Agent skill · nousresearch
weights-and-biases
W&B: log ML experiments, sweeps, model registry, dashboards.
What it needs
About 5k tokens when loaded.
What this skill does
Weights & Biases: ML Experiment Tracking & MLOps When to Use This Skill Use Weights & Biases (W&B) when you need to: Track ML experiments with automatic metric logging Visualize training in real-time dashboards Compare runs across hyperparameters and configurations Optimize hyperparameters with automated sweeps Manage model registry with versioning and lineage Collaborate on ML projects with team workspaces Track artifacts (datasets, models, code) with lineage Users: 200,000+ ML practitioners GitHub Stars: 10.5k+ Integrations: 100+ Installation Quick Start Basic Experiment Tracking With PyTorch Core Concepts 1. Projects and Runs Project: Collection of related experiments Run: Single execution of your training script 2. Configuration Tracking Track hyperparameters automatically: 3. Metric Logging 4. Model Checkpointing Hyperparameter Sweeps Automatically search for optimal hyperparameters. Define Sweep Configuration Define Training Function Sweep Strategies Artifacts Track datasets, models, and other files with lineage. Log Artifacts Use Artifacts Model Registry Integration Examples HuggingFace Transformers PyTorch Lightning Keras/TensorFlow Visualization & Analysis Custom Charts Reports Create shareable reports in W&B UI: Combine runs, charts, and text Markdown support Embeddable visualizations Team collaboration Best Practices 1. Organize with Tags and Groups 2. Log Everything Relevant 3. Use Descriptive Names 4. Save Important Artifacts 5. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nousresearch/weights-and-biases--e81c01