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name: training-core description: "Routes AXLearn trainer configs, fake-data smoke checks, launcher usage, and tokenizer setup." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

training-core

Use this sub-skill for AXLearn's shared training machinery:

• config_for_function, config_for_class, and Configurable/Module patterns.
• SpmdTrainer, SpmdEvaler, learners, optimizers, checkpointers, and schedules.
• axlearn.common.launch_trainer_main and axlearn.common.launch_trainer.
• Fake-data / CPU-safe tutorial workflows such as the logistic-regression example.
• SentencePiece training and tokenizer plumbing when the task is about training setup rather than a specific model family.

If the task names GPT/Fuji/Gala/Honeycrisp/Qwen/MoE, jump to ../language-models/. If the task names ImageNet/ResNet/vision models, jump to ../vision-workflows/. If the task names ASR/Conformer/LibriSpeech, jump to ../audio-asr/. If the task names axlearn gcp ..., jump to ../cli-cloud/.

What to read

• references/workflows.md for trainer and fake-data workflows.
• references/troubleshooting.md for install/import/config pitfalls.
• scripts/inspect_trainer_config.py for a safe config-inspection helper.

Typical workflows

Inspect a trainer config

Use this when you want to see what a named trainer config resolves to without launching a long run:

python scripts/inspect_trainer_config.py --module axlearn.experiments.logistic_regression.tutorial --config LogisticRegression

Run a CPU-safe tutorial smoke check

The logistic-regression tutorial is the best small local probe because it uses synthetic data and a short config path:

DATA_DIR=FAKE python -m axlearn.common.launch_trainer_main \
  --module=axlearn.experiments.logistic_regression.tutorial \
  --config=LogisticRegression \
  --trainer_dir=/tmp/axlearn-logreg \
  --data_dir=FAKE \
  --jax_backend=cpu

Inspect launcher helpers

When debugging launcher behavior, check the shared trainer entrypoints rather than the experiment module first:

• axlearn.common.launch_trainer_main
• axlearn.common.launch_trainer.get_trainer_config
• axlearn.common.trainer.SpmdTrainer

SentencePiece training

Use this sub-skill for tokenizer setup when the user is creating or validating a sentencepiece model. The command is CPU-oriented and can be memory-hungry, so treat it as a setup workflow, not a quick unit test.

Decision points

• Prefer fake-data or synthetic-data configs when the goal is to validate wiring.
• Prefer named trainer configs when the goal is to inspect config composition or mesh settings.
• Do not route GPT-family catalogs here if the task specifically names Fuji, Gala, Honeycrisp, Qwen, MoE, or flash attention.
• Do not route cloud launch/bundle behavior here; that belongs to cli-cloud.