Table Of Contents
- TensorRT Command-Line Wrapper: trtexec
- License
- Changelog
- Known issues
Included in the samples directory is a command line wrapper tool, called trtexec. trtexec is a tool to quickly utilize TensorRT without having to develop your own application. The trtexec tool has two main purposes:
- It’s useful for benchmarking networks on random or user-provided input data.
- It’s useful for generating serialized engines from models.
Benchmarking network - If you have a model saved as an ONNX file, you can use the trtexec tool to test the performance of running inference on your network using TensorRT. The trtexec tool has many options for specifying inputs and outputs, iterations for performance timing, precision allowed, and other options.
Serialized engine generation - If you generate a saved serialized engine file, you can pull it into another application that runs inference. For example, you can use the TensorRT Laboratory to run the engine with multiple execution contexts from multiple threads in a fully pipelined asynchronous way to test parallel inference performance. Also, in INT8 mode, random weights are used, meaning trtexec does not provide calibration capability.
Using custom input data - By default trtexec will run inference with randomly generated inputs. To provide custom inputs for an inference run, trtexec expects a binary file containing the data for each input tensor. It is recommended that this binary file be generated through numpy. For example, to create custom data of all ones to an ONNX model with one input named data with shape (1,3,244,244) and type FLOAT:
import numpy as np
data = np.ones((1,3,244,244), dtype=np.float32)
data.tofile("data.bin")
This binary file can be be loaded by trtexec during inference by using the --loadInputs flag:
./trtexec --onnx=model.onnx --loadInputs="data":data.bin
trtexec can be used to build engines, using different TensorRT features (see command line arguments), and run inference. trtexec also measures and reports execution time and can be used to understand performance and possibly locate bottlenecks.
Compile the sample by following build instructions in TensorRT README.
trtexec can build engines from models in ONNX format.
You can profile a custom layer using the IPluginRegistry for the plugins and trtexec. You’ll need to first register the plugin with IPluginRegistry.
If you are using TensorRT shipped plugins, you should load the libnvinfer_plugin.so file, as these plugins are pre-registered.
If you have your own plugin, then it has to be registered explicitly. The following macro can be used to register the plugin creator YourPluginCreator with the IPluginRegistry.
REGISTER_TENSORRT_PLUGIN(YourPluginCreator);
To run the MNIST network on NVIDIA DLA (Deep Learning Accelerator) using trtexec in FP16 mode, issue:
./trtexec --onnx=data/mnist/mnist.onnx --useDLACore=1 --fp16 --allowGPUFallback
To run the MNIST network on DLA using trtexec in INT8 mode, issue:
./trtexec --onnx=data/mnist/mnist.onnx --useDLACore=1 --int8 --allowGPUFallback
To run the MNIST network on DLA using trtexec, issue:
./trtexec --onnx=data/mnist/mnist.onnx --useDLACore=0 --fp16 --allowGPUFallback
For more information about DLA, see Working With DLA.
To run an ONNX model in full-dimensions mode with static input shapes:
./trtexec --onnx=model.onnx
The following examples assumes an ONNX model with one dynamic input with name input and dimensions [-1, 3, 244, 244]
To run an ONNX model in full-dimensions mode with an given input shape:
./trtexec --onnx=model.onnx --shapes=input:32x3x244x244
To benchmark your ONNX model with a range of possible input shapes:
./trtexec --onnx=model.onnx --minShapes=input:1x3x244x244 --optShapes=input:16x3x244x244 --maxShapes=input:32x3x244x244 --shapes=input:5x3x244x244
When running, trtexec prints the measured performance, but can also export the measurement trace to a json file:
./trtexec --onnx=data/mnist/mnist.onnx --exportTimes=trace.json
Once the trace is stored in a file, it can be printed using the tracer.py utility. This tool prints timestamps and duration of input, compute, and output, in different forms:
./tracer.py trace.json
Similarly, profiles can also be printed and stored in a json file. The utility profiler.py can be used to read and print the profile from a json file.
Tuning throughput may require running multiple concurrent streams of execution. This is the case for example when the latency achieved is well within the desired threshold, and we can increase the throughput, even at the expense of some latency. For example, saving engines with different precisions and assume that both execute within 2ms, the latency threshold:
trtexec --onnx=resnet50.onnx --saveEngine=g1.trt --int8 --skipInference
trtexec --onnx=resnet50.onnx --saveEngine=g2.trt --best --skipInference
Now, the saved engines can be tried to find the combination precision/streams below 2 ms that maximizes the throughput:
trtexec --loadEngine=g1.trt --streams=2
trtexec --loadEngine=g1.trt --streams=3
trtexec --loadEngine=g1.trt --streams=4
trtexec --loadEngine=g2.trt --streams=2
This flag will create a network with the NetworkDefinitionCreationFlag::kSTRONGLY_TYPED flag where tensor data types are inferred from network input types
and operator type specification. Use of specific builder precision flags such as --int8 or --best with this option is not allowed.
./trtexec --onnx=model.onnx --stronglyTyped
To see the full list of available options and their descriptions, issue the ./trtexec --help command.
Note: Specifying the --safe parameter turns the safety mode switch ON. By default, the --safe parameter is not specified; the safety mode switch is OFF. The layers and parameters that are contained within the --safe subset are restricted if the switch is set to ON. The switch is used for prototyping the safety restricted flows until the TensorRT safety runtime is made available. This parameter is required when loading or saving safe engines with the standard TensorRT package. For more information, see the Working With Automotive Safety section in the TensorRT Developer Guide.
The following resources provide more details about trtexec:
Documentation
For terms and conditions for use, reproduction, and distribution, see the TensorRT Software License Agreement documentation.
April 2019
This is the first release of this README.md file.
There are no known issues in this sample.