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Resources and Tips for Machine Learning Researchers at Princeton
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Details
Overview of the Research Computing Systems for ML
- GPU nodes on Adroit, Della, Stellar, Tiger and Citadel
- MIG GPUs on Della
- Multi-GPU training with PyTorch
- Tips for using Hugging Face
- Large ML datasets that are already available (e.g. ImageNet)
- JAX, R, Spark, scikit-learn, Julia, MATLAB, cuML
- Using jobstats and the web interface (preview of new features)
- Cryo-EM nodes
Overview of the A.I. Lab GPU Nodes
- Node specifications
- Who has access?
- OpenAI API
- New H200 GPUs on the way
- Social hour and seminars
Other Hardware Resources
- Some departments have their own GPU nodes
Center for Statistics and Machine Learning
- Faculty seminars
- Classes
- Graduate certificate program
- Minor program
Easy to use LLMs
- A.I. Sandbox for secure and free chat and API access to LLMs
- Web app for using LLMs by DDSS (Blackfish)
- Local inference API for Open LLMs on Research Computing systems
- Microsoft Copilot
- GitHub Copilot (free access)
ML in the Cloud
- Learn about obtaining free cloud credits (AWS, GCP, Azure)
Training Workshops and Classes
- Learn about educational resources
Update on plan for new PPPL cluster
See the full Research Computing training schedule or subscribe to the Research Computing mailing list.
Speakers
Hubert Strauss
Hubert is a Research Software Engineer II in Princeton Language and Intelligence.
Anushka Acharya
Associate Research Engineer, Research Computing
Princeton University
Background: Bachelor of Science in Computer Science and minor in Mathematics
Prior to joining the RSE group at Princeton, Anushka worked in the Digital Humanities Lab at Ramapo College as a Software Developer Intern. There, Anushka developed a web centric ETL tool and applied Machine Learning models to analyze an expansive archive of historical documents. She also worked as a Software Engineer intern at Memorial Sloan Kettering Cancer Center, where she developed and implemented different workflows to automate Hospital Management System processes.
Jonathan Halverson
Research Software and Computing Training Lead
Princeton University
Jonathan Halverson is the Research Software and Computing Training Lead with Research Computing. He has an expertise in data science and he is a founding organizer of the TensorFlow & PyTorch User Group at Princeton. Prior to his current position, Jonathan performed polymer physics research at the Max Planck Institute for Polymer Research and nanoscience research at Brookhaven National Laboratory. He holds a Ph.D. in Chemical Engineering from CUNY.