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weights-and-biases

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

Updated Sep 20, 2026
Search Tags:#mlops#machine learning#pytorch#hyperparameter tuning#experiment tracking#wandb#model registry
One-click install
npx skills add https://github.com/GreenyZA/neo-light --skill weights-and-biases-greenyza
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: weights-and-biases
Source: https://github.com/GreenyZA/neo-light/tree/main/.hermes-home/skills/mlops/evaluation/weights-and-biases
Command: npx skills add https://github.com/GreenyZA/neo-light --skill weights-and-biases-greenyza

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: Log metrics, configs, media, and system stats from PyTorch, TensorFlow, Keras, HuggingFace, PyTorch Lightning, Fast.ai, XGBoost, and LightGBM training loops. - Hyperparameter Sweeps: Run grid, random, or Bayesian optimization searches with early termination (Hyperband) and parallel multi-GPU agents. - Artifacts & Model Registry: Version datasets and models with automatic lineage tracking, aliases for deployment stages, and a central registry for promotion workflows. - Use Case: A data scientist fine-tuning a BERT classifier can launch a Bayesian sweep over learning rate and batch size, compare 50 runs in a real-time dashboard, and promote the best checkpoint to a production model registry. ## Quick Start Ask the AI to instrument your PyTorch training script with wandb so that losses, accuracy, and the final model checkpoint are logged to a W&B project.

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 call wandb.log inside your training loop to record losses and accuracy per epoch or batch. 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, then create it with wandb.sweep. Launch one or more agents with wandb.agent pointing to your training function, which reads hyperparameters from wandb.config.

Does Weights & Biases 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.

Can I use wandb offline without an internet connection?▼

Yes, set the WANDB_MODE environment variable to "offline" before calling wandb.init, and all run data is stored locally. Later, run wandb sync on the run directory to upload results to the W&B cloud dashboard.

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

wandb.save uploads individual files associated with a run, while Artifacts create versioned, deduplicated objects with lineage tracking and aliases. Artifacts are the recommended approach for datasets and models, and they can be linked to the Model Registry for deployment staging.

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

Bayesian optimization is recommended for expensive training runs because it learns from previous trials and is the most sample-efficient. Use grid search for a few discrete parameters needing full coverage, and random search for quick exploration across many parameters.

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