I’m struggling with nVidia releases. Lets say, I want our product to use TensorRT 8.5.2, deploying in an official nVidia TensorRT container.
The available TensorRT downloads only support CUDA 11.8
The v23.01 of the container, the first version to support 8.5.2 (v22.12 is 8.5.1), ships with CUDA 12.0.1.
What is the expectation here? Mine are that either the development package is compatible with the Docker image, or vice versa. Too much?
TensorRT Version :GPU Type :Nvidia Driver Version :CUDA Version :CUDNN Version :Operating System + Version :Python Version (if applicable) :TensorFlow Version (if applicable) :PyTorch Version (if applicable) :Baremetal or Container (if container which image + tag) :
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Hi ,
These support matrices provide a look into the supported platforms, features, and hardware capabilities of the NVIDIA TensorRT 8.6.1 APIs, parsers, and layers.
You can refer below link for all the supported operators list.
<!--- SPDX-License-Identifier: Apache-2.0 -->
# Supported ONNX Operators
TensorRT 8.6 supports operators up to Opset 17. Latest information of ONNX operators can be found [here](https://github.com/onnx/onnx/blob/master/docs/Operators.md)
TensorRT supports the following ONNX data types: DOUBLE, FLOAT32, FLOAT16, INT8, and BOOL
> Note: There is limited support for INT32, INT64, and DOUBLE types. TensorRT will attempt to cast down INT64 to INT32 and DOUBLE down to FLOAT, clamping values to `+-INT_MAX` or `+-FLT_MAX` if necessary.
See below for the support matrix of ONNX operators in ONNX-TensorRT.
## Operator Support Matrix
| Operator | Supported | Supported Types | Restrictions |
|---------------------------|------------|-----------------|------------------------------------------------------------------------------------------------------------------------|
| Abs | Y | FP32, FP16, INT32 |
| Acos | Y | FP32, FP16 |
| Acosh | Y | FP32, FP16 |
| Add | Y | FP32, FP16, INT32 |
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Thanks!
Sorry, but I don’t believe this answers my question. My question was about 3-way release compatibility between TensorRT, CUDA and TensorRT Docker image, specifically when applied to v8.5.2 of TensorRT.
Your answer is about ONNX operations compatibility in TensorRT 8.6.
Did I miss some part of it which answers my question?
Phrased differently: if I use TensorRT 8.5.2 in development, which version of nvcr.io/nvidia/tensorrt should the resulting software be deployed on – considering v22.12 of it still uses TensorRT 8.5.1, and v23.01 of it already wants CUDA 12.0.
Hi,
We recommend that you please use the latest TensorRT version 8.6.1.
alexm5m91:
Phrased differently: if I use TensorRT 8.5.2 in development, which version of nvcr.io/nvidia/tensorrt should the resulting software be deployed on – considering v22.12 of it still uses TensorRT 8.5.1, and v23.01 of it already wants CUDA 12.0.
Please refer to the notes added in the support matrix document.
These support matrices provide a look into the supported platforms, features, and hardware capabilities of the NVIDIA TensorRT 8.6.1 APIs, parsers, and layers.
If we have supported driver version, we can use the CUDA versions accordingly.
Thankyou.