weights-and-biases

name	weights-and-biases
description	Logs ML training with wandb.init/log, hyperparameter sweeps, artifact lineage, and the model registry. Use when tracking experiments, comparing runs, or sweeping hyperparameters in PyTorch, TensorFlow, or HuggingFace. Not for LLM app tracing and prompts (langfuse) or local Hub eval harnesses (community-evals).
version	1.0.1
author	Orchestra Research
license	MIT
dependencies	["wandb"]
platforms	["linux","macos","windows"]
metadata	{"hermes":{"tags":["MLOps","Weights And Biases","WandB","Experiment Tracking","Hyperparameter Tuning","Model Registry","Collaboration","Real-Time Visualization","PyTorch","TensorFlow","HuggingFace"]}}

Weights & Biases: ML Experiment Tracking & MLOps

When to Use

Use this skill when you need to:

• Track ML experiments with automatic metric, config, and system logging
• Visualize training in real-time dashboards and compare runs across hyperparameters
• Run hyperparameter sweeps (grid, random, Bayesian) to optimize model performance
• Manage model registry with versioning, aliases, and lineage
• Track artifacts (datasets, models, code) with full provenance
• Integrate W&B into PyTorch, TensorFlow/Keras, HuggingFace, or PyTorch Lightning workflows
• Collaborate on ML projects with shared team workspaces and reports

Trigger keywords: wandb, weights and biases, experiment tracking, sweep, hyperparameter tuning, model registry, artifact lineage, training dashboard, wandb.init, wandb.log, wandb.sweep

Prerequisites

• Python 3.8+
• A W&B account (free tier sufficient for public projects)
• pip install wandb
• API key obtained from https://wandb.ai/authorize

Authentication

# Windows PowerShell (primary host)
$env:WANDB_API_KEY = "YOUR_KEY"
wandb login

# Linux / macOS
export WANDB_API_KEY=YOUR_KEY
wandb login

Never commit live API keys. Use YOUR_KEY placeholder in all shared files. Store real keys in environment variables or a secrets manager.

Procedure

1. Install and Authenticate

pip install wandb
wandb login   # prompts for API key from https://wandb.ai/authorize

Or set the key programmatically before any wandb.init call:

export WANDB_API_KEY=YOUR_KEY

2. Initialize a Run

import wandb

run = wandb.init(
    project="my-project",
    name=,
    tags=[, ],
    group=,
    job_type=,
    notes=,
    config={
        : ,
        : ,
        : ,
        : ,
    },
)

()
()