Designs PyTorch distributed training on AIRE NVIDIA L40S GPUs (Ada, 48GB, PCIe, max 3 GPUs per node). Covers DDP vs FSDP, bf16, batch sizing, NCCL and torchrun with Slurm, TMP_SHARED staging, and SCRATCH checkpointing. Use for multi-GPU or multi-node training on AIRE gpu partition.
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.
Orchestrates work on the University of Leeds AIRE HPC cluster using the aire-agent toolkit. Use when starting AIRE sessions, submitting Slurm jobs, choosing between raw Slurm and aire-agent tools, wiring MCP for Claude Code, or following generate-validate-submit-debug loops on AIRE.
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.
Diagnoses PyTorch DDP and NCCL failures on AIRE L40S GPU jobs. Use when distributed training hangs, NCCL errors or timeouts occur, only one GPU is active, Slurm resources do not match torchrun, or seff shows wasted GPUs on AIRE.
Scaffolds and maintains modular, reproducible ML research repositories for the AIRE HPC cluster. Use for project layout (src/, configs/, scripts/, tests/), Hydra or YAML configuration, git hygiene, SCRATCH-aware paths, and integrating runs with aire-agent log_experiment. Complements domain skills such as medical-cv-research-engineer.