---

name: "training-evaluation" description: "Routes BasicTS training and evaluation workflows for forecasting, classification, imputation, and foundation-model launches." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

training-evaluation

Use this sub-skill when the task is to launch, resume, or evaluate a BasicTS run rather than to redesign the model, dataset layout, or callback logic.

Typical triggers

• "Train a BasicTS forecasting model"
• "Run the classification demo"
• "Evaluate a saved checkpoint"
• "Launch an imputation job"
• "Show me the BasicTS quick start"

What this sub-skill covers

• BasicTSLauncher.launch_training
• BasicTSLauncher.launch_evaluation
• BasicTSForecastingConfig
• BasicTSClassificationConfig
• BasicTSImputationConfig
• BasicTSFoundationModelConfig
• checkpoint save/load flow
• CPU-safe smoke runs and quick-start usage
• common task selection for forecasting, classification, imputation, and foundation-model runs

Read these bundled references first

• references/workflows.md for verified launch patterns and config selection.
• references/troubleshooting.md for dataset, checkpoint, and launch failures.
• scripts/run_mini_forecasting_smoke.py for a safe CPU smoke run that creates its own tiny fixture.

Route here when the user asks for

• a new training run or a resumed run
• a saved-model evaluation
• a quick start example that uses BasicTSLauncher
• a task-specific config choice for forecasting, classification, imputation, or foundation-model work
• checkpoint path selection or evaluation after training

Route elsewhere when the user asks for

• dataset file layout, raw conversion, or fixture validation → data-preparation
• custom model forward contracts or model family selection → model-development
• callbacks, metrics, scalers, taskflows, or config behavior beyond basic launch usage → pipeline-extension

Working guidance

1. Choose the right task config class before writing a runnable command.
2. Match dataset_name and dataset_params to the dataset files that exist or that your helper creates.
3. Keep gpus=None for CPU smoke unless the user explicitly wants a GPU run.
4. Use num_epochs or num_steps, but not both.
5. Use launch_evaluation only after you know the checkpoint path you want to load.

When to read the helper script

Run scripts/run_mini_forecasting_smoke.py when you want a safe CPU proof that the launcher, config packing, and checkpoint flow still work. It creates a tiny temporary dataset and does not depend on the original repository checkout.

Why this sub-skill exists

BasicTS is often used through a launcher plus config object. This sub-skill keeps that entry path separate from the lower-level dataset, model, and pipeline customization routes so future agents can answer common run requests quickly.