This baseline project shows how to get the most out of Python on Cloudera Data Science Workbench.

Modify the default files to get started with your own project.

• README.md -- This project's readme in Markdown format.
• analysis.py -- An example Python analysis script.
• requirements.txt -- Dependencies that need to be manually installed if you are running analysis.py in ML Runtimes (not necessary if you are using Legacy Engines).
• cdsw-build.sh -- A custom build script used for models and experiments. This will pip install our dependencies, primarily the scikit-learn library.
• fit.py -- A model training example to be run as an experiment. Generates the model.pkl file that contains the fitted parameters of our model.
• predict.py -- A sample function to be deployed as a model. Uses model.pkl produced by fit.py to make predictions about petal width.

1. Click "Open Workbench".
2. Launch a new Python session.
3. Run analysis.py in the workbench.

1. Click "Open Workbench".
2. Run an experiment with fit.py as the input script.
3. Once the experiment is complete, save the model.pkl file to the project.
4. Deploy a model using predict.py. Specify predict as the input function.

For detailed instructions on how to run these scripts, see the documentation.

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