Client Examples¶
The inference server includes a couple of example applications that show how to use the client libraries:
C++ and Python versions of image_client, an example application that uses the C++ or Python client library to execute image classification models on the TensorRT Inference Server.
C++ version of perf_client, an example application that issues a large number of concurrent requests to the inference server to measure latency and throughput for a given model. You can use this to experiment with different model configuration settings for your models.
A number of simple C++ and Python samples that show various aspects of the inference server. The name of these examples begins with simple_.
You can also communicate with the inference server by using the protoc compiler to generate the GRPC client stub in a large number of programming languages. As an example, grpc_image_client, is a Python application that is functionally equivalent to image_client but that uses a generated GRPC client stub to communicate with the inference server (instead of the client library).
Getting the Client Examples¶
The provided Dockerfile.client and CMake support can be used to build the examples, or the pre-built examples can be downloaded from GitHub.
Build Using Dockerfile¶
To build the examples using Docker follow the description in Build Using Dockerfile.
After the build completes the tensorrtserver_client docker image will contain the built client examples, and will also be configured with all the dependencies required to run those examples within the container. The easiest way to try the examples described in the following sections is to run the client image with --net=host so that the client examples can access the inference server running in its own container. To be able to use shared memory you need to run the client and server image with --ipc=host so that the inference server can access the shared memory in the client container. Additionally, to create shared memory regions that are larger than 64MB, the --shm-size=1g flag is needed while running the client image (see Running The Inference Server for more information about running the inference server):
$ docker run -it --rm --net=host tensorrtserver_client
In the tensorrtserver_client image you can find the example executables in /workspace/install/bin, and the Python examples in /workspace/install/python.
Build Using CMake¶
To build the examples using CMake follow the description in Build Using CMake.
Ubuntu 16.04 / Ubuntu 18.04¶
When the build completes the examples can be found in trtis-clients/install. To use the examples, you need to include the path to the client library in environment variable “LD_LIBRARY_PATH”, by default it is /path/to/tensorrtserver/repo/build/trtis-clients/install/lib. In addition to that, you also need to install the tensorrtserver Python package and other packages required by the examples:
$ pip install trtis-clients/install/python/tensorrtserver-*.whl numpy pillow
Windows 10¶
When the build completes the examples can be found in trtis-clients/install. The C++ client examples will not be generated as those examples have not yet been ported to Windows. However, you can use the Python examples to test if the build is successful. To use the Python examples, you need to install the tensorrtserver Python package and other packages required by the examples:
> pip install trtis-clients/install/python/tensorrtserver-*.whl numpy pillow
Download From GitHub¶
To download the examples follow the description in Download From GitHub.
To use the C++ examples you must install some dependencies. What dependencies you need to install depends on your OS. For Ubuntu 16.04:
$ apt-get update
$ apt-get install curl libcurl3-dev
For Ubuntu 18.04:
$ apt-get update
$ apt-get install curl libcurl4-openssl-dev
The Python examples require that you additionally install the wheel file and some other dependencies:
$ apt-get install python python-pip
$ pip install --user --upgrade python/tensorrtserver-*.whl numpy pillow
The C++ image_client example uses OpenCV for image manipulation so for that example you must install the following:
$ apt-get install libopencv-dev libopencv-core-dev
Image Classification Example Application¶
The image classification example that uses the C++ client API is available at src/clients/c++/image_client.cc. The Python version of the image classification client is available at src/clients/python/image_client.py.
To use image_client (or image_client.py) you must first have a running inference server that is serving one or more image classification models. The image_client application requires that the model have a single image input and produce a single classification output. If you don’t have a model repository with image classification models see Example Model Repository for instructions on how to create one.
Follow the instructions in Running The Inference Server to launch the server using the model repository. Once the server is running you can use the image_client application to send inference requests to the server. You can specify a single image or a directory holding images. Here we send a request for the resnet50_netdef model from the example model repository for an image from the qa/images directory:
$ image_client -m resnet50_netdef -s INCEPTION qa/images/mug.jpg
Request 0, batch size 1
Image '../qa/images/mug.jpg':
504 (COFFEE MUG) = 0.723991
The Python version of the application accepts the same command-line arguments:
$ python image_client.py -m resnet50_netdef -s INCEPTION qa/images/mug.jpg
Request 0, batch size 1
Image '../qa/images/mug.jpg':
504 (COFFEE MUG) = 0.778078556061
The image_client and image_client.py applications use the inference server client library to talk to the server. By default image_client instructs the client library to use HTTP protocol to talk to the server, but you can use GRPC protocol by providing the -i flag. You must also use the -u flag to point at the GRPC endpoint on the inference server:
$ image_client -i grpc -u localhost:8001 -m resnet50_netdef -s INCEPTION qa/images/mug.jpg
Request 0, batch size 1
Image '../qa/images/mug.jpg':
504 (COFFEE MUG) = 0.723991
By default the client prints the most probable classification for the image. Use the -c flag to see more classifications:
$ image_client -m resnet50_netdef -s INCEPTION -c 3 qa/images/mug.jpg
Request 0, batch size 1
Image '../qa/images/mug.jpg':
504 (COFFEE MUG) = 0.723991
968 (CUP) = 0.270953
967 (ESPRESSO) = 0.00115996
The -b flag allows you to send a batch of images for inferencing. The image_client application will form the batch from the image or images that you specified. If the batch is bigger than the number of images then image_client will just repeat the images to fill the batch:
$ image_client -m resnet50_netdef -s INCEPTION -c 3 -b 2 qa/images/mug.jpg
Request 0, batch size 2
Image '../qa/images/mug.jpg':
504 (COFFEE MUG) = 0.778078556061
968 (CUP) = 0.213262036443
967 (ESPRESSO) = 0.00293014757335
Image '../qa/images/mug.jpg':
504 (COFFEE MUG) = 0.778078556061
968 (CUP) = 0.213262036443
967 (ESPRESSO) = 0.00293014757335
Provide a directory instead of a single image to perform inferencing on all images in the directory:
$ image_client -m resnet50_netdef -s INCEPTION -c 3 -b 2 qa/images
Request 0, batch size 2
Image '../qa/images/car.jpg':
817 (SPORTS CAR) = 0.836187
511 (CONVERTIBLE) = 0.0708251
751 (RACER) = 0.0597549
Image '../qa/images/mug.jpg':
504 (COFFEE MUG) = 0.723991
968 (CUP) = 0.270953
967 (ESPRESSO) = 0.00115996
Request 1, batch size 2
Image '../qa/images/vulture.jpeg':
23 (VULTURE) = 0.992326
8 (HEN) = 0.00231854
84 (PEACOCK) = 0.00201471
Image '../qa/images/car.jpg':
817 (SPORTS CAR) = 0.836187
511 (CONVERTIBLE) = 0.0708251
751 (RACER) = 0.0597549
The grpc_image_client.py application at available at src/clients/python/grpc_image_client.py behaves the same as the image_client except that instead of using the inference server client library it uses the GRPC generated client library to communicate with the server.
Ensemble Image Classification Example Application¶
In comparison to the image classification example above, this example uses an ensemble of an image-preprocessing model implemented as a custom backend and a Caffe2 ResNet50 model. This ensemble allows you to send the raw image binaries in the request and receive classification results without preprocessing the images on the client. The ensemble image classification example that uses the C++ client API is available at src/clients/c++/ensemble_image_client.cc. The Python version of the image classification client is available at src/clients/python/ensemble_image_client.py.
To use ensemble_image_client (or ensemble_image_client.py) you must first have a running inference server that is serving the “preprocess_resnet50_ensemble” model and the models it depends on. The models are provided in example ensemble model repository see Example Model Repository for instructions on how to create one.
Follow the instructions in Running The Inference Server to launch the server using the ensemble model repository. Once the server is running you can use the ensemble_image_client application to send inference requests to the server. You can specify a single image or a directory holding images. Here we send a request for the ensemble from the example ensemble model repository for an image from the qa/images directory:
$ ensemble_image_client qa/images/mug.jpg
Image 'qa/images/mug.jpg':
504 (COFFEE MUG) = 0.723991
The Python version of the application accepts the same command-line arguments:
$ python ensemble_image_client.py qa/images/mug.jpg
Image 'qa/images/mug.jpg':
504 (COFFEE MUG) = 0.778078556061
Similar to image_client, by default ensemble_image_client instructs the client library to use HTTP protocol to talk to the server, but you can use GRPC protocol by providing the -i flag. You must also use the -u flag to point at the GRPC endpoint on the inference server:
$ ensemble_image_client -i grpc -u localhost:8001 qa/images/mug.jpg
Image 'qa/images/mug.jpg':
504 (COFFEE MUG) = 0.723991
By default the client prints the most probable classification for the image. Use the -c flag to see more classifications:
$ ensemble_image_client -c 3 qa/images/mug.jpg
Image 'qa/images/mug.jpg':
504 (COFFEE MUG) = 0.723991
968 (CUP) = 0.270953
967 (ESPRESSO) = 0.00115996
Provide a directory instead of a single image to perform inferencing on all images in the directory. If the number of images exceeds the maximum batch size of the ensemble, only the images within the maximum batch size will be sent:
$ ensemble_image_client -c 3 qa/images
Image 'qa/images/car.jpg':
817 (SPORTS CAR) = 0.836187
511 (CONVERTIBLE) = 0.0708251
751 (RACER) = 0.0597549
Image 'qa/images/mug.jpg':
504 (COFFEE MUG) = 0.723991
968 (CUP) = 0.270953
967 (ESPRESSO) = 0.00115996
Image 'qa/images/vulture.jpeg':
23 (VULTURE) = 0.992326
8 (HEN) = 0.00231854
84 (PEACOCK) = 0.00201471
Performance Example Application¶
The perf_client example application located at src/clients/c++/perf_client.cc uses the C++ client API to send concurrent requests to the server to measure latency and inferences-per-second under varying client loads.
To create each load level the perf_client maintains a constant number of outstanding inference requests to the server. The lowest load level is created by having one outstanding request to the server. When that request completes (i.e. the response is received from the server), the perf_client immediately sends another request. The next highest load level is created by having two outstanding requests to the server. When one of those requests completes, the perf_client immediately sends another request so that there are always exactly two inference requests in-flight at all times. The next highest load level is created with three outstanding requests, etc.
At each load level the perf_client measures the throughput and latency over a time window, and then repeats the measurements until it gets stable results. The perf_client then increases the load level and measures again. This repeats until the perf_client reaches one of the specified limits: either the maximum latency value is reached or the maximum concurrency value is reached. To determine stable results perf_client uses average request latency unless the –percentile flag is specified. If the –percentile flag is specified, perf_client will stabilize the results based on that confidence level. For example, if –percentile=99 is used the results will be stabilized using the 99-th percentile request latency.
To use perf_client you must first have a running inference server that is serving one or more models. The perf_client application works with any type of model by sending random data for all input tensors and by reading and ignoring all output tensors. If you don’t have a model repository see Example Model Repository for instructions on how to create one.
Follow the instructions in Running The Inference Server to launch the inference server using the model repository.
The perf_client application has two major modes. In the first mode you specify how many concurrent outstanding inference requests you want and perf_client finds a stable latency and inferences/second for that level of concurrency. Use the -t flag to control concurrency and -v to see verbose output. The following example uses four outstanding inference requests to the inference server:
$ perf_client -m resnet50_netdef -p3000 -t4 -v
*** Measurement Settings ***
Batch size: 1
Measurement window: 3000 msec
Request concurrency: 4
Pass [1] throughput: 207 infer/sec. Avg latency: 19268 usec (std 910 usec)
Pass [2] throughput: 206 infer/sec. Avg latency: 19362 usec (std 941 usec)
Pass [3] throughput: 208 infer/sec. Avg latency: 19252 usec (std 841 usec)
Client:
Request count: 624
Throughput: 208 infer/sec
p50 latency: 19985 usec
p90 latency: 22524 usec
p95 latency: 23401 usec
p99 latency: 24866 usec
Avg latency: 19252 usec (standard deviation 841 usec)
Avg HTTP time: 19224 usec (send 714 usec + response wait 18486 usec + receive 24 usec)
Server:
Request count: 749
Avg request latency: 17886 usec (overhead 55 usec + queue 26 usec + compute 17805 usec)
In the second mode perf_client will generate an inferences/second vs. latency curve by increasing request concurrency until a specific latency limit or concurrency limit is reached. This mode is enabled by using the -d option and -l option to specify the latency limit, and optionally the -c option to specify a maximum concurrency limit. By default the initial concurrency value is one, but the -t option can be used to select a different starting value. The following example measures latency and inferences/second starting with request concurrency one and increasing until request concurrency equals three or average request latency exceeds 50 milliseconds:
$ perf_client -m resnet50_netdef -p3000 -d -l50 -c 3
*** Measurement Settings ***
Batch size: 1
Measurement window: 3000 msec
Latency limit: 50 msec
Concurrency limit: 3 concurrent requests
Request concurrency: 1
Client:
Request count: 327
Throughput: 109 infer/sec
Avg latency: 9191 usec (standard deviation 822 usec)
Avg HTTP time: 9188 usec (send/recv 1007 usec + response wait 8181 usec)
Server:
Request count: 391
Avg request latency: 7661 usec (overhead 90 usec + queue 68 usec + compute 7503 usec)
Request concurrency: 2
Client:
Request count: 521
Throughput: 173 infer/sec
Avg latency: 11523 usec (standard deviation 616 usec)
Avg HTTP time: 11448 usec (send/recv 711 usec + response wait 10737 usec)
Server:
Request count: 629
Avg request latency: 10018 usec (overhead 70 usec + queue 41 usec + compute 9907 usec)
Request concurrency: 3
Client:
Request count: 580
Throughput: 193 infer/sec
Avg latency: 15518 usec (standard deviation 635 usec)
Avg HTTP time: 15487 usec (send/recv 779 usec + response wait 14708 usec)
Server:
Request count: 697
Avg request latency: 14083 usec (overhead 59 usec + queue 30 usec + compute 13994 usec)
Inferences/Second vs. Client Average Batch Latency
Concurrency: 1, 109 infer/sec, latency 9191 usec
Concurrency: 2, 173 infer/sec, latency 11523 usec
Concurrency: 3, 193 infer/sec, latency 15518 usec
Use the -f option to generate a file containing CSV output of the results:
$ perf_client -m resnet50_netdef -p3000 -d -l50 -c 3 -f perf.csv
You can then import the CSV file into a spreadsheet to help visualize the latency vs inferences/second tradeoff as well as see some components of the latency. Follow these steps:
Open this spreadsheet
Make a copy from the File menu “Make a copy…”
Open the copy
Select the A1 cell on the “Raw Data” tab
From the File menu select “Import…”
Select “Upload” and upload the file
Select “Replace data at selected cell” and then select the “Import data” button