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weights-and-biases

Track ML experiments, run hyperparameter sweeps, and manage model artifacts with Weights & Biases.

Updated May 4, 2026
Search Tags:#mlops#artifacts#pytorch#hyperparameter tuning#experiment tracking#wandb#model registry
One-click install
npx skills add https://github.com/Junkfooooood/hermes-governance --skill weights-and-biases-junkfooooood
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: weights-and-biases
Source: https://github.com/Junkfooooood/hermes-governance/tree/main/skills/mlops/evaluation/weights-and-biases
Command: npx skills add https://github.com/Junkfooooood/hermes-governance --skill weights-and-biases-junkfooooood

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires wandb, and includes references (resource) components.

What problem does it solve? Machine learning teams lose track of experiments, hyperparameters, and model versions when training runs are scattered across notebooks and scripts. This Skill provides complete guidance for using Weights & Biases (W&B) to log metrics, compare runs, optimize hyperparameters, and version datasets and models with full lineage. ## Core Features & Use Cases - Experiment Tracking: Log metrics, configs, media, and system stats from PyTorch, TensorFlow, Keras, HuggingFace, and PyTorch Lightning training loops. - Hyperparameter Sweeps: Run grid, random, or Bayesian optimization searches with early termination and parallel agents across multiple GPUs. - Artifacts & Model Registry: Version datasets and models with automatic lineage tracking, aliases for deployment stages, and a central model registry. - Use Case: A data scientist fine-tuning a BERT model can initialize a W&B run, launch a Bayesian sweep over learning rate and batch size, compare 50 trials in a real-time dashboard, and promote the best checkpoint to a production model registry. ## Quick Start Set up a W&B experiment tracking run for my PyTorch training script that logs loss and accuracy each epoch and saves the final model as an artifact.

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 PyTorch training experiments with Weights & Biases?▼

Initialize a run with wandb.init(project, config), then call wandb.log() inside your training loop to record metrics like loss and accuracy. Call wandb.finish() at the end, and use wandb.save() or Artifacts to upload model checkpoints.

How do I run a hyperparameter sweep with W&B?▼

Define a sweep_config with a search method (grid, random, or bayes), a target metric, and parameter distributions. Create the sweep with wandb.sweep(), then launch trials using wandb.agent(sweep_id, function=train, count=N), where train reads hyperparameters from wandb.config.

Does W&B integrate with HuggingFace Transformers?▼

Yes. Set report_to="wandb" in TrainingArguments and the HuggingFace Trainer automatically logs metrics, evaluation results, and checkpoints to W&B. You can also add custom WandbCallback subclasses for additional logging.

What is the difference between W&B Artifacts and the Model Registry?▼

Artifacts version any files (datasets, models, predictions) with automatic lineage tracking between runs. The Model Registry is a curated layer where model artifacts are linked and promoted through stages like staging and production using aliases.

Can I use W&B offline without an internet connection?▼

Yes. Set the WANDB_MODE environment variable to "offline" before initializing your run. Metrics are stored locally and can be uploaded later by running wandb sync on the run directory.

Which sweep search method should I use in W&B?▼

Use Bayesian optimization for expensive training runs with limited compute, as it learns from previous trials. Use random search for quick exploration across many parameters, and grid search only when you have few discrete parameters needing exhaustive coverage.

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