Contents

**~~ ~~ ~~ Work in progress! ~~ ~~ ~~**

This guide to HPC is intentionally fragmented into parts -- to make it easier to digest and refer to, and to elaborate on each topic. It is not a step-by-step start-to-finish guide on training a model, but by going through it in the following order, that process should become evident. Please report issues if the documentation is unclear, or certain steps were missed out, or if there are any inaccuracies.

Introduction (this page)

Account setup

Data storage and file systems

Data transfer

Requesting Resources

Modules, packages and virtual environments

Useful commands and troubleshooting

Running Jupyter notebooks

Examples workflows for popular ML repos

This documentation relies heavily on the notes shared by Cristobal Valenzuela and Dan Oved - big thanks to them.

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HPC Introduction

• What is HPC?

  • HPC stands for High Performance Computing. Essentially, a lot of big computers to run tasks that are too intensive for your computer, or that your computer simply can't do because of compatibility issues or limited hardware. If you come from 3D world, it's similar to the concept of render farms.

  • There are computers sitting in some physical location owned by NYU, without monitors and mouses and keyboards, but they are connected to the internet. You can get on the NYU network and access them on the command line (terminal).

  • The computers have a whole lot of software installed onto them already, such as Tensorflow and CUDA.

• Why would I want to use it?

  • As an ITP student, machine learning is probably one of the main reasons HPC would be useful to you. Training models is not always feesible on one's personal computer, and sometimes simply not possible because of hardware constraints.

  • While there's a lot of models out there that you can use, its sometime more interesting / fun / useful to train your own models. The relationship between input data and output can become a lot clearer when you train your own models, helping one to understand the workings and limitations of the algorithms, how they can be harnessed as tools, and how they can be exploited and misused. While there might be rhetoric around Machine Learning being a science, it is really more of an art of tuning and tweaking models to get them to do what you want them to do. Developing this intuition will not only help you use ML as a tool for your own work, but will also help you ask questions of how ML is being used in the world.

  • It's free, while you are at NYU. Access to powerful machines like this could easily add up to hundreds or thousands of dollars otherwise.

• What do I need to know before using HPC?

  • Familarity / comfort with the command line (terminal). There's no point-and-click interface for HPC, so its easy to get lost without some basic command line understanding.

  • Instructions or a basic workflow of the model you want to train. HPC only provides you with the computers - you will need to bring the code you want to run, and know how to run it, and what packages or modules it might require. You might find all the requirements / dependencies on HPC or you might need to install some of them

• What are the broad steps I need to do?

  • Make an account and set up SSH access
  • Transfer training data to servers
  • Setting up the repo you want to use and all the requirements / modules
  • Figuring out the appropriate amount of resources you need
  • Queuing a job! (Or running a quick job in 'interactive' mode ).
  • Transferring your model / data back