What problem does it solve? Machine learning training runs produce scattered metrics, configs, and artifacts that are hard to compare and reproduce. This Skill provides guidance for logging experiments with SwanLab, an open-source tracker that works in cloud, local, or self-hosted modes. ## Core Features & Use Cases - Metric and Config Logging: Initialize runs with swanlab.init, capture hyperparameters, and log scalars with grouped namespaces like train/loss and val/accuracy. - Media and Chart Visualization: Log images, audio, text, GIFs, point clouds, molecules, and pyecharts-based charts for richer run inspection. - Framework Integrations: Connect with PyTorch, HuggingFace Transformers (report_to="swanlab" or SwanLabCallback), PyTorch Lightning (SwanLabLogger), and Fastai (SwanLabCallback). - Use Case: A researcher fine-tuning a BERT classifier can set report_to="swanlab" in TrainingArguments, then compare validation loss across seeds in the SwanLab dashboard, or run fully offline with mode="local" and inspect logs via swanlab watch. ## Quick Start Use the SwanLab skill to set up experiment tracking for my PyTorch training loop with metric logging and a local dashboard.
