Installation

Segger Installation Guide

Select the appropriate installation method based on your requirements.

Micromamba Installation

micromamba create -n segger-rapids --channel-priority 1 \
  -c rapidsai -c conda-forge -c nvidia -c pytorch -c pyg \
  rapids=24.10 python=3.* 'cuda-version>=12.0,<=12.1' jupyterlab \
  'pytorch=*=*cuda*' 'pyg=*=*cu121' pyg-lib pytorch-sparse
micromamba install -n segger-rapids --channel-priority 1 --file mamba_environment.yml
micromamba run -n segger-rapids pip install --no-deps ./

Conda Installation

conda create -n segger-env python=3.10
conda activate segger-env
conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
conda install pyg -c pyg
pip install .

Docker Installation

docker pull danielunyi42/segger_dev:cuda121

The Docker image comes with all required packages pre-installed, including PyTorch, RAPIDS, and PyTorch Geometric. The current images support CUDA 11.8 and CUDA 12.1, which can be specified in the image tag.

For users who prefer Singularity:

singularity pull docker://danielunyi42/segger_dev:cuda121

:octocat: Github Installation

git clone https://github.com/EliHei2/segger_dev.git
cd segger_dev
pip install -e "."

Pip Installation of RAPIDS with CUDA 11

pip install "segger[rapids11]"

Pip Installation of RAPIDS with CUDA 12

pip install "segger[rapids12]"

Common Installation Issues

• Python Version: Ensure you are using Python >= 3.10. Check your version with:

  python --version
  

  If necessary, upgrade to the correct version.

• CUDA Compatibility (GPU): For GPU installations, ensure the correct CUDA drivers are installed. Verify your setup with:

  nvidia-smi
  

  Ensure your CUDA version is compatible with the package.

• Permissions: If you encounter permission errors, use the --user flag to install without admin rights:

  pip install --user .