KellenSunderland commented on a change in pull request #14860: [Review, don't merge before 1.5] Update TRT tutorial with new APIs URL: https://github.com/apache/incubator-mxnet/pull/14860#discussion_r291717679
########## File path: docs/tutorials/tensorrt/inference_with_trt.md ########## @@ -17,29 +17,23 @@ # Optimizing Deep Learning Computation Graphs with TensorRT -NVIDIA's TensorRT is a deep learning library that has been shown to provide large speedups when used for network inference. MXNet 1.3.0 is shipping with experimental integrated support for TensorRT. This means MXNet users can noew make use of this acceleration library to efficiently run their networks. In this blog post we'll see how to install, enable and run TensorRT with MXNet. We'll also give some insight into what is happening behind the scenes in MXNet to enable TensorRT graph execution. +NVIDIA's TensorRT is a deep learning library that has been shown to provide large speedups when used for network inference. MXNet 1.5.0 and later versions ship with experimental integrated support for TensorRT. This means MXNet users can now make use of this acceleration library to efficiently run their networks. In this tutorial we'll see how to install, enable and run TensorRT with MXNet. We'll also give some insight into what is happening behind the scenes in MXNet to enable TensorRT graph execution. ## Installation and Prerequisites -Installing MXNet with TensorRT integration is an easy process. First ensure that you are running Ubuntu 16.04, that you have updated your video drivers, and you have installed CUDA 9.0 or 9.2. You'll need a Pascal or newer generation NVIDIA gpu. You'll also have to download and install TensorRT libraries [instructions here](https://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html). Once your these prerequisites installed and up-to-date you can install a special build of MXNet with TensorRT support enabled via PyPi and pip. Install the appropriate version by running: +Installing MXNet with TensorRT integration is an easy process. First ensure that you are running Ubuntu 18.04, and that you have updated your video drivers, and you have installed CUDA 10.0. You'll need a Pascal or newer generation NVIDIA GPU. You'll also have to download and install TensorRT libraries [instructions here](https://docs.nvidia.com/deeplearning/sdk/tensorrt-install-guide/index.html). Once you have these prerequisites installed and up-to-date you can install a special build of MXNet with TensorRT support enabled via PyPi and pip. Install the appropriate version by running: Review comment: The CI is currently targeting 10.0 but it should work with newer versions. I'll update to say 10 or newer. ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: [email protected] With regards, Apache Git Services
