Introduction: This workshop will discuss how the various Python related tools work together on the cluster and introduce a series of best practices for managing these environments.
Course Goals:
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.
Notes:
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.
If you have existing conda environments, or have installed your own version of Anaconda, then the examples within this workshop might provide different results.
You will need to modify user and project names to apply to yourself.
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.
Python, Conda and Pip: Suggested Best Practices: Suggest best practices and summarize workshop.
Link to main training page. |