Abstract
NVIDIA TensorRT is a C++ library that facilitates high performance inference on NVIDIA GPUs. It is designed to work in connection with deep learning frameworks that are commonly used for training. TensorRT focuses specifically on running an already trained network quickly and efficiently on a GPU for the purpose of generating a result; also known as inferencing. These release notes describe the key features, software enhancements and improvements, and known issues for the TensorRT 8.6.13 product package.
1. TensorRT for DRIVE OS
1.1. DRIVE OS QNX "Standard"
1.3. DRIVE OS for Safety Proxy
- The TensorRT proxy runtime is a version of the safety runtime for platforms that are not safety certified. This includes NVIDIA DRIVE OS x86 SDK, NVIDIA DRIVE OS Linux SDK, NVIDIA DRIVE OS Linux PDK, NVIDIA DRIVE OS QNX SDK and NVIDIA DRIVE OS QNX PDK. The proxy runtime is part of the development flow for safety but it is not certified itself. The proxy runtime only supports engines with engine capability kSAFETY (safe engines).
- Headers allow applications to compile against the proxy runtime and the safety runtime.
- The safety runtime is also a library that allows applications to load serialized engine plans and perform inference. It is only available for QNX safety. The safety runtime only supports engines with engine capability kSAFETY (safe engines).
2. New Features and Enhancements
TensorRT Standard Build
The TensorRT 8.6 release includes changes to the TensorRT 8.6.1 standard builder and runtime that appear in TensorRT for DRIVE OS 6.0. For more information, refer to the NVIDIA TensorRT 8.6.1 Release Notes.
Documentation Changes
- The NVIDIA TensorRT 8.6.13 Developer Guide for DRIVE OS is based on the enterprise TensorRT 8.6.1 release. We have modified the TensorRT 8.6.1 Developer Guide documentation for DRIVE OS 6.0.9.2 accuracy. The TensorRT safety content has been removed.
- The TensorRT safety content is in the NVIDIA TensorRT 8.6.13 Safety Developer Guide Supplement for DRIVE OS. Refer to this PDF for all TensorRT safety specific documentation.
3. Fixed Issues
| Reference ID | Module | Description |
|---|---|---|
| 4323665 | TensorRT runtime | The TensorRT safety runtime does not set the CUDA API mode by invoking the API cudaSafeEXSelectAPIMode() at the initialization state. This bug has been fixed in this release. |
| 4350817 | TensorRT runtime | Some networks containing the softmax layer may fail with SafeCaskError when switching to the Operational State using NvDVMS (DRIVE OS VM State Management), which helps to verify DOS_RES_107. This bug has been fixed in this release. |
| 4369190 | TensorRT safety samples | The sample sampleSafeINT8 cannot run on the safety runtime due to a bug in the inference phase. This bug has been fixed in this release. |
4. Known Limitations
5. Known Issues
| Reference ID | Module | Description |
|---|---|---|
| 3656116 | TensorRT runtime |
What is the issue? There is an up to 7% performance regression for the 3D-UNet networks compared to TensorRT 8.4 EA when running in INT8 precision on NVIDIA Orin due to a functionality fix. How does it impact the customer? When running 3D-UNet networks in INT8 precision, the latency will be up to 7% longer than in TensorRT 8.4 EA. If there is a workaround, what is it? To work around this issue, set the input type and format to kINT8 and kCHW32, respectively. When can we expect the fix? We do not plan to fix this performance regression since it was caused by a necessary fix for an accuracy issue. Is it for Standard/Safety, SDK/PDK? Standard, SDK |
| 3263411 | TensorRT builder |
What is the issue? For some networks, building and running an engine in the standard runtime will have better performance than the safety runtime. This can be due to various limitations in scope of the safety runtime including more limited tactics, tensor size limits, and operations supported in the safety scope. How does it impact the customer? Inference in the safety runtime may be significantly slower than in the standard runtime. If there is a workaround, what is it? Depending on the network, it may or may not be possible to reorganize operations into a more efficient form matching the safety runtime scope. What is the recommendation? It is recommended to work with NVIDIA and provide proxy networks as early as possible that demonstrate key performance metrics close to actual production networks. Is it for Standard/Safety, SDK/PDK? Safety, SDK |
| 3793130 | TensorRT runtime |
What is the issue? Enabling the CUDA-graph option may cause the safety runtime to perform less efficiently compared to the proxy runtime for some networks. This discrepancy is due to the different objectives of the safety and proxy runtime. The safety runtime has more restrictive constraints to fulfill safety goals, resulting in different implementations between safety and proxy runtime. How does it impact the customer? Using the CUDA-graph for inference in the safety runtime may result in slower performance compared to the proxy runtime. However, this can vary depending on the inference network. If there is a workaround, what is it? It is recommended to check whether enabling CUDA-graph improves performance on the networks in production. Since the safety implementation with CUDA-graph comes with additional error checking and more deterministic execution, it is recommended to conduct cost-benefit analysis to decide if using CUDA-graph is beneficial to the use case. It is also recommended to work with NVIDIA and provide proxy networks as early as possible that demonstrate key performance metrics close to actual production networks. When can we expect the fix? In order to achieve safety, the implementation might require further support on error-checking and robustness measures. This could demand extra CPU/GPU cycles. However, in certain scenarios, the safety implementation might be faster since it does not support some features in proxy runtime. We do not intend to address this issue within the DRIVE OS 6.0 release timeframe. Is it for Standard/Safety, SDK/PDK? Safety, SDK |
| 4489498 | TensorRT safety samples |
What is the issue? The trtexec_safe built with make TRT_STATIC=1 may report error when loading the dynamically built plugin .so file. How does it impact the customer? The error may occur when customers use the manually built trtexec_safe with option make TRT_STATIC=1 to load the dynamically built plugin file. If there is a workaround, what is it? If the customer uses the trtexec_safe in the package, or uses the make without TRT_STATIC=1 option, the error will not occur. When can we expect the fix? The trtexec_safe will not be fixed for the future release. The safety team will provide the workflow for the plugin and the use case in the bug will not be triggered. Is it for Standard/Safety, SDK/PDK? Safety, SDK |
6. TensorRT Release Properties
| QNX AArch64 | ||
|---|---|---|
| QNX Safety | QNX Standard | |
| Supported NVIDIA CUDA? versions | 11.4.28 | 11.4.28 |
| Supported NVIDIA cuDNN versions | No | 8.9.2.19 |
| TensorRT Python API | No | No |
| NvOnnxParser | No | Yes |
6.1. Hardware Precision
| CUDA Compute Capability | Example Device | TF32 | FP32 | FP16 | INT8 | FP16 Tensor Cores | INT8 Tensor Cores | DLA |
|---|---|---|---|---|---|---|---|---|
| 8.7 | NVIDIA Orin |
No (TensorRT safe) Yes (TensorRT standard) |
Yes | Yes | Yes | Yes | Yes | Yes |
| 8.6 | NVIDIA A10 | Yes | Yes | Yes | Yes | Yes | Yes | No |
| 8.0 | NVIDIA PG199 | Yes | Yes | Yes | Yes | Yes | Yes | No |
6.2. Software Versions Per Platform
| Platform | Compiler Version | Python Version |
|---|---|---|
| QNX AArch64 | QNX 7.1.0 Q++ 8.3.0 | N/A |
6.3. Compatibility
- CUDA 11.4.28
- cuDNN 8.9.2.19
- TensorFlow 1.15.5
- PyTorch 1.13.1
- ONNX 1.12.0 and opset 17
- DLA 3.14.3
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