To help you get started, we've collected the most common ways that wandb is being used within popular public projects.

wandb / client / edgeml_tests / test_tensorflow_keras.py View on Github

def test_keras_log_weights(dummy_model, dummy_data, wandb_init_run):
    dummy_model.fit(*dummy_data, epochs=2, batch_size=36, validation_data=dummy_data,
                    callbacks=[WandbCallback(data_type="image", log_weights=True)])
    assert wandb_init_run.history.rows[0]['parameters/dense.weights']['_type'] == "histogram"

View more ways to use wandb

Weights and Biases

Use W&B to build better models faster. Track and visualize all the pieces of your machine learning pipeline, from datasets to production machine learning models. Get started with W&B today, sign up for an account!

See the W&B Developer Guide and API Reference Guide for a full technical description of the W&B platform.

Quickstart

Get started with W&B in four steps:

1. First, sign up for a W&B account.

2. Second, install the W&B SDK with pip. Navigate to your terminal and type the following command:

pip install wandb

3. Third, log into W&B:

wandb.login()

4. Use the example code snippet below as a template to integrate W&B to your Python script:

import wandb

# Start a W&B Run with wandb.init
run = wandb.init(project="my_first_project")

# Save model inputs and hyperparameters in a wandb.config object
config = run.config
config.learning_rate = 0.01

# Model training code here ...

# Log metrics over time to visualize performance with wandb.log
for i in range(10):
    run.log({"loss": ...})

# Mark the run as finished, and finish uploading all data
run.finish()

For example, if the preceding code was stored in a script called train.py:

python train.py

You will see a URL in your terminal logs when your script starts and finishes. Data is staged locally in a directory named wandb relative to your script. Navigate to the W&B App to view a dashboard of your first W&B Experiment. Use the W&B App to compare multiple experiments in a unified place, dive into the results of a single run, and much more!

Integrations

Use your favorite framework with W&B. W&B integrations make it fast and easy to set up experiment tracking and data versioning inside existing projects. For more information on how to integrate W&B with the framework of your choice, see W&B Integrations in the W&B Developer Guide.

Python Version Support

We are committed to supporting our minimum required Python version for at least six months after its official end-of-life (EOL) date, as defined by the Python Software Foundation. You can find a list of Python EOL dates here.

When we discontinue support for a Python version, we will increment the library’s minor version number to reflect this change.

Contribution guidelines

Weights & Biases ❤️ open source, and we welcome contributions from the community! See the Contribution guide for more information on the development workflow and the internals of the wandb library. For wandb bugs and feature requests, visit GitHub Issues or contact support@wandb.com.

Academic Researchers

Reach out to W&B Support at support@wandb.com to get a free academic license for you and your research group.

W&B Community

Be a part of the growing W&B Community and interact with the W&B team in our Discord. Stay connected with the latest ML updates and tutorials with W&B Fully Connected.

License

MIT License