Enable running the LLM tier on Imperial's HPC (scheduler: PBS Pro / qsub). - hpc/probe.pbs: 10-min 1-GPU reconnaissance job resolving the two unknowns the RCS docs omit -- compute-node internet access and the L40S driver's CUDA version -- plus TMPDIR/disk and available python/cuda modules. - hpc/llm_merge.pbs: scaled run on an L40S (48 GB), offline HF-cache wired, runs configs/llm/merge_hpc.yaml. - configs/llm/merge_hpc.yaml: Qwen2.5-7B-Instruct (fits the L40S) to reduce the noise that left the 0.5B prototype's overall-exceeds sign marginal. - hpc/README.md: the git-based workflow (login-node uv env + model pre-download -> qsub -> rsync results back), confirmed PBS/GPU directives, and the code TODOs for the definitive run (multi-seed, more families, directed/dilution-resistant merge). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
3.7 KiB
3.7 KiB
Running the Lamarckian Society on Imperial's HPC (CX3, PBS Pro)
The LLM experiments are the only part that wants more than a laptop GPU. This directory holds the
PBS job scripts for Imperial's CX3 cluster (scheduler: PBS Pro — qsub, not Slurm). The
analytic (Layer 1) and small-neural (Layer 1.5) tiers all run locally and need nothing here.
Confirmed facts (Imperial RCS user guide)
- Submit / monitor / cancel:
qsub <script>·qstat -u $USER(Q=queued, R=running) ·qdel <jobid>. Output lands in<script>.o<jobid>(stdout) and.e<jobid>(stderr). - GPU resource line:
#PBS -l select=1:ncpus=4:mem=24gb:ngpus=1:gpu_type=L40S(leave:gpu_type=…off for the default). GPUs: L40S 48 GB (default), RTX6000 24 GB, A100 40 GB (scarce). Queue gpu72 (~72 h), up to 8 GPUs/node. - Filesystem: jobs start in
$HOME;$PBS_O_WORKDIR= the submit directory;$TMPDIR= fast node-local scratch (copy large inputs in, results out); keep a job under ~100 GB. - Modules: e.g.
module load Python/3.12.3-GCCcore-13.3.0(we useuvinstead — see below).
Two unknowns the docs don't cover — resolved by probe.pbs
- Do compute nodes have internet? If not, models and packages must be fetched on the login node and used offline on the compute node.
- What CUDA version does the L40S driver support? Our env ships torch cu13; an older driver needs a pinned torch (cu124/cu121).
Run the probe first: git pull on the login node, then qsub hpc/probe.pbs, then read
probe.o<jobid>. It prints the GPU + driver CUDA, the internet test, $TMPDIR/disk, and the
available python/cuda modules. (Paste that output back and the real scripts get finalised.)
One-time setup on the LOGIN node (which has internet)
git clone <this repo> && cd LamarckianAI
curl -LsSf https://astral.sh/uv/install.sh | sh # uv -> ~/.local/bin (no sudo)
uv sync --extra dev --extra neural --extra llm # builds .venv (Python 3.14 + torch + transformers/peft)
# If the probe shows the L40S driver is < CUDA 13, pin torch to match first, e.g.:
# uv pip install --python .venv "torch==2.*" --index-url https://download.pytorch.org/whl/cu124
# Pre-download the base model into a cache the compute node can read:
HF_HOME=$HOME/hf_cache uv run python -c "from transformers import AutoModelForCausalLM, AutoTokenizer as T; \
n='Qwen/Qwen2.5-7B-Instruct'; T.from_pretrained(n); AutoModelForCausalLM.from_pretrained(n)"
Run the experiment
qsub hpc/llm_merge.pbs # L40S, ~4 h; runs configs/llm/merge_hpc.yaml
qstat -u $USER # watch it
Results are written to results/llm_merge_hpc/ (the .parquet is gitignored). Sync it back to a
machine with the plotting env to analyse:
rsync -avz <user>@login.hpc.ic.ac.uk:'~/LamarckianAI/results/llm_merge_hpc/' results/llm_merge_hpc/
python figures/plot_llm_merge.py results/llm_merge_hpc
Notes
- Why
uv, not the Python module:uvinstalls its own Python 3.14 and the exact pinned deps, so the HPC env matches the laptop env reproducibly and is independent of the cluster's module set. The only cluster-specific adjustment is the torch CUDA build if the driver is old (above). HF_HUB_OFFLINE=1is set inllm_merge.pbson the assumption compute nodes are offline; delete that line if the probe shows internet works.- The definitive "firm up the sign" run (not yet coded) also wants: several seeds with mean±CI;
more task families; and a dilution-resistant / offspring-selected ("directed sex") merge.
merge_hpc.yamlonly bumps the base model for now — enough to reduce noise, but the code changes are the real fix.