[FR] Implement Paddle Distributed Training Visualization based on MLFlow

[tizhou86]

Thank you for submitting a feature request. Before proceeding, please review MLflow's Issue Policy for feature requests and the MLflow Contributing Guide.

Please fill in this feature request template to ensure a timely and thorough response.

Willingness to contribute

The MLflow Community encourages new feature contributions. Would you or another member of your organization be willing to contribute an implementation of this feature (either as an MLflow Plugin or an enhancement to the MLflow code base)?

• Yes. I can contribute this feature independently.
• Yes. I would be willing to contribute this feature with guidance from the MLflow community.
• No. I cannot contribute this feature at this time.

Proposal Summary

(In a few sentences, provide a clear, high-level description of the feature request)

Motivation

• What is the use case for this feature?
  PaddlePaddle framework users can use MLFlow for PaddlePaddle training experiment visualization.

• Why is this use case valuable to support for MLflow users in general?
  MLFlow users can use PaddlePaddle framework natively.

• Why is this use case valuable to support for your project(s) or organization?
  PaddlePaddle framework users can use MLFlow for PaddlePaddle training experiment visualization.

• Why is it currently difficult to achieve this use case? (please be as specific as possible about why related MLflow features and components are insufficient)
  We need to figure out MLFlow capability to support distributed training currently.

What component(s), interfaces, languages, and integrations does this feature 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: MLflow Model server, model deployment tools, Spark UDFs
• area/server-infra: MLflow Tracking server backend
• area/tracking: Tracking Service, tracking client APIs, autologging

Interfaces

• 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

Languages

• 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
• integrations/paddlepaddle: PaddlePaddle integrations

Details

PaddlePaddle team has implemented the deep learning training visualization through PaddlePaddle high-level API integration. For this project, visualization capability of Paddle distributed training based on MLFlow should be implemented.

(Use this section to include any additional information about the feature. If you have a proposal for how to implement this feature, please include it here. For implementation guidelines, please refer to the Contributing Guide.)