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Developer tools for machine learning

Build better models faster with experiment tracking, dataset versioning, and model management

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Iterate on models faster with lightweight experiment tracking

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.

Stay focused on the hard machine learning problems

Let W&B take care of the legwork of tracking and visualizing performance metrics, example predictions, and even system metrics to identify performance issues.

Share research findings with collaborators transparently

It's never been easier to share project updates. Explain how your model works, show graphs of how  model versions improved, discuss bugs, and share progress towards milestones.

01

Integrate quickly

Track, compare, and visualize ML experiments with 5 lines of code. Free for academic and open source projects.

try a live notebook
# 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)
02

Visualize Seamlessly

Add W&B's lightweight integration to your existing ML code and quickly get live metrics, terminal logs, and system stats streamed to the centralized dashboard.

Watch Demo
03

Collaborate in real time

Explain how your model works, show graphs of how model versions improved, discuss bugs, and demonstrate progress towards milestones.

View Reports

Trusted by 100,000+ machine learning practitioners at
200+ companies and research institutions

See Case Study

"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

Featured Projects

Once you’re using W&B to track and visualize ML experiments, it’s seamless to create a report to showcase your work.

VIEW GALLERY

Never lose track of another ML project. Try Weights & Biases today.