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Try in Colab DeepChecks helps you validate your machine learning models and data, such as verifying your dataโ€™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 ->

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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"})

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Example

This Report shows off the power of using DeepChecks and W&B.
Deepchecks data validation results
Any questions or issues about this W&B integration? Open an issue in the DeepChecks github repository and weโ€™ll catch it and get you an answer.