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distributed-and-backends

distributed-and-backends

Choose and troubleshoot DALLE-pytorch CUDA, DeepSpeed, Horovod, Apex, Docker, sparse attention, and distributed checkpoint paths.

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Documentation de la Skill

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name: distributed-and-backends description: "Choose and troubleshoot DALLE-pytorch CUDA, DeepSpeed, Horovod, Apex, Docker, sparse attention, and distributed checkpoint paths." metadata: disco-role: operating disable-model-invocation: true license: MIT

Distributed and backend workflows

Use this sub-skill for CUDA readiness, DeepSpeed, Horovod, Apex AMP, Docker GPU images, sparse attention, distributed parser flags, and checkpoint behavior.

Read first

  • references/distributed-backends.md for backend flags, module availability, sparse attention, precision, and checkpoint notes.
  • references/docker-and-system-setup.md for Docker/source-build caveats.
  • references/troubleshooting.md for backend import, CUDA, Apex, Horovod, and DeepSpeed failures.
  • scripts/check_backend_availability.py to inspect safe backend availability without launching training.

Typical routes

RequestAction
"Can I use DeepSpeed?"Run/describe backend availability check, confirm deepspeed import, decide whether sparse attention or only distributed training is needed.
"What does --deepspeed do?"Explain parser wrapping and that args.deepspeed maps to the DeepSpeed backend when available.
"Use Horovod"Confirm horovod.torch import and use horovodrun-style launch; batch size semantics differ from DeepSpeed.
"Install Apex or sparse attention"Treat as CUDA/source-build task requiring explicit approval; do not run bundled source install scripts blindly.
"Choose attn_types"Use package attention options; only sparse needs DeepSpeed sparse attention, while full, axial_row, axial_col, and conv_like are package-level options.

Boundary notes

  • Single-process VAE training belongs to ../vae-training/SKILL.md.
  • Single-process DALL-E training/data/tokenizer issues belong to ../dalle-training/SKILL.md.
  • Generation belongs to ../generation-and-ranking/SKILL.md.

Safety rules

  • Do not run source builds, sudo apt-get, Docker builds, or distributed launchers without explicit approval.
  • A CUDA allocation smoke is not proof that full training works; it only proves torch can see a device.
  • Keep DeepSpeed/Apex/Horovod optional unless the user's selected workflow requires them.
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