| name | omnihub-conventions |
| description | Documents OmniHub repo layout, app configs, cluster and tools config, results layout, and when to use runner manual vs torchrun. Use when the user asks about conventions, app config, cluster config, tools config, results layout, or runner manual vs torchrun. |
OmniHub conventions
Repo layout
| Path | Purpose |
|---|
| config/ | Cluster YAMLs (e.g. hpcfund.yaml, frontier.yaml), job.template, config/tools/*.yaml |
| applications/ | One dir per app: config-example.yaml, entrypoint script, optional parse.py |
| scripts/ | Shared utilities sourced by job.template and apps (sanity checks, env setup) |
| omnihub/generate/ | job.py (omnihub-generate-job), sweep.py (omnihub-sweep), app_config.py |
| omnihub/process/ | processor.py (omnihub-process), parsers.py |
| omnihub/index/ | indexer.py (omnihub-index) |
| omnihub/run/ | runner.py (omnihub-run, used inside the container by the job) |
scripts/ directory
| Script | Purpose |
|---|
| sanity-check.sh | Consolidated sanity checks: ROCm (rocminfo), GPU compute-mode, and PyTorch NCCL (broadcast + all_reduce via torchrun). Called by job.template before the app. |
| sanity_torch_dist.py | Minimal PyTorch distributed test (init_process_group, broadcast, all_reduce). Used by sanity-check.sh under torchrun. |
| omnihub-apptainer-env.sh | Sourced inside Apptainer; sets ROCm paths and per-task pip cache. |
App config (YAML)
Paths are relative to the OmniHub repo root (e.g. applications/hf-infer/config-example.yaml).
- entrypoint — Required. Script to run (e.g.
applications/hf-infer/infer.py or /app/vllm/benchmarks/benchmark_latency.py).
- App-specific keys — ModelArguments, VllmArguments, tensor_parallel_size, input_len, output_len, etc. Depend on the application; see each app’s config-example.yaml.
Templates for sweeps use the same structure but with list values for dimensions to sweep (see the sweeps skill in .cursor/skills/sweeps/).
Cluster config (config/.yaml)
Under cluster::
- name, models-dir, shared-dir, data-dir, results-dir (template, e.g.
$WORK/results/omnihub/$SLURM_JOB_ID)
- subsets — List of partition definitions:
partition, gpu, num-gpus, num-nodes
Example: config/hpcfund.yaml defines partitions such as mi2104x, mi2508x, mi3008x, mi3258x.
Tools config (config/tools/*.yaml)
Each YAML defines one or more tools (name, conflicts, etc.). The job generator loads all config/tools/*.yaml and builds the tool list. Use omnihub-generate-job --help to see the current tool list. No hardcoded tool list in skills; tools are discovered at runtime.
Results layout and env vars
Jobs write to results_dir (from the cluster config, typically $WORK/results/omnihub/$SLURM_JOB_ID). The job script sets:
- OMNIHUB_RESULTS_DIR — Same as results_dir; used by omnihub-run and apps (e.g. Primus workspace).
- tools/ — omnihub-monitor, omnistat, rocprofv3-stats, etc., per job.
- logs/ — srun stdout/stderr per rank.
- job.sh, job.yaml, app.yaml, job-status.yaml — For post-processing.
Processed output goes in processed-data/ under each job dir (created by omnihub-process).
Job template pre-flight checks
config/job.template runs scripts/sanity-check.sh on all allocated nodes before the main workload. It checks ROCm availability (rocminfo), GPU compute-mode, and PyTorch NCCL collectives under torchrun.
These run inside the Apptainer container. If the sanity step fails, fix cluster networking (NCCL_SOCKET_IFNAME, MASTER_ADDR) before debugging application code.
Apptainer overlay
Each Slurm task gets its own overlay ($results_dir/.overlay.$SLURM_PROCID). scripts/omnihub-apptainer-env.sh is sourced inside the container to set ROCm paths and create a per-task pip cache. The overlay provides a writable /opt/venv layer; pip install inside the container writes to this overlay rather than the read-only image.
Known benign warning: fuse-overlayfs exited: fuse: reading device: Software caused connection abort at job exit is an Apptainer/fuse-overlayfs cleanup race condition. It does not affect results.
Runner: manual vs torchrun
- --runner torchrun — OmniHub’s job script launches torchrun; one process per GPU/task. Use when the app is a standard single-process script that expects to be launched by torchrun.
- --runner manual — No torchrun from the job script; one task per node (or override with --tasks-per-node). Use when:
- The app is multi-node and starts its own distributed launcher (e.g. Primus CLI that runs torchrun internally), or
- You need one process per node and the app uses NNODES / NODE_RANK / torchrun itself.
For single-node jobs, runner is often omitted. For multi-node, always set --runner; choose manual for frameworks that manage their own launcher (e.g. Primus pretrain).
