원클릭으로 Manus에서 모든 스킬 실행

setup-benchmark-inputs

스타257

포크21

업데이트2026년 7월 14일 19:56

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.

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

Manus에서 실행

출처

mlc-ai

mlc-ai/pith-train

GitHub 저장소 열기 Creator 저장소 보기

다운로드

Manus에서 실행

Setup Benchmark Inputs

Setup the minimal artifacts needed to benchmark, profile, or regression-test a MoE model in PithTrain: a single DCLM corpus shard tokenized for the target model, and the released HuggingFace checkpoint converted to DCP format. Each step is idempotent (skips if its output already exists).

Prerequisites

Python environment: activate .venv in the repo root (source .venv/bin/activate).

Usage

mkdir -p logging

# Single-node (DeepSeek-V2-Lite)
bash .agents/skills/setup-benchmark-inputs/scripts/launch_setup.sh --model deepseek-v2-lite 2>&1 | tee logging/setup-deepseek-v2-lite.log

# Multi-node via SLURM (Qwen3-30B-A3B)
srun -W 0 -o logging/setup-qwen3-30b-a3b.log .agents/skills/setup-benchmark-inputs/scripts/launch_setup.sh --model qwen3-30b-a3b

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Validates that code changes do not break training correctness by comparing per-step loss curves between a base branch and the current feature branch. Use when user asks to "validate correctness", "check if changes break training", "compare loss curves", "run a regression test", or "verify my changes are correct". The user specifies which model to validate and at which parallelism mesh (PP/EP/CP) — do not infer this from git diff.

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Query a captured PithTrain Nsight Systems profile to measure compute/communication overlap, locate exposed comm by DualPipeV stage, and inspect per-rank stream behavior. Use when the user asks to "analyze an nsys profile", "check overlap quality", "find exposed comm", "which stage is the bottleneck", or any question that starts from an existing `.nsys-rep` file. Assumes the trace was already captured (see capture-nsys-profile); provides query primitives the agent composes for the specific question being asked.

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name

setup-benchmark-inputs

description

Setup Benchmark Inputs

Prerequisites

Python environment: activate .venv in the repo root (source .venv/bin/activate).

Usage

mkdir -p logging

# Single-node (DeepSeek-V2-Lite)
bash .agents/skills/setup-benchmark-inputs/scripts/launch_setup.sh --model deepseek-v2-lite 2>&1 | tee logging/setup-deepseek-v2-lite.log

# Multi-node via SLURM (Qwen3-30B-A3B)
srun -W 0 -o logging/setup-qwen3-30b-a3b.log .agents/skills/setup-benchmark-inputs/scripts/launch_setup.sh --model qwen3-30b-a3b