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weights-and-biases

Track ML experiments, hyperparameters, artifacts, and model versions across training runs.

Updated Jul 7, 2026
Search Tags:#mlops#artifacts#experiment tracking#wandb#framework integrations#model registry#hyperparameter sweeps
One-click install
npx skills add https://github.com/TitoPrausee/nexus-toti --skill weights-and-biases-titoprausee
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: weights-and-biases
Source: https://github.com/TitoPrausee/nexus-toti/tree/main/data/skills/mlops/evaluation/weights-and-biases
Command: npx skills add https://github.com/TitoPrausee/nexus-toti --skill weights-and-biases-titoprausee

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill replaces scattered experiment notes and manual tracking with a centralized workflow for monitoring machine learning runs, comparing results, and preserving model lineage.

Core Features & Use Cases

  • Experiment tracking: Log metrics, configs, media, and system stats in real time.
  • Hyperparameter sweeps: Search parameter spaces with grid, random, or Bayesian optimization.
  • Artifacts and registry: Version datasets, checkpoints, and production models with lineage and aliases.
  • Framework integrations: Use it with PyTorch, TensorFlow, HuggingFace, PyTorch Lightning, Fast.ai, XGBoost, and LightGBM.
  • Use case: A training team can compare fine-tuning runs, promote the best checkpoint to the registry, and share the results with collaborators.

Quick Start

Use the weights-and-biases skill to set up a W&B run for your current training project, log the key metrics and configuration, and then add any model artifact or sweep configuration you want to track.

Frequently Asked Questions about weights-and-biases

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I track machine learning experiments and compare hyperparameters across training runs?▼

Track machine learning experiments by logging metrics, configs, media, and system stats in real time to a centralized dashboard. This creates a single source of truth for comparing hyperparameters and model performance across multiple training runs.

Can I use experiment tracking with PyTorch, TensorFlow, and HuggingFace models?▼

Yes, experiment tracking supports framework integrations for PyTorch, TensorFlow, HuggingFace, PyTorch Lightning, Keras, XGBoost, and LightGBM. You can log metrics, configurations, and artifacts directly from these training environments.

What's the best way to run hyperparameter sweeps for model training?▼

Run hyperparameter sweeps by searching parameter spaces using grid, random, or Bayesian optimization. This automates the search for optimal configurations and logs all results for reliable experiment comparison.

How do I version datasets and manage model registry promotion for production?▼

Version datasets and checkpoints by logging them as artifacts with full lineage tracking. You can promote the best model checkpoint to the registry using aliases, preserving model lineage from training to production.

Why do I need artifact lineage and repeatable configuration capture for MLOps?▼

Artifact lineage and repeatable configuration capture ensure reliable experiment comparison by linking datasets, code, and model versions across training runs. This maintains a consistent audit trail for team collaboration and MLOps workflows.

How do I set up a W&B run to log metrics and model artifacts for my training project?▼

Set up a W&B run by initializing the tracking API for your current training project. Log key metrics, capture configuration, and add any model artifact or sweep configuration you want to track for visualization.

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