b b summary python json id conversations from human value from bot value json id data xx xx json xverse 13b chat python json json import json with open file json as fp for i line in enumerate fp start 1 data json loads line data id data str i print json dumps data ensure ascii false json open enumerate json loads id json dumps ensure ascii false ascii details details summary b b summary 3 3 60 xverse 13b chat 3 3 60 540 details details summary b b summary xverse 13b chat details details summary b b summary n n 201 n n n xverse 13b chat details details summary b b summary how many legs does a horse have xverse 13b chat a horse has four legs xverse 13b chat combien de pattes a un cheval xverse 13b chat un cheval a quatre pattes xverse 13b chat 4 details details summary b b summary xverse 13b chat details int8 int4 int8 python model automodelforcausallm from pretrained xverse xverse 13b chat torch dtype torch bfloat16 trust remote code true model model quantize 8 cuda int4 python model automodelforcausallm from pretrained xverse xverse 13b chat torch dtype torch bfloat16 trust remote code true model model quantize 4 cuda mmlu gb mmlu xverse 13b chat bf16 fp16 28 2 60 2 xverse 13b chat int8 16 8 60 3 xverse 13b chat int4 10 9 55 0 xverse 13b xverse 13b chat llama efficient tuning https github com hiyouga llama efficient tuning xverse 13b 8 nvidia a800 80 gb deepspeed llama efficient tuning https github com hiyouga llama efficient tuning getting started model path xverse 13b xverse 13b chat bfloat16 bfloat16 bash deepspeed num gpus 8 src train bash py stage sft model name or path model path do train dataset alpaca gpt4 en template default finetuning type full output dir output model path overwrite cache per device train batch size 4 per device eval batch size 4 gradient accumulation steps 4 preprocessing num workers 16 lr scheduler type cosine logging steps 10 save steps 200 eval steps 200 learning rate 2e 5 max grad norm 0 5 num train epochs 2 0 evaluation strategy steps load best model at end plot loss bf16 padding side right deepspeed deepspeed json deep speed json json train micro batch size per gpu auto gradient accumulation steps auto gradient clipping auto zero allow untested optimizer true bf16 enabled true zero optimization stage 2 allgather partitions true reduce scatter true overlap comm false contiguous gradients true xverse 13b llm xverse 13b xverse 13b xverse 13b apache 2 0 license xverse 13b model license pdf xverse 13b opensource xverse cn | ai |
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Facial-Keypoint-Detection | facial keypoint detection project overview this repository contains project files for computer vision it combine knowledge of computer vision techniques and deep learning architectures to build a facial keypoint detection system that takes in any image with faces and predicts the location of 68 distinguishing keypoints on each face facial keypoints include points around the eyes nose and mouth on a face and are used in many applications facial tracking facial pose recognition facial filters and emotion recognition examples detect all faces using haar cascade classifiers using opencv p align center img src images obamas png align middle alt drawing width 400px p detect facial keypoint with a convolutional neural network p align center img src images obamas detected png align middle alt drawing width 300px p project structure the project will be broken up into a few main parts in four python notebooks models py notebook 1 loading and visualizing the facial keypoint data notebook 2 defining and training a convolutional neural network cnn to predict facial keypoints notebook 3 facial keypoint detection using haar cascades and your trained cnn notebook 4 fun filters and keypoint uses local environment instructions 1 clone the repository and navigate to the downloaded folder this may take a minute or two to clone due to the included image data git clone https github com nalbert9 facial keypoint detection git 2 create and activate a new anaconda environment python 3 6 download via anaconda https www anaconda com distribution linux or mac conda create n cv nd python 3 6 source activate cv nd windows conda create name cv nd python 3 6 activate cv nd 3 install pytorch and torchvision this should install the latest version of pytorch conda install pytorch torchvision cudatoolkit 9 0 c pytorch 6 install a few required pip packages which are specified in the requirements text file including opencv pip install r requirements txt licence this project is licensed under the terms of the license mit https img shields io badge license mit yellow svg https opensource org licenses mit | facial-keypoints facial-landmarks udacity haar-cascades opencv computer-vision pytorch | ai |
MachineLearning | machine learning https github com xxg1413 coursera tree master machine 20learning andrew 20ng 100 octave octave 100 jupyter notebook https github com xxg1413 coursera tree master machine 20learning andrew 20ng jupyter 20notebook numpy pandas scikit learn matplotlib tensorflow python3 12 https github com xxg1413 coursera tree master machine 20learning 20foundations https github com xxg1413 coursera tree master machine 20learning 20techniques cs231n fei fei li andrej karpathy justin johnson https github com xxg1413 coursera tree master cs231n 20convolutional 20neural 20networks 20for 20visual 20recognition 2 1 2 3 geoffrey hinton https github com xxg1413 coursera tree master neural 20networks 20for 20machine 20learning lecture2 octave octave tensorflow https github com xxg1413 machinelearning tree master building 20machine 20learning 20systems 100 https github com xxg1413 machinelearning tree master machine 20learning 20in 20action 2 python numpy https github com xxg1413 machinelearning tree master numpy 20beginner s 20guide 100 numpy python https github com xxg1413 machinelearning tree master learning 20data 20mining 20with 20python chapter3 https github com xxg1413 machinelearning tree master programming 20collective 20intelligence 100 r https github com xxg1413 machinelearning tree master r 20for 20everyone 100 python https github com xxg1413 machinelearning tree master web 20scraping 20with 20python 100 kaggle https github com xxg1413 machinelearning tree master kaggle euler https github com xxg1413 machinelearning tree master euler numpy https github com xxg1413 machinelearning tree master numpy tutorial pandas https github com xxg1413 machinelearning tree master pandas tutorial sklearn https github com xxg1413 machinelearning tree master sklearn tutorial tensorflow https github com xxg1413 tensorflow tree master tutorial spark introduction to apache spark https github com xxg1413 edx tree master introduction 20to 20apache 20spark distributed machine learning with apache spark https github com xxg1413 edx tree master distributed 20machine 20learning 20with 20apache 20spark big data analysis with apache spark | ai |
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origami | origami project goals lowest barrier of entry to image processing computer vision and neural networks on the jvm by using opencv and when possible clojure works out of the box on your device osx windows linux raspberry jetson and in your language clojure java kotlin scala binary via google s bazel clojars project https clojars org origami latest version svg image https circleci com gh hellonico origami svg style svg at a glance full support for opencv 4 7 0 https github com opencv opencv wiki changelog version470 circleci passing https app circleci com pipelines github hellonico origami cuda enabled nvidia support https developer nvidia com embedded jetson nano developer kit included architecture support matrix http origamidocs hellonico info units compatibility id origami 470 compatibility matrix on going support for yolo v6 https github com meituan yolov6 img style height 20px width 20px src doc yolo png alt img style height 16px width 16px src doc doc png alt a href https hellonico github io origami docs origami docs a codox https github com weavejester codox generated api is here http origamidocs hellonico info codox index html ready to use jupyter clojure notebooks https github com hellonico origami fun tree master jupyter java notebooks https github com hellonico opencv4 java tutorial tree master jupyter deep neural networks with origami dnn https github com hellonico origami dnn run on aws lambdas https github com hellonico origami aws lambdas real time streaming server app https github com hellonico opencv live video stream over http with origami sources https github com hellonico origami sources use various sources folders zip files dropbox webcam flickr etc to lazily load opencv mat objects use various handlers you tube https etc to load videocapture objects origami apps booth https github com hellonico opencv javafx camera yololi https github com hellonico yololi streamer https github com hellonico opencv live video stream over http viewer https github com hellonico origami viewer developed with a href https www jetbrains com idea img title idea width 64 height 64 src doc idea png a a href https cursive ide com img title cursive width 64 height 64 src doc cursive png a a href https ko fi com hellonico img title cursive width 140 height 64 src doc ko fi webp a | clojure computer-vision kotlin opencv deep-learning dnn java yolov8 | ai |
bedrock-ui | bedrock ui a bedrock for building design systems documentation https bedrock ui github io bedrock ui license https badgen net npm license bedrock ui core bedrock ui core npm https www npmjs com package bedrock ui core size https img shields io bundlephobia minzip bedrock ui core npm downloads https img shields io npm dt bedrock ui core svg https www npmjs com package bedrock ui core | os |
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plinth-18-teaser | plinth 18 teaser teaser for plinth the techno management literary fest the lnm institute of information technology jaipur | server |
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lfg-nav | navigation with large language models semantic guesswork as a heuristic for planning lfg dhruv shah michael equi b a ej osi ski fei xia brian ichter sergey levine berkeley ai research project page https sites google com view lfg nav home arxiv https arxiv org abs 2310 10103 summary video https youtu be juqyqow7e 4 si iqrkrkgpfvn bujq a href https colab research google com github michael equi lfg demo blob main lfg demo colab ipynb img src https colab research google com assets colab badge svg height 22 5 a introduction this repository contains code used in navigation with large language models semantic guesswork as a heuristic for planning by dhruv shah michael equi b a ej osi ski fei xia brian ichter and sergey levine jupyter experiment ipynb contains the prompts and code used to score the frontiers lfg demo ipynb notebook with examples of using the language scoring to score different frontiers lfg demo colab ipynb colab version of the above notebook you can easily run it in your browser https colab research google com github michael equi lfg demo blob main lfg demo colab ipynb installation to run locally install the package pip install r requirements txt then simply open lfg demo ipynb in jupyter notebook llms apis to use the api locally please provide an api key and organization id in a env file in the root directory of the repository openai api key sk openai org org citation if you find this work useful please consider citing misc shah2023lfg title navigation with large language models semantic guesswork as a heuristic for planning author dhruv shah and michael equi and blazej osinski and fei xia and brian ichter and sergey levine year 2023 eprint 2310 10103 archiveprefix arxiv primaryclass cs ro | ai |
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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 | os |
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spark | spark a classy web framework for rapid development spark is a full stack framework built on top of silex made for rapid development in the same spirit as ruby on rails spark is a framework for people who believe the structure of most applications are nearly the same convention configuration asset management should be shipped out of the box orms solve 80 of problems building a framework is of course a huge project so there isn t nearly all done this is what works for now application generator asset pipeline based on pipe http github com chh pipe with deployment support action controllers with action and view helpers built on traits and support for multiple templating strategies basic framework for generators extensible command line utility based on symfony console silex http silex sensiolabs org quick start create an application first get the spark executable wget http chh github com spark spark phar then invoke the create command and grab some coffee while composer installs all dependencies php spark phar create myapp go into the application directory and start a development server cd myapp vendor bin spark server then open http localhost 3000 in your browser you should see hello world in big letters controllers controllers make your application do things when someone views it in their browser controllers go into the directory app controllers and are best generated by the generate controller command vendor bin spark generate controller user if you open up app controllers myapp usercontroller php you should see this php php namespace myapp class usercontroller function indexaction each controller consists of actions each action is a public method of the controller class and ends with action the html goes into a view the view s file name gets taken from the controller and action name for example for the index action in the usercontroller the view user index phtml gets used put this into app views user index phtml html h1 hello world this name h1 the view has access to each property of the controller class this allows you to pass variables from the controller to the view also add the parameter name to the method we will need it later php php namespace myapp class usercontroller function indexaction name this name name the last part of getting our controller to do something remotely useful is to add a route a route connects a uri to a controller and action routes are configured in config routes php this will do for now php routes match user name user index that thing within the curly braces is a variable which gets extracted from the uri and then gets assigned the name name we ve previously declared that our action needs an argument name so spark figures this out and passes the route variable along to the action last but not least we assign the controller and action to the route this is done with controller action controller names are converted from under score to underscore and then suffixed with controller so user gets transformed to usercontroller if you open http localhost 3000 user john 20doe in your browser you should see hello world john doe in big letters license copyright c 2012 christoph hochstrasser permission is hereby granted free of charge to any person obtaining a copy of this software and associated documentation files the software to deal in the software without restriction including without limitation the rights to use copy modify merge publish distribute sublicense and or sell copies of the software and to permit persons to whom the software is furnished to do so subject to the following conditions the above copyright notice and this permission notice shall be included in all copies or substantial portions of the software the software is provided as is without warranty of any kind express or implied including but not limited to the warranties of merchantability fitness for a particular purpose and noninfringement in no event shall the authors or copyright holders be liable for any claim damages or other liability whether in an action of contract tort or otherwise arising from out of or in connection with the software or the use or other dealings in the software | front_end |
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caRtosm | cartographic explorations of the openstreetmap database with r https rcarto github io cartosm index html img barsosm png br blog post https rgeomatic hypotheses org p 1244 | cartography maps openstreetmap r | os |
ml-system-design | machine learning system design an interview framework interviewers will generally ask you to design a machine learning system for a particular task this question is usually broad the first thing you need to do is to ask questions to narrow down the scope of the problem and ensure your system s requirements you should also ask questions about performance and capacity considerations of the system images de ds ml png figure from pooyan jamshidi uofsc csce 585 machine learning systems https pooyanjamshidi github io mls lectures images building blocks png figure from https developers google com machine learning crash course production ml systems overview 1 clarify requirements 2 how the ml system fits into the overall product backend 3 data related activities 4 model related activities 5 scaling requirements goals functional requirements what is the goal any secondary goal e g for ctr maximizing the number of clicks a secondary goal might be the quality of the ads content batch prediction hourly weekly etc processing accumulated data when you don t need immediate results e g recommendation high throughput online prediction as soon as requests come when predictions are needed as soon as data sample is generated e g fraud detection low latency non functional requirements 1 reliability ml system fails may not give an error give garbage outputs 2 scalability ask questions about the scale of the system how many users how much content 3 maintainability data distribution might get changed how to re train and update the model 4 adaptability new data with added features changes in business objectives should be flexible overview data related activities 1 data explore what the dataset looks like 2 understand different features and their relationship with the target is the data balanced is there a missing value not an issue for tree based models is there an unexpected value for one more data columns how do you know if it s a typo etc and decide to ignore it 3 feature importance partial dependency plot shap values 4 ml pipeline think of data ingestion services storage 5 ml pipeline feature engineering encoding categorical features embedding generation etc 6 ml pipeline data split train set validation set test set embeddings enable us to encode entities e g words docs images person in a low dimensional vector space in order to capture their semantic information model related activities 1 ml pipeline model train and evaluation how to select a model and hyperparameters 2 ml pipeline model train and evaluation once the model is built do a bias variance tradeoff it will give you an idea of overfitting vs underfitting you need different approaches to make your model better 3 draw the ml pipeline images classic setup png 4 model debug 5 model deployment 6 ml pipeline performance monitoring metrics auc f1 mse accuracy ndcg for ranking problems etc when to use which metrics you should carefully choose your system s performance metrics for both online and offline testing these metrics will differ depending on the problem your system is trying to solve for example if you are performing binary classification you will use the following offline metrics area under curve auc log loss precision recall and f1 score when deciding on online metrics you may need both component wise and end to end metrics component wise metrics are used to evaluate the performance of ml systems that are plugged in to and used to improve other ml systems end to end metrics evaluate a system s performance after an ml model has been applied for example a metric for a search engine would be the users engagement and retention rate after your model has been plugged in offline experiment model training challenge different use cases in different teams not the same features not the same model settings model training steps should be configurable by using configuration objects formalizing the testing protocol keeping track of the experiment results evaluation metrics tp fp tn fn confusion matrix accuracy precision recall sensitivity specificity f score how do you choose among these imbalanced datasets roc curve tpr vs fpr threshold selection auc model comparison architecture the next step is to design your system s architecture you need to think about the system s components and how the data will flow through those components in this step you aim to design a model that can scale easily images architectural ml system png model serving embedded model model deployed as a separate service model published as data testing and quality in ml unit tests to check features are calculated correctly numeric is normalized one hot vector missing value test that the exported model still produces the same results offline vs online validating the model quality collecting and monitoring metrics validating model bias and fairness integration test distribute a holdout dataset along with model and allow to reassess the model s performance against the holdout dataset after it is integrated model deployment multiple models more than one model performing the same task shadow models deploy the new model side by side with the current one send the dame production traffic to gather data on how the shadow model performs before promoting it into the production competing models 1 multiple versions of the model in production like an a b test take some time to gather enough data to make statistically significant decisions 2 evaluating multiple competing models is multi armed bandits online learning models constantly learning in production extra complexities version model and data orchestration in ml pipelines provisioning of infrastructure and the execution of the ml pipelines to train models and capture metrics building testing and deploying data pipelines testing and validation to decide which models to promote provisioning of infrastructure and deployment of models to production scaling to build a scalable system your design needs to efficiently deal with a large and continually increasing amount of data scaling for increased demand same as in distributed systems scaling web app and serving system data partitioning data parallelism model parallelism for instance an ml system that displays relevant ads to users can t process every ad in the system at once you could use the funnel approach wherein each stage has fewer ads to process this will yield a scalable system that quickly determines relevant ads for users despite the increase in data real world examples machine learning platforms platform md ad click prediction for social networks ad md cota improving uber customer care with nlp machine learning cota md facebook newsfeed architecture newsfeed md how machine learning powers facebook s news feed ranking algorithm ranking md using approximate nearest neighbor search in real world applications ann md reference machine learning system design a framework for the interview day https leetcode com discuss interview question system design 566057 machine learning system design 3a a framework for the interview day a look at machine learning system design https www analyticsvidhya com blog 2021 01 a look at machine learning system design cracking the machine learning interview system design approaches https www educative io blog cracking machine learning interview system design machine learning for adtech in action with cyrille dubarry and han ju https www slideshare net databricks machine learning for adtech in action with cyrille dubarry and han ju designing computer systems for machine learning https pooyanjamshidi github io mls lectures | os |
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wasp-software-course | wasp software course code for assignment 2 in the wasp software engineering and cloud computing course kristoffer bergman per bostr m and shervin parvini wasp software course this repository contains code for assignment 2 in the wasp software engineering and cloud computing course with some java source code see src main java some junit tests see src test java an ant build file with targets for build test jar and clean see build xml the source files are organized according to the maven standard directory layout see https maven apache org guides introduction introduction to the standard directory layout html this facilitates integration with other tools like the continuous integration tool travis we don t use any dependencies to download libraries instead all dependent libraries are in included in the project in the lib folder installation to run this project you need to have java and ant installed do ruby e curl fssl https raw githubusercontent com homebrew install master install to install homebrew package manager brew install ant to install ant build build status https travis ci org kribe48 wasp software course svg branch master https travis ci org kribe48 wasp software course do ant to compile ant test to additionally run all tests ant jar to generate a product jar file ant clean to clean away generated files run run the generated jar file by java jar product jar | cloud |
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Awesome-NLP-Resources | awesome nlp resources awesome https awesome re badge svg https github com robofied awesome nlp resources maintenance https img shields io badge maintained 3f yes green svg https github com akshat4112 awesome nlp resources graphs commit activity mit license https img shields io badge license mit blue svg https lbesson mit license org this repository contains landmark research papers and blogs in natural language processing that came out in this century contents how to read a paper how to read a paper list of blogs list of blogs machine translation machine translation language models language models image to text image to text transformers transformers list of reasearch papers list of reasearch papers machine translation machine translation image to text image to text transformers transformers how to read a paper page with curl reading a paper is not the same as reading a blogpost or a novel here are a few handy resources to help you get started how to read an academic article https organizationsandmarkets com 2010 08 31 how to read an academic article br advice on reading academic papers https www cc gatech edu akmassey posts 2012 02 15 advice on reading academic papers html br how to read and understand a scientific paper https violentmetaphors com 2013 08 25 how to read and understand a scientific paper 2 br should i read papers http michaelrbernste in 2014 10 21 should i read papers html br the refreshingly rewarding realm of research papers https www youtube com watch v 8erx5wo3xya br list of research papers machine translation sequence to sequence learning with neural network https papers nips cc paper 5346 sequence to sequence learning with neural networks pdf lstmn based approach for sequence problems learning phase representations using rnn encoder decoder for statistical machine translation https arxiv org pdf 1406 1078 pdf attention model neural machine translation by jointly learning to align and translate https arxiv org pdf 1409 0473 pdf attention model architecture modified version for encoder decoder models don t confuse with i b attention is all you need b paper i transformers i e for transformers concept understanding back translation at scale https arxiv org pdf 1808 09381 pdf muse parallel multi scale attention for sequence to sequence learning https arxiv org abs 1911 09483 scaling neural machine translation https arxiv org abs 1806 00187 the best of both worlds combining recent advances in neural machine translation https arxiv org abs 1804 09849 convolutional sequence to sequence learning https arxiv org abs 1705 03122 modified attention model with convolutional layer language models scalable hierarchical distributed language model https www cs toronto edu amnih papers hlbl final pdf bag of tricks for efficient text classification https arxiv org pdf 1607 01759 pdf fasttext by facebook ai research trained on billion of words for text classification language models are unsupervised multitask learners https d4mucfpksywv cloudfront net better language models language models are unsupervised multitask learners pdf hierarchical probabilistic neural network language model https www iro umontreal ca lisa pointeurs hierarchical nnlm aistats05 pdf speed up training and recogintion by 200 yoshua bengio word embeddings distributed representations of sentences and documents https cs stanford edu quocle paragraph vector pdf sentence document to vectors by tomas mikolov by google distributed representations of words and phrases and their compositionality https papers nips cc paper 5021 distributed representations of words and phrases and their compositionality pdf word2vec representation by tomas mikolov google efficient estimation of word representations in vector space https arxiv org pdf 1301 3781 pdf high quality vector representation from huge data sets by tomas mikolov google deep contextualized word representations https arxiv org pdf 1802 05365 pdf based on deep birectional language model by allen institute for artificial intelligence enriching word vectors with subword information https arxiv org pdf 1607 04606 pdf handles morphology and generates vectors for words not present in training dataset by facebook ai research img src fb icon png width 20 height 20 misspelling oblivious word embeddings https arxiv org abs 1905 09755 image to text neural image caption generation with visual attention https arxiv org pdf 1502 03044 pdf deep visual semantic alignments for generating image descriptions https cs stanford edu people karpathy cvpr2015 pdf transformers attention is all you need https arxiv org abs 1706 03762 bert pre training of deep bidirectional transformers for language understanding https arxiv org pdf 1810 04805 pdf multimodal recurrent neural network architecture for image description by andrej kaparthy img src andreaj svg width 20 height 20 http karpathy github io and le fei fei list of blogs machine translation google machine translation blog https ai googleblog com 2016 09 a neural network for machine html email autoreply and auto suggestion https ai googleblog com 2018 05 smart compose using neural networks to html find code errors and repair https medium com martin monperrus sequence to sequence learning program repair e39dc5c0119b image to text image captioning using keras https towardsdatascience com image captioning with keras teaching computers to describe pictures c88a46a311b8 transformers the illustrated transformer http jalammar github io illustrated transformer transformers research paper core details explained by jalammar http jalammar github io the illustrated bert http jalammar github io illustrated bert bert is explained by jalammar http jalammar github io a visual guide to using bert for the first time boom http jalammar github io a visual guide to using bert for the first time very beautifully explained bert architecture with the help of visuals back to top contents | nlp natural-language-processing machine-learning artificial-intelligence bert papers language language-models vectors | ai |
elastic-cloud-engineering | elastic cloud engineering demo repository this is the demo repository for my elastic cloud engineeering talk at the aws meetup in amsterdam installing dependencies bundle install build cloudformation template rake | cloud |
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trekhleb.github.io | trekhleb dev ci https github com trekhleb trekhleb github io workflows ci badge svg https github com trekhleb trekhleb github io actions query workflow 3aci branch 3amaster my personal website https trekhleb dev with a list of my projects that help people learn and blog posts about life web development and machine learning trekhleb dev https trekhleb dev static assets images site meta image 01 png development the website is built on gatsby https www gatsbyjs com see the gatsby cheat sheet https www gatsbyjs com gatsby cheat sheet pdf for development hints running the project locally install proper version of node bash nvm use install dependencies bash npm i to run the project locally in development mode on http localhost 8000 http localhost 8000 bash npm run develop view graphiql an in browser ide to explore your site s data and schema on http localhost 8000 graphql https localhost 8000 graphql to build the production version of the project bash npm run build serve the production build for testing on http localhost 9000 http localhost 9000 bash npm run serve to do eslint and type checking bash npm run lint npm run type working with icons use react icons github io https react icons github io react icons to search for available icons styling components use tailwindcss com https tailwindcss com to search for available css classes custom domain support to serve the trekhleb github io https trekhleb github io web site on a custom domain trekhleb dev https trekhleb dev make sure that the public branch has cname file in the root folder for more details read this https docs github com en free pro team latest github working with github pages configuring a custom domain for your github pages site | portfolio personal-website gatsby react reactjs typescript javascript tailwindcss | front_end |
blockcore | p align center p align center img src https avatars3 githubusercontent com u 53176002 s 200 v 4 height 100 alt blockcore p h3 align center about blockcore h3 p align center open source net bitcoin based blockchain node in c p p align center a href https github com block core blockcore actions img src https github com block core blockcore workflows build badge svg a a href https github com block core blockcore actions img src https github com block core blockcore workflows publish 20release 20packages badge svg a p p do you want to build your own blockchain based upon blockcore then check out our blockcore node repository https github com block core blockcore node which is the best starting point for most custom blockchains introduction bitcoin implementation in c and net 6 what is blockcore blockcore is a platform to build layer 1 consensus networks based on the bitcoin protocol built on the net core https dotnet github io framework and written entirely in c blockcore aims to maintain an alternative c bitcoin implementation based on the nbitcoin https github com metacosa nbitcoin stratis https github com stratisproject stratisbitcoinfullnode projects blockcore is neither a coin or a for profit business why blockcore we see a need within the crypto ecosystem for development of the c full node technology stratis https github com stratisproject stratisbitcoinfullnode has provided an excellent starting point but their focus is enterprise and business we feel strongly that there is significant value focusing on open public blockchains using open source software blockcore objectives continue development of the c stratis fullnode maintain the c bitcoin fullnode support projects and teams that use the underlying technology extend the technology by building developer and user tools provide a forum for developers and teams to collaborate and improve on the technology build relationships with potential sponsors partners to help boost the pace of development blockcore principles we help each other and all projects that utilise the underlying technology we encourage contribution to the blockcore open source software we aim to make it easier for everyone to contribute to the ecosystem we encourage projects to adopt blockcore technology as we believe every project has something to offer and help make the technology stronger join our community on discord https www blockcore net discord getting started guide more details on getting started are available here https github com block core blockcore blob master documentation development if you are up for some blockchain development check this guides for more info contributing guide documentation contributing md coding style documentation coding style md there is a lot to do and we welcome contributions from developers and testers who want to gain some blockchain experience you can find tasks on the issues https github com block core blockcore issues tab or visit us on discord https www blockcore net discord other points testing guidelines documentation testing guidelines md official website https www blockcore net | blockchain |
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celo-blockchain | celo blockchain official golang implementation of the celo blockchain based off of the official golang implementation of the ethereum protocol https github com ethereum go ethereum circleci https img shields io circleci build github celo org celo blockchain master https circleci com gh celo org celo blockchain tree master codecov https img shields io codecov c github celo org celo blockchain https codecov io gh celo org celo blockchain discord https img shields io badge discord join 20chat blue svg https chat celo org prebuilt docker https en wikipedia org wiki docker software images are available for immediate use us gcr io celo org geth https us gcr io celo org geth see docs celo org getting started https docs celo org getting started choosing a network for a guide to the celo networks and how to get started documentation for celo more generally can be found at docs celo org https docs celo org most functionality of this client is similar to go ethereum also known as geth from which it was forked if you do not find your question answered by celo specific documentation try searching the geth wiki https github com ethereum go ethereum wiki building the source building geth requires both go min version 1 19 and a c compiler you can install them using your favourite package manager once the dependencies are installed run shell make geth or to build the full suite of utilities shell make all mobile clients there are two different commands in the makefile to build the ios and the android clients shell make ios and shell make android note the android command it applies a git patch patches mobilelibsforbuild patch required to swap some libs from the go mod for the client to work installs those libs builds the client and then reverts the patch executables the celo blockchain client comes with several wrappers executables found in the cmd directory command description geth the main celo blockchain client it is the entry point into the celo network capable of running as a full node default archive node retaining all historical state light node retrieving data live or lightest node retrieving minimum number of block headers to verify existing validator set it can be used by other processes as a gateway into the celo network via json rpc endpoints exposed on top of http websocket and or ipc transports geth help and the ethereum cli wiki page https github com ethereum go ethereum wiki command line options for command line options abigen source code generator to convert celo contract definitions into easy to use compile time type safe go packages it operates on plain ethereum contract abis https github com ethereum wiki wiki ethereum contract abi with expanded functionality if the contract bytecode is also available however it also accepts solidity source files making development much more streamlined please see ethereum s native dapps https github com ethereum go ethereum wiki native dapps go bindings to ethereum contracts wiki page for details bootnode stripped down version of the celo client implementation that only takes part in the network node discovery protocol but does not run any of the higher level application protocols it can be used as a lightweight bootstrap node to aid in finding peers in private networks evm developer utility version of the evm ethereum virtual machine that is capable of running bytecode snippets within a configurable environment and execution mode its purpose is to allow isolated fine grained debugging of evm opcodes e g evm code 60ff60ff debug run gethrpctest developer utility tool to support the ethereum rpc test https github com ethereum rpc tests test suite which validates baseline conformity to the ethereum json rpc https github com ethereum wiki wiki json rpc specs please see the ethereum test suite s readme https github com ethereum rpc tests blob master readme md for details rlpdump developer utility tool to convert binary rlp recursive length prefix https github com ethereum wiki wiki rlp dumps data encoding used by the celo protocol both network as well as consensus wise to user friendlier hierarchical representation e g rlpdump hex ce0183ffffffc4c304050583616263 running tests prior to running tests you will need to run make prepare this will run two sub rules without first running this make prepare certain tests will fail prepare system contracts this will shallow checkout the celo monorepo https github com celo org celo monorepo under celo blockchain monorepo checkout relative to this project s root at the commit defined in the file monorepo commit then it will compile the system contracts for use in full network tests the rule will copy the compiled contracts from celo monorepo to compiled system contracts if you subsequently edit the system contracts source or monorepo commit running the make rule again will re checkout the monorepo re compile the contracts and copy them into place monorepo commit may contain a commit hash or a tag branch names are forbidden in the case that you would like to change the default monorepo checkout location or that you would like to have multiple checkouts of the monorepo at different versions you can set the monorepo path variable in the make command for example make prepare system contracts monorepo path alt monorepo note that monorepo path should not be set to point at checkouts other than those checked out by the prepare system contracts rule and the checkouts created by the prepare system contracts rule should not be manually modified aside from changing the contract source prepare ethersjs project this will install dependencies for the ethersjs api check typescript project running celo please see the docs celo org getting started https docs celo org getting started choosing a network for instructions on how to run a node connected to the celo network using the prebuilt docker image going through all the possible command line flags is out of scope here please consult geth help for more complete information we ve enumerated a few common parameter combos to get you up to speed quickly on how you can run your own celo blockchain client instance full node on the main celo network by default the celo client will connect to the mainnet running the following command will create a full node that will sync with the celo network and allow access to all of its functionality shell geth console this command will start geth in full sync mode which will download and execute all historical block information start up geth s built in interactive javascript console https github com ethereum go ethereum wiki javascript console via the trailing console subcommand through which you can invoke all official web3 methods https github com ethereum wiki wiki javascript api as well as geth s own management apis https github com ethereum go ethereum wiki management apis this tool is optional and if you leave it out you can always attach to an already running geth instance with geth attach a full node on the alfajores test network smart contract developers will be most interested in the alfajores testnet on alfajores you can receive testnet celo through the alfajores faucet https celo org developers faucet and deploy smart contracts in an environment very similar to mainnet more information about the alfajores testnet can be found on docs celo org https docs celo org getting started alfajores testnet shell geth alfajores console note although there are some internal protective measures to prevent transactions from crossing over between the main network and test network you should make sure to always use separate accounts for testnet tokens and real tokens unless you manually move accounts geth will by default correctly separate the two networks and will not make any accounts available between them full node on the baklava test network validators and full node operators will be most interested in the baklava testnet on baklava you can receive a distribution of testnet celo gold to become a validator on the network and test out running a validator for the first time or try out new infrastructure more information about the baklava testnet can be found on docs celo org https docs celo org getting started baklava testnet a full guide to getting started as a validator on baklava can be found in the getting started guides https docs celo org getting started baklava testnet running a validator in baklava shell geth baklava console configuration as an alternative to passing the numerous flags to the celo binary you can also pass a configuration file via shell geth config path to your config toml to get an idea of how the file should look like you can use the dumpconfig subcommand to export your existing configuration shell geth your favourite flags dumpconfig programmatically interfacing geth nodes as a developer sooner rather than later you ll want to start interacting with geth and the celo network via your own programs and not manually through the console to aid this geth has built in support for a json rpc based apis standard apis https github com ethereum wiki wiki json rpc and geth specific apis https github com ethereum go ethereum wiki management apis these can be exposed via http websockets and ipc unix sockets on unix based platforms and named pipes on windows the ipc interface is enabled by default and exposes all the apis supported by geth whereas the http and ws interfaces need to manually be enabled and only expose a subset of apis due to security reasons these can be turned on off and configured as you d expect http based json rpc api options http enable the http rpc server http addr http rpc server listening interface default localhost http port http rpc server listening port default 8545 http api api s offered over the http rpc interface default eth net web3 http corsdomain comma separated list of domains from which to accept cross origin requests browser enforced ws enable the ws rpc server ws addr ws rpc server listening interface default localhost ws port ws rpc server listening port default 8546 ws api api s offered over the ws rpc interface default eth net web3 ws origins value origins from which to accept websockets requests graphql enable graphql on the http rpc server note that graphql can only be started if an http server is started as well graphql corsdomain value comma separated list of domains from which to accept cross origin requests browser enforced graphql vhosts value comma separated list of virtual hostnames from which to accept requests server enforced accepts wildcard default localhost ipcdisable disable the ipc rpc server ipcapi api s offered over the ipc rpc interface default admin debug eth miner net personal shh txpool web3 ipcpath filename for ipc socket pipe within the datadir explicit paths escape it you ll need to use your own programming environments capabilities libraries tools etc to connect via http ws or ipc to a geth node configured with the above flags and you ll need to speak json rpc https www jsonrpc org specification on all transports you can reuse the same connection for multiple requests note please understand the security implications of opening up an http ws based transport before doing so hackers on the internet are actively trying to subvert celo nodes with exposed apis further all browser tabs can access locally running web servers so malicious web pages could try to subvert locally available apis contribution thank you for considering to help out with the source code we welcome contributions from anyone on the internet and are grateful for even the smallest of fixes if you d like to contribute to celo blockchain please fork fix commit and send a pull request for the maintainers to review and merge into the main code base if you wish to submit more complex changes though please check up with the core devs first on the official celo forum https forum celo org c protocol to ensure those changes are in line with the general philosophy of the project and or get some early feedback which can make both your efforts much lighter as well as our review and merge procedures quick and simple please make sure your contributions adhere to our coding guidelines code must adhere to the official go formatting https golang org doc effective go html formatting guidelines i e uses gofmt https golang org cmd gofmt code must be documented adhering to the official go commentary https golang org doc effective go html commentary guidelines pull requests need to be based on and opened against the master branch commit messages should be prefixed with the package s they modify e g eth rpc make trace configs optional submitting an issue if you come across a bug please open a github issue https github com celo org celo blockchain issues new with information about your build and what happened ci testing and automerge we run a circle ci test suite on each pr the following tests are required to merge a pr unit tests make test or build env sh go run build ci go test lint make lint fix go format errors with gofmt s build make end to end sync and transfer tests check imports scripts check imports sh celo blockchain is based on go ethereum but the import path has been renamed from github com ethereum go ethereum to github com celo org celo blockchain developers are encouraged to run scripts setup git hooks sh to enable checking that import path has been changed to celo org on git merge and git commit imports can automatically be renamed with scripts rename imports sh individual package tests can be run with build env sh go test github com celo org celo blockchain path to go package if you don t have gopath set up once a pr is approved adding on the automerge label will keep it up to date and do a squash merge once all the required tests have passed benchmarking golang has built in support for running benchmarks with go tool go test run thisisnotatestname bench package name will run all benchmarks in a package one note around running benchmarks is that benchmarkhandlepreprepare is quite takes a while to run particularly when testing with a larger number of validators substituting bench regex for bench will specify which tests to run adding cpuprofile cpu out which can be visualized with go tool pprof html 8080 cpu out if graphviz is installed see the go testing flags https golang org cmd go hdr testing flags and go docs https golang org pkg testing hdr benchmarks for more information on benchmarking license the celo blockchain library i e all code outside of the cmd directory is licensed under the gnu lesser general public license v3 0 https www gnu org licenses lgpl 3 0 en html also included in our repository in the copying lesser file the celo blockchain binaries i e all code inside of the cmd directory is licensed under the gnu general public license v3 0 https www gnu org licenses gpl 3 0 en html also included in our repository in the copying file | blockchain hacktoberfest web3 | blockchain |
image-processing-projects | image processing and computer vision a place for my smaller projects experiments in image processing computer vision and computational photography anomoly detection for watch faces watch faces this project was a proof of concept for a small watch company with the goal of using computer vision to detect errors in the manufacturing process of watches an image is taken of a watch face and analyzed for errors in its design these errors can include things like misplaced numbers unwanted color variations misalignment of features on the watch etc my algorithm works by comparing a positive example of a watch pre determined to be manufactured correctly with the new query watch the images are aligned with opencv s sift detector matches are filtered using a custom statistcal model based of a gaussian kernel density estimation of the sift transformation features a homography transform is aligned so all of the watch features should line up if both watches are identical if the query watch has errors they will become immediately visible by comparison once the two images are aligned they are normalized and blurred gaussian and subtracted from eachother to visualize the differences these differences are highlighted for easy localization and detection future work of this project includes automation of a pipeline to capture the images and sort the watches in the examples below the top image shows a watch face with errors denoted by the highlighted iii which is missing the second exmaple is a perfect watch and does not show this error watch face with errors img src watch faces img error jpg width 1000 alt raw watch face without errors img src watch faces img no error jpg width 1000 alt raw cell counting with hough circles cell counting for my work with the synethetic biology company amyris i built an in depth pipeline to detect cells on a 96 well plate agar dish this was for a project which involved a larger pipeline of cell counting and colony picking with a robot i included parameters to filter the desired radius size of the cell the separation of cells within each plate close by cells can otherwise be incorrecly grouped as the same colony even though they have genetic differences this avoids that my algorithm was 4 times faster and improved average cell localization accuracy to 93 previously 79 it was based on using opencv hough circles as the shape finding algorithm with a whole lot of numpy to itemize the well plates and cell ranking img src cell counting img disp png width 1000 alt raw optical heart rate recognition via webcam optical heart rate this software uses real time computer vision to measure heart rate from changes in optical intensity measured via a webcam this is a sort of ensemble implementation drawing from software written by others in academia and as a hobby specifically i draw heaviest from the techniques of ming zher poh et al while making the processing lighter weight and more readable the technique uses feature extraction independent component analysis ica and a fast fourrier transform to detect heart rate img src optical heart rate img disp png width 900 alt raw img src optical heart rate img ica png width 900 alt raw ascii characters to pixels ascii mosaic this code is inspired from the bitsofcode https bitesofcode wordpress com 2017 01 19 converting images to ascii art part 1 blog post on converting pixel values of an image to ascii characters i built a similar processor using my own technique in python i also went one step further and created a method to convert specifc text in my case a speech transcript to create map pixel intensities to specific text the results are quite cool p align center img src ascii mosaic obama obama ascii compressed gif width 600 alt raw p video background removal with singular value decompostion background removal this notebook shows the use of singular value decomposition for the purpose of background removal from a survelliance video stream the project idea originally comes from rachel thomas fastai course for computational linear algebra chapter 3 https github com fastai numerical linear algebra the dataset comes from the bmc 2012 background models challenge dataset http bmc iut auvergne com page id 24 img src background removal output png width 900 alt raw math gifs math gifs because math is beautiful p align center img src math gifs toroid revolver toroid compressed gif width 400 alt raw p seam carving https github com momonala imaging and vision tree master seam carving an experiment with a technique that looks like magic content aware image resizing a technique invented by avidan and shamir from mitsubishi electric research labs in 2007 p align center a href https www youtube com watch v givqbkqdsgs target blank img src seam carving img png alt width 700 border 10 a p i used python s opencv and scikit image to build a few seam carving algroithms the first seam carving slider py is a tool that allows the user to upload an image and use a trackbar to compress the image as desired my second algorthim seam carving iter py is a loop implementation that allows the user to input a percent of the original image to compress into and save consecutive images as one seam is removed per iteration my algorithm takes in inputs for direction compression ratio and number of seams per iteration hardcoded is the energy mapping function which does gaussian smoothing and measures the sobel gradient magnitude in opencv much faster than scikit in the loop i recursively carve scikit and recompute the energy map which makes the seam cutting more seamless image layering with open source nasa map images earth layers the code works as a series of transformations and image overlays on equirectangular maps i used population data and earth at night lights data from nasa s image repo visible earth https www visibleearth nasa gov you can adjust the parameters of one image to highlight certain aspects of the image data and overlap images to see how population and light interact p align center img src earth layers earth layers2 jpg width 800 alt combined image p cython ising model cython ising following along a tutorial posting for cython reference in the future | ai |
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Advanced-Computer-Vision-Projects | advanced computer vision projects video this is the code repository for advanced computer vision projects video https www packtpub com big data and business intelligence advanced computer vision projects video utm source github utm medium repository utm campaign 9781788620772 published by packt https www packtpub com utm source github it contains all the supporting project files necessary to work through the video course from start to finish about the video course python s wealth of powerful packages along with its clear syntax make state of the art computer vision and machine learning accessible to developers with a variety of backgrounds this video course will equip you with the tools and skills to utilize the latest and greatest algorithms in computer vision making applications that weren t possible until recent years in this course you ll continue to use tensorflow and extend it to generate full captions from images later you ll see how to read text from license plates from real world images using google s tesseract software finally you ll see how to track human body poses using deepercut within tensorflow at the end of this course you ll develop an application that can estimate human poses within images and will be able to take on the world with best practices in computer vision with machine learning h2 what you will learn h2 div class book info will learn text ul li apply lstms to automated image captioning li know how to read text from real world images li see how to extract human pose data from images li understand the tensorflow workflow model li ul div instructions and navigation assumed knowledge to fully benefit from the coverage included in this course you will need br this video course is for python developers who wish to learn the latest cutting edge algorithms to solve computer vision problems that were impossible until recently technical requirements this course has the following software requirements br this course has the following software requirements this course has been tested on the following system configuration os windows 10 processor intel i7 4th generation mobile memory 32 gb hard disk space 1 tb video card geforce gtx 970m related products computer vision projects with python 3 video https www packtpub com big data and business intelligence computer vision projects python 3 video utm source github utm medium repository utm campaign 9781788835565 real world machine learning projects with scikit learn video https www packtpub com big data and business intelligence real world machine learning projects scikit learn video utm source github utm medium repository utm campaign 9781789131222 java machine learning for computer vision video https www packtpub com big data and business intelligence java machine learning computer vision video utm source github utm medium repository utm campaign 9781789130652 | ai |
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liquid | liquid join us on github discussions https img shields io badge join 20us on 20github 20discussions blue style flat color 0f69af https github com emdgroup liquid liquid discussions join us on teams https img shields io badge join 20us on 20teams blue style flat color 503291 https teams microsoft com l channel 19 3aeae3b35b0cbf42659e45c2b5592e0c0e 40thread tacv2 general groupid 88f23881 53e2 4a99 ad5c 8188c1087bbf tenantid db76fb59 a377 4120 bc54 59dead7d39c9 semantic release https img shields io badge 20 20 f0 9f 93 a6 f0 9f 9a 80 semantic release e10079 svg style flat color b93679 https github com semantic release semantic release browser support https img shields io static v1 label browsers message evergreen color 01884c https caniuse com search css variables coverage status https coveralls io repos github emdgroup liquid liquid badge svg branch main https coveralls io github emdgroup liquid liquid branch main open in github codespaces https img shields io badge open 20in github 20codespaces black style flat color 24292f https github com codespaces new hide repo select true ref main repo 344421806 machine standardlinux32gb location westeurope liquid is a ui component library based on the liquid design system https www figma com file 8gycaoepm8tt9qqj7gnv99 liquid oxygen share node id 3 3a14310 focusing on accessibility and interoperability it is built with stencil js https stenciljs com and contains inter alia css components and web components bundled in several bundle formats which you can use in a wide veriaty of plattforms and projects links getting started https liquid emd design liquid introduction getting started sandbox apps https liquid emd design liquid guides sandbox applications feature backlog https github com emdgroup liquid liquid issues q is 3aissue sort 3areactions 2b1 desc label 3afeature label 3aduplicate is 3aopen changelog https github com emdgroup liquid liquid releases breaking changes https github com emdgroup liquid liquid releases q 0 0 liquid design system on figma https www figma com file 8gycaoepm8tt9qqj7gnv99 liquid oxygen share node id 3 3a14310 contributing if you d like to contribute to the project please read our code of conduct code of conduct md then proceed to the contributing docs contributing md license liquid oxygen is available under a custom license https liquid emd design liquid legal license which restricts its usage to applications created for or by merck kgaa darmstadt germany as well as its vendors | components webcomponents ui-components css-components ui-library | os |
natural-language-processing | coverphoto treefrontsentiment png natural language processing nlp is one of the most important technologies of the information age understanding complex language utterances is also a crucial part of artificial intelligence applications of nlp are everywhere because people communicate most everything in language web search advertisement emails customer service language translation radiology reports etc there are a large variety of underlying tasks and machine learning models behind nlp applications recently deep learning approaches have obtained very high performance across many different nlp tasks these models can often be trained with a single end to end model and do not require traditional task specific feature engineering in this winter quarter course students will learn to implement train debug visualize and invent their own neural network models the course provides a thorough introduction to cutting edge research in deep learning applied to nlp on the model side we will cover word vector representations window based neural networks recurrent neural networks long short term memory models recursive neural networks convolutional neural networks as well as some recent models involving a memory component through lectures and programming assignments students will learn the necessary engineering tricks for making neural networks work on practical problems table of contents lectures lecture 1 introduction to nlp and deep learning video https www youtube com watch v oqq w 63ugq list pl3fw7lu3i5jsnh1rnuwq tcylnr7ekre6 index 1 slides review https github com khanhnamle1994 natural language processing tree master lecture 1 intro to nlp and deep learning lecture 2 word vector representations word2vec video https www youtube com watch v eribwqs9p38 list pl3fw7lu3i5jsnh1rnuwq tcylnr7ekre6 index 2 slides readings https github com khanhnamle1994 natural language processing tree master lecture 2 word vector representations lecture 3 glove global vectors for word representation video https www youtube com watch v asn7exxlzws list pl3fw7lu3i5jsnh1rnuwq tcylnr7ekre6 index 3 slides readings https github com khanhnamle1994 natural language processing tree master lecture 3 advanced word vector representations lecture 4 word window classification and neural networks video https www youtube com watch v uc2 iwvqrri list pl3fw7lu3i5jsnh1rnuwq tcylnr7ekre6 index 4 slides readings https github com khanhnamle1994 natural language processing tree master lecture 4 word window classification and neural networks lecture 5 backpropagation and project advice video https www youtube com watch v ispie dbagm index 5 list pl3fw7lu3i5jsnh1rnuwq tcylnr7ekre6 slides readings https github com khanhnamle1994 natural language processing tree master lecture 5 backpropagation and project advice lecture 6 dependency parsing video https www youtube com watch v pvshkzgxznc list pl3fw7lu3i5jsnh1rnuwq tcylnr7ekre6 index 6 slides readings https github com khanhnamle1994 natural language processing tree master lecture 7 dependency parsing lecture 7 introduction to tensorflow video https www youtube com watch v picxu81owcs list pl3fw7lu3i5jsnh1rnuwq tcylnr7ekre6 index 7 slides readings https github com khanhnamle1994 natural language processing tree master lecture 6 introduction to tensorflow lecture 8 recurrent neural networks and language models video https www youtube com watch v keqep pkry8 index 8 list pl3fw7lu3i5jsnh1rnuwq tcylnr7ekre6 slides readings https github com khanhnamle1994 natural language processing tree master lecture 8 recurrent neural nets and language models lecture 9 machine translation and advanced recurrent lstms and grus video https www youtube com watch v queliw8tbx8 list pl3fw7lu3i5jsnh1rnuwq tcylnr7ekre6 index 9 slides readings https github com khanhnamle1994 natural language processing tree master lecture 9 machine translation and advanced recurrent lstms gsu lecture 10 neural machine translation and models with attention video https www youtube com watch v ixqtk2sjwwm index 11 list pl3fw7lu3i5jsnh1rnuwq tcylnr7ekre6 slides readings https github com khanhnamle1994 natural language processing tree master lecture 10 neural machine translation and models with attention lecture 11 gated recurrent units and further topics in nmt video https www youtube com watch v 6 mo12fpc 0 index 12 list pl3fw7lu3i5jsnh1rnuwq tcylnr7ekre6 slides readings https github com khanhnamle1994 natural language processing tree master lecture 11 grus and further topics in nmt lecture 12 end to end models for speech processing video https www youtube com watch v 3mjikwxxigm list pl3fw7lu3i5jsnh1rnuwq tcylnr7ekre6 index 13 slides https github com khanhnamle1994 natural language processing tree master lecture 12 end to end model speech processing lecture 13 convolutional neural networks video https www youtube com watch v lg6mzw ooli list pl3fw7lu3i5jsnh1rnuwq tcylnr7ekre6 index 14 slides readings https github com khanhnamle1994 natural language processing tree master lecture 13 convolutional neural networks lecture 14 tree recursive neural networks and constituency parsing video https www youtube com watch v rfwgqpkwz1w list pl3fw7lu3i5jsnh1rnuwq tcylnr7ekre6 index 15 slides readings https github com khanhnamle1994 natural language processing tree master lecture 14 tree recursive neural networks and constituency parsing lecture 15 coreference resolution video https www youtube com watch v rpwewlauerk index 16 list pl3fw7lu3i5jsnh1rnuwq tcylnr7ekre6 slides readings https github com khanhnamle1994 natural language processing tree master lecture 15 coreference resolution lecture 16 dynamic neural networks for question answering video https www youtube com watch v t3octnte7is index 17 list pl3fw7lu3i5jsnh1rnuwq tcylnr7ekre6 slides readings https github com khanhnamle1994 natural language processing tree master lecture 16 dynamic neural networks for question answering lecture 17 issues in nlp and possible architectures for nlp video https www youtube com watch v b4v545v3dq0 list pl3fw7lu3i5jsnh1rnuwq tcylnr7ekre6 index 18 slides readings https github com khanhnamle1994 natural language processing tree master lecture 17 issues in nlp and possible architectures in nlp lecture 18 tackling the limits of deep learning for nlp video https www youtube com watch v jywnmse4hqe list pl3fw7lu3i5jsnh1rnuwq tcylnr7ekre6 index 19 slides readings https github com khanhnamle1994 natural language processing tree master lecture 18 tackling the limits of deep learning for nlp assignments assignment 1 problem https github com khanhnamle1994 natural language processing blob master assignment guidelines and solutions assignment1 pdf solutions https github com khanhnamle1994 natural language processing blob master assignment guidelines and solutions assignment1 solutions pdf folder https github com khanhnamle1994 natural language processing tree master assignment1 assignment 2 problem https github com khanhnamle1994 natural language processing blob master assignment guidelines and solutions assignment2 pdf solutions https github com khanhnamle1994 natural language processing blob master assignment guidelines and solutions assignment2 solutions pdf folder https github com khanhnamle1994 natural language processing tree master assignment2 assignment 3 problem https github com khanhnamle1994 natural language processing blob master assignment guidelines and solutions assignment3 pdf solutions https github com khanhnamle1994 natural language processing blob master assignment guidelines and solutions assignment3 solutions pdf folder https github com khanhnamle1994 natural language processing tree master assignment3 | natural-language-processing deep-learning word2vec machine-learning glove | ai |
nbn-service-check | note the launtel api has been disabled and as a result this wrapper will not function and has been archived refer to this map for latest nbn information https github com lukeprior nbn upgrade map nbn service check api this is an unnofficial api that can return the nbn availability information for properties including technology type maximum line speed and co existance status if available using the api the main api has been limited to extension users to remain within free plan limits bots and other applications can access this mirror hosted on deta without restirctions the api can be accessed at https nbn service check deta dev check address and will automatically attempt to match any input to a valid address powered by vercel https raw githubusercontent com lukeprior nbn availability extension main powered by vercel svg https vercel com | server |
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Multiuser-Tour-Website | project instruction a react web app which allows users to schedule their tour visits to beaches of goa for paddle boarding this multi user web app built using react and express framework which allows users to schedule their tours it implements bcryptjs a hashing algorithm for user authentication and express session for session implementation used libraries like nedb a javascript database to handle json data and mocha test framework for testing the application during the development to launch this app one has to follow the below steps 1 install nodejs express devproxy react br 2 install git br 3 clone this repository into local file system br 4 now to run the below command from the tourserver directory to get the server running from command line br 5 command node serverrun js br 6 to launch the react app run this command form reacttour directory from command line br 7 command node devproxy js br 8 command npm install g parcel bundler if warning like please upgrade your dependencies to the actual version of core js 3 displays execute the command below br 9 command npm install save core js 3 now install the parcel bundler after upgrade and run devproxy br 10 now launch the app with link url for react app from chrome or firefox br functionality guest users can visit home and about page coming tours br to schedule a tour he she can login with details from usertours json file now this is customer role with this each user can schedule of his own interest br only admin can add and delete the coming tours in the app via tour management test tests are written for login tour management using chai assertions and mocha framework to run the tests user can execute the below commands from tourserver directory command mocha test tourtest js br command mocha test logintest js br command mocha test addtourtest js br command mocha test deletetest js br command mocha test gettourtest js br aws hosting https dev d17l673sh63you amplifyapp com | server |
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EntecLabLog | enteclablog project using javafx that connects to a mysql database to log students entering the mdc engineering technology lab | server |
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system-design-primer-zh-tw | english readme md readme ja md readme zh hans md readme zh tw md brazilian portuguese https github com donnemartin system design primer issues 40 italian https github com donnemartin system design primer issues 104 korean https github com donnemartin system design primer issues 102 persian https github com donnemartin system design primer issues 110 polish https github com donnemartin system design primer issues 68 russian https github com donnemartin system design primer issues 87 turkish https github com donnemartin system design primer issues 39 vietnamese https github com donnemartin system design primer issues 127 add translation https github com donnemartin system design primer issues 28 the system design primer p align center img src http i imgur com jj3a5n8 png br p motivation learn how to design large scale systems prep for the system design interview learn how to design large scale systems learning how to design scalable systems will help you become a better engineer system design is a broad topic there is a vast amount of resources scattered throughout the web on system design principles this repo is an organized collection of resources to help you learn how to build systems at scale learn from the open source community this is an early draft of a continually updated open source project contributions contributing are welcome prep for the system design interview in addition to coding interviews system design is a required component of the technical interview process at many tech companies practice common system design interview questions and compare your results with sample solutions discussions code and diagrams additional topics for interview prep study guide study guide how to approach a system design interview question how to approach a system design interview question system design interview questions with solutions system design interview questions with solutions object oriented design interview questions with solutions object oriented design interview questions with solutions additional system design interview questions additional system design interview questions anki flashcards p align center img src http i imgur com zdcakb3 png br p the provided anki flashcard decks https apps ankiweb net use spaced repetition to help you retain key system design concepts system design deck resources flash cards system 20design apkg system design exercises deck resources flash cards system 20design 20exercises apkg object oriented design exercises deck resources flash cards oo 20design apkg great for use while on the go coding resource interactive coding challenges looking for resources to help you prep for the coding interview https github com donnemartin interactive coding challenges p align center img src http i imgur com b4ytaen png br p check out the sister repo interactive coding challenges https github com donnemartin interactive coding challenges which contains an additional anki deck coding deck https github com donnemartin interactive coding challenges tree master anki cards coding apkg contributing learn from the community feel free to submit pull requests to help fix errors improve sections add new sections translate https github com donnemartin system design primer issues 28 content that needs some polishing is placed under development under development review the contributing guidelines contributing md index of system design topics summaries of various system design topics including pros and cons everything is a trade off each section contains links to more in depth resources p align center img src http i imgur com jrubaf7 png br p system design topics start here system design topics start here step 1 review the scalability video lecture step 1 review the scalability video lecture step 2 review the scalability article step 2 review the scalability article next steps next steps performance vs scalability performance vs scalability latency vs throughput latency vs throughput availability vs consistency availability vs consistency cap theorem cap theorem cp consistency and partition tolerance cp consistency and partition tolerance ap availability and partition tolerance ap availability and partition tolerance consistency patterns consistency patterns weak consistency weak consistency eventual consistency eventual consistency strong consistency strong consistency availability patterns availability patterns fail over fail over replication replication domain name system domain name system content delivery network content delivery network push cdns push cdns pull cdns pull cdns load balancer load balancer active passive active passive active active active active layer 4 load balancing layer 4 load balancing layer 7 load balancing layer 7 load balancing horizontal scaling horizontal scaling reverse proxy web server reverse proxy web server load balancer vs reverse proxy load balancer vs reverse proxy application layer application layer microservices microservices service discovery service discovery database database relational database management system rdbms relational database management system rdbms master slave replication master slave replication master master replication master master replication federation federation sharding sharding denormalization denormalization sql tuning sql tuning nosql nosql key value store key value store document store document store wide column store wide column store graph database graph database sql or nosql sql or nosql cache cache client caching client caching cdn caching cdn caching web server caching web server caching database caching database caching application caching application caching caching at the database query level caching at the database query level caching at the object level caching at the object level when to update the cache when to update the cache cache aside cache aside write through write through write behind write back write behind write back refresh ahead refresh ahead asynchronism asynchronism message queues message queues task queues task queues back pressure back pressure communication communication transmission control protocol tcp transmission control protocol tcp user datagram protocol udp user datagram protocol udp remote procedure call rpc remote procedure call rpc representational state transfer rest representational state transfer rest security security appendix appendix powers of two table powers of two table latency numbers every programmer should know latency numbers every programmer should know additional system design interview questions additional system design interview questions real world architectures real world architectures company architectures company architectures company engineering blogs company engineering blogs under development under development credits credits contact info contact info license license study guide suggested topics to review based on your interview timeline short medium long imgur http i imgur com ofvllex png q for interviews do i need to know everything here a no you don t need to know everything here to prepare for the interview what you are asked in an interview depends on variables such as how much experience you have what your technical background is what positions you are interviewing for which companies you are interviewing with luck more experienced candidates are generally expected to know more about system design architects or team leads might be expected to know more than individual contributors top tech companies are likely to have one or more design interview rounds start broad and go deeper in a few areas it helps to know a little about various key system design topics adjust the following guide based on your timeline experience what positions you are interviewing for and which companies you are interviewing with short timeline aim for breadth with system design topics practice by solving some interview questions medium timeline aim for breadth and some depth with system design topics practice by solving many interview questions long timeline aim for breadth and more depth with system design topics practice by solving most interview questions short medium long read through the system design topics index of system design topics to get a broad understanding of how systems work 1 1 1 read through a few articles in the company engineering blogs company engineering blogs for the companies you are interviewing with 1 1 1 read through a few real world architectures real world architectures 1 1 1 review how to approach a system design interview question how to approach a system design interview question 1 1 1 work through system design interview questions with solutions system design interview questions with solutions some many most work through object oriented design interview questions with solutions object oriented design interview questions with solutions some many most review additional system design interview questions additional system design interview questions some many most how to approach a system design interview question how to tackle a system design interview question the system design interview is an open ended conversation you are expected to lead it you can use the following steps to guide the discussion to help solidify this process work through the system design interview questions with solutions system design interview questions with solutions section using the following steps step 1 outline use cases constraints and assumptions gather requirements and scope the problem ask questions to clarify use cases and constraints discuss assumptions who is going to use it how are they going to use it how many users are there what does the system do what are the inputs and outputs of the system how much data do we expect to handle how many requests per second do we expect what is the expected read to write ratio step 2 create a high level design outline a high level design with all important components sketch the main components and connections justify your ideas step 3 design core components dive into details for each core component for example if you were asked to design a url shortening service solutions system design pastebin readme md discuss generating and storing a hash of the full url md5 solutions system design pastebin readme md and base62 solutions system design pastebin readme md hash collisions sql or nosql database schema translating a hashed url to the full url database lookup api and object oriented design step 4 scale the design identify and address bottlenecks given the constraints for example do you need the following to address scalability issues load balancer horizontal scaling caching database sharding discuss potential solutions and trade offs everything is a trade off address bottlenecks using principles of scalable system design index of system design topics back of the envelope calculations you might be asked to do some estimates by hand refer to the appendix appendix for the following resources use back of the envelope calculations http highscalability com blog 2011 1 26 google pro tip use back of the envelope calculations to choo html powers of two table powers of two table latency numbers every programmer should know latency numbers every programmer should know source s and further reading check out the following links to get a better idea of what to expect how to ace a systems design interview https www palantir com 2011 10 how to rock a systems design interview the system design interview http www hiredintech com system design intro to architecture and systems design interviews https www youtube com watch v zgds0eumn70 system design interview questions with solutions common system design interview questions with sample discussions code and diagrams solutions linked to content in the solutions folder question design pastebin com or bit ly solution solutions system design pastebin readme md design the twitter timeline or facebook feed br design twitter search or facebook search solution solutions system design twitter readme md design a web crawler solution solutions system design web crawler readme md design mint com solution solutions system design mint readme md design the data structures for a social network solution solutions system design social graph readme md design a key value store for a search engine solution solutions system design query cache readme md design amazon s sales ranking by category feature solution solutions system design sales rank readme md design a system that scales to millions of users on aws solution solutions system design scaling aws readme md add a system design question contribute contributing design pastebin com or bit ly view exercise and solution solutions system design pastebin readme md imgur http i imgur com 4edxg0t png design the twitter timeline and search or facebook feed and search view exercise and solution solutions system design twitter readme md imgur http i imgur com jrubaf7 png design a web crawler view exercise and solution solutions system design web crawler readme md imgur http i imgur com bwxptqa png design mint com view exercise and solution solutions system design mint readme md imgur http i imgur com v5q57vu png design the data structures for a social network view exercise and solution solutions system design social graph readme md imgur http i imgur com cdcv5g7 png design a key value store for a search engine view exercise and solution solutions system design query cache readme md imgur http i imgur com 4j99mhe png design amazon s sales ranking by category feature view exercise and solution solutions system design sales rank readme md imgur http i imgur com mzexp06 png design a system that scales to millions of users on aws view exercise and solution solutions system design scaling aws readme md imgur http i imgur com jj3a5n8 png object oriented design interview questions with solutions common object oriented design interview questions with sample discussions code and diagrams solutions linked to content in the solutions folder note this section is under development question design a hash map solution solutions object oriented design hash table hash map ipynb design a least recently used cache solution solutions object oriented design lru cache lru cache ipynb design a call center solution solutions object oriented design call center call center ipynb design a deck of cards solution solutions object oriented design deck of cards deck of cards ipynb design a parking lot solution solutions object oriented design parking lot parking lot ipynb design a chat server solution solutions object oriented design online chat online chat ipynb design a circular array contribute contributing add an object oriented design question contribute contributing system design topics start here new to system design first you ll need a basic understanding of common principles learning about what they are how they are used and their pros and cons step 1 review the scalability video lecture scalability lecture at harvard https www youtube com watch v w9f d3oy4 topics covered vertical scaling horizontal scaling caching load balancing database replication database partitioning step 2 review the scalability article scalability http www lecloud net tagged scalability topics covered clones http www lecloud net post 7295452622 scalability for dummies part 1 clones databases http www lecloud net post 7994751381 scalability for dummies part 2 database caches http www lecloud net post 9246290032 scalability for dummies part 3 cache asynchronism http www lecloud net post 9699762917 scalability for dummies part 4 asynchronism next steps next we ll look at high level trade offs performance vs scalability latency vs throughput availability vs consistency keep in mind that everything is a trade off then we ll dive into more specific topics such as dns cdns and load balancers performance vs scalability a service is scalable if it results in increased performance in a manner proportional to resources added generally increasing performance means serving more units of work but it can also be to handle larger units of work such as when datasets grow sup a href http www allthingsdistributed com 2006 03 a word on scalability html 1 a sup another way to look at performance vs scalability if you have a performance problem your system is slow for a single user if you have a scalability problem your system is fast for a single user but slow under heavy load source s and further reading a word on scalability http www allthingsdistributed com 2006 03 a word on scalability html scalability availability stability patterns http www slideshare net jboner scalability availability stability patterns latency vs throughput latency is the time to perform some action or to produce some result throughput is the number of such actions or results per unit of time generally you should aim for maximal throughput with acceptable latency source s and further reading understanding latency vs throughput https community cadence com cadence blogs 8 b sd archive 2010 09 13 understanding latency vs throughput availability vs consistency cap theorem p align center img src http i imgur com bglmi2u png br i a href http robertgreiner com 2014 08 cap theorem revisited source cap theorem revisited a i p in a distributed computer system you can only support two of the following guarantees consistency every read receives the most recent write or an error availability every request receives a response without guarantee that it contains the most recent version of the information partition tolerance the system continues to operate despite arbitrary partitioning due to network failures networks aren t reliable so you ll need to support partition tolerance you ll need to make a software tradeoff between consistency and availability cp consistency and partition tolerance waiting for a response from the partitioned node might result in a timeout error cp is a good choice if your business needs require atomic reads and writes ap availability and partition tolerance responses return the most recent version of the data available on the a node which might not be the latest writes might take some time to propagate when the partition is resolved ap is a good choice if the business needs allow for eventual consistency eventual consistency or when the system needs to continue working despite external errors source s and further reading cap theorem revisited http robertgreiner com 2014 08 cap theorem revisited a plain english introduction to cap theorem http ksat me a plain english introduction to cap theorem cap faq https github com henryr cap faq consistency patterns with multiple copies of the same data we are faced with options on how to synchronize them so clients have a consistent view of the data recall the definition of consistency from the cap theorem cap theorem every read receives the most recent write or an error weak consistency after a write reads may or may not see it a best effort approach is taken this approach is seen in systems such as memcached weak consistency works well in real time use cases such as voip video chat and realtime multiplayer games for example if you are on a phone call and lose reception for a few seconds when you regain connection you do not hear what was spoken during connection loss eventual consistency after a write reads will eventually see it typically within milliseconds data is replicated asynchronously this approach is seen in systems such as dns and email eventual consistency works well in highly available systems strong consistency after a write reads will see it data is replicated synchronously this approach is seen in file systems and rdbmses strong consistency works well in systems that need transactions source s and further reading transactions across data centers http snarfed org transactions across datacenters io html availability patterns there are two main patterns to support high availability fail over and replication fail over active passive with active passive fail over heartbeats are sent between the active and the passive server on standby if the heartbeat is interrupted the passive server takes over the active s ip address and resumes service the length of downtime is determined by whether the passive server is already running in hot standby or whether it needs to start up from cold standby only the active server handles traffic active passive failover can also be referred to as master slave failover active active in active active both servers are managing traffic spreading the load between them if the servers are public facing the dns would need to know about the public ips of both servers if the servers are internal facing application logic would need to know about both servers active active failover can also be referred to as master master failover disadvantage s failover fail over adds more hardware and additional complexity there is a potential for loss of data if the active system fails before any newly written data can be replicated to the passive replication master slave and master master this topic is further discussed in the database database section master slave replication master slave replication master master replication master master replication domain name system p align center img src http i imgur com ioylj4i jpg br i a href http www slideshare net srikrupa5 dns security presentation issa source dns security presentation a i p a domain name system dns translates a domain name such as www example com to an ip address dns is hierarchical with a few authoritative servers at the top level your router or isp provides information about which dns server s to contact when doing a lookup lower level dns servers cache mappings which could become stale due to dns propagation delays dns results can also be cached by your browser or os for a certain period of time determined by the time to live ttl https en wikipedia org wiki time to live ns record name server specifies the dns servers for your domain subdomain mx record mail exchange specifies the mail servers for accepting messages a record address points a name to an ip address cname canonical points a name to another name or cname example com to www example com or to an a record services such as cloudflare https www cloudflare com dns and route 53 https aws amazon com route53 provide managed dns services some dns services can route traffic through various methods weighted round robin http g33kinfo com info archives 2657 prevent traffic from going to servers under maintenance balance between varying cluster sizes a b testing latency based geolocation based disadvantage s dns accessing a dns server introduces a slight delay although mitigated by caching described above dns server management could be complex and is generally managed by governments isps and large companies http superuser com questions 472695 who controls the dns servers 472729 dns services have recently come under ddos attack http dyn com blog dyn analysis summary of friday october 21 attack preventing users from accessing websites such as twitter without knowing twitter s ip address es source s and further reading dns architecture https technet microsoft com en us library dd197427 v ws 10 aspx wikipedia https en wikipedia org wiki domain name system dns articles https support dnsimple com categories dns content delivery network p align center img src http i imgur com h9taugi jpg br i a href https www creative artworks eu why use a content delivery network cdn source why use a cdn a i p a content delivery network cdn is a globally distributed network of proxy servers serving content from locations closer to the user generally static files such as html css js photos and videos are served from cdn although some cdns such as amazon s cloudfront support dynamic content the site s dns resolution will tell clients which server to contact serving content from cdns can significantly improve performance in two ways users receive content at data centers close to them your servers do not have to serve requests that the cdn fulfills push cdns push cdns receive new content whenever changes occur on your server you take full responsibility for providing content uploading directly to the cdn and rewriting urls to point to the cdn you can configure when content expires and when it is updated content is uploaded only when it is new or changed minimizing traffic but maximizing storage sites with a small amount of traffic or sites with content that isn t often updated work well with push cdns content is placed on the cdns once instead of being re pulled at regular intervals pull cdns pull cdns grab new content from your server when the first user requests the content you leave the content on your server and rewrite urls to point to the cdn this results in a slower request until the content is cached on the cdn a time to live ttl https en wikipedia org wiki time to live determines how long content is cached pull cdns minimize storage space on the cdn but can create redundant traffic if files expire and are pulled before they have actually changed sites with heavy traffic work well with pull cdns as traffic is spread out more evenly with only recently requested content remaining on the cdn disadvantage s cdn cdn costs could be significant depending on traffic although this should be weighed with additional costs you would incur not using a cdn content might be stale if it is updated before the ttl expires it cdns require changing urls for static content to point to the cdn source s and further reading globally distributed content delivery http repository cmu edu cgi viewcontent cgi article 2112 context compsci the differences between push and pull cdns http www travelblogadvice com technical the differences between push and pull cdns wikipedia https en wikipedia org wiki content delivery network load balancer p align center img src http i imgur com h81n9ik png br i a href http horicky blogspot com 2010 10 scalable system design patterns html source scalable system design patterns a i p load balancers distribute incoming client requests to computing resources such as application servers and databases in each case the load balancer returns the response from the computing resource to the appropriate client load balancers are effective at preventing requests from going to unhealthy servers preventing overloading resources helping eliminate single points of failure load balancers can be implemented with hardware expensive or with software such as haproxy additional benefits include ssl termination decrypt incoming requests and encrypt server responses so backend servers do not have to perform these potentially expensive operations removes the need to install x 509 certificates https en wikipedia org wiki x 509 on each server session persistence issue cookies and route a specific client s requests to same instance if the web apps do not keep track of sessions to protect against failures it s common to set up multiple load balancers either in active passive active passive or active active active active mode load balancers can route traffic based on various metrics including random least loaded session cookies round robin or weighted round robin http g33kinfo com info archives 2657 layer 4 layer 4 load balancing layer 7 layer 7 load balancing layer 4 load balancing layer 4 load balancers look at info at the transport layer communication to decide how to distribute requests generally this involves the source destination ip addresses and ports in the header but not the contents of the packet layer 4 load balancers forward network packets to and from the upstream server performing network address translation nat https www nginx com resources glossary layer 4 load balancing layer 7 load balancing layer 7 load balancers look at the application layer communication to decide how to distribute requests this can involve contents of the header message and cookies layer 7 load balancers terminates network traffic reads the message makes a load balancing decision then opens a connection to the selected server for example a layer 7 load balancer can direct video traffic to servers that host videos while directing more sensitive user billing traffic to security hardened servers at the cost of flexibility layer 4 load balancing requires less time and computing resources than layer 7 although the performance impact can be minimal on modern commodity hardware horizontal scaling load balancers can also help with horizontal scaling improving performance and availability scaling out using commodity machines is more cost efficient and results in higher availability than scaling up a single server on more expensive hardware called vertical scaling it is also easier to hire for talent working on commodity hardware than it is for specialized enterprise systems disadvantage s horizontal scaling scaling horizontally introduces complexity and involves cloning servers servers should be stateless they should not contain any user related data like sessions or profile pictures sessions can be stored in a centralized data store such as a database database sql nosql or a persistent cache cache redis memcached downstream servers such as caches and databases need to handle more simultaneous connections as upstream servers scale out disadvantage s load balancer the load balancer can become a performance bottleneck if it does not have enough resources or if it is not configured properly introducing a load balancer to help eliminate single points of failure results in increased complexity a single load balancer is a single point of failure configuring multiple load balancers further increases complexity source s and further reading nginx architecture https www nginx com blog inside nginx how we designed for performance scale haproxy architecture guide http www haproxy org download 1 2 doc architecture txt scalability http www lecloud net post 7295452622 scalability for dummies part 1 clones wikipedia https en wikipedia org wiki load balancing computing layer 4 load balancing https www nginx com resources glossary layer 4 load balancing layer 7 load balancing https www nginx com resources glossary layer 7 load balancing elb listener config http docs aws amazon com elasticloadbalancing latest classic elb listener config html reverse proxy web server p align center img src http i imgur com n41azff png br i a href https upload wikimedia org wikipedia commons 6 67 reverse proxy h2g2bob svg source wikipedia a i br p a reverse proxy is a web server that centralizes internal services and provides unified interfaces to the public requests from clients are forwarded to a server that can fulfill it before the reverse proxy returns the server s response to the client additional benefits include increased security hide information about backend servers blacklist ips limit number of connections per client increased scalability and flexibility clients only see the reverse proxy s ip allowing you to scale servers or change their configuration ssl termination decrypt incoming requests and encrypt server responses so backend servers do not have to perform these potentially expensive operations removes the need to install x 509 certificates https en wikipedia org wiki x 509 on each server compression compress server responses caching return the response for cached requests static content serve static content directly html css js photos videos etc load balancer vs reverse proxy deploying a load balancer is useful when you have multiple servers often load balancers route traffic to a set of servers serving the same function reverse proxies can be useful even with just one web server or application server opening up the benefits described in the previous section solutions such as nginx and haproxy can support both layer 7 reverse proxying and load balancing disadvantage s reverse proxy introducing a reverse proxy results in increased complexity a single reverse proxy is a single point of failure configuring multiple reverse proxies ie a failover https en wikipedia org wiki failover further increases complexity source s and further reading reverse proxy vs load balancer https www nginx com resources glossary reverse proxy vs load balancer nginx architecture https www nginx com blog inside nginx how we designed for performance scale haproxy architecture guide http www haproxy org download 1 2 doc architecture txt wikipedia https en wikipedia org wiki reverse proxy application layer p align center img src http i imgur com yb5sywm png br i a href http lethain com introduction to architecting systems for scale platform layer source intro to architecting systems for scale a i p separating out the web layer from the application layer also known as platform layer allows you to scale and configure both layers independently adding a new api results in adding application servers without necessarily adding additional web servers the single responsibility principle advocates for small and autonomous services that work together small teams with small services can plan more aggressively for rapid growth workers in the application layer also help enable asynchronism asynchronism microservices related to this discussion are microservices https en wikipedia org wiki microservices which can be described as a suite of independently deployable small modular services each service runs a unique process and communicates through a well defined lightweight mechanism to serve a business goal sup a href https smartbear com learn api design what are microservices 1 a sup pinterest for example could have the following microservices user profile follower feed search photo upload etc service discovery systems such as consul https www consul io docs index html etcd https coreos com etcd docs latest and zookeeper http www slideshare net sauravhaloi introduction to apache zookeeper can help services find each other by keeping track of registered names addresses and ports health checks https www consul io intro getting started checks html help verify service integrity and are often done using an http hypertext transfer protocol http endpoint both consul and etcd have a built in key value store key value store that can be useful for storing config values and other shared data disadvantage s application layer adding an application layer with loosely coupled services requires a different approach from an architectural operations and process viewpoint vs a monolithic system microservices can add complexity in terms of deployments and operations source s and further reading intro to architecting systems for scale http lethain com introduction to architecting systems for scale crack the system design interview http www puncsky com blog 2016 02 14 crack the system design interview service oriented architecture https en wikipedia org wiki service oriented architecture introduction to zookeeper http www slideshare net sauravhaloi introduction to apache zookeeper here s what you need to know about building microservices https cloudncode wordpress com 2016 07 22 msa getting started database p align center img src http i imgur com xkm5cxz png br i a href https www youtube com watch v w95murbkymu source scaling up to your first 10 million users a i p relational database management system rdbms a relational database like sql is a collection of data items organized in tables acid is a set of properties of relational database transactions https en wikipedia org wiki database transaction atomicity each transaction is all or nothing consistency any transaction will bring the database from one valid state to another isolation executing transactions concurrently has the same results as if the transactions were executed serially durability once a transaction has been committed it will remain so there are many techniques to scale a relational database master slave replication master master replication federation sharding denormalization and sql tuning master slave replication the master serves reads and writes replicating writes to one or more slaves which serve only reads slaves can also replicate to additional slaves in a tree like fashion if the master goes offline the system can continue to operate in read only mode until a slave is promoted to a master or a new master is provisioned p align center img src http i imgur com c9iogtn png br i a href http www slideshare net jboner scalability availability stability patterns source scalability availability stability patterns a i p disadvantage s master slave replication additional logic is needed to promote a slave to a master see disadvantage s replication disadvantages replication for points related to both master slave and master master master master replication both masters serve reads and writes and coordinate with each other on writes if either master goes down the system can continue to operate with both reads and writes p align center img src http i imgur com krahlgg png br i a href http www slideshare net jboner scalability availability stability patterns source scalability availability stability patterns a i p disadvantage s master master replication you ll need a load balancer or you ll need to make changes to your application logic to determine where to write most master master systems are either loosely consistent violating acid or have increased write latency due to synchronization conflict resolution comes more into play as more write nodes are added and as latency increases see disadvantage s replication disadvantages replication for points related to both master slave and master master disadvantage s replication there is a potential for loss of data if the master fails before any newly written data can be replicated to other nodes writes are replayed to the read replicas if there are a lot of writes the read replicas can get bogged down with replaying writes and can t do as many reads the more read slaves the more you have to replicate which leads to greater replication lag on some systems writing to the master can spawn multiple threads to write in parallel whereas read replicas only support writing sequentially with a single thread replication adds more hardware and additional complexity source s and further reading replication scalability availability stability patterns http www slideshare net jboner scalability availability stability patterns multi master replication https en wikipedia org wiki multi master replication federation p align center img src http i imgur com u3qv33e png br i a href https www youtube com watch v w95murbkymu source scaling up to your first 10 million users a i p federation or functional partitioning splits up databases by function for example instead of a single monolithic database you could have three databases forums users and products resulting in less read and write traffic to each database and therefore less replication lag smaller databases result in more data that can fit in memory which in turn results in more cache hits due to improved cache locality with no single central master serializing writes you can write in parallel increasing throughput disadvantage s federation federation is not effective if your schema requires huge functions or tables you ll need to update your application logic to determine which database to read and write joining data from two databases is more complex with a server link http stackoverflow com questions 5145637 querying data by joining two tables in two database on different servers federation adds more hardware and additional complexity source s and further reading federation scaling up to your first 10 million users https www youtube com watch v w95murbkymu sharding p align center img src http i imgur com wu8x5id png br i a href http www slideshare net jboner scalability availability stability patterns source scalability availability stability patterns a i p sharding distributes data across different databases such that each database can only manage a subset of the data taking a users database as an example as the number of users increases more shards are added to the cluster similar to the advantages of federation federation sharding results in less read and write traffic less replication and more cache hits index size is also reduced which generally improves performance with faster queries if one shard goes down the other shards are still operational although you ll want to add some form of replication to avoid data loss like federation there is no single central master serializing writes allowing you to write in parallel with increased throughput common ways to shard a table of users is either through the user s last name initial or the user s geographic location disadvantage s sharding you ll need to update your application logic to work with shards which could result in complex sql queries data distribution can become lopsided in a shard for example a set of power users on a shard could result in increased load to that shard compared to others rebalancing adds additional complexity a sharding function based on consistent hashing http www paperplanes de 2011 12 9 the magic of consistent hashing html can reduce the amount of transferred data joining data from multiple shards is more complex sharding adds more hardware and additional complexity source s and further reading sharding the coming of the shard http highscalability com blog 2009 8 6 an unorthodox approach to database design the coming of the html shard database architecture https en wikipedia org wiki shard database architecture consistent hashing http www paperplanes de 2011 12 9 the magic of consistent hashing html denormalization denormalization attempts to improve read performance at the expense of some write performance redundant copies of the data are written in multiple tables to avoid expensive joins some rdbms such as postgresql https en wikipedia org wiki postgresql and oracle support materialized views https en wikipedia org wiki materialized view which handle the work of storing redundant information and keeping redundant copies consistent once data becomes distributed with techniques such as federation federation and sharding sharding managing joins across data centers further increases complexity denormalization might circumvent the need for such complex joins in most systems reads can heavily outnumber writes 100 1 or even 1000 1 a read resulting in a complex database join can be very expensive spending a significant amount of time on disk operations disadvantage s denormalization data is duplicated constraints can help redundant copies of information stay in sync which increases complexity of the database design a denormalized database under heavy write load might perform worse than its normalized counterpart source s and further reading denormalization denormalization https en wikipedia org wiki denormalization sql tuning sql tuning is a broad topic and many books https www amazon com s ref nb sb noss 2 url search alias 3daps field keywords sql tuning have been written as reference it s important to benchmark and profile to simulate and uncover bottlenecks benchmark simulate high load situations with tools such as ab http httpd apache org docs 2 2 programs ab html profile enable tools such as the slow query log http dev mysql com doc refman 5 7 en slow query log html to help track performance issues benchmarking and profiling might point you to the following optimizations tighten up the schema mysql dumps to disk in contiguous blocks for fast access use char instead of varchar for fixed length fields char effectively allows for fast random access whereas with varchar you must find the end of a string before moving onto the next one use text for large blocks of text such as blog posts text also allows for boolean searches using a text field results in storing a pointer on disk that is used to locate the text block use int for larger numbers up to 2 32 or 4 billion use decimal for currency to avoid floating point representation errors avoid storing large blobs store the location of where to get the object instead varchar 255 is the largest number of characters that can be counted in an 8 bit number often maximizing the use of a byte in some rdbms set the not null constraint where applicable to improve search performance http stackoverflow com questions 1017239 how do null values affect performance in a database search use good indices columns that you are querying select group by order by join could be faster with indices indices are usually represented as self balancing b tree https en wikipedia org wiki b tree that keeps data sorted and allows searches sequential access insertions and deletions in logarithmic time placing an index can keep the data in memory requiring more space writes could also be slower since the index also needs to be updated when loading large amounts of data it might be faster to disable indices load the data then rebuild the indices avoid expensive joins denormalize denormalization where performance demands it partition tables break up a table by putting hot spots in a separate table to help keep it in memory tune the query cache in some cases the query cache http dev mysql com doc refman 5 7 en query cache could lead to performance issues https www percona com blog 2014 01 28 10 mysql performance tuning settings after installation source s and further reading sql tuning tips for optimizing mysql queries http 20bits com article 10 tips for optimizing mysql queries that dont suck is there a good reason i see varchar 255 used so often http stackoverflow com questions 1217466 is there a good reason i see varchar255 used so often as opposed to another l how do null values affect performance http stackoverflow com questions 1017239 how do null values affect performance in a database search slow query log http dev mysql com doc refman 5 7 en slow query log html nosql nosql is a collection of data items represented in a key value store document store wide column store or a graph database data is denormalized and joins are generally done in the application code most nosql stores lack true acid transactions and favor eventual consistency eventual consistency base is often used to describe the properties of nosql databases in comparison with the cap theorem cap theorem base chooses availability over consistency basically available the system guarantees availability soft state the state of the system may change over time even without input eventual consistency the system will become consistent over a period of time given that the system doesn t receive input during that period in addition to choosing between sql or nosql sql or nosql it is helpful to understand which type of nosql database best fits your use case s we ll review key value stores document stores wide column stores and graph databases in the next section key value store abstraction hash table a key value store generally allows for o 1 reads and writes and is often backed by memory or ssd data stores can maintain keys in lexicographic order https en wikipedia org wiki lexicographical order allowing efficient retrieval of key ranges key value stores can allow for storing of metadata with a value key value stores provide high performance and are often used for simple data models or for rapidly changing data such as an in memory cache layer since they offer only a limited set of operations complexity is shifted to the application layer if additional operations are needed a key value store is the basis for more complex systems such as a document store and in some cases a graph database source s and further reading key value store key value database https en wikipedia org wiki key value database disadvantages of key value stores http stackoverflow com questions 4056093 what are the disadvantages of using a key value table over nullable columns or redis architecture http qnimate com overview of redis architecture memcached architecture https www adayinthelifeof nl 2011 02 06 memcache internals document store abstraction key value store with documents stored as values a document store is centered around documents xml json binary etc where a document stores all information for a given object document stores provide apis or a query language to query based on the internal structure of the document itself note many key value stores include features for working with a value s metadata blurring the lines between these two storage types based on the underlying implementation documents are organized in either collections tags metadata or directories although documents can be organized or grouped together documents may have fields that are completely different from each other some document stores like mongodb https www mongodb com mongodb architecture and couchdb https blog couchdb org 2016 08 01 couchdb 2 0 architecture also provide a sql like language to perform complex queries dynamodb http www read seas harvard edu kohler class cs239 w08 decandia07dynamo pdf supports both key values and documents document stores provide high flexibility and are often used for working with occasionally changing data source s and further reading document store document oriented database https en wikipedia org wiki document oriented database mongodb architecture https www mongodb com mongodb architecture couchdb architecture https blog couchdb org 2016 08 01 couchdb 2 0 architecture elasticsearch architecture https www elastic co blog found elasticsearch from the bottom up wide column store p align center img src http i imgur com n16iogk png br i a href http blog grio com 2015 11 sql nosql a brief history html source sql nosql a brief history a i p abstraction nested map columnfamily rowkey columns colkey value timestamp a wide column store s basic unit of data is a column name value pair a column can be grouped in column families analogous to a sql table super column families further group column families you can access each column independently with a row key and columns with the same row key form a row each value contains a timestamp for versioning and for conflict resolution google introduced bigtable http www read seas harvard edu kohler class cs239 w08 chang06bigtable pdf as the first wide column store which influenced the open source hbase https www mapr com blog in depth look hbase architecture often used in the hadoop ecosystem and cassandra http docs datastax com en archived cassandra 2 0 cassandra architecture architectureintro c html from facebook stores such as bigtable hbase and cassandra maintain keys in lexicographic order allowing efficient retrieval of selective key ranges wide column stores offer high availability and high scalability they are often used for very large data sets source s and further reading wide column store sql nosql a brief history http blog grio com 2015 11 sql nosql a brief history html bigtable architecture http www read seas harvard edu kohler class cs239 w08 chang06bigtable pdf hbase architecture https www mapr com blog in depth look hbase architecture cassandra architecture http docs datastax com en archived cassandra 2 0 cassandra architecture architectureintro c html graph database p align center img src http i imgur com fncl65g png br i a href https en wikipedia org wiki file graphdatabase propertygraph png source graph database a i p abstraction graph in a graph database each node is a record and each arc is a relationship between two nodes graph databases are optimized to represent complex relationships with many foreign keys or many to many relationships graphs databases offer high performance for data models with complex relationships such as a social network they are relatively new and are not yet widely used it might be more difficult to find development tools and resources many graphs can only be accessed with rest apis representational state transfer rest source s and further reading graph graph database https en wikipedia org wiki graph database neo4j https neo4j com flockdb https blog twitter com 2010 introducing flockdb source s and further reading nosql explanation of base terminology http stackoverflow com questions 3342497 explanation of base terminology nosql databases a survey and decision guidance https medium com baqend blog nosql databases a survey and decision guidance ea7823a822d wskogqenq scalability http www lecloud net post 7994751381 scalability for dummies part 2 database introduction to nosql https www youtube com watch v qi g07c q5i nosql patterns http horicky blogspot com 2009 11 nosql patterns html sql or nosql p align center img src http i imgur com wxgqg5f png br i a href https www infoq com articles transition rdbms nosql source transitioning from rdbms to nosql a i p reasons for sql structured data strict schema relational data need for complex joins transactions clear patterns for scaling more established developers community code tools etc lookups by index are very fast reasons for nosql semi structured data dynamic or flexible schema non relational data no need for complex joins store many tb or pb of data very data intensive workload very high throughput for iops sample data well suited for nosql rapid ingest of clickstream and log data leaderboard or scoring data temporary data such as a shopping cart frequently accessed hot tables metadata lookup tables source s and further reading sql or nosql scaling up to your first 10 million users https www youtube com watch v w95murbkymu sql vs nosql differences https www sitepoint com sql vs nosql differences cache p align center img src http i imgur com q6z24la png br i a href http horicky blogspot com 2010 10 scalable system design patterns html source scalable system design patterns a i p caching improves page load times and can reduce the load on your servers and databases in this model the dispatcher will first lookup if the request has been made before and try to find the previous result to return in order to save the actual execution databases often benefit from a uniform distribution of reads and writes across its partitions popular items can skew the distribution causing bottlenecks putting a cache in front of a database can help absorb uneven loads and spikes in traffic client caching caches can be located on the client side os or browser server side reverse proxy or in a distinct cache layer cdn caching cdns content delivery network are considered a type of cache web server caching reverse proxies reverse proxy web server and caches such as varnish https www varnish cache org can serve static and dynamic content directly web servers can also cache requests returning responses without having to contact application servers database caching your database usually includes some level of caching in a default configuration optimized for a generic use case tweaking these settings for specific usage patterns can further boost performance application caching in memory caches such as memcached and redis are key value stores between your application and your data storage since the data is held in ram it is much faster than typical databases where data is stored on disk ram is more limited than disk so cache invalidation https en wikipedia org wiki cache algorithms algorithms such as least recently used lru https en wikipedia org wiki cache algorithms least recently used can help invalidate cold entries and keep hot data in ram redis has the following additional features persistence option built in data structures such as sorted sets and lists there are multiple levels you can cache that fall into two general categories database queries and objects row level query level fully formed serializable objects fully rendered html generally you should try to avoid file based caching as it makes cloning and auto scaling more difficult caching at the database query level whenever you query the database hash the query as a key and store the result to the cache this approach suffers from expiration issues hard to delete a cached result with complex queries if one piece of data changes such as a table cell you need to delete all cached queries that might include the changed cell caching at the object level see your data as an object similar to what you do with your application code have your application assemble the dataset from the database into a class instance or a data structure s remove the object from cache if its underlying data has changed allows for asynchronous processing workers assemble objects by consuming the latest cached object suggestions of what to cache user sessions fully rendered web pages activity streams user graph data when to update the cache since you can only store a limited amount of data in cache you ll need to determine which cache update strategy works best for your use case cache aside p align center img src http i imgur com onjorqk png br i a href http www slideshare net tmatyashovsky from cache to in memory data grid introduction to hazelcast source from cache to in memory data grid a i p the application is responsible for reading and writing from storage the cache does not interact with storage directly the application does the following look for entry in cache resulting in a cache miss load entry from the database add entry to cache return entry def get user self user id user cache get user 0 user id if user is none user db query select from users where user id 0 user id if user is not none key user 0 format user id cache set key json dumps user return user memcached https memcached org is generally used in this manner subsequent reads of data added to cache are fast cache aside is also referred to as lazy loading only requested data is cached which avoids filling up the cache with data that isn t requested disadvantage s cache aside each cache miss results in three trips which can cause a noticeable delay data can become stale if it is updated in the database this issue is mitigated by setting a time to live ttl which forces an update of the cache entry or by using write through when a node fails it is replaced by a new empty node increasing latency write through p align center img src http i imgur com 0vbc0hn png br i a href http www slideshare net jboner scalability availability stability patterns source scalability availability stability patterns a i p the application uses the cache as the main data store reading and writing data to it while the cache is responsible for reading and writing to the database application adds updates entry in cache cache synchronously writes entry to data store return application code set user 12345 foo bar cache code def set user user id values user db query update users where id 0 user id values cache set user id user write through is a slow overall operation due to the write operation but subsequent reads of just written data are fast users are generally more tolerant of latency when updating data than reading data data in the cache is not stale disadvantage s write through when a new node is created due to failure or scaling the new node will not cache entries until the entry is updated in the database cache aside in conjunction with write through can mitigate this issue most data written might never read which can be minimized with a ttl write behind write back p align center img src http i imgur com rgsrvjg png br i a href http www slideshare net jboner scalability availability stability patterns source scalability availability stability patterns a i p in write behind the application does the following add update entry in cache asynchronously write entry to the data store improving write performance disadvantage s write behind there could be data loss if the cache goes down prior to its contents hitting the data store it is more complex to implement write behind than it is to implement cache aside or write through refresh ahead p align center img src http i imgur com kxtjqge png br i a href http www slideshare net tmatyashovsky from cache to in memory data grid introduction to hazelcast source from cache to in memory data grid a i p you can configure the cache to automatically refresh any recently accessed cache entry prior to its expiration refresh ahead can result in reduced latency vs read through if the cache can accurately predict which items are likely to be needed in the future disadvantage s refresh ahead not accurately predicting which items are likely to be needed in the future can result in reduced performance than without refresh ahead disadvantage s cache need to maintain consistency between caches and the source of truth such as the database through cache invalidation https en wikipedia org wiki cache algorithms cache invalidation is a difficult problem there is additional complexity associated with when to update the cache need to make application changes such as adding redis or memcached source s and further reading from cache to in memory data grid http www slideshare net tmatyashovsky from cache to in memory data grid introduction to hazelcast scalable system design patterns http horicky blogspot com 2010 10 scalable system design patterns html introduction to architecting systems for scale http lethain com introduction to architecting systems for scale scalability availability stability patterns http www slideshare net jboner scalability availability stability patterns scalability http www lecloud net post 9246290032 scalability for dummies part 3 cache aws elasticache strategies http docs aws amazon com amazonelasticache latest userguide strategies html wikipedia https en wikipedia org wiki cache computing asynchronism p align center img src http i imgur com 54gyssx png br i a href http lethain com introduction to architecting systems for scale platform layer source intro to architecting systems for scale a i p asynchronous workflows help reduce request times for expensive operations that would otherwise be performed in line they can also help by doing time consuming work in advance such as periodic aggregation of data message queues message queues receive hold and deliver messages if an operation is too slow to perform inline you can use a message queue with the following workflow an application publishes a job to the queue then notifies the user of job status a worker picks up the job from the queue processes it then signals the job is complete the user is not blocked and the job is processed in the background during this time the client might optionally do a small amount of processing to make it seem like the task has completed for example if posting a tweet the tweet could be instantly posted to your timeline but it could take some time before your tweet is actually delivered to all of your followers redis is useful as a simple message broker but messages can be lost rabbitmq is popular but requires you to adapt to the amqp protocol and manage your own nodes amazon sqs is hosted but can have high latency and has the possibility of messages being delivered twice task queues tasks queues receive tasks and their related data runs them then delivers their results they can support scheduling and can be used to run computationally intensive jobs in the background celery has support for scheduling and primarily has python support back pressure if queues start to grow significantly the queue size can become larger than memory resulting in cache misses disk reads and even slower performance back pressure http mechanical sympathy blogspot com 2012 05 apply back pressure when overloaded html can help by limiting the queue size thereby maintaining a high throughput rate and good response times for jobs already in the queue once the queue fills up clients get a server busy or http 503 status code to try again later clients can retry the request at a later time perhaps with exponential backoff https en wikipedia org wiki exponential backoff disadvantage s asynchronism use cases such as inexpensive calculations and realtime workflows might be better suited for synchronous operations as introducing queues can add delays and complexity source s and further reading it s all a numbers game https www youtube com watch v 1kryh75wgy4 applying back pressure when overloaded http mechanical sympathy blogspot com 2012 05 apply back pressure when overloaded html little s law https en wikipedia org wiki little 27s law what is the difference between a message queue and a task queue https www quora com what is the difference between a message queue and a task queue why would a task queue require a message broker like rabbitmq redis celery or ironmq to function communication p align center img src http i imgur com 5keocqs jpg br i a href http www escotal com osilayer html source osi 7 layer model a i p hypertext transfer protocol http http is a method for encoding and transporting data between a client and a server it is a request response protocol clients issue requests and servers issue responses with relevant content and completion status info about the request http is self contained allowing requests and responses to flow through many intermediate routers and servers that perform load balancing caching encryption and compression a basic http request consists of a verb method and a resource endpoint below are common http verbs verb description idempotent safe cacheable get reads a resource yes yes yes post creates a resource or trigger a process that handles data no no yes if response contains freshness info put creates or replace a resource yes no no patch partially updates a resource no no yes if response contains freshness info delete deletes a resource yes no no can be called many times without different outcomes http is an application layer protocol relying on lower level protocols such as tcp and udp source s and further reading http what is http https www nginx com resources glossary http difference between http and tcp https www quora com what is the difference between http protocol and tcp protocol difference between put and patch https laracasts com discuss channels general discussion whats the differences between put and patch page 1 transmission control protocol tcp p align center img src http i imgur com jdasdvg jpg br i a href http www wildbunny co uk blog 2012 10 09 how to make a multi player game part 1 source how to make a multiplayer game a i p tcp is a connection oriented protocol over an ip network https en wikipedia org wiki internet protocol connection is established and terminated using a handshake https en wikipedia org wiki handshaking all packets sent are guaranteed to reach the destination in the original order and without corruption through sequence numbers and checksum fields https en wikipedia org wiki transmission control protocol checksum computation for each packet acknowledgement https en wikipedia org wiki acknowledgement data networks packets and automatic retransmission if the sender does not receive a correct response it will resend the packets if there are multiple timeouts the connection is dropped tcp also implements flow control https en wikipedia org wiki flow control data and congestion control https en wikipedia org wiki network congestion congestion control these guarantees cause delays and generally result in less efficient transmission than udp to ensure high throughput web servers can keep a large number of tcp connections open resulting in high memory usage it can be expensive to have a large number of open connections between web server threads and say a memcached memcached server connection pooling https en wikipedia org wiki connection pool can help in addition to switching to udp where applicable tcp is useful for applications that require high reliability but are less time critical some examples include web servers database info smtp ftp and ssh use tcp over udp when you need all of the data to arrive intact you want to automatically make a best estimate use of the network throughput user datagram protocol udp p align center img src http i imgur com yzdrjta jpg br i a href http www wildbunny co uk blog 2012 10 09 how to make a multi player game part 1 source how to make a multiplayer game a i p udp is connectionless datagrams analogous to packets are guaranteed only at the datagram level datagrams might reach their destination out of order or not at all udp does not support congestion control without the guarantees that tcp support udp is generally more efficient udp can broadcast sending datagrams to all devices on the subnet this is useful with dhcp https en wikipedia org wiki dynamic host configuration protocol because the client has not yet received an ip address thus preventing a way for tcp to stream without the ip address udp is less reliable but works well in real time use cases such as voip video chat streaming and realtime multiplayer games use udp over tcp when you need the lowest latency late data is worse than loss of data you want to implement your own error correction source s and further reading tcp and udp networking for game programming http gafferongames com networking for game programmers udp vs tcp key differences between tcp and udp protocols http www cyberciti biz faq key differences between tcp and udp protocols difference between tcp and udp http stackoverflow com questions 5970383 difference between tcp and udp transmission control protocol https en wikipedia org wiki transmission control protocol user datagram protocol https en wikipedia org wiki user datagram protocol scaling memcache at facebook http www cs bu edu jappavoo jappavoo github com 451 papers memcache fb pdf remote procedure call rpc p align center img src http i imgur com if4mkb5 png br i a href http www puncsky com blog 2016 02 14 crack the system design interview source crack the system design interview a i p in an rpc a client causes a procedure to execute on a different address space usually a remote server the procedure is coded as if it were a local procedure call abstracting away the details of how to communicate with the server from the client program remote calls are usually slower and less reliable than local calls so it is helpful to distinguish rpc calls from local calls popular rpc frameworks include protobuf https developers google com protocol buffers thrift https thrift apache org and avro https avro apache org docs current rpc is a request response protocol client program calls the client stub procedure the parameters are pushed onto the stack like a local procedure call client stub procedure marshals packs procedure id and arguments into a request message client communication module os sends the message from the client to the server server communication module os passes the incoming packets to the server stub procedure server stub procedure unmarshalls the results calls the server procedure matching the procedure id and passes the given arguments the server response repeats the steps above in reverse order sample rpc calls get someoperation data anid post anotheroperation data anid anotherdata another value rpc is focused on exposing behaviors rpcs are often used for performance reasons with internal communications as you can hand craft native calls to better fit your use cases choose a native library aka sdk when you know your target platform you want to control how your logic is accessed you want to control how error control happens off your library performance and end user experience is your primary concern http apis following rest tend to be used more often for public apis disadvantage s rpc rpc clients become tightly coupled to the service implementation a new api must be defined for every new operation or use case it can be difficult to debug rpc you might not be able to leverage existing technologies out of the box for example it might require additional effort to ensure rpc calls are properly cached http etherealbits com 2012 12 debunking the myths of rpc rest on caching servers such as squid http www squid cache org representational state transfer rest rest is an architectural style enforcing a client server model where the client acts on a set of resources managed by the server the server provides a representation of resources and actions that can either manipulate or get a new representation of resources all communication must be stateless and cacheable there are four qualities of a restful interface identify resources uri in http use the same uri regardless of any operation change with representations verbs in http use verbs headers and body self descriptive error message status response in http use status codes don t reinvent the wheel hateoas http restcookbook com basics hateoas html interface for http your web service should be fully accessible in a browser sample rest calls get someresources anid put someresources anid anotherdata another value rest is focused on exposing data it minimizes the coupling between client server and is often used for public http apis rest uses a more generic and uniform method of exposing resources through uris representation through headers https github com for get know your http well blob master headers md and actions through verbs such as get post put delete and patch being stateless rest is great for horizontal scaling and partitioning disadvantage s rest with rest being focused on exposing data it might not be a good fit if resources are not naturally organized or accessed in a simple hierarchy for example returning all updated records from the past hour matching a particular set of events is not easily expressed as a path with rest it is likely to be implemented with a combination of uri path query parameters and possibly the request body rest typically relies on a few verbs get post put delete and patch which sometimes doesn t fit your use case for example moving expired documents to the archive folder might not cleanly fit within these verbs fetching complicated resources with nested hierarchies requires multiple round trips between the client and server to render single views e g fetching content of a blog entry and the comments on that entry for mobile applications operating in variable network conditions these multiple roundtrips are highly undesirable over time more fields might be added to an api response and older clients will receive all new data fields even those that they do not need as a result it bloats the payload size and leads to larger latencies rpc and rest calls comparison operation rpc rest signup post signup post persons resign post resign br br personid 1234 br delete persons 1234 read a person get readperson personid 1234 get persons 1234 read a person s items list get readusersitemslist personid 1234 get persons 1234 items add an item to a person s items post additemtousersitemslist br br personid 1234 br itemid 456 br post persons 1234 items br br itemid 456 br update an item post modifyitem br br itemid 456 br key value br put items 456 br br key value br delete an item post removeitem br br itemid 456 br delete items 456 p align center i a href https apihandyman io do you really know why you prefer rest over rpc source do you really know why you prefer rest over rpc a i p source s and further reading rest and rpc do you really know why you prefer rest over rpc https apihandyman io do you really know why you prefer rest over rpc when are rpc ish approaches more appropriate than rest http programmers stackexchange com a 181186 rest vs json rpc http stackoverflow com questions 15056878 rest vs json rpc debunking the myths of rpc and rest http etherealbits com 2012 12 debunking the myths of rpc rest what are the drawbacks of using rest https www quora com what are the drawbacks of using restful apis crack the system design interview http www puncsky com blog 2016 02 14 crack the system design interview thrift https code facebook com posts 1468950976659943 why rest for internal use and not rpc http arstechnica com civis viewtopic php t 1190508 security this section could use some updates consider contributing contributing security is a broad topic unless you have considerable experience a security background or are applying for a position that requires knowledge of security you probably won t need to know more than the basics encrypt in transit and at rest sanitize all user inputs or any input parameters exposed to user to prevent xss https en wikipedia org wiki cross site scripting and sql injection https en wikipedia org wiki sql injection use parameterized queries to prevent sql injection use the principle of least privilege https en wikipedia org wiki principle of least privilege source s and further reading security guide for developers https github com fallibleinc security guide for developers owasp top ten https www owasp org index php owasp top ten cheat sheet appendix you ll sometimes be asked to do back of the envelope estimates for example you might need to determine how long it will take to generate 100 image thumbnails from disk or how much memory a data structure will take the powers of two table and latency numbers every programmer should know are handy references powers of two table power exact value approx value bytes 7 128 8 256 10 1024 1 thousand 1 kb 16 65 536 64 kb 20 1 048 576 1 million 1 mb 30 1 073 741 824 1 billion 1 gb 32 4 294 967 296 4 gb 40 1 099 511 627 776 1 trillion 1 tb source s and further reading powers of two https en wikipedia org wiki power of two latency numbers every programmer should know latency comparison numbers l1 cache reference 0 5 ns branch mispredict 5 ns l2 cache reference 7 ns 14x l1 cache mutex lock unlock 100 ns main memory reference 100 ns 20x l2 cache 200x l1 cache compress 1k bytes with zippy 10 000 ns 10 us send 1 kb bytes over 1 gbps network 10 000 ns 10 us read 4 kb randomly from ssd 150 000 ns 150 us 1gb sec ssd read 1 mb sequentially from memory 250 000 ns 250 us round trip within same datacenter 500 000 ns 500 us read 1 mb sequentially from ssd 1 000 000 ns 1 000 us 1 ms 1gb sec ssd 4x memory disk seek 10 000 000 ns 10 000 us 10 ms 20x datacenter roundtrip read 1 mb sequentially from 1 gbps 10 000 000 ns 10 000 us 10 ms 40x memory 10x ssd read 1 mb sequentially from disk 30 000 000 ns 30 000 us 30 ms 120x memory 30x ssd send packet ca netherlands ca 150 000 000 ns 150 000 us 150 ms notes 1 ns 10 9 seconds 1 us 10 6 seconds 1 000 ns 1 ms 10 3 seconds 1 000 us 1 000 000 ns handy metrics based on numbers above read sequentially from disk at 30 mb s read sequentially from 1 gbps ethernet at 100 mb s read sequentially from ssd at 1 gb s read sequentially from main memory at 4 gb s 6 7 world wide round trips per second 2 000 round trips per second within a data center latency numbers visualized https camo githubusercontent com 77f72259e1eb58596b564d1ad823af1853bc60a3 687474703a2f2f692e696d6775722e636f6d2f6b307431652e706e67 source s and further reading latency numbers every programmer should know 1 https gist github com jboner 2841832 latency numbers every programmer should know 2 https gist github com hellerbarde 2843375 designs lessons and advice from building large distributed systems http www cs cornell edu projects ladis2009 talks dean keynote ladis2009 pdf software engineering advice from building large scale distributed systems https static googleusercontent com media research google com en people jeff stanford 295 talk pdf additional system design interview questions common system design interview questions with links to resources on how to solve each question reference s design a file sync service like dropbox youtube com https www youtube com watch v pe4gwstwhmc design a search engine like google queue acm org http queue acm org detail cfm id 988407 br stackexchange com http programmers stackexchange com questions 38324 interview question how would you implement google search br ardendertat com http www ardendertat com 2012 01 11 implementing search engines br stanford edu http infolab stanford edu backrub google html design a scalable web crawler like google quora com https www quora com how can i build a web crawler from scratch design google docs code google com https code google com p google mobwrite br neil fraser name https neil fraser name writing sync design a key value store like redis slideshare net http www slideshare net dvirsky introduction to redis design a cache system like memcached slideshare net http www slideshare net oemebamo introduction to memcached design a recommendation system like amazon s hulu com http tech hulu com blog 2011 09 19 recommendation system html br ijcai13 org http ijcai13 org files tutorial slides td3 pdf design a tinyurl system like bitly n00tc0d3r blogspot com http n00tc0d3r blogspot com design a chat app like whatsapp highscalability com http highscalability com blog 2014 2 26 the whatsapp architecture facebook bought for 19 billion html design a picture sharing system like instagram highscalability com http highscalability com flickr architecture br highscalability com http highscalability com blog 2011 12 6 instagram architecture 14 million users terabytes of photos html design the facebook news feed function quora com http www quora com what are best practices for building something like a news feed br quora com http www quora com activity streams what are the scaling issues to keep in mind while developing a social network feed br slideshare net http www slideshare net danmckinley etsy activity feeds architecture design the facebook timeline function facebook com https www facebook com note php note id 10150468255628920 br highscalability com http highscalability com blog 2012 1 23 facebook timeline brought to you by the power of denormaliza html design the facebook chat function erlang factory com http www erlang factory com upload presentations 31 eugeneletuchy erlangatfacebook pdf br facebook com https www facebook com note php note id 14218138919 id 9445547199 index 0 design a graph search function like facebook s facebook com https www facebook com notes facebook engineering under the hood building out the infrastructure for graph search 10151347573598920 br facebook com https www facebook com notes facebook engineering under the hood indexing and ranking in graph search 10151361720763920 br facebook com https www facebook com notes facebook engineering under the hood the natural language interface of graph search 10151432733048920 design a content delivery network like cloudflare cmu edu http repository cmu edu cgi viewcontent cgi article 2112 context compsci design a trending topic system like twitter s michael noll com http www michael noll com blog 2013 01 18 implementing real time trending topics in storm br snikolov wordpress com http snikolov wordpress com 2012 11 14 early detection of twitter trends design a random id generation system blog twitter com https blog twitter com 2010 announcing snowflake br github com https github com twitter snowflake return the top k requests during a time interval ucsb edu https icmi cs ucsb edu research tech reports reports 2005 23 pdf br wpi edu http davis wpi edu xmdv docs edbt11 diyang pdf design a system that serves data from multiple data centers highscalability com http highscalability com blog 2009 8 24 how google serves data from multiple datacenters html design an online multiplayer card game indieflashblog com http www indieflashblog com how to create an asynchronous multiplayer game html br buildnewgames com http buildnewgames com real time multiplayer design a garbage collection system stuffwithstuff com http journal stuffwithstuff com 2013 12 08 babys first garbage collector br washington edu http courses cs washington edu courses csep521 07wi prj rick pdf design an api rate limiter https stripe com blog https stripe com blog rate limiters add a system design question contribute contributing real world architectures articles on how real world systems are designed p align center img src http i imgur com tcuo2fw png br i a href https www infoq com presentations twitter timeline scalability source twitter timelines at scale a i p don t focus on nitty gritty details for the following articles instead identify shared principles common technologies and patterns within these articles study what problems are solved by each component where it works where it doesn t review the lessons learned type system reference s data processing mapreduce distributed data processing from google research google com http static googleusercontent com media research google com zh cn us archive mapreduce osdi04 pdf data processing spark distributed data processing from databricks slideshare net http www slideshare net agrishchenko apache spark architecture data processing storm distributed data processing from twitter slideshare net http www slideshare net previa storm 16094009 data store bigtable distributed column oriented database from google harvard edu http www read seas harvard edu kohler class cs239 w08 chang06bigtable pdf data store hbase open source implementation of bigtable slideshare net http www slideshare net alexbaranau intro to hbase data store cassandra distributed column oriented database from facebook slideshare net http www slideshare net planetcassandra cassandra introduction features 30103666 data store dynamodb document oriented database from amazon harvard edu http www read seas harvard edu kohler class cs239 w08 decandia07dynamo pdf data store mongodb document oriented database slideshare net http www slideshare net mdirolf introduction to mongodb data store spanner globally distributed database from google research google com http research google com archive spanner osdi2012 pdf data store memcached distributed memory caching system slideshare net http www slideshare net oemebamo introduction to memcached data store redis distributed memory caching system with persistence and value types slideshare net http www slideshare net dvirsky introduction to redis file system google file system gfs distributed file system research google com http static googleusercontent com media research google com zh cn us archive gfs sosp2003 pdf file system hadoop file system hdfs open source implementation of gfs apache org https hadoop apache org docs r1 2 1 hdfs design html misc chubby lock service for loosely coupled distributed systems from google research google com http static googleusercontent com external content untrusted dlcp research google com en us archive chubby osdi06 pdf misc dapper distributed systems tracing infrastructure research google com http static googleusercontent com media research google com en pubs archive 36356 pdf misc kafka pub sub message queue from linkedin slideshare net http www slideshare net mumrah kafka talk tri hug misc zookeeper centralized infrastructure and services enabling synchronization slideshare net http www slideshare net sauravhaloi introduction to apache zookeeper add an architecture contribute contributing company architectures company reference s amazon amazon architecture http highscalability com amazon architecture cinchcast producing 1 500 hours of audio every day http highscalability com blog 2012 7 16 cinchcast architecture producing 1500 hours of audio every d html datasift realtime datamining at 120 000 tweets per second http highscalability com blog 2011 11 29 datasift architecture realtime datamining at 120000 tweets p html dropbox how we ve scaled dropbox https www youtube com watch v pe4gwstwhmc espn operating at 100 000 duh nuh nuhs per second http highscalability com blog 2013 11 4 espns architecture at scale operating at 100000 duh nuh nuhs html google google architecture http highscalability com google architecture instagram 14 million users terabytes of photos http highscalability com blog 2011 12 6 instagram architecture 14 million users terabytes of photos html br what powers instagram http instagram engineering tumblr com post 13649370142 what powers instagram hundreds of instances justin tv justin tv s live video broadcasting architecture http highscalability com blog 2010 3 16 justintvs live video broadcasting architecture html facebook scaling memcached at facebook https cs uwaterloo ca brecht courses 854 emerging 2014 readings key value fb memcached nsdi 2013 pdf br tao facebook s distributed data store for the social graph https cs uwaterloo ca brecht courses 854 emerging 2014 readings data store tao facebook distributed datastore atc 2013 pdf br facebook s photo storage https www usenix org legacy event osdi10 tech full papers beaver pdf br how facebook live streams to 800 000 simultaneous viewers http highscalability com blog 2016 6 27 how facebook live streams to 800000 simultaneous viewers html flickr flickr architecture http highscalability com flickr architecture mailbox from 0 to one million users in 6 weeks http highscalability com blog 2013 6 18 scaling mailbox from 0 to one million users in 6 weeks and 1 html netflix netflix what happens when you press play http highscalability com blog 2017 12 11 netflix what happens when you press play html pinterest from 0 to 10s of billions of page views a month http highscalability com blog 2013 4 15 scaling pinterest from 0 to 10s of billions of page views a html br 18 million visitors 10x growth 12 employees http highscalability com blog 2012 5 21 pinterest architecture update 18 million visitors 10x growth html playfish 50 million monthly users and growing http highscalability com blog 2010 9 21 playfishs social gaming architecture 50 million monthly user html plentyoffish plentyoffish architecture http highscalability com plentyoffish architecture salesforce how they handle 1 3 billion transactions a day http highscalability com blog 2013 9 23 salesforce architecture how they handle 13 billion transacti html stack overflow stack overflow architecture http highscalability com blog 2009 8 5 stack overflow architecture html tripadvisor 40m visitors 200m dynamic page views 30tb data http highscalability com blog 2011 6 27 tripadvisor architecture 40m visitors 200m dynamic page view html tumblr 15 billion page views a month http highscalability com blog 2012 2 13 tumblr architecture 15 billion page views a month and harder html twitter making twitter 10000 percent faster http highscalability com scaling twitter making twitter 10000 percent faster br storing 250 million tweets a day using mysql http highscalability com blog 2011 12 19 how twitter stores 250 million tweets a day using mysql html br 150m active users 300k qps a 22 mb s firehose http highscalability com blog 2013 7 8 the architecture twitter uses to deal with 150m active users html br timelines at scale https www infoq com presentations twitter timeline scalability br big and small data at twitter https www youtube com watch v 5cktp36hvgi br operations at twitter scaling beyond 100 million users https www youtube com watch v z8lu0cj6bou uber how uber scales their real time market platform http highscalability com blog 2015 9 14 how uber scales their real time market platform html br lessons learned from scaling uber to 2000 engineers 1000 services and 8000 git repositories http highscalability com blog 2016 10 12 lessons learned from scaling uber to 2000 engineers 1000 ser html whatsapp the whatsapp architecture facebook bought for 19 billion http highscalability com blog 2014 2 26 the whatsapp architecture facebook bought for 19 billion html youtube youtube scalability https www youtube com watch v w5wvu624fy8 br youtube architecture http highscalability com youtube architecture company engineering blogs architectures for companies you are interviewing with questions you encounter might be from the same domain airbnb engineering http nerds airbnb com atlassian developers https developer atlassian com blog autodesk engineering http cloudengineering autodesk com blog aws blog https aws amazon com blogs aws bitly engineering blog http word bitly com box blogs https www box com blog engineering cloudera developer blog http blog cloudera com blog dropbox tech blog https tech dropbox com engineering at quora http engineering quora com ebay tech blog http www ebaytechblog com evernote tech blog https blog evernote com tech etsy code as craft http codeascraft com facebook engineering https www facebook com engineering flickr code http code flickr net foursquare engineering blog http engineering foursquare com github engineering blog http githubengineering com google research blog http googleresearch blogspot com groupon engineering blog https engineering groupon com heroku engineering blog https engineering heroku com hubspot engineering blog http product hubspot com blog topic engineering high scalability http highscalability com instagram engineering http instagram engineering tumblr com intel software blog https software intel com en us blogs jane street tech blog https blogs janestreet com category ocaml linkedin engineering http engineering linkedin com blog microsoft engineering https engineering microsoft com microsoft python engineering https blogs msdn microsoft com pythonengineering netflix tech blog http techblog netflix com paypal developer blog https devblog paypal com category engineering pinterest engineering blog http engineering pinterest com quora engineering https engineering quora com reddit blog http www redditblog com salesforce engineering blog https developer salesforce com blogs engineering slack engineering blog https slack engineering spotify labs https labs spotify com twilio engineering blog http www twilio com engineering twitter engineering https engineering twitter com uber engineering blog http eng uber com yahoo engineering blog http yahooeng tumblr com yelp engineering blog http engineeringblog yelp com zynga engineering blog https www zynga com blogs engineering source s and further reading looking to add a blog to avoid duplicating work consider adding your company blog to the following repo kilimchoi engineering blogs https github com kilimchoi engineering blogs under development interested in adding a section or helping complete one in progress contribute contributing distributed computing with mapreduce consistent hashing scatter gather contribute contributing credits credits and sources are provided throughout this repo special thanks to hired in tech http www hiredintech com system design the system design process cracking the coding interview https www amazon com dp 0984782850 high scalability http highscalability com checkcheckzz system design interview https github com checkcheckzz system design interview shashank88 system design https github com shashank88 system design mmcgrana services engineering https github com mmcgrana services engineering system design cheat sheet https gist github com vasanthk 485d1c25737e8e72759f a distributed systems reading list http dancres github io pages cracking the system design interview http www puncsky com blog 2016 02 14 crack the system design interview contact info feel free to contact me to discuss any issues questions or comments my contact info can be found on my github page https github com donnemartin license i am providing code and resources in this repository to you under an open source license because this is my personal repository the license you receive to my code and resources is from me and not my employer facebook copyright 2017 donne martin creative commons attribution 4 0 international license cc by 4 0 http creativecommons org licenses by 4 0 | system system-design translation interview-questions traditional-chinese | os |
cloud-conversion-tool-api | description cloud conversion tool is an api gateway project in python with flask to receive files and convert to another file format this is a project for cloud software engineering made with python https img shields io badge python 2b5b84 style for the badge logo python logocolor white labelcolor 000000 flask https img shields io badge flask 000000 style for the badge logo flask logocolor white labelcolor 000000 prerequirements python redis docker docker compose optional for linux and mac you can use makefile for windows you can use bash function important it is important to have all the projects and repositories in the next folder screenshot 2023 10 23 at 00 23 41 https github com zearkiatos cloud conversion tool api assets 10298615 34ee3120 3c58 4bc8 bedb 4ec8bdf83e12 how to execute if you want execute without docker then execute the next commands in your terminal note firstable is important that you have your python virtual environmente created 1 firstly you should have the env please follow the file env example you should have something like this txt flask app flaskr app flask env development app name cloud conversion tool api redis broker base url your redis broker secret key your secret key jwt secret key your secret key data base uri your postgres uri topic task posted task convert path storage app videos app url app url 2 first step you should activate the python environment sh with linux or mac make activate with windows source run sh activate with python flask python3 m venv venv source venv bin activate 3 second step you should install all the dependencies and python packages sh with linux or mac make install with windows source run sh install with python flask pip install r requirements txt 4 finally into directory flaskr execute bash with linux or mac you can send a specific port with port 8000 param make run or make run port 8000 with windows you can send a specific port with 8000 param source run sh run or source run sh run 8000 with python flask flask run how to execute with docker 1 step one locate in the root of the project bash cd cloud conversion tool api 2 run in docker bash with linux or mac make docker dev up command to run with nginx and proxy reverse make docker up with windows source run sh docker dev up command to run with nginx and proxy reverse source run sh docker up with docker compose for all operative systems docker compose f docker compose develop yml up build command to run with nginx and proxy reverse docker compose up build 3 make sure that all microservices are running executing this command bash docker ps screenshot 2023 10 23 at 00 39 06 https github com zearkiatos cloud conversion tool api assets 10298615 6394dc98 6df5 45c0 a019 a122183a0b66 5 finally shutdown the environment in docker bash with linux or mac make docker dev down with windows source run sh docker dev down with docker compose for all operative systems docker compose f docker compose develop yml down | cloud |
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ux-pattern-library | edx pattern library the working ui library and front end styleguide for edx open edx applications and sites github version https badge fury io gh edx 2fux pattern library svg https badge fury io gh edx 2fux pattern library npm version https badge fury io js edx pattern library svg https badge fury io js edx pattern library bower version https badge fury io bo edx pattern library svg https badge fury io bo edx pattern library a href https nodei co npm edx pattern library img src https nodei co npm edx pattern library png downloads true downloadrank true stars true a table of contents 1 overview overview 2 license license 3 dependencies dependencies 4 documentation documentation 5 getting started getting started 6 contributions contributions overview this library contains the following a working preview and documentation system for edx application ui http ux edx org known as pldoc styleguides and standards for general front end https github com edx ux pattern library wiki styleguide general html https github com edx ux pattern library wiki styleguide html sass css https github com edx ux pattern library wiki styleguide sass css and accessibility minded https github com edx ux pattern library wiki styleguide accessibility development portable sass css utilities and modules for use within edx applications license the code in this repository is licensed the apache 2 0 license unless otherwise noted please see the license file https github com edx ux pattern library blob master license for details dependencies using the edx pattern library source code in a project current requires locally installing a package manager either node npm https nodejs org or bower http bower io use of modern web browsers see open edx edx browser support http docstrings readthedocs org en latest front matter browsers html the use and compilation of sass into css using perferrably libsass http sass lang com libsass if using the sass method for including the pattern library third party dependencies also the pattern library currently relies on the following thrd party libraries library version purpose bourbon https github com thoughtbot bourbon 4 2 6 basic sass css utilities bi app sass https github com anasnakawa bi app sass latest right to left left to right directional support for layouts breakpoint sass https github com at import breakpoint 2 6 1 css breakpoint media query management font awesome https github com fortawesome font awesome 4 6 3 accessibility minded iconic font documentation the edx pattern library has its own living documentation site at http ux edx org additionally we have many styleguides and how to wiki documents https github com edx ux pattern library wiki in the github repository getting started if you want to use the edx pattern library in your project or work please see how to work and develop on documentation site pldoc https github com edx ux pattern library wiki how to use and deploy the uxpl in your project if you d like to set up or work on the living documentation site locally check out how to work and develop on documentation site pldoc https github com edx ux pattern library wiki how to work and develop on documentation site pldoc if you d like to work on or contribute to the edx pattern library package read how to work develop on the edx pattern library package https github com edx ux pattern library wiki how to workdevelop on the ux pattern library package contributions contributions are very welcome the easiest way is to fork this repo and then make a pull request from your fork the first time you make a pull request you may be asked to sign a contributor agreement please refer to our contributor guidelines https github com edx edx platform blob master contributing rst in the main edx platform repo for important additional information contributing and the edx pattern library there are a few additional details alongside our general guidelines to keep in mind contributing to the edx pattern library pattern library features ideas and improvements if you re looking to suggest an idea or you re thinking about developing a feature start a discussion by visiting the open edx jira site https openedx atlassian net secure dashboard jspa and create a new issue by selecting the create button at the top of the page choose the project edx pattern library and the issue type new feature or improvement you may first need to create a free jira account https openedx atlassian net admin users sign up bugs and issues if you notice an issue or a bug with the pattern library we would love ot hear about it follow the above instructions on logging a new edx pattern library jira issue and then assign the issue type of bug to your issue an edx ux front end development team member will then take it from there and triage your bug conversely if you want to help resolve any known bugs issues https openedx atlassian net projects uxpl issues which are tracked in jira you can create a free jira account https openedx atlassian net admin users sign up and then comment on the ticket to indicate that you are working on it don t hesitate to ask clarifying questions on the ticket as needed too if anything is unclear submitting code for code contributions please open up a pull request prs will get ospr tickets assigned to them as mentioned in the above contributing guidelines approval by ux and front end team members an edx ux or front end development team member will be working with you on any pull requests you make they will be evaulating your pull request from a design point of view as well as from a front end development perspective other team members as well as ui front end developers may also lend a hand tests the edx pattern library source code doesn t currently leverage the open edx test suite nor are there any automated tests configured for this codebase currently front end development standards in addition to the general contributor documentation any contributions should meet specific front end development requirements including the guidelines and principles listed in general front end styleguide https github com edx ux pattern library wiki styleguide general accessibility styleguide https github com edx ux pattern library wiki styleguide accessibility html styleguide https github com edx ux pattern library wiki styleguide html forms styleguide https github com edx ux pattern library wiki styleguide forms sass css styleguide https github com edx ux pattern library wiki styleguide sass css including right to left support https github com edx ux pattern library wiki styleguide sass css right to left rtl support javascript styleguide https github com edx ux pattern library wiki styleguide javascript | front_end |
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enrichment_models | a name readme top a contributors contributors shield contributors url forks forks shield forks url stargazers stars shield stars url issues issues shield issues url mit license license shield license url linkedin linkedin shield linkedin url project logo br div align center a href https github com ntropy network enrichment models img src https developers ntropy com img ntropy logo svg alt logo width 80 height 80 a h2 align center financial transaction models benchmark h2 h3 align center this repository provides a benchmark of the ntropy api and different large language models openai chatgpt and llama finetuned models in the task of transaction enrichment it also contains an easy to use wrapper that enables using the llms to perform transaction enrichment llama adapters were open sourced and are available on huggingface hub h3 div table of contents details summary table of contents summary ol li a href benchmark benchmark a li li a href usage installation a li li a href usage usage a li li a href contributing contributing a li li a href license license a li li a href contact contact a li li a href ressources ressources a li ol details benchmark benchmark we benchmarked ntropy s api and a set of llms in the task of extracting the following fields label merchant and website ntropy s api is compared against openai s llm s gpt 4 using a straightforward prompt llama finetuned models 7b 13b params on consumer transactions data with lora adapters the dataset used can be found here datasets 100 labeled consumer transactions csv https github com ntropy network enrichment models blob main datasets 100 labeled consumer transactions csv all predictions can be found here datasets benchmark predictions csv https github com ntropy network enrichment models blob main datasets benchmark predictions csv it consists of a random subset of 100 anonymized consumer transactions the full label list can be found here https api ntropy com v2 labels hierarchy consumer gpt 4 llama finetuned 7b llama finetuned 13b ntropy api labeler accuracy 0 71 0 72 0 78 0 86 labeler f1 score 0 64 0 56 0 65 0 73 labeler label similarity 0 85 0 82 0 87 0 91 labeler latency s tx 1 47 0 27 0 34 0 01 merchant accuracy 0 66 0 87 website accuracy 0 69 0 87 normalizer latency s tx 4 45 0 01 label similarity is an approximate metric that uses embeddings distance to give a smoother metric than the accuracy ex 2 similar labels will have a score close to 1 while 2 very different semantically will have a score close to 0 you can see more details in tests integration test openai test label similarity score https github com ntropy network enrichment models blob main tests integration test openai py l29 among the models evaluated ntropy demonstrates the best metrics in terms of accuracy and latency this superiority can be attributed to several factors including its access to web search engines and internal merchant databases moreover ntropy s internal models have been fine tuned specifically for financial tasks contributing to their effectiveness to get accurate labels we noticed that when a llama model is fine tuned on consumer transactions even without having access to external information about merchants it achieves a higher accuracy compared to gpt 4 by 7 points this suggests that llm s models possess a considerable amount of knowledge about companies even though measuring this knowledge directly can be challenging additionally retrieving cleaned company names and websites appears to be more difficult for these models based on this dataset it is interesting to note that gpt 4 has the ability to generate websites that appear to be correct at first glance but in reality do not exist for instance kwikcash http www kwikcash com instead of https www kwikcashonline com clark s pump n shop https pumpnshop com instead of https www myclarkspns com note llama models were benchmarked on a single a100 gpu p align right a href readme top back to top a p installation installation this project uses python 3 10 the python package that can be installed either using poetry or pip poetry poetry install poetry shell pip pip install depending on which model you want to run you need at least one of the following or all for running the full benchmark ntropy api key for using the ntropy api you need an api key go to https dashboard ntropy com create an account you can login with a google account but you must use a company domain on the left side menu you can click on api keys and then click on create api key copy the api key and paste it here enrichment models init py https github com ntropy network enrichment models blob main enrichment models init py note you will get a limit of 10 000 transactions with a free account if you need more please contact us openai api key for using the openai models you ll need an api key go to https platform openai com create an account on the dropdown menu click on view api keys then create new secret key copy the api key and paste it here enrichment models init py https github com ntropy network enrichment models blob main enrichment models init py llama requirements the llama adapters are open sourced and can be used from the huggingface hub the models have 2 variants 7b params 13b params 16bits and can be found at the following urls https huggingface co ntropydev ntropy labeler llama lora 7b https huggingface co ntropydev ntropy labeler llama lora 13b note minimum 32gb of ram are needed to run llama models better if you have access to some gpu s with enough vram p align right a href readme top back to top a p usage examples usage benchmark if you want to run the full benchmark after setting up api key s in enrichment models init py https github com ntropy network enrichment models blob main enrichment models init py you can just run make benchmark or python scripts full benchmark py this will print results on the terminal as well as dumping metrics and predictions in the datasets folder model integration if you want to integrate one of these models you can just take examples on the notebooks in the notebooks folder also if you want to integrate ntropy s api you might want to have a look at the documentation https developers ntropy com there is one notebook per model ntropy openai and llama contributing contributing we welcome and appreciate any pull request that suggests enhancements or introduces new models apis and so on to add to the benchmark table p align right a href readme top back to top a p license license distributed under the mit license see license https github com ntropy network enrichment models blob main license for more information p align right a href readme top back to top a p contact contact support ntropy com support ntropy com acknowledgments ressources main project dependencies alpaca lora https github com tloen alpaca lora openai python https github com openai openai python ntropy sdk https github com ntropy network ntropy sdk p align right a href readme top back to top a p markdown links images https www markdownguide org basic syntax reference style links contributors shield https img shields io github contributors ntropy network enrichment models svg style for the badge contributors url https github com ntropy network enrichment models graphs contributors forks shield https img shields io github forks ntropy network enrichment models svg style for the badge forks url https github com ntropy network enrichment models network members stars shield https img shields io github stars ntropy network enrichment models svg style for the badge stars url https github com ntropy network enrichment models stargazers issues shield https img shields io github issues ntropy network enrichment models svg style for the badge issues url https github com ntropy network enrichment models issues license shield https img shields io github license ntropy network enrichment models svg style for the badge license url https github com ntropy network enrichment models blob main license txt linkedin shield https img shields io badge linkedin black svg style for the badge logo linkedin colorb 555 linkedin url https www linkedin com company ntropy | financial-data gpt-4 llama llm machine-learning nlp ntropy openai transactions | ai |
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