---

name: "training-evaluation-deployment" description: "Chronos-2 fine-tuning, training configs, evaluation workflows, aggregate relative scores, and SageMaker deployment references." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

training-evaluation-deployment

Use this sub-skill for Chronos non-inference workflows:

• Chronos-2 fit() and LoRA/full fine-tuning
• original Chronos training configs and KernelSynth data generation
• fev-based evaluation and aggregate relative scores
• SageMaker deployment shape, payloads, and reference-only cloud constraints

Route elsewhere

• Forecast usage and output interpretation: ../chronos-2-forecasting/ or ../chronos-bolt-and-original/
• DataFrame schema, covariates, and validation: ../data-formats-and-validation/
• Full benchmark reproduction, cloud endpoint execution, and large GPU runs stay reference-only unless the user explicitly supplies credentials, hardware, and time.

Bundled runtime files

• references/training-evaluation.md
• references/sagemaker-deployment.md
• references/troubleshooting.md
• scripts/aggregate_relative_scores.py
• scripts/chronos2_fit_smoke_template.py

Safe defaults

• Scripts do not train, download, or call AWS unless the user explicitly opts in.
• Prefer tiny synthetic data, low step counts, and local outputs for smoke checks.