CML is a CLI from from the creators of DVC - Iterative AI - that helps integrate your machine learning projects in your CI pipeline.
The example here is written for GitLab CI, but the same principles apply in other CI systems.
Export SuiteResult as a Markdown and HTML files#
from deepchecks.tabular.datasets.classification.adult import load_data, load_fitted_model from deepchecks.tabular.suites import train_test_validation model = load_fitted_model() train, test = load_data() ttvs = train_test_validation() # run the suite and get a SuiteResult result = ttvs.run(train_dataset=train, test_dataset=test, model=model) # save the SuiteResult as a GitLab- or GitHub- compliant markdown result.save_as_cml_markdown(file='report_gitlab.md', format='gitlab') # a full html report - report_gitlab.html - is produced alongside report_gitlab.md # its relative path to the .md file must stay consistent for cml to find it.
Use CML post the report to a Pull/Merge Request#
test-data-integrity: stage: test_data script: - dvc pull # say this command produces ./report_gitlab.md and ./report_gitlab.html - dvc repro train_test_validation # make cml make a comment in the PR/MR with the produced summary report file - cml comment create report_gitlab.md --publish --publish-native