CPU heavy consumption

[david-oliveira-br]

Hello guys , during some simple local tests I noticed my cpu processing topping 100% while running examples (mic or audio file). I spent some hours reviewing the api in a attempt to find a possible bottleneck but didnt find anything relevant yet. Do you guys have some recommendation or thoughts about it? thanks in advance

[laar789]

Hi. I just notice this, and your are totally right. It's like the moment we run client.start_stream_transcription, one core of the CPU gets completely occupied, and that will be a problem. @david-oliveira-br, have you found any solution to this?

[mikedavidson-evertz]

Hello! I noticed this same issue as well, where my CPU would spike to 100%. Interestingly enough, this only happens when building my code using python:3.9.16-slim-bullseye, ubuntu:20.04 or ubuntu:22.04 Docker images.

When I use python:3.9-alpine for my docker image, the CPU inside the container sits at around 1 - 5%!

Stepping through the transcribe sdk code and monitoring my CPU usage, I found the exact spot where my CPU spikes. The method that triggers the spike is here:

https://github.com/awslabs/amazon-transcribe-streaming-sdk/blob/develop/amazon_transcribe/client.py#L174

response = await self._session_manager.make_request(
            signed_request.uri,
            method=signed_request.method,
            headers=signed_request.headers.as_list(),
            body=signed_request.body,
        )

and more specifically, when stepping through that request call above, the spike happens when the stream is activated here: https://github.com/awslabs/amazon-transcribe-streaming-sdk/blob/develop/amazon_transcribe/httpsession.py#L56

def _set_stream(self, stream: http.HttpClientStream):
        if self._stream is not None:
            raise HTTPException("Stream already set on AwsCrtHttpResponse object")
        self._stream = stream
        self._stream.completion_future.add_done_callback(self._on_complete)
        self._stream.activate() # <- this call triggers the spike

self._stream.active() calls into the awscrt lib here and this is where the spike happens: https://github.com/awslabs/aws-crt-python/blob/main/awscrt/http.py#L286

  def activate(self):
        """Begin sending the request.

        The HTTP stream does nothing until this is called. Call activate() when you
        are ready for its callbacks and events to fire.
        """
        _awscrt.http_client_stream_activate(self) # <-- 100% CPU SPIKE HAPPENS HERE 

C code for the awscrt http_client_stream_activate python bindings above:

https://github.com/awslabs/aws-crt-python/blob/58de212a9288e64cdb5f698f782abf4281ba8bf6/source/http_stream.c#L301

Also, this spike occurs before I even begin transcribing any audio! It happens the moment this stream is activated.

Do you guys have any idea what's causing this? Thanks.

[mikedavidson-evertz]

Steps to recreate this issue

If you're on ubuntu 20.04 or 22.04, you can run this code directly using python3.9 and monitor you cpu usage with top

Note: Change the region in the transcribe client to the region you want to test with.

from amazon_transcribe.client import TranscribeStreamingClient

import asyncio


async def start_stream():
    transcribe_client = TranscribeStreamingClient(region="us-east-1")
    transcribe_stream = await transcribe_client.start_stream_transcription(
        language_code="en-US",
        media_sample_rate_hz=16000,
        media_encoding="pcm",
        language_model_name=None,
        vocabulary_name=None,
        vocab_filter_method=None,
        vocab_filter_name=None,
        show_speaker_label=None,
        enable_channel_identification=None,
        number_of_channels=None,
        enable_partial_results_stabilization=None,
        partial_results_stability=None,
        session_id=None,
    )

    # put a breakpoint here and look at your CPU usage.
    print("put breakpoint here")

    # loop so we can monitor cpu usage
    while True:
        pass


def main():
    asyncio.run(start_stream())


if __name__ == "__main__":
    main()

top CPU output:

Build the python code using docker

If you're not using ubuntu, you can build the code using Docker. Put that python code above in a main.py and the Dockerfile below in the same directory:

Note: Fill in your AWS creds in the dockerfile so it can authenticate with transcribe.

FROM ubuntu:20.04

ENV AWS_ACCESS_KEY_ID=<AWS_ACCESS_KEY_ID>
ENV AWS_SECRET_ACCESS_KEY=<AWS_SECRET_ACCESS_KEY>
ENV AWS_SESSION_TOKEN=<AWS_SESSION_TOKEN>
      
ARG DEBIAN_FRONTEND=noninteractive

RUN apt-get update && apt-get install -y software-properties-common && \
    add-apt-repository -y ppa:deadsnakes/ppa

RUN apt-get install --no-install-recommends -y \
    python3.9=3.9.16-1+focal1 \
    python3-pip \
    && apt-get clean && rm -rf /var/lib/apt/lists/*

RUN python3.9 -m pip install amazon-transcribe==0.6.1

WORKDIR /transcribe_high_cpu_test

COPY main.py ./

CMD ["python3.9", "main.py"]

Now build the image and run the image while monitoring your CPU usage. Run these commands in the same directory as the python/Dockerfile.

docker build -t transcribe-cpu-usage-test .

docker run transcribe-cpu-usage-test:latest