DeepChecks

Integrate W&B with Deepchecks to validate ML models and datasets with automated testing and experiment tracking.

DeepChecks helps you validate your machine learning models and data, such as verifying your data’s integrity, inspecting its distributions, validating data splits, evaluating your model and comparing between different models, all with minimal effort.

Read more about DeepChecks and the wandb integration ->

Getting started

To use DeepChecks with W&B you will first need to sign up for a W&B account. With the W&B integration in DeepChecks you can quickly get started like so:

import wandb

wandb.login()

# import your check from deepchecks
from deepchecks.checks import ModelErrorAnalysis

# run your check
result = ModelErrorAnalysis()

# push that result to wandb
result.to_wandb()

You can also log an entire DeepChecks test suite to W&B.

import wandb

wandb.login()

# import your full_suite tests from deepchecks
from deepchecks.suites import full_suite

# create and run a DeepChecks test suite
suite_result = full_suite().run(...)

# push thes results to wandb
# here you can pass any wandb.init configs and arguments you need
suite_result.to_wandb(project="my-suite-project", config={"suite-name": "full-suite"})

Example

This Report shows off the power of using DeepChecks and W&B.

Any questions or issues about this W&B integration? Open an issue in the DeepChecks github repository and we’ll catch it and get you an answer.