Documentation
English
Search…
⌃K

DeepChecks

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 with minimal effort.

Getting Started

To use DeepChecks with Weights & Biases you will first need to sign up for a Weights & Biases account here. With the Weights & Biases 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 Weights & Biases
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 Weights & Biases
Any questions or issues about this Weights & Biases integration? Open an issue in the DeepChecks github repository and we'll catch it and get you an answer :)
Last modified 8mo ago