setup-benchmark-inputs

Set up the minimal set of artifacts (tokenized DCLM corpus shard + released HuggingFace checkpoint converted to DCP) required to benchmark, profile, or regression-test a MoE model in PithTrain. Use when the user asks to "prepare benchmark inputs", "set up the benchmark workspace", "download the DCLM shard", "fetch and convert the released checkpoint", "tokenize DCLM for DeepSeek/Qwen3", or when a downstream skill (capture-nsys-profile, validate-correctness, or any short canonical run) needs its workspace pre-populated. Produce `workspace/datasets/dclm-baseline/toktxt/<model>` and `workspace/checkpoints/<model>/torch-dcp/step-00000000`. Idempotent and safe to re-run.

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Repository: mlc-ai/pith-train
Last source activity: August 22, 2026 at 00:53
Detected SKILL.md language: English
Stars: 345
Forks: 34

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add-new-model

Adds support for a new MoE language model to PithTrain. Use when the user asks to "add support for model X", "implement model Y in pithtrain", "port model Z", or otherwise integrate a new MoE architecture. Scope covers the model file, all framework wiring (setup_model, apply_fsdp, test_dualpipev), optional checkpoint conversion, and running training + inference tests from pp=1/ep=1 up to pp=2/ep=2.

2026-08-22

capture-nsys-profile

Capture a Nsight Systems (.nsys-rep) profile of a short PithTrain run for performance analysis. Use when the user asks to "capture an nsys profile", "profile training", or "grab an nsys trace", or wants to inspect kernel timelines / pipeline behavior / all-to-all overheads. Adaptive over pipeline-parallel (PP), expert-parallel (EP), context-parallel (CP), and sequence length; size the global batch so the pipeline reaches steady state without producing a multi-GB .nsys-rep. Run 5 warmup steps + 1 profiled step from a released checkpoint.

2026-08-22

launch-with-slurm

Reference for launching jobs inside a SLURM allocation via srun (single-node or multi-node). Use whenever work needs to run on allocated compute — from direct user requests ("run on the cluster", "use my running job", "on my allocation", "launch on slurm", "train across N nodes", "dispatch the job") OR from within another skill's workflow (e.g., validate-correctness running validation on the allocation, add-new-model reaching pp=2/ep=2). Covers finding the target job ($SLURM_JOB_ID or squeue, including array-job ID resolution), reading the allocation with scontrol, the srun flags that matter (--jobid, -W 0, -N, -o, --open-mode, --nodelist, --overlap), and gotchas like GPU sharing with live steps and distributed-aware output redirection.

2026-08-22

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