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versioning
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visualization
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WEIGHTS & BIASES IS AVAILABLE IN THE CLOUD OR INSTALLED ON YOUR PRIVATE
INFRASTRUCTURE.


LEARN ABOUT SELF-HOSTING
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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

Any Framework
TensorFlow
PyTorch
Keras
Scikit-LEARN
Hugging Face
XGBoost
# 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)
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.

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DESIGNED FOR ALL USE CASES

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Train + Evaluate + Share
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Scale + Collaborate
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CENTRAL DASHBOARD

A SYSTEM OF RECORD FOR YOUR MODEL RESULTS

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.

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HYPERPARAMETER SWEEP

TRY DOZENS OF MODEL VERSIONS QUICKLY

Optimize models with our massively scalable hyperparameter search tool. Sweeps
are lightweight, fast to set up, and plug in to your existing infrastructure for
running models.

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ARTIFACT TRACKING

LIGHTWEIGHT MODEL AND DATASET VERSIONING

Save every detail of your end-to-end machine learning pipeline — data
preparation, data versioning, training, and evaluation.

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COLLABORATIVE DOCUMENTS

EXPLORE RESULTS AND SHARE FINDINGS

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

Try it Out

Collaboration
Reproducibility
Debugging
Transparency


COLLABORATION

SEAMLESSLY SHARE PROGRESS ACROSS PROJECTS.

Manage team projects with a lightweight system of record. It's easy to hand off
projects when every experiment is automatically well documented and saved
centrally.

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REPRODUCE RESULTS

EFFORTLESSLY CAPTURE CONFIGURATIONS

With Weights & Biases experiment tracking, your team can standardize tracking
for experiments and capture hyperparameters, metrics, input data, and the exact
code version that trained each model

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DEBUG ML MODELS

FOCUS YOUR TEAM ON THE HARD MACHINE LEARNING PROBLEMS

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

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TRANSPARENCY

SHARE UPDATES ACROSS YOUR ORGANIZATION

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

try w&b

Governance
Provenance
efficiency
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GOVERNANCE

PROTECT AND MANAGE VALUABLE IP

Use this central platform to reliably track all your organization's machine
learning models, from experimentation to production. Centrally manage access
controls and artifact audit logs, with a complete model history that enables
traceable model results.

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DATA PROVENANCE

RELIABLE RECORDS FOR AUDITING MODELS

Capture all the inputs, transformations, and systems involved in building a
production model. Safeguard valuable intellectual property with all the
necessary context to understand and build upon models, even after team members
leave.

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ORGANIZATIONAL EFFICIENCY

UNLOCK PRODUCTIVITY, ACCELERATE RESEARCH

With a well integrated pipeline, your machine learning teams move quickly and
build valuable models in less time. Use Weights & Biases to empower your team to
share insights and build models faster.

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DATA SECURITY

INSTALL IN PRIVATE CLOUD AND ON-PREM

Data security is a cornerstone of our machine learning platform. We support
enterprise installations in private cloud and on-prem clusters, and plug in
easily with other enterprise-grade tools in your machine learning workflow.

try W&B



TRUSTED BY 500,000+ MACHINE LEARNING PRACTITIONERS AT
500+ COMPANIES AND RESEARCH INSTITUTIONS

See Case Studies


"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


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.


NEVER LOSE TRACK OF ANOTHER ML PROJECT. TRY WEIGHTS & BIASES TODAY.

Sign UPRequest Demo


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

OpenAI Jukebox
01


Exploring generative models that create music based on raw audio

Lighting Effects
02


Use RGB-space geometry to generate digital painting lighting effects

Political Advertising
03


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

Visualize Predictions
04


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
06


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

OpenAI Jukebox
01


Exploring generative models that create music based on raw audio

Lighting Effects
02


Use RGB-space geometry to generate digital painting lighting effects

Political Advertising
03


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

Visualize Predictions
04


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
06


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

OpenAI Jukebox
01


Exploring generative models that create music based on raw audio

Lighting Effects
02


Use RGB-space geometry to generate digital painting lighting effects

Political Advertising
03


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

Visualize Predictions
04


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
06


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

OpenAI Jukebox
01


Exploring generative models that create music based on raw audio

Lighting Effects
02


Use RGB-space geometry to generate digital painting lighting effects

Political Advertising
03


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

Visualize Predictions
04


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
06


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







ACCESS THE WHITE PAPER

Read how building the right technical stack for your machine learning team
supports core business efforts and safeguards IP

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