Description
With official ngc tensorrt docker, when use python interface, call tensorrt.Runtime(TRT_LOGGER) or trt.Builder(TRT_LOGGER) first time will cost almost 20 seconds.
And even with c++ interface, call nvinfer1::createInferBuilder function also cost a long time.
On the host machine, the same python function call just cost less than 2 second.
Environment
In Docker environment 20.09
TensorRT Version: 7.1.3
GPU Type: P100
Nvidia Driver Version: 465.24.02
CUDA Version: 11.3
CUDNN Version:
Operating System + Version: Ubuntu18.04 host
Python Version (if applicable): 3.6.9
TensorFlow Version (if applicable): None
PyTorch Version (if applicable): None
Baremetal or Container (if container which image + tag):
Relevant Files
Please attach or include links to any models, data, files, or scripts necessary to reproduce your issue. (Github repo, Google Drive, Dropbox, etc.)
Steps To Reproduce
python code
import tensorrt as trt
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
runtime = trt.Runtime(TRT_LOGGER)
#builder = trt.Builder(TRT_LOGGER) #or this call
c++ code, edit example add log info
bool SampleINT8::build(DataType dataType)
{
**sample::gLogInfo << "before builder initilze" << std::endl;**
auto builder = SampleUniquePtr<nvinfer1::IBuilder>(nvinfer1::createInferBuilder(sample::gLogger.getTRTLogger()));
**sample::gLogInfo << "after builder initilze" << std::endl;**
below is log, the cost time is 27 seconds
[06/11/2021-09:25:04] [I] before builder initilze
[06/11/2021-09:25:31] [I] after builder initilze
Please include:
- Exact steps/commands to build your repro
- Exact steps/commands to run your repro
- Full traceback of errors encountered