Use the W&B Python Library to track machine learning experiments with a few lines of code. You can then review the results in an interactive dashboard or export your data to Python for programmatic access using our Public API.
Utilize W&B Integrations if you use use popular frameworks such as PyTorch, Keras, or Scikit. See our Integration guides for a for a full list of integrations and information on how to add W&B to your code.
How it works
W&B Experiments are composed of the following building blocks:
wandb.init(): Initialize a new run at the top of your script. This returns a
Runobject and creates a local directory where all logs and files are saved, then streamed asynchronously to a W&B server. If you want to use a private server instead of our hosted cloud server, we offer Self-Hosting.
wandb.config: Save a dictionary of hyperparameters such as learning rate or model type. The model settings you capture in config are useful later to organize and query your results.
wandb.log(): Log metrics over time in a training loop, such as accuracy and loss. By default, when you call
wandb.logit appends a new step to the
historyobject and updates the
history: An array of dictionary-like objects that tracks metrics over time. These time series values are shown as default line plots in the UI.
summary: By default, the final value of a metric logged with wandb.log(). You can set the summary for a metric manually to capture the highest accuracy or lowest loss instead of the final value. These values are used in the table, and plots that compare runs — for example, you could visualize at the final accuracy for all runs in your project.
wandb.log_artifact: Save outputs of a run, like the model weights or a table of predictions. This lets you track not just model training, but all the pipeline steps that affect the final model.
The proceeding pseudocode demonstrates a common W&B Experiment tracking workflow:
# Flexible integration for any Python script
# 1. Start a W&B Run
# 2. Save mode inputs and hyperparameters
config = wandb.config
config.learning_rate = 0.01
# Set up model and data
model, dataloader = get_model(), get_data()
# Model training goes here
# 3. Log metrics over time to visualize performance
# 4. Log an artifact to W&B
How to get started
Depending on your use case, explore the following resources to get started with W&B Experiments:
- If this is your first time using W&B Experiments, we recommend you read the Quick Start. The Quickstart walks you through setting up your first experiment.
- Explore topics about Experiments in the W&B Developer Guide such as:
- Create an experiment
- Configure experiments
- Log data from experiments
- View results from experiments
- Explore the W&B Python Library within the W&B API Reference Guide.