7880ea8d79
Organize podman/ directory into logical subdirectories: New structure: - docs/ - ML_TOOLS_GUIDE.md, jupyter_workflow.md - configs/ - environment*.yml, security_policy.json - containers/ - *.dockerfile, *.podfile - scripts/ - *.sh, *.py (secure_runner, cli_integration, etc.) - jupyter/ - jupyter_cookie_secret (flattened from jupyter_runtime/runtime/) - workspace/ - Example projects (cleaned of temp files) Cleaned workspace: - Removed .DS_Store, mlflow.db, cache/ - Removed duplicate cli_integration.py Removed unnecessary nesting: - Flattened jupyter_runtime/runtime/ to just jupyter/ Improves maintainability by grouping files by purpose and eliminating root directory clutter.
68 lines
1.3 KiB
Markdown
68 lines
1.3 KiB
Markdown
# ML Tools Integration Guide
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Data scientists can now use their preferred ML tools securely!
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## Available Tools
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- **MLflow** - Experiment tracking and model registry
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- **Weights & Biases** - Experiment tracking and visualization
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- **Streamlit** - Interactive web apps
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- **Dash** - Plotly-based web dashboards
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- **Panel** - Data apps and dashboards
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- **Bokeh** - Interactive visualizations
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## Quick Start
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### 1. Test Tools Available
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```bash
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cd podman
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python test_ml_tools.py
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```
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### 2. Use MLflow
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```python
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import mlflow
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# Start tracking
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with mlflow.start_run():
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mlflow.log_param("epochs", 10)
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mlflow.log_metric("accuracy", 0.95)
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```
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### 3. Launch Streamlit App
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```bash
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# In your secure container
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streamlit run my_app.py --server.port 8501
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```
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### 4. Use Dash Dashboard
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```python
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import dash
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import dash_core_components as dcc
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import dash_html_components as html
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app = dash.Dash(__name__)
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app.run_server(debug=True, host='0.0.0.0', port=8050)
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```
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## Security Features
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- Network access limited to localhost only
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- Tools pre-approved in security policy
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- Container isolation maintained
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- No external internet access
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## Custom Requirements
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Add your own tools via `requirements.txt`:
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```
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mlflow==2.7.0
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wandb==0.16.0
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streamlit==1.28.0
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```
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## Access URLs
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- Streamlit: http://localhost:8501
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- Dash: http://localhost:8050
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- MLflow UI: http://localhost:5000
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