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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 |
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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 pythonPython, using pip, and creating Conda conda environments. |
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Notes:
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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 bringing together concepts from the workshop.
Python, Conda and Pip: Exercise: Provide an exercise to work through that puts together the various concepts covered within this workshop.
Python, Conda and Pip: Summary: Summary of this workshop.
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