aws sagemaker real-time endpoint, does not process requests concurrently?

Question

I deployed stable diffusion v2.0 by aws sagemaker, and create endpoint for real-time inference.(instance type is ml.g4dn.xlarge)
Also, i used aws apigateway and aws lambda.

my question is that concurrency of prediction process by invoked request.
when i check cloudwatch log, i see that request is sequentially process.(one prediction finished, then next prediction proceeded)

i expected that the requests are concurrently dealt with, but not.

at real-time endpoint, there is no max concurrent options, then invokeendpoint always sequentially procedeed??
no way for making requests are parellely dealt with, except increase instance number?

Answer

The concurrency of a real-time endpoint depends on the number of workers maintained inside your algorithm container. For each worker, a copy of the model weights need to be loaded. In other words, we need to first configure the container to maintain multiple workers and make sure there is enough CPU & GPU memory to host multiple models.

I think for stable diffusion, the officially recommended GPU memory is 10 GB and g4dn.xlarge only comes with 16, which is not sufficient for 2 models running concurrently ?

Could you please check the runtime GPU utilization as well as the configured number of workers in your container ?

aws sagemaker real-time endpoint, does not process requests concurrently?

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