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Preamble

To run the code included in this tutorial, we will leverage on a pretty "standard" Python/PyData stack: numpy, pandas, matplotlib, and scikit-learn for all the data science and Machine learning parts, and pytorch (w/ torchvision) for the Deep Learning examples.

Moreover, a few extra / specialised packages will be also featured:

  • PySyft: A platform for Remote Data Science
  • Opacus: A library to train PyTorch models with differential privacy
  • PHE: A Python 3 library implementing the Paillier Partially Homomorphic Encryption

As for the Python version/distribution: any Python 3.10+ version should be fine.

The repository contains the files to recreate the Python environment with all the required packages, either you are using Miniconda(i.e. environment.yml) or Standard Python Distribution (i.e. ppml_requirements.txt).

Set up the Environment

Before we start:

All the instructions reported below will consider the Terminal and hence the command-line interface to run all the commands.

Similarly, instructions to recreate the environment will consider using pyenv and pyenv-virtualenv to download the Python distribution, and install the environment.

If you haven't already, let's download (or git clone) the current repository on your local computer.

git clone https://github.com/leriomaggio/ppml-tutorial
cd ppml-tutorial

Setup the environment:

The repository contains a ppml_requirements.txt file that can be used to automatically recreate the environment with all the required packages.

First, let's download the shims of the Python version we want to use. We will be using Python 3.12:

pyenv install 3.12

Once this is complete, you should now have the shims of Python 3.12 available in your system.

The next step is to now point to this version of the interpreter when creating the new virtual environment.

pyenv virtualenv 3.12 ppml

This will create a new virtual environment called ppml. We now need to activate the environment:

pyenv virtualenv activate ppml

Finally, you could install all the required packages using pip:

pip install -f ppml_requirements.txt

Well Done! 🎉

Test your Environment

If you followed all the steps reported in the previous section to setup your local machine, you should be ready to proceed with testing your environment.

To do so, please open the Get-Ready.ipynb notebook to check that everything works properly on your end:

jupyter lab Get-Ready.ipynb