deepspeed
Use DeepSpeed for distributed training, inference acceleration, ZeRO configuration, parallelism/MoE design, profiling, autotuning, and operational diagnostics.
Skill by VectorSpaceLab
Chalk Labs
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Install
Works with Claude Code, Claude apps, Codex, Cursor, Gemini CLI, GitHub Copilot, VS Code, OpenCode, Goose, Windsurf. Read the repository before you give any tool access to your data.
npx agentmarket install skill/vectorspacelab-deepspeed
Asks which of your tools to add it to (Claude Code, Codex, Cursor, VS Code, Windsurf, Gemini CLI, Claude Desktop) and shows the change first. See https://agentmart.dev/cli.
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About
Use cases: design, ml
Reviews
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Permissions
What this skill can do and what it asks for, from its SKILL.md. Checked Oct 25, 2026.
- Tools
- No tools found
- Secrets
- None asked for
- Install scripts
- None found
- Runs as
- Python package
Network hosts it talks to 1
codeload.github.com· found in install command
Files it touches
~/.claude/skills/deepspeed
What's inside
- Skill name
deepspeed- Description
- Use DeepSpeed for distributed training, inference acceleration, ZeRO configuration, parallelism/MoE design, profiling, autotuning, and operational diagnostics.
- License
- Apache-2.0
- Metadata
- disco-role: operating
- Instructions
- 38 lines in SKILL.md · view on GitHub
Before installing
- Ships executable scripts
Files (29)
- SKILL.md
- install-and-environment.md · references/
- repo-provenance.md · references/
- repo-routing-metadata.json · references/
- troubleshooting.md · references/
- check_deepspeed_env.py · scripts/
- inspect_deepspeed_api.py · scripts/
- SKILL.md · sub-skills/inference-injection/
- api-reference.md · sub-skills/inference-injection/references/
- troubleshooting.md · sub-skills/inference-injection/references/
- workflows.md · sub-skills/inference-injection/references/
- inspect_inference_config.py · sub-skills/inference-injection/scripts/
- SKILL.md · sub-skills/ops-tooling/
- cli-reference.md · sub-skills/ops-tooling/references/
- troubleshooting.md · sub-skills/ops-tooling/references/
- workflows.md · sub-skills/ops-tooling/references/
- check_deepspeed_tools.py · sub-skills/ops-tooling/scripts/
- SKILL.md · sub-skills/parallelism-moe/
- api-reference.md · sub-skills/parallelism-moe/references/
- troubleshooting.md · sub-skills/parallelism-moe/references/
- workflows.md · sub-skills/parallelism-moe/references/
- inspect_parallelism_api.py · sub-skills/parallelism-moe/scripts/
- SKILL.md · sub-skills/training-config/
- api-reference.md · sub-skills/training-config/references/
- configuration.md · sub-skills/training-config/references/
- launcher-and-checkpointing.md · sub-skills/training-config/references/
- troubleshooting.md · sub-skills/training-config/references/
- launcher_resource_preview.py · sub-skills/training-config/scripts/
- validate_ds_config.py · sub-skills/training-config/scripts/
SKILL.md (first lines)
# 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.
From VectorSpaceLab/AREX-Skill, Apache-2.0.
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Signals
- Stars
- 341
- Forks
- 26
- Open issues
- 0
- Last commit (this folder)
- Aug 26, 2026 (2 months ago)
- Last release
- Sep 2, 2026 (2 months ago)
From GitHub (fetched Oct 24, 2026).
Details
- License
- Apache-2.0
- Repository
- github.com/VectorSpaceLab/AREX-Skill
- First seen
- Oct 24, 2026
- Last checked
- Oct 24, 2026
- Source
- GitHubgithub-graphql
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Cite this page
deepspeed skill. Agent Mart. https://agentmart.dev/skills/vectorspacelab-deepspeed (last updated 2026-10-24)