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--- |
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title: Image To Text App |
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emoji: 📹 |
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colorFrom: blue |
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colorTo: red |
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sdk: streamlit |
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app_file: app.py |
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pinned: false |
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--- |
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# Kidney-disease-classification-mlops |
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## Workflows |
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1. Update config.yaml |
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2. Update secrets.yaml [Optional] |
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3. Update params.yaml |
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4. Update the entity |
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5. Update the configuration manager in src config |
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6. Update the components |
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7. Update the pipeline |
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8. Update the main.py |
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9. Update the dvc.yaml |
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10. app.py |
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# How to run? |
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### STEPS: |
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Clone the repository |
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```bash |
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https://github.com/HAKIM-ML/ |
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Kidney-disease-classification-mlops |
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``` |
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### STEP 01- Create a conda environment after opening the repository |
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```bash |
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conda create -n cnncls python=3.8 -y |
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``` |
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```bash |
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conda activate cnncls |
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``` |
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### STEP 02- install the requirements |
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```bash |
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pip install -r requirements.txt |
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``` |
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```bash |
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# Finally run the following command |
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python app.py |
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``` |
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Now, |
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```bash |
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open up you local host and port |
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``` |
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## MLflow |
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- [Documentation](https://mlflow.org/docs/latest/index.html) |
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##### cmd |
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- mlflow ui |
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### dagshub |
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[dagshub](https://dagshub.com/) |
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MLFLOW_TRACKING_URI = https://dagshub.com/azizulhakim8291/Kidney-disease-classification-mlops.mlflow |
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python script.py |
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import dagshub |
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dagshub.init(repo_owner='azizulhakim8291', repo_name='Kidney-disease-classification-mlops', mlflow=True) |
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import mlflow |
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with mlflow.start_run(): |
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mlflow.log_param('parameter name', 'value') |
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mlflow.log_metric('metric name', 1) |
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### DVC cmd |
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1. dvc init |
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2. dvc repro |
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3. dvc dag |
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## About MLflow & DVC |
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MLflow |
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- Its Production Grade |
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- Trace all of your expriements |
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- Logging & taging your model |
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DVC |
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- Its very lite weight for POC only |
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- lite weight expriements tracker |
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- It can perform Orchestration (Creating Pipelines) |
