weights-and-biases
Category: ml-inference-and-ops · License: MIT · Version: 1.0.0
Tags: MLOps, Weights And Biases, WandB, Experiment Tracking, Hyperparameter Tuning, Model Registry, Collaboration, Real-Time Visualization, PyTorch, TensorFlow, HuggingFace
When to use it
Logs and tracks machine learning experiments with Weights & Biases (W&B, wandb): metrics, hyperparameters, checkpoints, sweeps, artifacts with lineage, model registry, custom charts and shareable reports. Includes integrations for PyTorch, HuggingFace Transformers, PyTorch Lightning and Keras/TensorFlow. Use when logging training runs and comparing them across configurations, running automated hyperparameter sweeps, versioning datasets and models as artifacts, registering model versions, or sharing results with a team workspace. Use when setting up wandb.init for offline or unstable-connection training. Not for general data versioning outside ML runs.Example requests
Ask your agent in plain language, for example:- “How do I log my loss curves to wandb?”
- “I’m fine-tuning bert-base-uncased with HuggingFace Trainer in train_glue.py on a cluster with a flaky network, and my run keeps crashing with ‘wandb: ERROR Error communicating with wandb process’ and ‘CommError: Run initialization has timed out’. I want to keep the metrics and upload them later. How should I set this up?”
- “I’ve trained 40 ResNet variants with different learning rates, batch sizes and weight decays. I want the search over those settings automated so I can see which combination gives the best validation accuracy, and then share a summary with my labmates. What should I do?”
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.