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README.md

Tutorials for SparseML

Tutorials, which are implemented as Jupyter Notebooks for easy consumption and editing, are provided under the notebooks directory.

Assuming you are running the notebooks from within a virtual environment (recommended), you may follow the steps below to prepare and launch your notebooks:

  1. Create a kernel attached to the environment: 
    python -m ipykernel install --user --name your_env --display-name "Python (your_env)".

    This kernel should then be available for you under the "Kernel > Change kernel" menu item.

  2. If a notebook displays TensorBoard and you are running it from a remote server, you may forward the port that TensorBoard uses (by default 6006) to your local machine: 
    ssh -N -f -L localhost:6006:localhost:6006 user@remote_ip_address

    Tip: If the port is unavailable, you may look for the process using it with sudo lsof -i :6006 and release it with kill -9 <PROCESS_ID>. The above binding command also allows you to view TensorBoard outside your notebook by going to localhost:6006 from your local machine.

  3. Some notebooks may make use of the [ipywidgets](https://github.com/jupyter-widgets/ipywidgets) package. You may need to enable the Jupyter extension to properly see the UIs with the following command: 
    jupyter nbextension enable --py widgetsnbextension.
  4. Start a Jupyter session in the `notebooks` directory, optionally using an available port of your choice (e.g., 8890): 
    cd notebooks
jupyter notebook --port=8890

    Again, if you are running the Jupyter server from a remote server, you may bind the notebook port as you did with TensorBoard, then view it from your local machine with localhost:8890.

Note, the TensorFlow V1 notebooks are tested with TensorFlow version ~= 1.15.0. For best results, confirm your system matches that version.

Script Description
Keras Classification Notebook demonstrating how to prune a Keras classification model using SparseML
PyTorch Classification Notebook demonstrating how to prune a PyTorch classification model using SparseML
PyTorch Detection Notebook demonstrating how to prune a PyTorch detection model using SparseML
TensorFlow V1 Classification Notebook demonstrating how to prune a TensorFlow V1 classification model using SparseML