An Entire ML Toolbox
in 5 Lines of Code
We’re proud to partner with NVIDIA’s Base Command Platform. Contact us to get started or head here to read more.
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
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.
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.
# 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)
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 (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.
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.
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…
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.
About Weights & Biases
Our mission is to build the best tools for machine learning. Use W&B for experiment tracking, dataset versioning, and collaborating on ML projects.
