Description
This tutorial is a Software Carpentry-style introduction to Conda for (data) scientists. This tutorial motivates the use of Conda as a development tool for building and sharing project specific software environments that facilitate reproducible (data) science workflows. Particular attention is given to using Conda to create reproducible environments with NVIDIA GPU dependencies (including environments for Horovod, TensorFlow, PyTorch, and NVIDIA RAPIDS) as well as a discussion of best practices for using Conda in HPC environments.
Tutorial Prerequisites: Basic familiarity with Python programming and Bash shell concepts (i.e., basic commands, environment variables, etc). Familiarity installing NVIDIA CUDA Toolkit would be beneficial for NVIDIA GPU focused episodes.