TensorRT | NVIDIA® TensorRT™, an SDK for high-performance deep learning inference, includes a deep learning inf | Machine Learning library

by NVIDIA C++ Version: v8.6.1 License: Apache-2.0

X-Ray Key Features Code Snippets(3)Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | TensorRT Summary

TensorRT is a C++ library typically used in Artificial Intelligence, Machine Learning, Pytorch applications. TensorRT has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

This repository contains the Open Source Software (OSS) components of NVIDIA TensorRT. Included are the sources for TensorRT plugins and parsers (Caffe and ONNX), as well as sample applications demonstrating usage and capabilities of the TensorRT platform. These open source software components are a subset of the TensorRT General Availability (GA) release with some extensions and bug-fixes.

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Security

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Support

TensorRT has a medium active ecosystem.

It has 7338 star(s) with 1777 fork(s). There are 133 watchers for this library.

It had no major release in the last 12 months.

There are 218 open issues and 2476 have been closed. On average issues are closed in 82 days. There are 22 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of TensorRT is v8.6.1

Quality

TensorRT has 0 bugs and 0 code smells.

Security

TensorRT has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

TensorRT code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

TensorRT is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

TensorRT releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

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TensorRT Key Features

No Key Features are available at this moment for TensorRT.

TensorRT Examples and Code Snippets

Convert TensorFlow to TensorRT graph .

python

Lines of Code : 37

License : Non-SPDX (Apache License 2.0)

Copy

def convert_with_tensorrt(args):
  """Function triggered by 'convert tensorrt' command.

  Args:
    args: A namespace parsed from command line.
  """
  # Import here instead of at top, because this will crash if TensorRT is
  # not installed
  from

Find the TensorRT configuration file .

python

Lines of Code : 26

License : Non-SPDX (Apache License 2.0)

Copy

def _find_tensorrt_config(base_paths, required_version):

  def get_header_version(path):
    version = (
        _get_header_version(path, name)
        for name in ("NV_TENSORRT_MAJOR", "NV_TENSORRT_MINOR",
                     "NV_TENSORRT_PATCH")

Set the TensorRT version .

python

Lines of Code : 15

License : Non-SPDX (Apache License 2.0)

Copy

def set_tf_tensorrt_version(environ_cp):
  """Set TF_TENSORRT_VERSION."""
  if not (is_linux() or is_windows()):
    raise ValueError('Currently TensorRT is only supported on Linux platform.')

  if not int(environ_cp.get('TF_NEED_TENSORRT', False)):

Community Discussions

Trending Discussions on TensorRT

Cannot create the calibration cache for the QAT model in tensorRT

Quantized model gives negative accuracy after conversion from pytorch to ONNX

‘numeric_limits’ is not a member of ‘std’

Install tensorrt with custom plugins

nsys profile multiple processes

Improving code performance by loading binary data instead of text and converting

Training with GPU an object detection model on colab with Turicreate

CLion IDE does not resolve header files when use remote host

function IBuilder::buildEngineWithConfig() returns null

How to include a OneHot in an ONNX coming from PyTorch

QUESTION

Cannot create the calibration cache for the QAT model in tensorRT

Asked 2022-Mar-14 at 21:20

I've trained a quantized model (with help of quantized-aware-training method in pytorch). I want to create the calibration cache to do inference in INT8 mode by TensorRT. When create calib cache, I get the following warning and the cache is not created:

...

ANSWER

Answered 2022-Mar-14 at 21:20

If the ONNX model has Q/DQ nodes in it, you may not need calibration cache because quantization parameters such as scale and zero point are included in the Q/DQ nodes. You can run the Q/DQ ONNX model directly in TensorRT execution provider in OnnxRuntime (>= v1.9.0).

Source https://stackoverflow.com/questions/71368760

QUESTION

Quantized model gives negative accuracy after conversion from pytorch to ONNX

Asked 2022-Mar-06 at 07:24

I'm trying to train a quantize model in pytorch and convert it to ONNX. I employ the quantized-aware-training technique with help of pytorch_quantization package. I used the below code to convert my model to ONNX:

...

ANSWER

Answered 2022-Mar-06 at 07:24

After some tries, I found that there is a version conflict. I changed the versions accordingly:

Source https://stackoverflow.com/questions/71362729

QUESTION

‘numeric_limits’ is not a member of ‘std’

Asked 2022-Feb-28 at 15:12

I am trying to compile an application from source, FlyWithLua, which includes the sol2 library.

I am following the instructions but when I run cmake --build ./build I get the following error:

...

ANSWER

Answered 2022-Feb-28 at 15:12

Source https://stackoverflow.com/questions/71296302

QUESTION

Install tensorrt with custom plugins

Asked 2022-Jan-18 at 13:25

I'm able to install the desired version of TensorRT from official nvidia guide (https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html#maclearn-net-repo-install)

...

ANSWER

Answered 2022-Jan-18 at 13:25

It's quite easy to "install" custom plugin if you registered it. So the steps are the following:

Install tensorRT

Source https://stackoverflow.com/questions/70533826

QUESTION

nsys profile multiple processes

Asked 2021-Oct-16 at 17:44

I'd like to experiment with MPS on Nvidia GPUs, therefore I'd like to be able to profile two process running in parallel. With the, now deprecated, nvprof, there used to be an option "--profile-all-processes". Is there a equivalent for nsys ?

I tried generating multiple report with MPS OFF and them importing them on the same timeline with this code (from this question) :

...

ANSWER

Answered 2021-Oct-15 at 13:30

I guess your question is why do the kernels from separate processes appear to overlap, even though MPS is off.

The reason for this is due to the low level GPU task/context scheduler (behavior).

It used to be that the scheduler would run one kernel/process/context/task to completion, then schedule another kernel from some waiting process/context/task. In this scenario, the profiler would depict the kernel execution without overlap.

More recently (let's say sometime after 2015 when your reference presentation was created), the GPU scheduler switched to time-slicing on various newer GPUs and newer CUDA versions. This means that at a high level, the tasks appear to be running "concurrently" from the profiler perspective, even though MPS is off. Kernel A from process 1 is not necessarily allowed to run to completion, before the context scheduler halts that kernel in its tracks, does a context-switch, and allows kernel B from process 2 to begin executing for a time-slice.

A side effect of this for an ordinary kernel is that due to time-slicing, those kernels which seem to be running concurrently will usually take longer to run. For your time-delay kernel(s), the time-delay mechanism is "fooled" by the time slicing (effectively the SM clock continues to increment) so they don't appear to take any longer runtime even though time-sliced sharing is going on.

This answer (i.e. the one you already linked) has a similar description.

Source https://stackoverflow.com/questions/69581288

QUESTION

Improving code performance by loading binary data instead of text and converting

Asked 2021-Sep-20 at 00:06

Hi I am working with existing C++ code, I normally use VB.NET and much of what I am seeing is confusing and contradictory to me.

The existing code loads neural network weights from a file that is encoded as follows:

...

ANSWER

Answered 2021-Sep-20 at 00:06

Here's an example program that will convert the text format shown into a binary format and back again. I took the data from the question and converted to binary and back successfully. My feeling is it's better to cook the data with a separate program before consuming it with the actual application so the app reading code is single purpose.

There's also an example of how to read the binary file into the Weights class at the end. I don't use TensorRT so I copied the two classes used from the documentation so the example compiles. Make sure you don't add those to your actual code.

If you have any questions let me know. Hope this helps and makes loading faster.

Source https://stackoverflow.com/questions/69240957

QUESTION

Training with GPU an object detection model on colab with Turicreate

Asked 2021-Sep-02 at 08:55

It's days that I'm trying to train an object detection model on Google Colab using GPU with TuriCreate.

According to the TuriCreate's repository, to use gpu during training you must follow these instructions:

https://github.com/apple/turicreate/blob/main/LinuxGPU.md

However, everytime I start the training, the shell produces this output before starting the training:

...

ANSWER

Answered 2021-Sep-02 at 08:55

I managed to solve this: the problem is due to the version of tensorflow pre-installed on the colab machine.

Source https://stackoverflow.com/questions/68957000

QUESTION

CLion IDE does not resolve header files when use remote host

Asked 2021-Jun-02 at 09:10

I use CLion IDE for a small TensorRT project. The project and related libraries (Cuda, TensorRT) are both located on a ssh server. One version of the project is cloned from the server and run locally. I managed to sync project between the server and local and build the project successfully (using command line cmake and make). One problem is CLion can not resolve header files (that are located remotely, for example NvInfer.h in TensorRT libraries), therefore code auto completion also does not work. I have tried flowing workarounds:

Include path to the header files to CMakeLists.txt by using include_directories()
Tool -> Resync with remote hosts.
Create toolchain and map remote host like in CLion official guide.
I also referred to this question and other similar questions but it still does not work.

If you have successfully setup CLion for remote development, please help me. Thank you for reading.

More information:

A few days ago. I found that the header files are silently installed in .cache/JetBrains/CLion2020.3/.remote/MyHostName_PortNumber/usr/include/x86_64-linux-gnu/the_header_files.h. But now their aren't. How can i make CLion to install them again.

...

ANSWER

Answered 2021-Mar-24 at 05:07

I have just found the answer. The reason is CLion does not install header files to local because I am using a cmake version that is not supported by CLion. I uninstall cmake on the ssh server and reinstall it with CLion-supported version (3.17.1). Thank you!

Source https://stackoverflow.com/questions/66769074

QUESTION

function IBuilder::buildEngineWithConfig() returns null

Asked 2021-Mar-26 at 03:33

I am using tensorRT to build a small model as below:

...

ANSWER

Answered 2021-Mar-26 at 03:33

After a few days of rolling over this problem. I have found that if layers in the model does not match the weight passed in, there is no error will appear but you can not create an TensorRT engine to do later tasks. Therefore, the best way to do in this situation is carefully checking layer by layer and the .wts file.

Source https://stackoverflow.com/questions/66741628

QUESTION

How to include a OneHot in an ONNX coming from PyTorch

Asked 2021-Mar-22 at 13:05

I'm using PyTorch to train neural-net and output them into ONNX. I use these models in a Vespa index, which loads ONNXs through TensorRT. I need one-hot-encoding for some features but this is really hard to achieve within the Vespa framework.

Is it possible to embed a one-hot-encoding for some given features inside my ONNX net (e.g. before the network's representation) ? If so, how should I achieve this based on a PyTorch model ?

I already noticed two things:

ONNX format includes the OneHot operator : see ONNX doc
PyTorch built-in ONNX exporting system not not support OneHot operator : see torch.onnx doc

EDIT 2021/03/11: Here is my workflow:

training learning-to-rank models via PyTorch
exporting them as ONNX
importing these ONNX into my Vespa index in order to rank any query's results thanks to the ONNX model. Under the hood, Vespa uses TensorRT for inference (so I use Vespa's ONNX model evaluation)

...

ANSWER

Answered 2021-Mar-10 at 08:27

If PyTorch can't export the OneHot operator to ONNX I think your best option is to ask them to fix that?

Or, if you can extract the conversion from your model, such that the one-hot-encoded tensor is an input to your network, you can do that conversion on the Vespa side by writing a function supplying the one-hot tensor by converting the source data to it, e.g

Source https://stackoverflow.com/questions/66544994

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install TensorRT

If using the TensorRT OSS build container, TensorRT libraries are preinstalled under /usr/lib/x86_64-linux-gnu and you may skip this step. Else download and extract the TensorRT GA build from NVIDIA Developer Zone.
Download TensorRT OSS git clone -b master https://github.com/nvidia/TensorRT TensorRT cd TensorRT git submodule update --init --recursive
(Optional - if not using TensorRT container) Specify the TensorRT GA release build If using the TensorRT OSS build container, TensorRT libraries are preinstalled under /usr/lib/x86_64-linux-gnu and you may skip this step. Else download and extract the TensorRT GA build from NVIDIA Developer Zone. Example: Ubuntu 18.04 on x86-64 with cuda-11.4 cd ~/Downloads tar -xvzf TensorRT-8.2.3.0.Linux.x86_64-gnu.cuda-11.4.cudnn8.2.tar.gz export TRT_LIBPATH=`pwd`/TensorRT-8.2.3.0 Example: Windows on x86-64 with cuda-11.4 cd ~\Downloads Expand-Archive .\TensorRT-8.2.3.0.Windows10.x86_64.cuda-11.4.cudnn8.2.zip $Env:TRT_LIBPATH = '$(Get-Location)\TensorRT-8.2.3.0' $Env:PATH += 'C:\Program Files (x86)\Microsoft Visual Studio\2017\Professional\MSBuild\15.0\Bin\'
(Optional - for Jetson builds only) Download the JetPack SDK Download and launch the JetPack SDK manager. Login with your NVIDIA developer account. Select the platform and target OS (example: Jetson AGX Xavier, Linux Jetpack 4.6), and click Continue. Under Download & Install Options change the download folder and select Download now, Install later. Agree to the license terms and click Continue. Move the extracted files into the <TensorRT-OSS>/docker/jetpack_files folder.
For Linux platforms, we recommend that you generate a docker container for building TensorRT OSS as described below. For native builds, on Windows for example, please install the prerequisite System Packages.
Generate the TensorRT-OSS build container. The TensorRT-OSS build container can be generated using the supplied Dockerfiles and build script. The build container is configured for building TensorRT OSS out-of-the-box. Example: Ubuntu 18.04 on x86-64 with cuda-11.4.2 (default) ./docker/build.sh --file docker/ubuntu-18.04.Dockerfile --tag tensorrt-ubuntu18.04-cuda11.4 Example: CentOS/RedHat 7 on x86-64 with cuda-10.2 ./docker/build.sh --file docker/centos-7.Dockerfile --tag tensorrt-centos7-cuda10.2 --cuda 10.2 Example: Ubuntu 18.04 cross-compile for Jetson (aarch64) with cuda-10.2 (JetPack SDK) ./docker/build.sh --file docker/ubuntu-cross-aarch64.Dockerfile --tag tensorrt-jetpack-cuda10.2 --cuda 10.2 Example: Ubuntu 20.04 on aarch64 with cuda-11.4.2 ./docker/build.sh --file docker/ubuntu-20.04-aarch64.Dockerfile --tag tensorrt-aarch64-ubuntu20.04-cuda11.4
Launch the TensorRT-OSS build container. Example: Ubuntu 18.04 build container ./docker/launch.sh --tag tensorrt-ubuntu18.04-cuda11.4 --gpus all NOTE: Use the --tag corresponding to build container generated in Step 1. NVIDIA Container Toolkit is required for GPU access (running TensorRT applications) inside the build container. sudo password for Ubuntu build containers is 'nvidia'. Specify port number using --jupyter <port> for launching Jupyter notebooks.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

Find more information at: