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Start a run before logging a model #4256

Merged
merged 1 commit into from Dec 6, 2021
Merged

Start a run before logging a model #4256

merged 1 commit into from Dec 6, 2021

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einsmein
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@einsmein einsmein commented Apr 16, 2021

What changes are proposed in this pull request?

When logging a model, start a run if none is active and save the run id.
Without a run id in an autogenerated MLmodel, UI throws an error for an experiment that contains the model.

How is this patch tested?

Since loading a model only loads crated function, it's not possible to check whether a run_id is specified in MLmodel. I'm open for suggestion if and how a test should be added for this.

Release Notes

Is this a user-facing change?

  • No. You can skip the rest of this section.
  • Yes. Give a description of this change to be included in the release notes for MLflow users.

(Details in 1-2 sentences. You can just refer to another PR with a description if this PR is part of a larger change.)

What component(s), interfaces, languages, and integrations does this PR affect?

Components

  • area/artifacts: Artifact stores and artifact logging
  • area/build: Build and test infrastructure for MLflow
  • area/docs: MLflow documentation pages
  • area/examples: Example code
  • area/model-registry: Model Registry service, APIs, and the fluent client calls for Model Registry
  • area/models: MLmodel format, model serialization/deserialization, flavors
  • area/projects: MLproject format, project running backends
  • area/scoring: Local serving, model deployment tools, spark UDFs
  • area/server-infra: MLflow server, JavaScript dev server
  • area/tracking: Tracking Service, tracking client APIs, autologging

Interface

  • area/uiux: Front-end, user experience, JavaScript, plotting
  • area/docker: Docker use across MLflow's components, such as MLflow Projects and MLflow Models
  • area/sqlalchemy: Use of SQLAlchemy in the Tracking Service or Model Registry
  • area/windows: Windows support

Language

  • language/r: R APIs and clients
  • language/java: Java APIs and clients
  • language/new: Proposals for new client languages

Integrations

  • integrations/azure: Azure and Azure ML integrations
  • integrations/sagemaker: SageMaker integrations
  • integrations/databricks: Databricks integrations

How should the PR be classified in the release notes? Choose one:

  • rn/breaking-change - The PR will be mentioned in the "Breaking Changes" section
  • rn/none - No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" section
  • rn/feature - A new user-facing feature worth mentioning in the release notes
  • rn/bug-fix - A user-facing bug fix worth mentioning in the release notes
  • rn/documentation - A user-facing documentation change worth mentioning in the release notes

@github-actions github-actions bot added the rn/none List under Small Changes in Changelogs. label Apr 16, 2021
Signed-off-by: Mind <ji@desupervised.io>
@lorenzwalthert
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LGTM. @tomasatdatabricks?

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@harupy harupy left a comment

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LGTM!

@harupy harupy merged commit 18c5265 into mlflow:master Dec 6, 2021
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3 participants