experiment-tracking-swanlab

To log and visualize machine learning experiments locally, you can use SwanLab to track metrics, configurations, and tags while generating scalar charts and media. It works locally or self-hosted, reducing dependency on external SaaS platforms.

Yes, SwanLab experiment tracking integrates natively with PyTorch, Transformers, PyTorch Lightning, and Fastai. You can log training metrics and media across these different frameworks within your existing MLOps workflows.

The best way to compare ML runs across different hyperparameters is by using an experiment tracking tool like SwanLab. It allows you to compare runs across different seeds, checkpoints, and hyperparameters using visual scalar charts.

To track media outputs like images and audio during model training, SwanLab requires the Pillow and Soundfile libraries. You can log these media types alongside your standard scalar metrics for comprehensive training visualization.

Yes, you can use a self-hosted dashboard for MLOps experiment visualization with SwanLab. It supports both local and self-hosted environments, allowing data scientists to compare experiments without relying on external SaaS platforms.

Before starting ML experiment tracking with SwanLab, you need to install swanlab (version 0.7.11 or higher), Pillow (9.0.0+), and Soundfile (0.12.0+). Pillow and Soundfile are specifically required for logging media files.