问题
I'm trying to deploy a TensorFlow model to Google AI Platform for Online Prediction. I'm having latency and throughput issues.
The model runs on my machine in less than 1 second (with only an Intel Core I7 4790K CPU) for a single image. I deployed it to AI Platform on a machine with 8 cores and an NVIDIA T4 GPU.
When running the model on AI Platform on the mentioned configuration, it takes a little less than a second when sending only one image. If I start sending many requests, each with one image, the model eventually blocks and stops responding. So I'm instead sending batches of images on each request (from 2 to 10, depending on external factors).
The problem is that I expected the batched requests to be almost constant in time. When sending 1 image, the CPU utilization was around 10% and GPU 12%. So I expected that a batch of 9 images would use ~100% of the hardware and respond in the same time ~1 sec, but this is not the case. A batch of 7 to 10 images takes anywhere from 15 to 50 seconds to be processed.
I already tried to optimize my model. I was using map_fn, replaced that with manual loops, switched from Float 32 to Float 16, tried to vectorize the operations as much as possible, but it's still in the same situation.
What am I missing here?
I'm using the latest AI Platform runtime for online prediction (Python 3.7, TensorFlow 2.1, CUDA 10.1).
The model is a large version of YOLOv4 (~250MB in SavedModel format). I've built a few postprocessing algorithms in TensorFlow that operates on the output of the model.
Last but not least, I also tried debugging with TensorBoard, and it turns out that the YOLOv4 part of the TensorFlow Graph is taking ~90% of the processing time. I expected this particular part of the model to be highly parallel.
Thanks in advance for any help with this. Please ask me for any information that you may need to better understand the issue.
UPDATE 2020-07-13: as suggested in a comment below, I also tried running the model on CPU, but it's really slow and suffers of the same problems than with GPU. It doesn't seem to process images from a single request in parallel.
Also, I think I'm running into issues with TensorFlow Serving due to the rate and amount of requests. I used the tensorflow/serving:latest-gpu Docker image locally to test this further. The model answers 3 times faster on my machine (GeForce GTX 1650) than on AI Platform, but its really inconsistent with response times. I'm getting the following response times (<amount of images> <response time in milliseconds>):
3 9004
3 8051
11 4332
1 222
3 4386
3 3547
11 5101
9 3016
10 3122
11 3341
9 4039
11 3783
11 3294
Then, after running for a minute, I start getting delays and errors:
3 27578
3 28563
3 31867
3 18855
{
message: 'Request failed with status code 504',
response: {
data: { error: 'Timed out waiting for notification' },
status: 504
}
}
回答1:
From the Google Cloud documentation:
If you use a simple model and a small set of input instances, you'll find that there is a considerable difference between how long it takes to finish identical prediction requests using online versus batch prediction. It might take a batch job several minutes to complete predictions that are returned almost instantly by an online request. This is a side-effect of the different infrastructure used by the two methods of prediction. AI Platform Prediction allocates and initializes resources for a batch prediction job when you send the request. Online prediction is typically ready to process at the time of request.
This has to do, like the quote says, with the difference in node allocations, specially with:
Node allocation for online prediction:
- Keeps at least one node ready over a period of several minutes, to handle requests even when there are none to handle. The ready state ensures that the service can serve each prediction promptly.
You can learn more about that here
回答2:
The model is a large version of YOLOv4 (~250MB in SavedModel format). I've built a few postprocessing algorithms in TensorFlow that operates on the output of the model.
What are the postprocessing modifications have you made to the YOLOv4? Is it possible that the source of the slowdown are from those operations? One test you can do to validate this hypothesis locally is to benchmark an unmodified version of YOLOv4 against the benchmarks you've already made for your modified version.
Last but not least, I also tried debugging with TensorBoard, and it turns out that the YOLOv4 part of the TensorFlow Graph is taking ~90% of the processing time. I expected this particular part of the model to be highly parallel.
It would be interesting to take a look at the "debugging output" you're mentioning here. If you use https://www.tensorflow.org/guide/profiler#install_the_profiler_and_gpu_prerequisites, what are the breakdown of the most expensive operations? I've had some experience digging into TF ops -- I've found some strange bottlenecks due to CPU <-> GPU data transfer bottlenecks in some cases. Would be happy to hop on a call sometime and take a look with you if you shoot me a DM.
回答3:
For others with the same problem as me when using AI Platform:
As stated in a comment from the Google Cloud team here, AI Platform does not execute batches of instances at once. They plan on adding the feature, though.
We've since moved on from AI Platform to a custom deployment of NVIDIA's Triton Inference Server hosted on Google Cloud Compute Engine. We're getting much better performance than we expected, and we can still apply many more optimizations to our model provided by Triton.
Thanks to everyone who tried to help by replying to this answer.
来源：https://stackoverflow.com/questions/62844769/tensorflow-model-serving-on-google-ai-platform-online-prediction-too-slow-with-i