Setting up Conda Environment for ML Projects

This blogpost is written for myself as I keep forgetting the steps to set up a conda environment for my ML projects.

Install Conda

These four commands quickly and quietly install the latest 64-bit version of the installer and then clean up after themselves. To install a different version or architecture of Miniconda for Linux, change the name of the .sh installer in the wget command.

Visit the Miniconda website to find the latest version of Miniconda for Linux.

mkdir -p ~/miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
rm -rf ~/miniconda3/miniconda.sh

After installing, initialize your newly-installed Miniconda. The following commands initialize for bash and zsh shells:

~/miniconda3/bin/conda init bash
~/miniconda3/bin/conda init zsh

you can run conda -V to check if conda is installed correctly.

Creating Conda Environment

The below command will create the conda environment with the name EnvName and python version 3.9.

conda create --name EnvName python=3.9 jupyter

To activate the environment, run the below command:

conda activate EnvName

Installing Pytorch

Visit the Pytorch website to find the latest version of Pytorch. You can choose your system configuration and get the command to install Pytorch.

conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia

check if everything is installed correctly by running the below command:

python -c "import torch; print(torch.__version__)"

Installing other libraries and packages.