Track, compare, and visualize your ML models with 5 lines of code
Quickly and easily implement experiment logging by adding just a few lines to your script and start logging results. Our lightweight integration works with any Python script.
ANY FRAMEWORK
TENSORFLOW
PYTORCH
Keras
SCIKIT
HUGGING FACE
XGBOOST
ANY FRAMEWORK
# 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})
TENSORFLOW
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())
PYTORCH
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})
Keras
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()])
SCIKIT
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')
HUGGING FACE
# 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()
XGBOOST
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)
Visualize and compare every experiment
See model metrics stream live into interactive graphs and tables. It is easy to see how your latest ML model is performing compared to previous experiments, no matter where you are training your models.
Quickly find and re-run previous model checkpoints
W&B’s experiment tracking saves everything you need to reproduce models later— the latest git commit, hyperparameters, model weights, and even sample test predictions. You can save experiment files and datasets directly to W&B or store pointers to your own storage.
import wandb
from transformers import DebertaV2ForQuestionAnswering
# 1. Create a wandb run
run = wandb.init(project=’turkish-qa’)
# 2. Connect to the model checkpoint you want on W&B
wandb_model = run.use_artifact(‘sally/turkish-qa/
deberta-v2:v5′)
# 3. Download the model files to a directory
model_dir = wandb_model.download()
# 4. Load your model
model = DebertaV2ForQuestionAnswering.from_pretrained(model_dir)
From “When Inception-ResNet-V2 is too slow” by Stacey Svetlichnaya
Monitor your CPU and GPU usage
Visualize live metrics like GPU utilization to identify training bottlenecks and avoid wasting expensive resources.
Debug performance in real time
See how your model is performing and identify problem areas during training. We support rich media including images, video, audio, and 3D objects.
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
Dataset versioning with deduplication 100GB free storage
Automatically version logged datasets, with diffing and deduplication handled by Weights & Biases, behind the scenes.
MLOps Whitepaper
Read how building the right technical stack for your machine learning team supports core business efforts and safeguards IP
Accessible anywhere
Check the latest training model and results on desktop and mobile. Use collaborative hosted projects to coordinate across your team.
The Science of Debugging with W&B Reports
By Sarah Jane of Latent Space
Automatically version logged datasets, with diffing and deduplication handled by Weights & Biases, behind the scenes.
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.