---

name: ops-tooling description: "Diagnose DeepSpeed installs, build/JIT flags, CLI tools, autotuning, profiling, monitoring, DeepNVMe/GDS utilities, compression APIs, and repo policy checks safely." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

DeepSpeed Ops Tooling

Use this sub-skill when an agent needs to inspect or explain DeepSpeed operational tooling rather than author a training loop, inference kernel injection, or parallelism design.

Use For

• Installation and compatibility diagnosis with ds_report or python -m deepspeed.env_report.
• Build planning for JIT versus prebuilt ops, DS_BUILD_* flags, CUDA toolkit matching, optional extras, and TORCH_EXTENSIONS_DIR caching.
• Installed tool discovery for deepspeed, ds, ds_report, ds_io, ds_nvme_tune, and other safe CLI help checks.
• Autotuning launch planning, FLOPS profiler setup, monitor backend configuration, DeepNVMe/AIO/GDS guardrails, and compression API routing.
• DeepSpeed repo policy diagnostics such as CUDA/distributed import bans, license headers, and package index safety.

Do Not Use For

• Training-loop JSON ownership or ZeRO/offload choices; route to training-config.
• Inference injection policies, tensor-parallel inference kernels, or Triton inference details; route to inference-injection.
• Pipeline/model parallel architecture or MoE expert design; route to parallelism-moe.

Start Here

1. Run the bundled safe checker before destructive or long-running tooling:

   python scripts/check_deepspeed_tools.py
   

2. Use CLI Reference to choose the right installed tool and identify commands that only print help versus commands that run jobs or write storage.
3. Use Workflows for install triage, autotuning, profiling, monitors, DeepNVMe, and compression setup.
4. Use Troubleshooting for common failures and safety gates.

Safety Defaults

• Prefer read-only inspection: --help, imports, config parsing, and ds_report before running builds or I/O benchmarks.
• Treat ds_io, ds_nvme_tune, DeepNVMe write APIs, and GDS examples as potentially destructive unless the user explicitly provides a scratch directory and confirms writes.
• Treat autotuning as a real job launcher: it can start many training experiments and consume cluster GPUs.
• Never run ds_ssh or remote launch commands without explicit hostfile, SSH, and user approval.