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Experiment tracking
for deep learning

Record and visualize every detail of your research, collaborate easily, advance the state of the art

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Record Metrics

Store all the hyperparameters and output metrics in one place, to effortlessly track runs and reproduce models.

Visualize Training

Explore and compare every experiment with our training and inference visualizations to see trends across individual and multiple runs.

Share Progress

Create beautiful reports that showcase your work to seamlessly collaborate and communicate findings with your team.

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

Hamel Husain
GitHub

"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 Institute

Read the full interview with Peter Welinder from OpenAI ➞

Fast Integration

Add just a few lines of code to your script to get the power of W&B logging.
We're framework agnostic.

import torch
import torch.nn as nn
from torchvision import datasets
import wandb

wandb.init()
...

Log Anything

Easily log metrics from your script to visualize results in real time as your model trains.

wandb.log({"acc": accuracy, "val_acc": val_accuracy})

Explore Everything

Peek under the hood and see what your model is producing at each time step.

wandb.log({"point_cloud": wandb.Object3D(point_cloud)})

Massive Scale

Tag, filter, sort, and group runs easily. Our service was built to handle thousands of parallel runs.

Example Projects

Keras MNIST Example
This sample project is classifying images of handwritten digits from the MNIST dataset.
iNaturalist Fine-Tuning
Stacey walks you through fine-tuning a Keras model to classify images of plants and animals.
Semantic Segmentation of 3D Point Clouds
Nick trained a model to take input of a point cloud representing a real world object and provide segmentation of the object into different parts.

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