experiment-tracking

You can deploy a self-hosted MLflow tracking server by configuring PostgreSQL as the backend store and an S3-compatible artifact storage, enabling private logging of runs, metrics, and artifacts without relying on public services.

You can log metrics, parameters, and artifacts from PyTorch and TensorFlow training scripts by integrating MLflow or Weights & Biases hooks directly into your training loops to capture run data.

Using MLflow model registry in a self-hosted environment allows you to compare experiment runs, visualize training progress, and manage model versions privately without relying on public tracking services.

W&B local server mode supports team pipelines but may encounter offline syncing issues. This Skill provides guidance to troubleshoot offline syncing, data loss, and connectivity issues across your team's training pipelines.

Setting up local experiment tracking requires either SQLite or PostgreSQL as a backend store. SQLite suits lightweight local setups, while PostgreSQL provides scalable backend storage for team environments.