Getting Started with Machine Learning
This is User:Ben's guide to getting started with machine learning.
Contents
Dependencies
Here's some useful dependencies that I use:
- uv
- This is similar to Pip but written in Rust and is way faster
- It has nice management of virtual environments
- Can use Conda instead but it is much slower
- Github Copilot
- mlfab
- This is a Python package I made to help make it easy to quickly try out machine learning ideas in PyTorch
- Coding tools
Installing Starter Project
- Go to this project and install it
Opening the project in VSCode
- Create a VSCode config file that looks something like this:
{
"folders": [
{
"name": "Getting Started",
"path": "/home/ubuntu/Github/getting_started"
},
{
"name": "Workspaces",
"path": "/home/ubuntu/.code-workspaces"
}
],
"settings": {
"cmake.configureSettings": {
"CMAKE_CUDA_COMPILER": "/usr/bin/nvcc",
"CMAKE_PREFIX_PATH": [
"/home/ubuntu/.virtualenvs/getting-started/lib/python3.11/site-packages/torch/share/cmake"
],
"PYTHON_EXECUTABLE": "/home/ubuntu/.virtualenvs/getting-started/bin/python",
"TORCH_CUDA_ARCH_LIST": "'8.0'"
},
"python.defaultInterpreterPath": "/home/ubuntu/.virtualenvs/getting-started/bin/python",
"ruff.path": [
"/home/ubuntu/.virtualenvs/getting-started/bin/ruff"
]
}
}
- Install the VSCode SSH extension
- SSH into the cluster (see K-Scale Cluster for instructions)
- Open the workspace that you created in VSCode
Useful Brain Dump Stuff
- Use
breakpoint()
to debug code - Check out the mlfab examples directory for some ideas
- It is a good idea to try to write the full training loop yourself to figure out what's going on
- Run
nvidia-smi
to see the GPUs and their statuses/any active processes