Weights & Biases

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
wandb.init(project="pedestrian-detection")

# 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.
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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.