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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.

  • Suggested Best Practices.

Note:

  • The focus of this workshop is on how to use Python, Pip and Conda explicitly on the ARCC clusters (not on desktops).

  • This is NOT an introduction to Python programming. Users should be familiar with python, using pip, and creating conda environments.

Table of Contents
stylenone

Headers and Sections

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Code Examples

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Two Column Tables are nice ways to separate content/ Background info along with a code example on the same “Slide”. Please notice the table width. This should stop scroll bars from appearing

  • Bullets are nice to include for distinct points

  • yep

  • they

  • sure

  • are

    This is 14 lines

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Code Block
Please use the "code snippet" in the + button when creating code examples. 
Also please do not go past the width of the table. 
This is to prevent scroll bars appearing This is the Max number of code lines to show an example












Straight Code - No context

Code Block















Limit to 16 lines in the example. This is the end

Same Thing With Images

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Two Column Tables are nice ways to separate content/ Background info along with an image example on the same “Slide”. Please notice the table width. This should stop scroll bars from appearing

  • Bullets are nice to include for distinct points

  • yep

  • they

  • sure

  • are

    This is 14 lines

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Alternatively No Table

 

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Finally The End

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Link to Previous sub-module or Home Module

 

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Sections

  1. Python Pip Installs on the Cluster

  2. Pip Install within a Conda Environment

  3. Create a Shared Conda Environment within a Project

  4. Create a Module File to Load Your Conda Environment

  5. Python, Conda and Pip: Suggested Best Practices

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