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weights-and-biases

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

2|Updated Aug 15, 2026
Search Tags:#mlops#machine learning#PyTorch#hyperparameter tuning#experiment tracking#wandb#model registry
One-click install
npx skills add https://github.com/Jensen-Yao/agents-skills --skill weights-and-biases-jensen-yao
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: weights-and-biases
Source: https://github.com/Jensen-Yao/agents-skills/tree/main/skills/weights-and-biases
Command: npx skills add https://github.com/Jensen-Yao/agents-skills --skill weights-and-biases-jensen-yao

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve? Machine learning training runs produce scattered metrics, configs, and checkpoints that are hard to compare, reproduce, or share. This Skill provides complete guidance for instrumenting training code with Weights & Biases so every experiment is logged, visualized, and versioned automatically. ## 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 searches with early termination and parallel agents across multiple GPUs. - Artifacts & Model Registry: Version datasets and models with lineage tracking, aliases, and promotion through staging to production. - Use Case: A team fine-tuning a BERT classifier can launch a Bayesian sweep over learning rate and batch size, compare 50 runs in a shared dashboard, and promote the best checkpoint to the production model registry. ## Quick Start Instrument my PyTorch training script with W&B logging and set up a Bayesian hyperparameter sweep over learning rate and batch size.

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 experiments with Weights & Biases?▼

Call wandb.init with your project name and config, then call wandb.log with metrics inside your training loop. Use wandb.watch to automatically log gradients and model parameters, and wandb.finish when training completes.

How to run hyperparameter sweeps with wandb?▼

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 agents with wandb.agent pointing to your training function.

Does W&B integrate with HuggingFace Transformers?▼

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

Can I use wandb offline without internet access?▼

Yes. Set the WANDB_MODE environment variable to "offline" and runs are saved locally. Later, run wandb sync on the run directory to upload the logged data to the W&B server.

What is the difference between wandb.save and Artifacts?▼

wandb.save uploads files associated with a single run, while Artifacts create versioned, standalone objects with lineage tracking across runs. Artifacts are recommended for datasets and models because they support versioning, aliases, and the model registry.

Which sweep method should I use for expensive training runs?▼

Bayesian optimization is recommended for expensive runs because it learns from previous trials and samples promising parameter regions, requiring fewer runs than grid or random search. Combine it with hyperband early termination to stop underperforming trials.

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