Azure ML filters mlflow.source.* tags at run creation but not at runtime

[Harikrzna]

• Package Name: azureml-mlflow
• Package Version: 1.61.0.post1
• Operating System: Ubuntu 22.04.5 LTS
• Python Version: 3.12

Describe the bug
Azure ML's MLflow tracking backend filters mlflow.source.name and mlflow.source.type tags when passed to create_run(tags=...) but allows the same tags when set via set_tag() after run creation. This inconsistency breaks integrations with tools like PyTorch Lightning.

To Reproduce
Steps to reproduce the behavior:

1. Create a run with source tags at creation time:

   client.create_run(experiment_id="...", tags={
       "mlflow.source.name": "https://github.com/org/repo",
       "mlflow.source.type": "PROJECT"
   })

Result: Tags are filtered out ❌

2. Create a run and set tags afterwards
   run = client.create_run(experiment_id="...")
   client.set_tag(run.info.run_id, "mlflow.source.name", "https://github.com/org/repo")
   client.set_tag(run.info.run_id, "mlflow.source.type", "PROJECT")

Result: Tags are preserved ✅

Impact
PyTorch Lightning MLFlowLogger broken: Lightning passes tags to create_run(), causing all source tags to be lost
Standard MLflow patterns fail: The recommended MLflow pattern uses tags at creation
Confusing behavior: No documentation explains this filtering

Expected behavior
Either:
Option A: Allow mlflow.source.* tags in both scenarios (preferred)
Option B: Filter them in both scenarios and document why
Option C: Document the current behavior and provide guidance

Additional context
Noticed this difference when planning to migrate to Mlflow in AzureML from a self-hosted version of MlFlow v2.12.1.