| name | aire-github-installs |
| description | Installs Python packages and research code from GitHub on AIRE HPC. Use for pip install git+https, editable installs (-e), git submodules, private repositories, CUDA/C++ extension builds, or import failures between login and compute nodes on AIRE. |
AIRE GitHub Installs
Operating mode
Optimize for correct node type, pinned commits, and CUDA build hygiene.
Execution note: task type (install, editable, submodule, private, cuda-build, debug), public vs private repo, compile required?, code location ($HOME vs $SCRATCH).
Read: $AIRE/knowledge/ml-on-aire.md, $AIRE/docs/software/compilers/cuda.md, $AIRE/knowledge/troubleshooting.md, $AIRE/AGENTS.md.
Core rules
- Compile GPU extensions on compute nodes —
srun with GPU if CUDA needed.
- Load cuda before nvcc builds —
module load cuda/12.6.2; set CPATH=$CUDA_HOME/include.
- Same conda env as SBATCH — activate before
pip install.
- Pin commits in
environment.yaml under pip: or requirements-git.txt.
- No personal SSH keys on AIRE — use HTTPS+token, rsync from laptop, or CI artifacts.
- Batch jobs must re-load modules and activate env — login installs are not enough alone.
Clone
cd "${SCRATCH:-/mnt/scratch/$USER}/src"
git clone --recursive https://github.com/org/repo.git
cd repo && git submodule update --init --recursive
Editable install (development)
srun --partition=gpu --gres=gpu:1 --cpus-per-task=8 --mem=32G --time=2:00:00 --pty bash
module load cuda/12.6.2
module load miniforge/24.7.1
source "$(conda info --base)/etc/profile.d/conda.sh"
conda activate <env>
export CPATH="${CUDA_HOME}/include${CPATH:+:${CPATH}}"
export MAX_JOBS="${SLURM_CPUS_PER_TASK:-4}"
pip install -e ".[dev]"
python -c "import mypackage; print(mypackage.__file__)"
Pin in environment.yaml
- pip:
- monai @ git+https://github.com/Project-MONAI/MONAI.git@v1.3.0
Private repositories
| Approach | AIRE fit |
|---|
HTTPS + GITHUB_TOKEN env (not in git) | Best on-cluster |
Clone locally → rsync to $SCRATCH | No token on cluster |
git@github.com:... with user SSH key | Not supported |
SBATCH pattern
Install once during setup; in jobs only activate:
module load cuda/12.6.2
module load miniforge/24.7.1
source "$(conda info --base)/etc/profile.d/conda.sh"
conda activate <env>
python train.py
Do not pip install in every array task unless unavoidable.
Common failures
| Symptom | Fix |
|---|
No module named X in job | Add conda block; validate script |
| CUDA headers missing | export CPATH=$CUDA_HOME/include |
No kernel image available | PyTorch ≥2.1, pytorch-cuda=12.4, sm_89 |
| Build OK on login, fail in job | Repeat module load + activate in SBATCH |
| Empty submodule dir | git submodule update --init --recursive |
Anti-patterns
pip install git+... on login for GPU extensions- Editable install from
$TMP_SHARED
- Tokens in YAML or SBATCH
- Assuming
~/.bashrc conda init suffices in batch
Integration
- Prerequisite:
aire-conda-environments
- Training:
aire-l40s-distributed-training
- Workflow:
aire-agent-workflow
