experiment-tracking-swanlab

# SwanLab: Open-Source Experiment Tracking ## When to Use This Skill Use SwanLab when you need to: - **Track ML experiments** with metrics, configs, tags, and descriptions - **Visualize training** with scalar charts and logged media - **Compare runs** across seeds, checkpoints, and hyperparameters - **Work locally or self-hosted** instead of depending on managed SaaS - **Integrate** with PyTorch, Transformers, PyTorch Lightning, or Fastai **Deployment**: Cloud, local, or self-hosted | **Media**: images, audio, text, GIFs, point clouds, molecules | **Integrations**: PyTorch, Transformers, PyTorch Lightning, Fastai ## Installation ```bash # Install SwanLab plus the media dependencies used in this skill pip install "swanlab>=0.7.11" "pillow>=9.0.0" "soundfile>=0.12.0" # Add local dashboard support for mode="local" and swanlab watch pip install "swanlab[dashboard]>=0.7.11" # Optional framework integrations pip install transformers pytorch-lightning fastai # Login for cloud or self-hosted usage swanlab login ``` `pillow` and `soundfile` are the media dependencies used by the Image and Audio examples in this skill. `swanlab[dashboard]` adds the local dashboard dependency required by `mode="local"` and `swanlab watch`. ## Quick Start ### Basic Experiment Tracking ```python import swanlab run = swanlab.init( project="my-project", experiment_name="baseline", config={ "learning_rate": 1e-3, "epochs": 10, "batch_size": 32, "model"...