experiment-tracking-swanlab
Category: ml-inference-and-ops · License: MIT · Version: 1.0.0
Tags: MLOps, SwanLab, Experiment Tracking, Open Source, Visualization, PyTorch, Transformers, PyTorch Lightning, Fastai, Self-Hosted
When to use it
Tracks ML experiments with SwanLab, an open-source tool covering swanlab.init, config and metric logging, scalar charts, and media logging (images, audio, text, GIFs, point clouds, molecules), in cloud, local (mode=“local” with swanlab[dashboard]), or self-hosted setups. Includes integrations for PyTorch, Transformers, PyTorch Lightning, and Fastai. Use when logging metrics and hyperparameters for training runs, comparing runs across seeds, checkpoints, or hyperparameters, running an offline or self-hosted dashboard instead of managed SaaS, logging media alongside scalars, or adding tracking callbacks to a Transformers, Lightning, or Fastai training loop. Not for other trackers such as Weights & Biases or MLflow.Example requests
Ask your agent in plain language, for example:- “How do I log loss and accuracy per epoch to SwanLab?”
- “I’m fine-tuning bert-base-uncased on SST-2 with a HuggingFace Trainer in train_sst2.py. I want the loss curves and my learning rate / batch size / seed recorded so I can compare three seeds side by side. The cluster has no internet, so it has to work offline. How should I set this up?”
- “My lab doesn’t want training data going to a third-party SaaS. I need something open-source that I can host on our own server to see training curves and also look at the generated images and audio samples from my GAN next to the loss plots. What should I use and how do I wire it into my PyTorch loop?”
Install
/plugin marketplace add KalarisLabs/research-agent-skills and install the whole ml-inference-and-ops category with /plugin install ml-inference-and-ops@research-agent-skills.