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Submitted URL: https://www.wandb.com/papers
Effective URL: https://wandb.ai/site/papers
Submission: On October 18 via manual from US — Scanned from DE
Effective URL: https://wandb.ai/site/papers
Submission: On October 18 via manual from US — Scanned from DE
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FOR FANS OF TWO MINUTE PAPERS
DEVELOPER TOOLS FOR MACHINE LEARNING
Build better models faster with experiment tracking, dataset versioning, and
model management
Sign upRequest demo
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
Any Framework
TensorFlow
PyTorch
Keras
Scikit
Hugging Face
XGBoost
# 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
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.
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
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
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