[FR] Still show Run ID in the UI when a Run Name is specified

Willingness to contribute

No. I cannot contribute this feature at this time.

Proposal Summary

When you specify a run name, the run ID is no longer visible in the UI (on neither the Experiment page nor that Run’s page). It would be very helpful to keep the Run ID on at least one of those pages for easier access than searching for that run id in the backend database.

Motivation

What is the use case for this feature?

Run IDs are unique identifiers and as such, are valuable to use as reference to specific runs between scripts.

Why is this use case valuable to support for MLflow users in general?

Specifying a run name is a useful feature, but it comes at the expense of losing the Run ID in the UI, which is also a useful feature. It would be valuable to have both a run’s name and its unique identifier.

Why is this use case valuable to support for your project(s) or organization?

We use MLflow run names similar to git commit messages, so that there is a brief message about why that run happened. This helps with inter-developer collaboration, as well as keeps a quick reminder of what was changing between runs. We also use the MLflow run ID to identify the unique model artifact (e.g., model.joblib artifact) that we want to promote. For us, promotion comes in the form of uploading that model artifact from successful runs to an S3 bucket, using the run ID in the object key name within the bucket, and then using SageMaker’s Python SDK to deploy that model on AWS.

Why is it currently difficult to achieve this use case?

Run ID is lost in the UI when you specify a run name. It would be more convenient to avoid going to the backend database each time to check a run ID.

Details

No response

What component(s) does this bug affect?

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: MLflow Model server, model deployment tools, Spark UDFs
area/server-infra: MLflow Tracking server backend
area/tracking: Tracking Service, tracking client APIs, autologging

What interface(s) does this bug affect?

area/uiux: Front-end, user experience, plotting, JavaScript, JavaScript dev server
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