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Added a UC uri check to flavor_backend_registry #11990
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@kriscon-db Thank you for the contribution! Could you fix the following issue(s)? ⚠ DCO checkThe DCO check failed. Please sign off your commit(s) by following the instructions here. See https://github.com/mlflow/mlflow/blob/master/CONTRIBUTING.md#sign-your-work for more details. |
@@ -38,7 +38,8 @@ def _get_flavor_backend_for_local_model(model=None, build_docker=True, **kwargs) | |||
def get_flavor_backend(model_uri, **kwargs): | |||
if model_uri: | |||
with TempDir() as tmp: | |||
if ModelsArtifactRepository.is_models_uri(model_uri): | |||
from mlflow import get_registry_uri | |||
if ModelsArtifactRepository.is_models_uri(model_uri) and not is_databricks_unity_catalog_uri(get_registry_uri()): |
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AFAICT we're special casing model registry URIs here because the logic below
local_path = _download_artifact_from_uri(
append_to_uri_path(underlying_model_uri, MLMODEL_FILE_NAME), output_path=tmp.path()
)
May not work for models:/
URIs (or probably at least didn't when originally written) since URIs like models:/<modelname>/<version>/path/to/file
weren't supported
Instead of special-casing UC, it'd be better to just call get_artifact_repository
on model_uri
to get the artifact repo, then download_artifacts
similar to https://github.com/mlflow/mlflow/pull/8764/files#diff-c3efddc751d03e91a2284128768d6761b80f9b03ad48947c9f701dbf743b25aaR78
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Thanks @kriscon-db ! Could we test by configuring a Databricks CLI profile locally and then running import mlflow; mlflow.set_registry_uri("databricks-uc://<profile-name>"); mlflow.models.build_docker(...)
, passing UC model version info?
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Signed-off-by: mlflow-automation <mlflow-automation@users.noreply.github.com>
) | ||
root_uri, artifact_path = _get_root_uri_and_artifact_path(model_uri) | ||
artifact_repo = get_artifact_repository(root_uri) | ||
local_path = artifact_repo.download_artifacts(artifact_path, dst_path=tmp.path()) |
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Shouldn't we still be downloading the MLMODEL_FILE_NAME
here?
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Or maybe Model.load
is able to figure that out somehow?
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that leads me to believe the work is already being done? I can put that back in this method if you want tho.
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Great, no need, was just curious - if tests are passing that means it works :D
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LGTM!
Signed-off-by: mlflow-automation <mlflow-automation@users.noreply.github.com> Co-authored-by: mlflow-automation <mlflow-automation@users.noreply.github.com>
Signed-off-by: mlflow-automation <mlflow-automation@users.noreply.github.com> Co-authored-by: mlflow-automation <mlflow-automation@users.noreply.github.com>
Signed-off-by: mlflow-automation <mlflow-automation@users.noreply.github.com> Co-authored-by: mlflow-automation <mlflow-automation@users.noreply.github.com>
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Related Issues/PRs
#xxxWhat changes are proposed in this pull request?
Change
flavor_backend_registry.py
to use theget_artifact_repository
to make sure you get the specific repository for UC uri's.How is this PR tested?
Tested on my local environment with the repro from the ES ticket. This PR solves the incorrect artifact repo bug that exists in this code path for UC.
Does this PR require documentation update?
Release Notes
Is this a user-facing change?
What component(s), interfaces, languages, and integrations does this PR affect?
Components
area/artifacts
: Artifact stores and artifact loggingarea/build
: Build and test infrastructure for MLflowarea/deployments
: MLflow Deployments client APIs, server, and third-party Deployments integrationsarea/docs
: MLflow documentation pagesarea/examples
: Example codearea/model-registry
: Model Registry service, APIs, and the fluent client calls for Model Registryarea/models
: MLmodel format, model serialization/deserialization, flavorsarea/recipes
: Recipes, Recipe APIs, Recipe configs, Recipe Templatesarea/projects
: MLproject format, project running backendsarea/scoring
: MLflow Model server, model deployment tools, Spark UDFsarea/server-infra
: MLflow Tracking server backendarea/tracking
: Tracking Service, tracking client APIs, autologgingInterface
area/uiux
: Front-end, user experience, plotting, JavaScript, JavaScript dev serverarea/docker
: Docker use across MLflow's components, such as MLflow Projects and MLflow Modelsarea/sqlalchemy
: Use of SQLAlchemy in the Tracking Service or Model Registryarea/windows
: Windows supportLanguage
language/r
: R APIs and clientslanguage/java
: Java APIs and clientslanguage/new
: Proposals for new client languagesIntegrations
integrations/azure
: Azure and Azure ML integrationsintegrations/sagemaker
: SageMaker integrationsintegrations/databricks
: Databricks integrationsHow should the PR be classified in the release notes? Choose one:
rn/none
- No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" sectionrn/breaking-change
- The PR will be mentioned in the "Breaking Changes" sectionrn/feature
- A new user-facing feature worth mentioning in the release notesrn/bug-fix
- A user-facing bug fix worth mentioning in the release notesrn/documentation
- A user-facing documentation change worth mentioning in the release notesShould this PR be included in the next patch release?
Yes
should be selected for bug fixes, documentation updates, and other small changes.No
should be selected for new features and larger changes. If you're unsure about the release classification of this PR, leave this unchecked to let the maintainers decide.What is a minor/patch release?
Bug fixes, doc updates and new features usually go into minor releases.
Bug fixes and doc updates usually go into patch releases.