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https://github.com/princetonuniversity/intro_ml_libs


https://github.com/princetonuniversity/intro_ml_libs

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README

          

# Intro to the Machine Learning Libraries

## About

This workshop provides demonstrations of the effective usage of popular machine learning libraries on the HPC clusters at Princeton. It shows how to install each library as well as how to write Slurm scripts to take advantage of multi-threading and/or GPUs. The material covers PyTorch, TensorFlow, JAX, Julia, Spark, Rapids, Scikit-learn, R and Snap ML.

## Useful links
- [PyTorch at Princeton](https://github.com/PrincetonUniversity/install_pytorch) | [PyTorch.org](https://pytorch.org)
- [TensorFlow at Princeton](https://github.com/PrincetonUniversity/slurm_mnist) | [TensorFlow.org](https://www.tensorflow.org)
- [Spark at Princeton](https://researchcomputing.princeton.edu/faq/spark-via-slurm) | [Spark website for ML](https://spark.apache.org/docs/latest/ml-guide.html)
- [MATLAB at Princeton](https://researchcomputing.princeton.edu/matlab) | [MATLAB website for ML](https://www.mathworks.com/solutions/machine-learning.html)
- [Julia at Princeton](https://researchcomputing.princeton.edu/julia)
- [NVIDIA Rapids](https://rapids.ai/)
- [R at Princeton](https://researchcomputing.princeton.edu/R) | [R ML packages on CRAN](https://cran.r-project.org/web/views/MachineLearning.html)
- [Python at Princeton](https://researchcomputing.princeton.edu/support/knowledge-base/python)) | [Scikit-Learn website](https://scikit-learn.org/stable/)

## Getting Help

If you encounter any difficulties while working with the machine learning libraries on the HPC clusters then please send an email to cses@princeton.edu or attend a help session.

## Authorship

This workshop was created by Jonathan Halverson.