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weights-and-biases

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

Updated Sep 10, 2026
Search Tags:#mlops#machine learning#PyTorch#hyperparameter tuning#experiment tracking#wandb#model registry
One-click install
npx skills add https://github.com/loteiron/ZeusAgent --skill weights-and-biases-loteiron
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: weights-and-biases
Source: https://github.com/loteiron/ZeusAgent/tree/main/optional-skills/mlops/evaluation/weights-and-biases
Command: npx skills add https://github.com/loteiron/ZeusAgent --skill weights-and-biases-loteiron

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve? Machine learning teams lose track of training runs, hyperparameters, and model versions when experiments are managed manually, 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, and a central registry for staging and production promotion. - 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 shared dashboard, and promote the best checkpoint to the production model registry. ## Quick Start Ask the agent to set up W&B tracking for your training script by initializing a run, logging metrics each epoch, and saving the final model as an 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 with Weights & Biases?▼

Call wandb.init with your project name and config, then use wandb.log inside your training loop to record loss, accuracy, and learning rate per step or epoch. Finish with wandb.finish and optionally upload checkpoints via wandb.save or artifacts.

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 to execute trials.

Does wandb work with HuggingFace Transformers?▼

Yes, set report_to="wandb" in TrainingArguments and the Trainer automatically logs metrics, hyperparameters, and checkpoints to W&B. You can also add custom callbacks to log additional metrics or save the final model as an artifact.

Can I use wandb offline without internet access?▼

Yes, set WANDB_MODE=offline before initializing your run and all data is stored locally. Later, run wandb sync on the run directory to upload results when a connection is available.

What is the difference between wandb artifacts and model registry?▼

Artifacts version any files such as datasets or models with automatic lineage tracking between runs. The model registry is a curated layer where model artifacts are linked and promoted through stages like staging and production using aliases.

Why is my wandb sweep not improving results?▼

Poor sweep results usually come from overly wide parameter ranges, linear instead of log-uniform scales for learning rates, or too few trials. Start with random search to narrow ranges, then switch to Bayesian optimization with early termination.

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