Race conditions in Azure ML endpoint inference?

Question

Hello,

When using the auto-generated python scoring scripts for consuming models deployed as a managed online endpoint, I noticed that the scripts defines the loaded model as a global variable, global model.

This has me concerned if there is a risk of reaching a race condition when running inference?

If we submit requests A and B, where both uses the model.predict, and the inference from request B is returned before A. Do we not have a risk of crosstalk here, or are the requests handled in such a way that this doesn't happen?

Answer 1

Hello aot,

Thanks for raising this question in Q&A forum.

You have a valid concern regarding concurrency, but in the standard Azure Machine Learning (AML) Managed Online Endpoint architecture, using a global variable for the model in score.py is the recommended pattern and is generally safe from the "crosstalk" race condition you described, provided the underlying model's predict method itself is thread-safe or the server is configured correctly.

Here is the technical breakdown of why this works and where the edge cases lie:

1. Process Isolation vs. Threading

• Initialization (init()): This runs once when the container starts. The global model variable loads the heavy model object into memory.
• Inference (run()): Azure ML uses a web server (typically Gunicorn with Uvicorn workers for Python) to handle incoming HTTP requests.
• Default Behavior: By default, AML endpoints often use a synchronous worker model or a specific number of worker processes.
  • If the server uses multiple worker processes, each process has its own independent copy of the memory (and the global model). Request A goes to Process 1, Request B goes to Process 2. They cannot interfere with each other.
  • If the server uses threading within a single process, multiple requests might access the same global model object simultaneously.

2. The "Crosstalk" Scenario (Request A getting Request B's data)

This specific type of race condition (data leakage) is extremely unlikely in standard ML frameworks (Scikit-Learn, PyTorch, TensorFlow) because the predict() function usually does not store request-specific state on the model object itself.

• Safe: result = model.predict(data) -> The data flows through the function stack.
• Unsafe: model.last_input = data; result = model.compute() -> This would cause race conditions in a threaded environment. Standard libraries do not do this.

Summary & Recommendation

Using global model is efficient because it prevents reloading the model for every request (which would be too slow).

To ensure safety:

1. Check your scoring logic: Ensure you aren't storing any request-specific data in global variables or modifying attributes of the model object during the run() function.
2. Concurrency Settings: If your model framework is not thread-safe, you can configure the endpoint to use process-based concurrency rather than thread-based. You can tune the WORKER_COUNT environment variable in your deployment configuration to control how many independent processes run.

If helps, approve the answer.

Best Regards,

Jerald Felix