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name: deepspeed description: "Use DeepSpeed for distributed training, inference acceleration, ZeRO configuration, parallelism/MoE design, profiling, autotuning, and operational diagnostics." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

DeepSpeed Repo Skill

Use this skill when a user asks to work with DeepSpeed APIs, configuration files, launch commands, inference acceleration, model parallelism, MoE, profiling, autotuning, or operational tooling. DeepSpeed is a PyTorch-focused optimization library with many optional GPU, distributed, and compiled-op paths, so route first by the user's workflow and then check the relevant troubleshooting reference before running heavy commands.

First Checks

1. Confirm the user wants DeepSpeed itself, not a downstream framework integration that merely accepts a DeepSpeed config.
2. Read references/install-and-environment.md when installing, importing, or debugging optional CUDA/ROCm/vendor accelerator behavior.
3. Run scripts/check_deepspeed_env.py for a read-only environment check, then scripts/inspect_deepspeed_api.py when API fields or signatures may have drifted.
4. Check references/repo-provenance.md before refreshing this skill against a source checkout.
5. Do not run native examples, distributed tests, autotuning, NVMe/GDS tools, builds, downloads, or remote SSH commands without explicit safety confirmation.

Route By Task

• Use sub-skills/training-config/ for PyTorch training integration, ds_config.json authoring, launcher resource filters, ZeRO/offload choices, checkpoint save/load/export, and config validation.
• Use sub-skills/inference-injection/ for deepspeed.init_inference, kernel or manual injection policies, inference tensor parallelism, inference quantization, checkpoint reshaping, v2/FastGen routing, and hybrid-engine boundaries.
• Use sub-skills/parallelism-moe/ for PipelineModule, pipeline schedules, MoE layers and optimizer groups, expert tensor parallelism, sequence parallel APIs, AutoSP, and activation checkpointing.
• Use sub-skills/ops-tooling/ for ds_report, install/build diagnostics, JIT/prebuilt op flags, autotuning, FLOPS profiling, monitor backends, DeepNVMe/AIO/GDS tools, compression APIs, and repo policy checks.

Shared References

• references/install-and-environment.md: package installation, PyTorch-first requirement, optional extras, compiled ops, and backend safety tiers.
• references/troubleshooting.md: cross-cutting import, accelerator, config, launcher, and optional-tool failure modes.
• references/repo-provenance.md: source snapshot and refresh baseline.

Shared Scripts

• scripts/check_deepspeed_env.py: read-only import, package metadata, PyTorch, CUDA visibility, and installed CLI discovery.
• scripts/inspect_deepspeed_api.py: read-only signature and config-field inspection for the main DeepSpeed APIs covered by this skill.

Safety Defaults

• Prefer read-only commands first: imports, --help, config parsing, and tiny CPU checks.
• Treat deepspeed launcher commands, checkpoint tests, CUDA op builds, autotuning, ds_io, ds_nvme_tune, GDS/AIO code, and ds_ssh as real workload or infrastructure commands.
• Do not promise GPU, compiled-op, NVMe, GDS, monitor, or model-download behavior unless the current environment has been explicitly verified for that path.
• Keep generated examples self-contained. Do not tell future agents to open or execute original repo docs, tests, scripts, examples, or notebooks as runtime dependencies.