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NVIDIA TensorRT™ is a platform for high-performance deep learning inference. It includes a deep learning inference optimizer and runtime that delivers low latency and high-throughput for deep learning inference applications. TensorRT-based applications perform up to 40x faster than CPU-only platforms during inference. With TensorRT, you can optimize neural network models trained in all major frameworks, calibrate for lower precision with high accuracy, and finally deploy to hyperscale data centers, embedded, or automotive product platforms.


TensorRT Developer Guide


1. How to check TensorRT version?

There are two methods to check TensorRT version,

  • Symbols from library
$ nm -D /usr/lib//aarch64-linux-gnu/libnvinfer.so | grep "tensorrt"
0000000007849eb0 B tensorrt_build_svc_tensorrt_20181028_25152976
0000000007849eb4 B tensorrt_version_5_0_3_2

NOTE: 20181028 is the build date and 25152976 is the top changelist and 5_0_3_2 is the version information.

  • Macros from header file
$ cat /usr/include/aarch64-linux-gnu/NvInfer.h | grep "define NV_TENSORRT"
#define NV_TENSORRT_MAJOR 5 //!< TensorRT major version.
#define NV_TENSORRT_MINOR 0 //!< TensorRT minor version.
#define NV_TENSORRT_PATCH 3 //!< TensorRT patch version.
#define NV_TENSORRT_BUILD 2 //!< TensorRT build number.
#define NV_TENSORRT_SONAME_MAJOR 5 //!< Shared object library major version number.
#define NV_TENSORRT_SONAME_MINOR 0 //!< Shared object library minor version number.
#define NV_TENSORRT_SONAME_PATCH 3 //!< Shared object library patch version number.
2. Whether TRT support thread-safe?

TensorRT runtime is thread-safe in the sense that parallel threads using different TRT Execution Contexts can execute in parallel without interference.

3. Can INT8 calibration table be compatible among different TRT version or HW platform?

INT8 calibration table is absolutely NOT compatible between different TRT versions. This is because the optimized network graph is probably different among various TRT versions. If you enforce to use them, TRT may not find the corresponding scaling factor for given tensor.
As long as the installed TensorRT version is identical for different HW platforms, then the INT8 calibration table can be compatible. That means you can perform INT8 calibration on a faster computation platform, like V100 or P4 and then deploy the calibration table to Tegra for INT8 inferencing.

4. How to check GPU utilization?

On Tegra platform, we can use tegrastats to achieve that,

$ sudo /home/nvidia/tegrastats

On Desktop platform, like Tesla, we can use nvidia-smi to achieve that,

$ nvidia-smi --format=csv -lms 500 --query-gpu=index,timestamp,utilization.gpu,clocks.current.graphics,clocks.current.sm,clocks.current.video,clocks.current.memory,utilization.memory,memory.total,memory.free,memory.used,power.limit,power.draw,temperature.gpu,fan.speed,compute_mode,gpu_operation_mode.current,clocks_throttle_reasons.active,pstate,clocks_throttle_reasons.hw_slowdown,clocks_throttle_reasons.gpu_idle,clocks_throttle_reasons.applications_clocks_setting,clocks_throttle_reasons.sw_power_cap,clocks_throttle_reasons.sync_boost -i 0 | tee log.cs
5. What is kernel auto-tuning?

TensorRT contains various kernel implementations, including those existing in CUDNN and CUBLAS, to accommodate diverse neural network configurations (batch, input/output dims, filters, strides, pads, dilation rate and etc). During network building, TensorRT will profile all suitable kernels and find out the best one with the smallest latency, and then mark it as the final tactic to run the certain layer. We call this process as kernel auto-tuning.
Additionally, it’s not always true that INT8 kernel faster than FP16’s than FP32’s, so

  • if you run FP16 precision mode, it profiles all candidates in FP16 kernel pool and FP32 kernel pool.
  • if you run INT8 precision mode, it profiles all candidates in INT8 kernel pool and FP32 kernel pool.
  • if both FP16 and INT8 are enabled (we call it hybrid mode), it profiles all candidate in INT8 kernel pool, FP16 kernel pool and FP32 kernel pool.

If current layer chooses different mode as its bottom layer or top layer, TensorRT will insert a reformatting layer between them to do the tensor format conversion, and the time for this reformatting layer will be taken into account as the cost of current layer during auto-tuning.

6. How TensorRT behave when different batch size is being used?

For relatively deep network, if GPU is fully occupied, we couldn't obtain much performance gain from batching.
For relatively simper network, generally, GPU is not fully loaded, then we could obtain performance gain from batching.
In other words, inference time per frame can be improved with the bigger batch size only if GPU loading is not full.

7. What is maxWorkspaceSize?

maxWorkspaceSize indicates a threshold to filter the kernels/tactics of which desired workspace size is less than maxWorkspaceSize. In other words, if the workspace size a tactic requires(such as for convolution, we can use cudnnGetConvolutionForwardWorkspaceSize() to get the needed workspace size) is larger than what we specify to maxWorkspaceSize, then this tactic will be ignored during kernel auto-tuning.
NOTE: the final device memory TRT consumes has nothing to do with maxWorkspacesize.

8. What is the difference between enqueue() and execute()?
  • Enqueue will need user to create and synchronize cudaStream_t. When it's being invoked, it will return immediately. While with Execute, TensorRT will create/synchronize steam internally, so it will return until everything is completed.
  • Enqueue and Execute are the functions with respect to the whole network/cudaEngine, other than specific layer. There is no enqueue inference in layer's implementation, so Layer only has execute interface, including the IPlugin layer.
  • Both enqueue and execute support profiling now. The time consumption of each layer will be printed when the whole execution is done, other than real-time profiling.
  • setDebugSync is only supported by execute. If this flag is true, the builder will synchronize (cudaDeviceSynchronize) after timing each layer and report the layer name.
9. How to dump the output of certain layer?

TensorRT doesn’t store the intermediate result of your network, so you have to use the following API to mark the intended layer as output layer, and then interference again and save its result for further analysis,



  • You can set multiplier layers as the output at the same time, but setting the layer as output may break the network optimization and impact the inference performance, as TensorRT always runs output layer in FP32 mode, no matter which mode you have configured.
  • Don’t forget to adjust the dimension or output buffer size after you change the output layer.
10. How to analyze network performance?

First of all, we should be aware of the profiling command tool that TensorRT provides - trtexec.
If all your network layer has been supported by TensorRT through either native way or plugin way, you can always utilize this tool to profile your network very quickly.
Second, you can add profiling metrics for your application manually from CPU side (link) or GPU side (link).

  • Time collection should only contain the network enqueue() or execute() and any context set-up or memory initialization or refill operation should be excluded.
  • Add more iterations for the time collection, in order to avagage the GPU warm-up effect.

Third, if you would like to scope the time consumption of each layer, you can implement IProfiler to achieve that, or utilize SimpleProfiler TensorRT already provides (refer to below patch for sampleSSD),

--- sampleSSD.cpp.orig	2019-05-27 12:39:14.193521455 +0800
+++ sampleSSD.cpp	2019-05-27 12:38:59.393358775 +0800
@@ -428,8 +428,11 @@
     float* detectionOut = new float[N * kKEEP_TOPK * 7];
     int* keepCount = new int[N];
+    SimpleProfiler profiler (" layer time");
+    context->setProfiler(&profiler);
     // Run inference
     doInference(*context, data, detectionOut, keepCount, N);
+    std::cout << profiler;
     bool pass = true;