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weights-and-biases

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

Updated May 13, 2026
Search Tags:#mlops#machine learning#pytorch#hyperparameter tuning#experiment tracking#wandb#model registry
One-click install
npx skills add https://github.com/superfhp/lumi-agent-body --skill weights-and-biases-superfhp
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: weights-and-biases
Source: https://github.com/superfhp/lumi-agent-body/tree/main/skills/mlops/evaluation/weights-and-biases
Command: npx skills add https://github.com/superfhp/lumi-agent-body --skill weights-and-biases-superfhp

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, making results hard to reproduce and compare. ## Core Features & Use Cases - Experiment Tracking: Log metrics, configs, media, and system stats from PyTorch, TensorFlow, Keras, HuggingFace, and PyTorch Lightning training loops with automatic real-time dashboards. - 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 lineage tracking, aliases like 'production', and a central registry for deployment governance. - Use Case: A team fine-tuning a BERT classifier can log every training run, launch a Bayesian sweep over learning rates and batch sizes, then promote the best checkpoint to a production model registry with full lineage back to the training dataset. ## Quick Start Initialize a W&B run in my training script, log the loss and accuracy each epoch, and save the final model as a versioned 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?▼

Call wandb.init with your project name and config, then use 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 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 custom WandbCallback subclasses for additional logging.

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 using the wandb sync command on the run directory.

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

Artifacts version any file-based data such as datasets and model checkpoints with automatic lineage tracking. The Model Registry is a curated layer where you link model artifacts and manage deployment stages using aliases like staging and production.

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

Use Bayesian optimization, which learns from previous trials to sample promising parameter regions and is the most sample-efficient. Combine it with hyperband early termination to stop underperforming runs and save compute.

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