Description 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 …

Hi , We recommend you to check the supported features from the below link. Support Matrix :: NVIDIA Deep Learning TensorRT Documentation These support matrices provide a look into the supported platforms, features, and hardware capabilities of the NVIDIA TensorRT 8.6.1 APIs, parsers, a…

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. [image] 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, an…

nVidia release versions compatibility

AI & Data Science Deep Learning (Training & Inference) TensorRT

alexm5m91 September 7, 2023, 6:42am 6

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?

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nVidia release versions compatibility

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