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name: composer description: "Use MosaicML Composer for PyTorch training loops, speedup methods, loggers, checkpoints, distributed launch, profiling, and model export." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

MosaicML Composer Repo Skill

Use this repo skill when a task involves MosaicML Composer, the mosaicml Python distribution, or the importable composer package for PyTorch training engines, efficient training methods, callbacks/loggers, checkpoints, distributed launch, profiling, or model export.

Public install:

pip install mosaicml

Minimal import check:

python - <<'PY'
import composer
print(composer.__version__)
from composer import Trainer
print(Trainer)
PY

For optional integrations, read installation and package map before adding extras.

Route by task

• Build, run, resume, or debug a training workflow: read training. This route owns Trainer, ComposerModel, ComposerClassifier, DataSpec, Evaluator, Time, State, manual checkpoint load, autoresume, and basic device/precision choices.
• Add or debug Composer speedup methods: read methods. This route owns composer.algorithms, composer.functional, MixUp/CutMix/LabelSmoothing, model surgery, batch-key routing, and method recipe placement.
• Make run state visible: read observability. This route owns loggers, monitoring callbacks, local/remote file upload, profiler traces, composer_collect_env, and observability troubleshooting.
• Launch or reason about distributed/backend workflows: read distributed. This route owns the composer launcher, rank/world-size helpers, distributed samplers, get_device, FSDP/FSDP2/TP basics, and auto microbatching caveats.
• Export models for inference: read inference-export. This route owns export_for_inference, ExportForInferenceCallback, TorchScript/ONNX validation, checkpoint-backed export, and optional HuggingFace/PEFT caveats.

Root references and scripts

• Installation and package map: distribution/import names, optional extras, top-level namespaces, console entry points, and first checks.
• Troubleshooting: cross-cutting install/import, backend, optional dependency, CLI, and package-surface failures.
• Repo provenance: source commit, branch, package version, dirty-state baseline, and relative evidence paths.
• Routing metadata: structured scenario placement for managed repo-skill import.
• check_import.py: safe JSON import/backend probe for Composer and PyTorch.
• run_smokes.py: runs bundled sub-skill smoke scripts with the current Python.

Fast operating workflow

1. Verify installation with python scripts/check_import.py from this skill root.
2. Pick the closest sub-skill route from the task wording and load only the references/scripts it names.
3. Start with CPU/tiny-data scripts when debugging model, data, or API issues.
4. Add optional extras only for the integration in use; do not install mosaicml[all] unless the task truly spans many optional backends.
5. For GPU/distributed issues, run the distributed device_probe.py before launching training.
6. For checkpoint/export issues, decide whether the task is about training-state resume or inference artifact export; route accordingly.
7. Keep generated project code independent of this skill's source provenance and do not copy private machine paths into user scripts.

Composer concepts to recognize

• Trainer is the high-level training loop that manages fit/eval/predict, checkpoint save/load, callbacks, algorithms, logging, precision, devices, and distributed integration.
• ComposerModel is the model contract for batches; ComposerClassifier wraps simple (input, target) classification modules.
• Algorithm classes integrate methods through Trainer events; composer.functional helpers are for custom loops or one-off model/batch mutation.
• LoggerDestination subclasses receive metrics and uploaded files; callbacks and trace handlers generate many of those files.
• The composer console entry point launches multi-process distributed jobs and configures rank environment variables.
• export_for_inference and ExportForInferenceCallback produce TorchScript or ONNX artifacts after optional load/surgery/transform steps.

Avoid using this skill when

• The task is ordinary PyTorch with no Composer APIs, errors, configs, or artifacts.
• The user is actually using PyTorch Lightning, Accelerate, DeepSpeed, or another training framework without Composer.
• The task is model-family-specific and only incidentally mentions a project that once used Composer.
• The user asks to edit the Composer repository itself rather than operate the package; this skill is for package/runtime use, not maintainer development.

Verification posture

The generated skill favors CPU-safe, no-download workflows first and documents CUDA/distributed behavior with backend caveats. Optional CUDA was available during construction and a PyTorch CUDA smoke passed, but production-scale multi-rank training should still be verified in the user's target environment before relying on it.