Principle:Axolotl ai cloud Axolotl Experiment Tracking Integration
| Knowledge Sources | |
|---|---|
| Domains | Experiment_Tracking, RLHF, Training |
| Last Updated | 2026-02-07 00:00 GMT |
Overview
Configuration pattern for integrating third-party experiment tracking platforms into the training pipeline with support for metrics logging, completion tables, profiling, and team notifications.
Description
Experiment Tracking Integration addresses the need to monitor, compare, and share training runs across a team. In RLHF workflows (DPO, KTO, GRPO), tracking goes beyond standard loss metrics to include qualitative completion logging (chosen vs rejected response pairs), performance profiling of training step components, and real-time team notifications via messaging platforms. The integration must be rank-aware in distributed training (only rank 0 initializes the tracker) and should support multiple modes (cloud, local, offline, disabled). The pattern also handles authentication securely through environment variables rather than config files.
Usage
Apply this principle when setting up production training with comprehensive monitoring. It is especially relevant for RLHF workflows where qualitative evaluation of model outputs is as important as loss metrics. Choose an experiment tracking platform (SwanLab, W&B, MLflow, Comet) and configure the appropriate plugin and parameters.
Theoretical Basis
# Abstract experiment tracking integration
def setup_tracking(config, rank):
if rank != 0:
return NoOpTracker()
tracker = init_tracker(
project=config.project,
experiment=config.experiment_name,
mode=config.mode, # cloud | local | offline | disabled
)
if config.log_completions and is_rlhf_trainer(config):
register_completion_callback(tracker, config.log_interval)
if config.team_notifications:
register_notification_callback(tracker, config.webhook)
# Profiling is automatic when tracker is active
return tracker