aire-conda-environments

name	aire-conda-environments
description	Manages Miniforge/conda environments on the University of Leeds AIRE HPC cluster. Use when creating, updating, exporting, or debugging Python/R conda envs on AIRE; wiring activation into SBATCH or srun; fixing HOME quota; or choosing Miniforge, conda-pack, Apptainer, or Pixi for ML stacks.

AIRE Conda Environments

Operating mode

Optimize for AIRE policy, reproducibility, and HOME quota safety.

Execution note: task type (create, update, export, sbatch-wire, quota), framework, GPU need, target (login / srun / batch).

Read when needed: $AIRE/knowledge/ml-on-aire.md, $AIRE/docs/usage/dependency_management.md, $AIRE/knowledge/storage.md, $AIRE/templates/environments/*.yml.

Core rules

1. Miniforge only — module load miniforge/24.7.1; channel conda-forge.
2. Envs in $HOME — never relocate envs/ or .conda to $SCRATCH as a quota workaround.
3. Data and outputs on $SCRATCH — datasets, checkpoints, env-record.yaml snapshots at scale.
4. YAML-first — edit environment.yaml, then conda env update -f environment.yaml --prune.
5. Re-load modules + activate in every job — login setup does not propagate to compute nodes.
6. Heavy solves on compute — use srun (GPU node for CUDA stacks), not login nodes.

Create or update

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
conda env create -f environment.yaml   # or: conda env update -f environment.yaml --prune
conda activate <name>
python -c "import torch; print(torch.cuda.is_available())"

Templates: $AIRE/templates/environments/pytorch.yml, tensorflow.yml, medical-imaging.yml.

CUDA alignment: cuda/12.6.2 module + pytorch-cuda=12.4 in YAML (see ml-on-aire.md).

SBATCH activation block

module load cuda/12.6.2          # if GPU
module load miniforge/24.7.1
source "$(conda info --base)/etc/profile.d/conda.sh"
conda activate <env_name>

If conda activate fails in batch: source activate <env_name> ($AIRE/knowledge/troubleshooting.md).

Prefer $AIRE/tools/generate-script.sh → validate → sbatch.

Reproducibility exports

Artifact	Command	Use
Recipe	environment.yaml in git	Rebuild
Snapshot	conda env export > $SCRATCH/.../env-record-${SLURM_JOB_ID}.yaml	Tie to results
History	conda env export --from-history	Recover drifted envs

HOME quota triage

1. conda env update -f environment.yaml --prune on active envs
2. conda clean --all
3. conda remove -n <unused> --all
4. $AIRE/tools/check-quota.sh or aire-agent quota helpers
5. Request increase only after cleanup

Alternatives

Tool	When
conda-pack	Ship env to another machine
Apptainer	Frozen portable stack (module load apptainer/1.3.6)
Pixi	Non-conda workflow (module load pixi/0.41.4)
pytorch/2.5.1 module	Quick baseline without custom pip

Anti-patterns

• Ad-hoc conda install without YAML updates
• GPU env builds on login nodes
• Full conda env export as the only rebuild file
• Omitting source .../conda.sh in SBATCH

Integration

• Next: aire-github-installs for pip/git deps inside the env
• Jobs: aire-agent-workflow, aire-l40s-distributed-training