W&B for academics

Free for students, educators, and academic researchers. Always.

Trusted by leading universities, top researchers,
and over 300,000 ML practitioners

Trusted–and cited–by hundreds of cutting-edge researchers

From climate science to medical research to fundamental breakthroughs in NLP and computer vision, Weights & Biases is behind the scenes making research more reproducible and collaborative. Check out some of the 500+ papers that have cited W&B.

BROWSE OUR GOOGLE SCHOLAR CITATIONS →

W&B for research & education

These projects help showcase how you can use W&B for your next paper submission, class assignment, or interactive lesson

Automate Logging

Learn how to ensure your research is logged properly for so you can easily reproduce your findings

Research Together

Discover how a small team built DALL-E mini collaboratively with the help of W&B

Share Findings

W&B Reports help you share, store, and explain your findings to your team or the world at large

Publish Research

How to export gorgeous, publication-ready visualizations and plots with Weights & Biases

Semantic Segmentation

Semantic segmentation for scene parsing on Berkeley Deep Drive 100K

Debugging Models

How ML engineers at Latent Space quickly iterate on models with W&B reports receipt PDFs

OpenAI Jukebox

Exploring generative models that create music based on raw audio

Lighting Effects

Use RGB-space geometry to generate digital painting lighting effects

Political Advertising

Fuzzy string search (or binary matching) on entity names from receipt PDFs

Visualize Predictions

Visualize images, videos, audio, tables, HTML, metrics, plots, 3d objects, and more

Semantic Segmentation

Semantic segmentation for scene parsing on Berkeley Deep Drive 100K

Debugging Models

How ML engineers at Latent Space quickly iterate on models with W&B reports receipt PDFs

OpenAI Jukebox

Exploring generative models that create music based on raw audio

Lighting Effects

Use RGB-space geometry to generate digital painting lighting effects

Political Advertising

Fuzzy string search (or binary matching) on entity names from receipt PDFs

Visualize Predictions

Visualize images, videos, audio, tables, HTML, metrics, plots, 3d objects, and more

Semantic Segmentation

Semantic segmentation for scene parsing on Berkeley Deep Drive 100K

Debugging Models

How ML engineers at Latent Space quickly iterate on models with W&B reports receipt PDFs

OpenAI Jukebox

Exploring generative models that create music based on raw audio

Lighting Effects

Use RGB-space geometry to generate digital painting lighting effects

Political Advertising

Fuzzy string search (or binary matching) on entity names from receipt PDFs

Visualize Predictions

Visualize images, videos, audio, tables, HTML, metrics, plots, 3d objects, and more

Semantic Segmentation

Semantic segmentation for scene parsing on Berkeley Deep Drive 100K

Debugging Models

How ML engineers at Latent Space quickly iterate on models with W&B reports receipt PDFs

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How to cite Weights & Biases

If you used W&B and we helped make your research successful, we’d love to hear about it. You can cite us with the information to the right but what we’d like most is to hear from you about your work. Email us at research@wandb.com and we’ll get in touch.

LEARN MORE ABOUT HOW TO CITE W&B →

@misc{wandb,
title = {Experiment Tracking with Weights and Biases},
year = {2020},
note = {Software available from wandb.com},
url={https://www.wandb.com/},
author = {Biewald, Lukas},
}

Log everything so you lose nothing

Experiments you can’t reproduce aren’t going to help your next big discovery. With Weights & Biases, you choose what you log and when you log it so you can do less manual admin and a lot more model training.

Integrated with every popular framework and thousands of ML repos

Weights & Biases plays well with others. From PyTorch, Keras, and JAX to niche repos across the ML landscape, chances are, you’ll find us integrated there. Check out our most popular integrations (and how they work) in our docs.

Resources for Educators, Teaching Assistants, and Students

We've included introductory content to help get you and your students started using Weights & Biases to enable collaborative, repeatable machine and deep learning in your classroom, research lab, or student-run organization.
‍
CHECK OUT OUR FREE RESOURCES →

Learn about MLOps from the experts

Discover how to accelerate and scale your model development, improve your productivity, ensure reproducibility and model lineage, and above all else, train better models faster in this expansive, free course from W&B.

REGISTER FOR FREE →

Collaborate with your team in real-time

Weights & Biases is made for collaboration. With each and every experiment logged to a single system, your research team shares access to all dataset and model versions, git commits, and your recent experiments.

LEARN HOW TEAMS WORK →

Explore the Weights & Biases platform

Experiments

Experiment
tracking

Reports

Collaborative
dashboards

Artifacts

Dataset and model
versioning

Tables

Interactive data
visualization

Sweeps

Hyperparameter
optimization

Models

Centralized model registry

Easy to install, easy to use

Track, compare, and visualize all your ML experiments in a single place. Academics and researchers get full access to our entire suite of features and it takes just five lines of code to get started.

try a live notebook

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# Flexible integration for any Python script

import wandb

# 1. Start a W&B run

wandb.init(project='gpt4')

config = wandb.config

config.learning_rate = 0.01

# 2. Save model inputs and hyperparameters

# Model training here

# 3. Log metrics over time to visualize performance

wandb.log({"loss": loss})

import wandb

# 1. Start a W&B run

wandb.init(project='gpt3')

# 2. Save model inputs and hyperparameters

config = wandb.config

config.learning_rate = 0.01

# Model training here

# 3. Log metrics over time to visualize performance

with tf.Session() as sess:

# ...

wandb.tensorflow.log(tf.summary.merge_all())

import wandb

# 1. Start a new run

wandb.init(project="gpt-3")

# 2. Save model inputs and hyperparameters

config = wandb.config

config.learning_rate = 0.01

# 3. Log gradients and model parameters

wandb.watch(model)

for batch_idx, (data, target) in

enumerate(train_loader):

if batch_idx % args.log_interval == 0:

# 4. Log metrics to visualize performance

wandb.log({"loss": loss})

import wandb

from wandb.keras import WandbCallback

# 1. Start a new run

wandb.init(project="gpt-3")

# 2. Save model inputs and hyperparameters

config = wandb.config

config.learning_rate = 0.01

... Define a model

# 3. Log layer dimensions and metrics over time

model.fit(X_train, y_train, validation_data=(X_test, y_test),

callbacks=[WandbCallback()])

import wandb

wandb.init(project="visualize-sklearn")

# Model training here

# Log classifier visualizations

wandb.sklearn.plot_classifier(clf, X_train, X_test, y_train, y_test, y_pred, y_probas, labels, model_name='SVC', feature_names=None)

# Log regression visualizations

wandb.sklearn.plot_regressor(reg, X_train, X_test, y_train, y_test, model_name='Ridge')

# Log clustering visualizations

wandb.sklearn.plot_clusterer(kmeans, X_train, cluster_labels, labels=None, model_name='KMeans')

# 1. Import wandb and login

import wandb
wandb.login()

# 2. Define which wandb project to log to and name your run

wandb.init(project="gpt-3", run_name='gpt-3-base-high-lr')

# 3. Add wandb in your Hugging Face `TrainingArguments`

args = TrainingArguments(... , report_to='wandb')

# 4. W&B logging will begin automatically when your start training your Trainer

trainer = Trainer(... , args=args)
trainer.train()

import wandb

# 1. Start a new run

wandb.init(project="visualize-models", name="xgboost")

# 2. Add the callback

bst = xgboost.train(param, xg_train, num_round, watchlist, callbacks=[wandb.xgboost.wandb_callback()])

# Get predictions

pred = bst.predict(xg_test)

W&B for academics

Free for students, educators, and academic researchers. Always.

Trusted–and cited–by hundreds of cutting-edge researchers

W&B for research & education

How to cite Weights & Biases

Log everything so you lose nothing

Integrated with every popular framework and thousands of ML repos

Resources for Educators, Teaching Assistants, and Students

Learn about MLOps from the experts

Collaborate with your team in real-time

Easy to install, easy to use

Whether you're at the same university or on different continents, W&B makes academic research easy. And we're free for students, educators, and university researchers.