[GitHub] [incubator-tvm] trevor-m commented on a change in pull request #6395: [BYOC][TensorRT] TensorRT BYOC integration

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trevor-m commented on a change in pull request #6395:
URL: https://github.com/apache/incubator-tvm/pull/6395#discussion_r500554166



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File path: docs/deploy/tensorrt.rst
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+..  Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+..    http://www.apache.org/licenses/LICENSE-2.0
+
+..  Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+
+Relay TensorRT Integration
+==========================
+**Author**: `Trevor Morris <https://github.com/trevor-m>`_
+
+Introduction
+------------
+
+NVIDIA TensorRT is a library for optimized deep learning inference. This integration will offload as
+many operators as possible from Relay to TensorRT, providing a performance boost on NVIDIA GPUs
+without the need to tune schedules.
+
+This guide will demonstrate how to install TensorRT and build TVM with TensorRT BYOC and runtime
+enabled. It will also provide example code to compile and run a ResNet-18 model using TensorRT and
+how to configure the compilation and runtime settings. Finally, we document the supported operators
+and how to extend the integration to support other operators.
+
+Installing TensorRT
+-------------------
+
+In order to download TensorRT, you will need to create an NVIDIA Developer program account. Please
+see NVIDIA's documentation for more info:
+https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html. If you have a Jetson device
+such as a TX1, TX2, Xavier, or Nano, TensorRT will already be installed on the device via the
+JetPack SDK.
+
+There are two methods to install TensorRT:
+
+* System install via deb or rpm package.
+* Tar file installation.
+
+With the tar file installation method, you must provide the path of the extracted tar archive to
+USE_TENSORT_GRAPH_RUNTIME=/path/to/TensorRT. With the system install method,
+USE_TENSORT_GRAPH_RUNTIME=ON will automatically locate your installation.
+
+Building TVM with TensorRT support
+----------------------------------
+
+There are two separate build flags for TensorRT integration in TVM:
+
+* USE_TENSORT=ON/OFF - This flag will enable compiling a TensorRT module, which does not require any
+TensorRT library.
+* USE_TENSORT_GRAPH_RUNTIME=ON/OFF/path-to-TensorRT - This flag will enable the TensorRT runtime
+module. This will build TVM against the TensorRT libraries.

Review comment:
       USE_TENSORRT_GRAPH_RUNTIME is referring to TVM's graph runtime. However since this could be also be used with VM `USE_TENSORRT_RUNTIME` is perhaps a better name.




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