Principle:Cohere ai Cohere python Training Monitoring
| Field | Value |
|---|---|
| Type | Principle |
| Source | Cohere Python SDK |
| Domain | Fine-tuning MLOps Monitoring |
| Last Updated | 2026-02-15 |
| Implemented By | Implementation:Cohere_ai_Cohere_python_FinetuningClient_Monitoring |
Overview
A polling-based pattern for tracking fine-tuning job progress through status checks, event logs, and training metrics.
Description
Training Monitoring is the process of observing a fine-tuning job's lifecycle from submission to completion. The SDK provides three monitoring endpoints: get_finetuned_model (current status), list_events (lifecycle event log), and list_training_step_metrics (per-step loss and metric data). Status values include STATUS_FINETUNING, STATUS_DEPLOYING_API, STATUS_READY, STATUS_FAILED, etc. Monitoring is necessary because fine-tuning is asynchronous.
Usage
Poll get_finetuned_model periodically to check status. Use list_events for a timeline of lifecycle events. Use list_training_step_metrics for detailed training curves. Stop polling when status reaches STATUS_READY or STATUS_FAILED.
Theoretical Basis
Asynchronous job monitoring follows the observer pattern with polling. The three-endpoint design separates concerns: status (current state), events (state transitions), metrics (training progress). This mirrors the MLOps pattern of experiment tracking.