An Entire ML Toolbox in 5 Lines of Code

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W&B lets you debug your models, track your experiments, optimize hyperparameters, reproduce your best runs, and a whole lot more.

W&B is DGX-ready, SOC-2 compliant, and trusted by more than 100,000 ML practitioners from some of the most innovative organizations in the world:

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

Hamel Husain
GitHub

"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

"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
# Flexible integration for any Python script
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
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)

BCP and Weights & Biases complement each other perfectly.

BCP unlocks the world class compute you need to train the large, cutting-edge models of tomorrow.

Weights & Biases gives you the vital insights you need to understand how your models are performing on that compute infrastructure. It gives your team a single tool to experiment, debug,  reproduce, and collaborate on your best models, letting you push those models to production faster.

try a live notebook

Track experiments in real time

See live updates on model performance, check for overfitting, and visualize how a model performs on different classes.

COVID-19 main protease in complex N3 on left and COVID-19 main protease in complex with Z31792168 on right
COVID-19 main protease in complex N3 (left) and COVID-19 main protease in complex with Z31792168 (right) from “Visualizing Molecular Structure with Weights & Biases” by Nicholas Bardy

Understand every step of your pipeline

Get a bird’s eye view of every step of model development, understand model and dataset dependencies and automatically checksum and version datasets and models.

simple three part illustration of artifacts workflow

Discover your best runs faster

W&B’s visualizations and dashboard let you explore the space of possible models quickly, without getting bogged down setting up manual visualizations.

Preview of figures from The Science of Debugging with W&B Reports

The Science of Debugging with W&B Reports

By Sarah Jane of Latent Space

We use Weights & Biases as a way to share results and learnings such that we can build on top of each other's work. The W&B Reports feature has been one of the most critical...

View Report

Collaborate across large teams with ease

Customize real-time views of model training and evaluation, share automatically updating dashboards, and create interactive reports to share with stakeholders.

Never lose track of another ML project. Try W&B today.