[FR] Why is there no 'log_model()' function in mlflow.client?

Willingness to contribute

Yes. I would be willing to contribute this feature with guidance from the MLflow community.

Proposal Summary

I want to use mlflow by parallel runs. But, mlflow is not thread-safe. when using mlflow.start_run() code, run id becomes a global variable. So, every parallel runs become crashed.

I found a method of log_artifact() in mlflow.client(). This method is not log model and meta info of model to mlflow tracking server, but only save to registry server.

I want to method for mlflow.client like mlflow.skelarn.log_model() that is save model to registry and log to mlflow tracking server.

Is it possible?

Motivation

What is the use case for this feature?

When using mlflow.client, There is no way to log model to mlflow tracking server. log_artifact() method in mlflow.client is only save to registry not log.

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

This feature is necessary to users who want to use mlflow in parallel.

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

Why is it currently difficult to achieve this use case?

Now I’,m using mlflow.onnx.log_model(). So parallel runs become crashed.

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/recipes: Recipes, Recipe APIs, Recipe configs, Recipe 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

[jaehyeongAN]

@WeichenXu123 Cool, I want to do that.

[harupy]

@WeichenXu123 Got it.

I think we can add a method MLflowClient.log_model, like:

This approach sounds good to me.