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weights-and-biases

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

1|Updated Aug 6, 2026
Search Tags:#mlops#machine learning#PyTorch#hyperparameter tuning#experiment tracking#wandb#model registry
One-click install
npx skills add https://github.com/agtktID/indagis-agent --skill weights-and-biases-agtktid
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: weights-and-biases
Source: https://github.com/agtktID/indagis-agent/tree/main/skills/mlops/evaluation/weights-and-biases
Command: npx skills add https://github.com/agtktID/indagis-agent --skill weights-and-biases-agtktid

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 instrumenting training code with Weights & Biases so every run, metric, and artifact is logged, comparable, and reproducible. ## Core Features & Use Cases - Experiment Tracking: Initialize runs with wandb.init, log metrics, media, and configs, and compare runs in real-time dashboards. - Hyperparameter Sweeps: Automate grid, random, and Bayesian search with early termination via Hyperband and parallel agents across GPUs. - Artifacts & Model Registry: Version datasets and models with lineage tracking, aliases, and promotion through staging to production. - Framework Integrations: Native support for PyTorch, PyTorch Lightning, HuggingFace Transformers, Keras/TensorFlow, Fast.ai, XGBoost, and LightGBM. - Use Case: A data scientist fine-tuning a BERT classifier runs a Bayesian sweep over learning rate and batch size, logs validation F1 per trial, and links the best checkpoint to the production model registry. ## Quick Start Use the weights-and-biases skill to instrument my PyTorch training script with W&B experiment tracking and set up a Bayesian hyperparameter sweep.

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 ML experiments with Weights & Biases?▼

Call wandb.init with a project name and config dictionary, then use wandb.log inside your training loop to record metrics like loss and accuracy. Each run gets a unique ID and URL for dashboard comparison.

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, then create it with wandb.sweep. Launch trials using wandb.agent with your training function and a run count.

Does W&B integrate with HuggingFace Transformers and PyTorch Lightning?▼

Yes. Set report_to="wandb" in TrainingArguments for automatic HuggingFace logging, or pass WandbLogger to the PyTorch Lightning Trainer. Keras uses WandbMetricsLogger and WandbModelCheckpoint callbacks.

What are W&B Artifacts and how do I version models?▼

Artifacts are versioned datasets, models, or files with automatic lineage tracking. Create one with wandb.Artifact, add files or cloud references, and log it with aliases like "best" or "production" for registry promotion.

Can I use wandb offline without an internet connection?▼

Yes. Set WANDB_MODE=offline before initializing, and all metrics are stored locally. Sync them later to the cloud dashboard by running wandb sync on the run directory.

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

Bayesian optimization is recommended for expensive training runs since it learns from previous trials. Use grid search for few discrete parameters and random search for quick exploration across many parameters.

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