Creating a Python Project (PyTorch)

We will create a project to run PyTorch for our ml project

Setup

Use UV

I cannot recommend enough to use UV, install it and use it instead of poetry, pip, and others. It is multiple times faster and more convenient, and you can swap python versions like gloves.
Installation | uv

Let’s create the environment, the following will

1. Create a directory
2. Initiate a proper python project there
3. Install PyTorch

mkdir pytorch-sandbox && cd pytorch-sandbox
uv init
uv add torch torchvision torchaudio  --index https://download.pytorch.org/whl/cu128 

uv add creates .venv for us, so it auto executes uv venv [--python x.xx]
, but if we didn’t want to use uv add, we could go with uv pip install .

Enabling Jupyter

Using uv with Jupyter | Using Jupyter from VS Code | uv
I work in VSCode, UV has instruction on how we can enable Junyper to work with UV environment.
in

uv add --dev ipykernel

Our initial directory will contain all necessary boilerplate environment configurations to begin with coding:

For more info on pyproject.toml and other environment files, refer to: Working on projects | uv

If you use WSL 2

To enable CUDA on WSL Ubuntu 24.04, install the following:

wget https://developer.download.nvidia.com/compute/cuda/repos/wsl-ubuntu/x86_64/cuda-wsl-ubuntu.pin
sudo mv cuda-wsl-ubuntu.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/12.9.0/local_installers/cuda-repo-wsl-ubuntu-12-9-local_12.9.0-1_amd64.deb
sudo dpkg -i cuda-repo-wsl-ubuntu-12-9-local_12.9.0-1_amd64.deb
sudo cp /var/cuda-repo-wsl-ubuntu-12-9-local/cuda-*-keyring.gpg /usr/share/keyrings/
sudo apt-get update
sudo apt-get -y install cuda-toolkit-12-9

Then place the following variables in your ~/.bashrc or ~/.zshrc:

export PATH=${PATH}:/usr/local/cuda-12.9/bin
export LD_LIBRARY_PATH=${LD_LIBRARY_PATH}:/usr/local/cuda-12.9/lib64

So you can run this to check your graphic card visibility and CUDA information

nvidia-smi && nvcc --version