| # Perlmutter execution |
|
|
| The package does not encode Perlmutter paths, modules, or allocation policy. |
| Create the uv environment in the project location appropriate for your |
| account, select a site-compatible GPU/driver environment, and use the same |
| Hydra configuration as on a workstation. |
|
|
| ## Single GPU |
|
|
| ```bash |
| uv run gnn4colliders train \ |
| data.cache.path=/path/to/graphs.pt \ |
| environment=perlmutter \ |
| trainer.device=cuda \ |
| trainer.max_epochs=10 |
| ``` |
|
|
| The equivalent Slurm wrapper is: |
|
|
| ```bash |
| sbatch scripts/slurm/train_single_gpu.sh \ |
| data.cache.path=/path/to/graphs.pt trainer.max_epochs=10 |
| ``` |
|
|
| ## Single-node DDP |
|
|
| ```bash |
| GPUS_PER_NODE=4 sbatch scripts/slurm/train_multi_gpu.sh \ |
| data.cache.path=/path/to/graphs.pt trainer.max_epochs=10 |
| ``` |
|
|
| The wrapper uses `torchrun`; `batch_size` and `num_workers` are per GPU. |
| Effective batch size is `data.batch_size * number_of_processes`, and only rank |
| 0 writes the shared checkpoint/config/prediction artifacts. |
|
|
| ## Multi-node DDP |
|
|
| ```bash |
| GPUS_PER_NODE=4 sbatch scripts/slurm/train_multi_node.sh \ |
| data.cache.path=/path/to/graphs.pt trainer.max_epochs=10 |
| ``` |
|
|
| Use the provided script as a template and adapt only allocation/account |
| settings required by the site. Evaluation and prediction can use |
| `scripts/slurm/evaluate.sh`; prediction currently gathers moderate-size |
| results in memory. |
|
|
| ## Checks and common failures |
|
|
| ```bash |
| uv run python -c "import torch, dgl; print(torch.__version__, dgl.__version__, torch.cuda.is_available())" |
| uv run gnn4colliders --help |
| ``` |
|
|
| An unavailable DGL wheel, incompatible driver, missing cache, or mismatched |
| cache schema should be fixed in the environment/input rather than hidden with |
| package-level path changes. GPU kernels and distributed execution are not |
| promised bitwise deterministic. |
|
|
