Open dbczumar opened 2 years ago
@edoakes @architkulkarni @frascuchon Do you have bandwidth on your end to migrate the mlflow-ray-serve
plugin to the updated scoring protocol and adjust the Deployment Client predict()
API? By my estimates, it should only take a few hours of work at most. Apologies for the short notice.
Hi @dbczumar, thanks for the heads up! The Ray team won't have bandwidth in the short term for the migration, but we welcome outside contributions.
Is there a migration guide, or should we just compare the new docs with the old version of the docs?
Hi @dbczumar, thanks for the heads up! The Ray team won't have bandwidth in the short term for the migration, but we welcome outside contributions.
Is there a migration guide, or should we just compare the new docs with the old version of the docs?
Hi @architkulkarni , thanks for the context! Comparing the new docs with the old ones should be sufficient - the changes are pretty small here. Hoping the community can help here! :)
@architkulkarni any update on this issue?
We don't have a timeline for updating this unfortunately. Contributions are welcome as always!
Proposal Summary
In MLflow 2.0 (scheduled for release on Nov. 14), we will be making small modifications to the MLflow Model Server's RESTful scoring protocol (documented here: https://output.circle-artifacts.com/output/job/bb07270e-1101-421c-901c-01e72bc7b6df/artifacts/0/docs/build/html/models.html#deploy-mlflow-models) and the MLflow Deployment Client
predict()
API (documented here: https://output.circle-artifacts.com/output/job/bb07270e-1101-421c-901c-01e72bc7b6df/artifacts/0/docs/build/html/python_api/mlflow.deployments.html#mlflow.deployments.BaseDeploymentClient.predict).For compatibility with MLflow 2.0, the
mlflow-ray-serve
plugin will need to be updated to conform to the new scoring protocol and Deployment Client interface. The MLflow maintainers are happy to assist with this process, and we apologize for the short notice.Motivation
mlflow-ray-serve
plugin will break in MLflow 2.0.