Developer tools for machine learning

Experiment tracking, model optimization, and dataset versioning

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Central dashboard
A system of record for your model results
Add a few lines to your script, and each time you train a new version of your model, you'll see a new experiment stream live to your dashboard.
Fast integration
Set up your code in 5 minutes
Add a few lines to your script to start logging results. Our lightweight integration works with any Python script. See the docs →

import torch
import torch.nn as nn

import wandb

# Log any metric from your training script
wandb.log({"acc": accuracy, "val_acc": val_accuracy})

Collaborative reports
Share high level updates and detailed work logs
It's never been easier to share updates with your coworkers. Explain how your model works, show graphs of how your model versions improved, discuss bugs, and demonstrate progress towards milestones.

See an example report →
Hyperparameter sweeps
Try dozens of model versions quickly
Optimize models with our massively scalable hyperparameter search tool. Sweeps are lightweight, fast to set up, and plug in to your existing infrastructure for running models.
Reproducible models
Quickly find and re-run previous models
Save everything you need to reproduce models later— the latest git commit, hyperparameters, model weights, and even sample test predictions. You can save experiment files directly to W&B or store pointers to your own storage.
System metrics
CPU and GPU usage across runs
Visualize live metrics like GPU utilization to identify training bottlenecks and avoid wasting expensive resources.
Visualize predictions
Debug performance in real time
Log model predictions to see how your model is performing, and identify problem areas during training. We support rich media including images, video, audio, and 3D objects.
Lightweight, modular tools for your machine learning team
Machine learning experiment tracking
Hyperparameter search and model optimization
Dataset versioning and model management
Read the full interview with Peter Welinder from OpenAI ➞

"W&B was fundamental for launching our internal machine learning systems, as it enables collaboration across various teams."

Hamel Husain

"W&B allows us to scale up insights from a single researcher to the entire team and from a single machine to thousands."

Wojciech Zaremba
Cofounder of OpenAI

"W&B is a key piece of our fast-paced, cutting-edge, large-scale research workflow: great flexibility, performance, and user experience."

Adrien Gaidon
Toyota Research

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