[FR] Allow docker args to be passed into `mlflow models build-docker` and `mlflow sagemaker build-and-push-container`

Willingness to contribute

No. I cannot contribute this feature at this time.

Proposal Summary

During the build-docker process, mlflow installs packages as specified in a requirements.txt file that is logged together with the model in the mlflow registry. Some of these packages may be in a private package repository, whose access requires a token. There is currently no way to pass such a token into the build process.

Motivation

What is the use case for this feature?

During the build-docker process, mlflow installs packages as specified in a requirements.txt file that is logged together with the model in the mlflow registry. Some of these packages may be in a private package repository, whose access requires a token. There is currently no way to pass such a token (as an ARG) into the build process.

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

Currently the documentation recommends to add code not installable from pipy as code to the log_model call. This does not scale well since adding packages as code does not resolve their dependencies.

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

See above.

Why is it currently difficult to achieve this use case?

See above.

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/deployments: MLflow Deployments client APIs, server, and third-party Deployments integrations
• 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