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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] **..."
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]
** 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
* [https://github.com/features/copilot Github Copilot]
* [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
=== Installing Starter Project ===
* Go to [https://github.com/kscalelabs/getting-started this project] and install it
==== Opening the project in VSCode ====
* Create a VSCode config file that looks something like this:
<syntaxhighlight lang="json">
{
"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"
]
}
}
</syntaxhighlight>
* Install the [https://code.visualstudio.com/docs/remote/ssh VSCode SSH extension]
* SSH into the cluster (see [[K-Scale Cluster]] for instructions)
* Open the workspace that you created in VSCode
=== Dependencies ===
Here's some useful dependencies that I use:
* [https://astral.sh/blog/uv 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
* [https://github.com/features/copilot Github Copilot]
* [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
=== Installing Starter Project ===
* Go to [https://github.com/kscalelabs/getting-started this project] and install it
==== Opening the project in VSCode ====
* Create a VSCode config file that looks something like this:
<syntaxhighlight lang="json">
{
"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"
]
}
}
</syntaxhighlight>
* Install the [https://code.visualstudio.com/docs/remote/ssh VSCode SSH extension]
* SSH into the cluster (see [[K-Scale Cluster]] for instructions)
* Open the workspace that you created in VSCode