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training-workflows

training-workflows

Plan and safely launch SimpleTuner training workflows across CLI configuration, model quickstarts, distributed backends, validation, checkpointing, and runtime troubleshooting.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: training-workflows description: "Plan and safely launch SimpleTuner training workflows across CLI configuration, model quickstarts, distributed backends, validation, checkpointing, and runtime troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: AGPL 3.0

training-workflows

Use this sub-skill when a task is about planning or launching a SimpleTuner training run, choosing a platform install variant, selecting a model-family quickstart, deciding between CLI/configuration backends, planning distributed or memory-saving runtime, or diagnosing training-runtime failures.

Real model downloads, Hub access, dataset acquisition, cloud submissions, and actual training are manual/expensive actions. Do not start them unless the user explicitly approves the runtime cost, credentials, and target hardware.

Router

  1. Install and CLI/config route: start with training CLI and config to choose simpletuner configure, simpletuner train, simpletuner-train, ENV, CONFIG_BACKEND, CONFIG_PATH, and JSON/TOML/env/cmd config style.
  2. Model-family quickstart: use model family quickstarts to select the family/flavour/example level. If the request is about adapter formats, extraction, merge, conversion, LyCORIS internals, ControlNet adapter mechanics, or distillation method details, reroute to sub-skills/model-and-adapter-tooling/.
  3. Distributed and memory plan: use distributed and memory before changing num_processes, DeepSpeed, FSDP2, context parallelism, attention backends, quantization, offload, or resume topology.
  4. Validation, checkpointing, or failures: use troubleshooting for missing config/env, invalid backend, undersized dataset buckets, DeepSpeed+FSDP conflicts, context-parallel requirements, resume constraints, platform package mismatches, OOM, model access, and validation/checkpoint path issues.

Reroute boundaries

  • Dataloader schema, cache layout, dataset filtering, captions, image/video/audio/conditioning data, and data_backend_config internals: sub-skills/data-and-config/.
  • Adapter export/extraction/conversion/merge, LoRA format migration, LyCORIS details, model registry inspection scripts, CaptionFlow, Prompt2Effect, and distillation method internals: sub-skills/model-and-adapter-tooling/.
  • WebUI/API server modes, local job queue, workers, cloud providers, auth/quotas/notifications, and webhook operations: sub-skills/webui-and-operations/.
  • Repository tests, code changes, frontend E2E policy, docs/translations, and public-text privacy for contribution work: sub-skills/repo-development/.

Safe command builder

Use the bundled helper to print a command only; it never launches training:

python skills/disco/simple-tuner/sub-skills/training-workflows/scripts/build_training_command.py --help
python skills/disco/simple-tuner/sub-skills/training-workflows/scripts/build_training_command.py --env flux-lora --config-backend json -- max_train_steps=100 report_to=none
python skills/disco/simple-tuner/sub-skills/training-workflows/scripts/build_training_command.py --config-path config/flux-lora/config.json --config-backend json -- model_family=flux model_type=lora

Evidence base

This sub-skill was distilled from README.md, documentation/INSTALL.md, documentation/TUTORIAL.md, documentation/QUICKSTART.md, documentation/OPTIONS.md, documentation/DEEPSPEED.md, documentation/FSDP2.md, documentation/DISTRIBUTED.md, documentation/attention/FLEX.md, documentation/attention/SLA.md, documentation/evaluation/*.md, simpletuner/cli/train.py, simpletuner/train.py, simpletuner/helpers/training/*, simpletuner/examples/*/config.json, tests/test_attention_backend.py, tests/test_context_parallel_plans.py, and tests/test_fsdp_cmd_args.py. Source evidence is cited by repo-relative name only; do not require a future agent to reopen the source checkout to use this sub-skill.

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