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Understand where pip installs packages within a user’s home folder with respect to different versions of Python.
Understand how to use pip to install a package within a conda environment.
Demonstrate how to create a conda environment, with pip installs, that can be shared across a project.
Demonstrate creating a module file to load a conda/python environment.
Demonstrate extending a conda environment to act as a kernel within the Jupyter service.
Suggested Best Practices.

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Notewarning
This is not a workshop on learning the Python language, but on using Python, Pip and Conda explicitly on the ARCC clusters (not on desktops). Users should be familiar with python, using pip, and creating Conda environments.

Note

Notes:

The workshop modules work best in a sequential manner as a story introducing concepts and providing examples, but sections can be used separately to focus on a particular concept.
If you have existing Conda environments, or have installed your own version of Anaconda, then the examples within this workshop might provide different results.
We have tried to make the examples as generic as possible. You will need to replace <project-name> and <username> with appropriate values that apply to you.
This tutorial is available for download as a PDF here.

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Sections

Python Pip Installs on the Cluster: Understand where pip installs packages within a user’s home folder with respect to different versions of Python.
Pip Install within a Conda Environment: Understand how Conda’s pip works with a User’s Python pip package Installs.
Create a Shared TensorFlow Conda Environment: General process for creating and sharing a conda environment under a project.
Create a Module File to Load Your Conda Environment: Demonstrate creating a module file to load a Conda environment.
Extend Conda Environment to Jupyter Kernel: Demonstrate extending a Python related conda environment in to Juypter kernel.
Jupyter Python Packages and Issues: When using Jupyter how are python packages managed?
Conda and Pip Environments and Reproducibility: Introduce ideas and practices to assist in managing the reproducibility of environments created using Conda and Pip.
Python, Conda and Pip: Suggested Best Practices: Suggest best practices and summarize workshop.

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