Difference between revisions of "Getting Started with Machine Learning"
(Created page with "This is User:Ben's guide to getting started with machine learning. === Dependencies === Here's some useful dependencies that I use: * [https://astral.sh/blog/uv uv] **...") |
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* [https://github.com/kscalelabs/mlfab mlfab] | * [https://github.com/kscalelabs/mlfab mlfab] | ||
** This is a Python package I made to help make it easy to quickly try out machine learning ideas in PyTorch | ** This is a Python package I made to help make it easy to quickly try out machine learning ideas in PyTorch | ||
| + | * Coding tools | ||
| + | ** [https://mypy-lang.org/ mypy] static analysis | ||
| + | ** [https://github.com/psf/black black] code formatter | ||
| + | ** [https://docs.astral.sh/ruff/ ruff] alternative to flake8 | ||
=== Installing Starter Project === | === Installing Starter Project === | ||
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* SSH into the cluster (see [[K-Scale Cluster]] for instructions) | * SSH into the cluster (see [[K-Scale Cluster]] for instructions) | ||
* Open the workspace that you created in VSCode | * Open the workspace that you created in VSCode | ||
| + | |||
| + | === Useful Brain Dump Stuff === | ||
| + | |||
| + | * Use <code>breakpoint()</code> to debug code | ||
Revision as of 01:41, 20 May 2024
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
