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weights-and-biases

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

Updated Jun 7, 2026
Search Tags:#mlops#machine learning#PyTorch#hyperparameter tuning#experiment tracking#wandb#model registry
One-click install
npx skills add https://github.com/Chensihakniroth/ANAKOT-AGENT --skill weights-and-biases-chensihakniroth
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: weights-and-biases
Source: https://github.com/Chensihakniroth/ANAKOT-AGENT/tree/main/skills/mlops/evaluation/weights-and-biases
Command: npx skills add https://github.com/Chensihakniroth/ANAKOT-AGENT --skill weights-and-biases-chensihakniroth

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 terminals. This Skill provides complete guidance for logging metrics, comparing runs, optimizing hyperparameters, and managing model lineage with Weights & Biases (W&B). ## 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 GPUs. - Artifacts & Model Registry: Version datasets and models with automatic lineage tracking, aliases, and promotion through staging to production. - 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, and promote the best checkpoint to a production model registry. ## Quick Start Use the weights-and-biases skill to set up W&B experiment tracking in my PyTorch training script and log validation accuracy each epoch.

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=...) before training, then call wandb.log() with metrics like loss and accuracy inside your training loop. Call wandb.finish() at the end to close the run and sync results to the dashboard.

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 trials using wandb.agent(sweep_id, function=train, count=N).

Does W&B integrate with HuggingFace Transformers?▼

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

Can I use wandb offline without internet connection?▼

Yes, set the WANDB_MODE environment variable to "offline" before initializing runs. Metrics are stored locally and can be synced later to the cloud dashboard using the wandb sync command.

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

wandb.save uploads individual files to a run, while Artifacts provide versioned, deduplicated storage with lineage tracking across runs. Artifacts are recommended for datasets and models since they support aliases, versioning, and registry linking.

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