Tweak compile instructions

Compiling.md

@@ -15,7 +15,7 @@ As also mentioned in the instructions below but repeated here for visibility, if
       * Some version of g++ that supports at least C++14.
       * If using the OpenCL backend, a modern GPU that supports OpenCL 1.2 or greater, or else something like [this](https://software.intel.com/en-us/opencl-sdk) for CPU. But if using CPU, Eigen should be better.
       * If using the CUDA backend, CUDA 10.2 with CUDNN 7.6.5, or CUDA 11.1 with CUDNN 8.0.4 (https://developer.nvidia.com/cuda-toolkit) (https://developer.nvidia.com/cudnn) and a GPU capable of supporting them. I'm unsure how version compatibility works with CUDA, there's a good chance that later versions than these work just as well, but they have not been tested.
-      * If using the TensorRT backend, in addition to the dependencies for the CUDA backend, you also need TensorRT (https://developer.nvidia.com/tensorrt) on a version compatible with your CUDA and CUDNN versions.
+      * If using the TensorRT backend, in addition to the dependencies for the CUDA backend, you also need TensorRT (https://developer.nvidia.com/tensorrt) on a version compatible with your CUDA and CUDNN versions and at least version 8.2.
       * If using the Eigen backend, Eigen3. With Debian packages, (i.e. apt or apt-get), this should be `libeigen3-dev`.
       * zlib, libzip. With Debian packages (i.e. apt or apt-get), these should be `zlib1g-dev`, `libzip-dev`.
       * If you want to do self-play training and research, probably Google perftools `libgoogle-perftools-dev` for TCMalloc or some other better malloc implementation. For unknown reasons, the allocation pattern in self-play with large numbers of threads and parallel games causes a lot of memory fragmentation under glibc malloc that will eventually run your machine out of memory, but better mallocs handle it fine.