This is the Windows app named TensorRT Backend For ONNX whose latest release can be downloaded as ONNX-TensorRT8.6EARelease.zip. It can be run online in the free hosting provider OnWorks for workstations.
Download and run online this app named TensorRT Backend For ONNX with OnWorks for free.
Follow these instructions in order to run this app:
- 1. Downloaded this application in your PC.
- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 3. Upload this application in such filemanager.
- 4. Start any OS OnWorks online emulator from this website, but better Windows online emulator.
- 5. From the OnWorks Windows OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.
- 6. Download the application and install it.
- 7. Download Wine from your Linux distributions software repositories. Once installed, you can then double-click the app to run them with Wine. You can also try PlayOnLinux, a fancy interface over Wine that will help you install popular Windows programs and games.
Wine is a way to run Windows software on Linux, but with no Windows required. Wine is an open-source Windows compatibility layer that can run Windows programs directly on any Linux desktop. Essentially, Wine is trying to re-implement enough of Windows from scratch so that it can run all those Windows applications without actually needing Windows.
SCREENSHOTS:
TensorRT Backend For ONNX
DESCRIPTION:
Parses ONNX models for execution with TensorRT. Development on the main branch is for the latest version of TensorRT 8.4.1.5 with full dimensions and dynamic shape support. For previous versions of TensorRT, refer to their respective branches. Building INetwork objects in full dimensions mode with dynamic shape support requires calling the C++ and Python API. Current supported ONNX operators are found in the operator support matrix. For building within docker, we recommend using and setting up the docker containers as instructed in the main (TensorRT repository). Note that this project has a dependency on CUDA. By default the build will look in /usr/local/cuda for the CUDA toolkit installation. If your CUDA path is different, overwrite the default path. ONNX models can be converted to serialized TensorRT engines using the onnx2trt executable.
Features
- ONNX models can be converted to human-readable text
- ONNX models can be converted to serialized TensorRT engines
- ONNX models can be optimized by ONNX's optimization libraries
- Python Modules
- TensorRT 8.4.1.5 supports ONNX release 1.8.0
- The TensorRT backend for ONNX can be used in Python
Programming Language
C++
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/tensorrt-backend-onnx.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.