W&B for academics

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

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.

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

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
# 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)

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.