pyprobml

Python 3 code to reproduce the figures in the book series Probabilistic Machine Learning by Kevin Patrick Murphy. This is work in progress, so expect rough edges! (Some demos use code from our companion JAX State Space Library.)

Running the notebooks

The scripts needed to make all the figures for each chapter are automatically combined together into a series of Jupyter notebooks, one per chapter. * Volume 1 figure notebooks * Volume 2 figure notebooks. (Note: volume 2 is not finished yet.)

In addition to the automatically generated notebooks, there are a series of manually created notebooks, which create additional figures, and provide supplementary material for the book. These are stored in the notebooks repo, since they can be quite large. Some of these notebooks use the scripts mentioned above, but others are independent of the book content.

The easiest way to run these notebooks is inside Colab. This has most of the libraries you will need (e.g., scikit-learn, JAX) pre-installed, and gives you access to a free GPU and TPU. We have a created a intro to colab notebook with more details.

Running scripts in colab

The easiest way to run individual scripts is inside Colab. Just cut and paste this into a code cell: py %pip install superimport !git clone --depth 1 https://github.com/probml/pyprobml &> /dev/null # THIS CODEBASE Note: The superimport library will automatically install packages for any file which contains the line `import superimport'.

Then run a script from a cell like this: py %run pyprobml/scripts/softmax_plot.py

To edit a file locally and then run it, follow the example below. ```py

Make sure local changes to file are detected by runtime

%load_ext autoreload %autoreload 2

file = 'pyprobml/scripts/softmax_plot.py' # change this filename as needed from google.colab import files files.view(file) # open editor

%run $file ```

To download and run code from github, follow the example below. (Note the raw in the URL.) py !wget -q https://raw.githubusercontent.com/probml/pyprobml/master/scripts/softmax_plot.py %run softmax_plot.py

Running the scripts locally

We assume you have already installed JAX and Tensorflow and Torch, since the details on how to do this depend on whether you have a CPU, GPU, etc.

For the remaining python packages, do this: bash pip install superimport git clone --depth 1 https://github.com/probml/pyprobml &> /dev/null # THIS CODEBASE

Note: The superimport library will automatically install packages for any file which contains the line `import superimport'.

To manually execute an individual script from the command line, follow this example: bash python3 pyprobml/scripts/softmax_plot.py This will run the script, plot a figure, and save the result to the pyprobml/figures directory.

Running scripts for vol 2

Some demos for vol 2 use JSL (Jax State-space Library). This requires extra packages, see these installation instructions. Then you can run the pyprobml version of the JSL demos like this %run pyprobml/scripts/kf_tracking_demo.py # colab python3 pyprobml/scripts/kf_tracking_demo.py # locally

GCP, TPUs, and all that

When you want more power or control than colab gives you, you should get a Google Cloud Platform (GCP) account, and get access to a TPU VM. You can then use this as a virtual desktop which you can access via ssh from inside VScode. We have created various tutorials on Colab, GCP and TPUs with more information.

How to contribute

See this guide for how to contribute code.

Metrics

GSOC 2021

For a summary of some of the contributions to this codebase during Google Summer of Code 2021, see this link.

Acknowledgements

I would like to thank the following people for contributing to the code (list autogenerated from this page):

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