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Skill

weights-and-biases

From Orchestra-Research

Track ML experiments with automatic logging, visualize training in real-time, optimize hyperparameters with sweeps, and manage model registry with W&B - collaborative MLOps platform

Wraps the wandb Python SDK for logging ML experiment metrics, running hyperparameter sweeps, and managing model artifacts.

Use it when

  • Tracking training metrics/config across PyTorch or TensorFlow runs
  • Running Bayesian, grid, or random hyperparameter sweeps
  • Versioning models and datasets via W&B Artifacts
  • Comparing multiple experiment runs in a dashboard

Skip it if

  • Requires a W&B account and API key (wandb login)
  • Depends on external hosted service, not local-only
  • Thin wrapper around the wandb pip package's own API

Facts

Repository
Orchestra-Research/AI-Research-SKILLs
Status
Actively maintained
Last commit
Source file
13-mlops/weights-and-biases/SKILL.md

Source preview

The instructions Claude Code reads when this skill runs.

# Weights & Biases: ML Experiment Tracking & MLOps

## When to Use This Skill

Use Weights & Biases (W&B) when you need to:
- **Track ML experiments** with automatic metric logging
- **Visualize training** in real-time dashboards
- **Compare runs** across hyperparameters and configurations
- **Optimize hyperparameters** with automated sweeps
- **Manage model registry** with versioning and lineage
- **Collaborate on ML projects** with team workspaces
- **Track artifacts** (datasets, models, code) with lineage

**Users**: 200,000+ ML practitioners | **GitHub Stars**: 10.5k+ | **Integrations**: 100+

## Installation

```bash
# Install W&B
pip install wandb

# Login (creates API key)
wandb login

# Or set API key programmatically
export WANDB_API_KEY=your_api_key_here
```

## Quick Start

### Basic Experiment Tracking

```python
import wandb

# Initialize a run
run = wandb.init(
    project="my-project",
    config={
        "learning_rate": 0.001,
        "epochs": 10,
        "batch_size": 32,
        "architecture": "ResNet50"
    }
)

# Training loop
for epoch in range(run.config.epochs):
    # Your training code
    train_loss = train_epoch()
    val_loss = validate()

    # Log metrics
    wandb.log({
        "epoch": epoch,
        "train/loss": train_loss,
        "val/loss": val_loss,
        "train/accuracy": train_acc,
        "val/accuracy": val_acc
    })

# Finish the
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Reused elsewhere

An identical copy of this skill ships in 2 repositories.

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