[FR] Using MLflow from Julia? #6997
Description
Willingness to contribute
No. I cannot contribute this feature at this time.
Proposal Summary
I'd be interested in using MLflow from Julia, more specifically MLflow Tracking.
Motivation
What is the use case for this feature?
I have ML experiments in Julia and I'm looking for for some software to help me manage/track/compare them.
Why is this use case valuable to support for MLflow users in general?
It would benefit the whole Julia ML community.
Why is this use case valuable to support for your project(s) or organization?
It would help the iterative process in my research.
Why is it currently difficult to achieve this use case?
As far as I know there no integration with the Julia language yet.
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/pipelines: Pipelines, Pipeline APIs, Pipeline configs, Pipeline Templates -
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
What language(s) does this bug affect?
-
language/r: R APIs and clients -
language/java: Java APIs and clients -
language/new: Proposals for new client languages
What integration(s) does this bug affect?
-
integrations/azure: Azure and Azure ML integrations -
integrations/sagemaker: SageMaker integrations -
integrations/databricks: Databricks integrations