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2026SEL3-project-Brittle_St.../docs/HPC.md

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# HPC Guide
Full documentation: <https://docs.hpc.ugent.be/>
## Storage Overview
- **Run Outputs**: Written to `$VSC_SCRATCH` during the job (fast I/O) and copied to `$VSC_DATA` at the end for persistence.
- **Virtual Environments**: Managed on **`$VSC_DATA`** by mirroring configuration files. This avoids the 3GB home quota without requiring symlinks in the project root.
## Initial Environment Setup
Run **once** after cloning the repository. Ensure you are logged into a **compute node** on `donphan` or `joltik`.
> [!IMPORTANT]
> To avoid the **3GB home directory quota limit**, the installation script mirrors your configuration files to **`$VSC_DATA`** (25GB+ quota). The `vsc-venv` tool then automatically creates and manages the environment on the larger partition.
```bash
# 1. Start an interactive session (donphan for debug, joltik for training)
qsub -I -l nodes=1:ppn=8:gpus=1
# 2. Run the streamlined install script
cd "${PBS_O_WORKDIR}"
bash scripts/hpc/install.sh
```
## Interactive Debugging
You can use the same `install.sh` script to quickly activate your environment for interactive work.
```bash
# Request an interactive job
qsub -I -l nodes=1:ppn=4 -l walltime=1:00:00
# Change to project directory and run install.sh to sync and activate
cd "$PBS_O_WORKDIR"
bash scripts/hpc/install.sh
```
### Verification Commands
After installation, run these commands to ensure your environment is set up correctly:
1. **Verify Location**:
```bash
# Confirm that NO 'venvs' folder appeared in your project root
ls -d venvs 2>/dev/null # Should return 'not found'
# Confirm the environment is on the data partition
python -c "import torch; print(torch.__file__)"
# Expected: /kyukon/data/gent/vsc... or similar
```
2. **Verify GPU Access**:
```bash
python -c "import torch; import jax; print(f'Torch CUDA: {torch.cuda.is_available()}'); print(f'JAX Devices: {jax.devices()}')"
```
*Expected output: `Torch CUDA: True` and `JAX Devices: [CudaDevice(id=0)]`.*
3. **Verify Home Quota**:
```bash
df -h ~ # Should show low usage (< 1GB typically)
```
## Submitting Batch Training Jobs
```bash
# Submit to the default cluster (joltik)
qsub scripts/hpc/train.pbs
# To choose a different cluster (e.g. donphan debug) without touching code
module swap cluster/donphan && qsub scripts/hpc/train.pbs
```
The `train.pbs` script automatically handles its own activation using the mirrored configurations on `$VSC_DATA`.
## Managing Dependencies
`env/hpc/requirements.txt` is auto-generated from `pyproject.toml`. To regenerate:
```bash
uv run scripts/export_hpc_requirements.py
```
Modules listed in `env/hpc/modules.txt` are automatically excluded from the pip requirements to save space and use HPC-optimized binaries.