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refactor(hpc): use explicit PIP_CACHE_DIR/UV_CACHE_DIR, drop prose comments

Match comment style from previous cleanup commit:
- No section divider bars, no echo progress lines, no prose comments
  on obvious commands
- Replace XDG_CACHE_HOME with explicit PIP_CACHE_DIR and UV_CACHE_DIR
- Drop MPLCONFIGDIR (matplotlib cache is negligible in size)
This commit is contained in:
Tibo De Peuter 2026-04-04 18:48:06 +02:00
parent cd2e594d1b
commit 34a887cd58
3 changed files with 13 additions and 74 deletions

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@ -81,9 +81,8 @@ module swap cluster/donphan
qsub -I -l nodes=1:ppn=4 -l walltime=1:00:00
# Once inside the job — redirect caches to scratch first
export XDG_CACHE_HOME="$VSC_SCRATCH/.cache"
export PIP_CACHE_DIR="$VSC_SCRATCH/.cache/pip"
export UV_CACHE_DIR="$VSC_SCRATCH/.cache/uv"
export MPLCONFIGDIR="$VSC_SCRATCH/.config/matplotlib"
# Change to the project directory and activate environment
cd "$PBS_O_WORKDIR"

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@ -1,45 +1,21 @@
#!/bin/bash
# scripts/hpc/install.sh — Run once on a login node (donphan) to set up the
# project's Python virtual environment using the official vsc-venv wrapper.
# scripts/hpc/install.sh
#
# Usage (from project root):
# bash scripts/hpc/install.sh
#
# Prerequisites:
# - Connected to VSC via the web portal (HPC Login → Interactive Apps > Shell tmux)
# - Set cluster to "donphan (interactive/debug)"
# - Project cloned into $VSC_HOME (e.g. git clone <repo> && cd <repo>)
set -euo pipefail
# ---------------------------------------------------------------------------
# Redirect caches away from $VSC_HOME (very limited at ~3 GB) to scratch.
# This must be done before any pip/module activity.
# ---------------------------------------------------------------------------
export XDG_CACHE_HOME="$VSC_SCRATCH/.cache"
# Keep caches off $VSC_HOME (quota ~3 GB).
export PIP_CACHE_DIR="$VSC_SCRATCH/.cache/pip"
export UV_CACHE_DIR="$VSC_SCRATCH/.cache/uv"
export MPLCONFIGDIR="$VSC_SCRATCH/.config/matplotlib"
mkdir -p "$XDG_CACHE_HOME" "$UV_CACHE_DIR" "$MPLCONFIGDIR"
mkdir -p "$PIP_CACHE_DIR" "$UV_CACHE_DIR"
echo "==> Loading vsc-venv module..."
module load vsc-venv
echo "==> Activating virtual environment (created per-cluster in \$VSC_DATA)..."
# vsc-venv transparently creates and manages a per-cluster venv in $VSC_DATA,
# loading the modules listed in env/hpc/modules.txt and pip-installing anything
# in env/hpc/requirements.txt that is not already satisfied.
source vsc-venv --activate \
--modules env/hpc/modules.txt \
--requirements env/hpc/requirements.txt
echo "==> Registering Jupyter kernel..."
python -m ipykernel install --user --name="sel3_${VSC_INSTITUTE_CLUSTER}" \
--display-name "SEL3 (${VSC_INSTITUTE_CLUSTER})"
echo ""
echo "✅ Environment ready. Kernel: sel3_${VSC_INSTITUTE_CLUSTER}"
echo " To activate in future sessions:"
echo " module load vsc-venv && source vsc-venv --activate --modules env/hpc/modules.txt"
echo ""
echo " Remember to also set cache dirs in any interactive session:"
echo " export XDG_CACHE_HOME=\$VSC_SCRATCH/.cache"

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@ -1,80 +1,44 @@
#!/bin/bash
# scripts/hpc/train.pbs — Submit PPO training as a batch job with qsub.
# scripts/hpc/train.pbs
#
# Usage (from project root after cloning into $VSC_HOME):
# Usage (from project root):
# qsub scripts/hpc/train.pbs
#
# To target a different GPU cluster (default: joltik):
# To target a specific GPU cluster (default: joltik):
# module swap cluster/accelgor && qsub scripts/hpc/train.pbs
#
# Available GPU clusters: joltik, accelgor, litleo
# Debug / interactive: doduo (CPU) or donphan (interactive GPU)
# ---------------------------------------------------------------------------
# PBS job directives
# ---------------------------------------------------------------------------
#PBS -N brittlestar-ppo
#PBS -l nodes=1:ppn=8:gpus=1
#PBS -l walltime=24:00:00
#PBS -l mem=32gb
# Logs are redirected inside the script to $VSC_DATA so they persist.
#PBS -o /dev/null
#PBS -e /dev/null
# ---------------------------------------------------------------------------
set -euo pipefail
# Always change to the directory from which qsub was called (best practice).
cd "$PBS_O_WORKDIR"
# ---------------------------------------------------------------------------
# Storage routing (see https://docs.hpc.ugent.be/Linux/running_jobs_with_input_output_data/)
#
# $VSC_HOME (~3 GB) — project source code only; no writes during jobs
# $VSC_DATA (~25 GB) — persistent run artefacts (models, final logs)
# $VSC_SCRATCH (large) — fast I/O during the job; caches; intermediate files
# ---------------------------------------------------------------------------
# Redirect Python / pip / uv caches away from $VSC_HOME to scratch (fast I/O).
export XDG_CACHE_HOME="$VSC_SCRATCH/.cache"
# Keep caches off $VSC_HOME (quota ~3 GB).
export PIP_CACHE_DIR="$VSC_SCRATCH/.cache/pip"
export UV_CACHE_DIR="$VSC_SCRATCH/.cache/uv"
export MPLCONFIGDIR="$VSC_SCRATCH/.config/matplotlib"
mkdir -p "$XDG_CACHE_HOME" "$UV_CACHE_DIR" "$MPLCONFIGDIR"
# Run outputs: write to $VSC_SCRATCH during the job (fast), then copy to
# $VSC_DATA at the end for long-term storage.
# Write run outputs to scratch during the job (fast I/O),
# then stage out to $VSC_DATA at the end for persistence.
RUN_ID="brittlestar_${PBS_JOBID}"
SCRATCH_RUNDIR="$VSC_SCRATCH/runs/$RUN_ID"
DATA_RUNDIR="$VSC_DATA/runs/$RUN_ID"
mkdir -p "$SCRATCH_RUNDIR" "$DATA_RUNDIR"
mkdir -p "$PIP_CACHE_DIR" "$UV_CACHE_DIR" "$SCRATCH_RUNDIR" "$DATA_RUNDIR"
# PBS stdout/stderr: redirect into the data directory for persistence.
exec 1> "$DATA_RUNDIR/job.out" 2> "$DATA_RUNDIR/job.err"
# ---------------------------------------------------------------------------
# Environment
# ---------------------------------------------------------------------------
module load vsc-venv
source vsc-venv --activate --modules env/hpc/modules.txt
# EGL is required for headless MuJoCo — both the physics sim loop and
# on-demand video rendering to disk. Without this, MuJoCo tries to open an
# X11 display and fails on compute nodes.
export MUJOCO_GL=egl
# Tell WandB to store its local run cache on scratch (fast, large).
export WANDB_DIR="$SCRATCH_RUNDIR"
# ---------------------------------------------------------------------------
# Run
# ---------------------------------------------------------------------------
python src/train.py \
--config configs/production_training.yaml \
--run_dir "$SCRATCH_RUNDIR"
# ---------------------------------------------------------------------------
# Stage out: copy results to persistent VSC_DATA storage.
# ---------------------------------------------------------------------------
echo "Copying results from scratch to \$VSC_DATA..."
cp -r "$SCRATCH_RUNDIR/." "$DATA_RUNDIR/"
echo "Done. Results at: $DATA_RUNDIR"