Developer tools for machine learning

Experiment tracking, hyperparameter optimization, model and dataset versioning

Track, compare, and visualize ML experiments with 5 lines of code.

# 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. Install the wandb library
pip install wandb
‍
# 2. Run a script with the Trainer, which automatically logs losses, evaluation metrics, model topology and gradients
python run_glue.py \
--model_name_or_path bert-base-uncased \
--task_name MRPC \
--data_dir $GLUE_DIR/$TASK_NAME \
--do_train \
--evaluate_during_training \
--max_seq_length 128 \
--per_gpu_train_batch_size 32 \
--learning_rate 2e-5 \
--num_train_epochs 3 \
--output_dir /tmp/$TASK_NAME/ \
--overwrite_output_dir \
--logging_steps 50

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)

Free for academic and open source projects →

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 sweeps

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.

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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.

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.

Debug ML models

Identify performance issues quickly

Focus your team on the hard machine learning problems, and let Weights & Biases take care of the legwork of tracking and visualizing performance metrics, example predictions, and even system metrics to identify performance issues.

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.