leezu commented on a change in pull request #18434: URL: https://github.com/apache/incubator-mxnet/pull/18434#discussion_r435379132
########## File path: docs/python_docs/python/tutorials/deploy/inference/image_classification_jetson.md ########## @@ -0,0 +1,109 @@ +<!--- 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. --> + +# Image Classication using pretrained ResNet-50 model on Jetson module + +This tutorial shows how to install MXNet v1.6 with Jetson support and use it to deploy a pre-trained MXNet model for image classification on a Jetson module. + +## What's in this tutorial? + +This tutorial shows how to: + +1. Install MXNet v1.6 along with its dependencies on a Jetson module (This tutorial has been tested on Jetson Xavier AGX and Jetson Nano modules) + +2. Deploy a pre-trained MXNet model for image classifcation on the module + +## Who's this tutorial for? + +This tutorial would benefit developers working on Jetson modules implementing deep learning applications. It assumes that readers have a Jetson module setup with Jetpack installed, are familiar with the Jetson working environment and are somewhat familiar with deep learning using MXNet. + +## Prerequisites + +To complete this tutorial, you need: + +* A [Jetson module](https://developer.nvidia.com/embedded/develop/hardware) setup with [Jetpack 4.4](https://docs.nvidia.com/jetson/jetpack/release-notes/) installed using NVIDIA [SDK Manager](https://developer.nvidia.com/nvidia-sdk-manager) + +* An SSH connection to the module OR display and keyboard setup to directly open shell on the module + +* [Swapfile](https://help.ubuntu.com/community/SwapFaq) installed, especially on Jetson Nano for additional memory (increase memory if the inference script terminates with a `Killed` message) + +## Installing MXNet v1.6 with Jetson support + +We start by installing MXNet dependencies +```bash +sudo apt-get update +sudo apt-get install -y git build-essential libopenblas-dev libopencv-dev python3-pip +sudo pip3 install -U pip +``` + +Then we download and install MXNet v1.6 wheel with Jetson support +```bash +wget https://mxnet-public.s3.us-east-2.amazonaws.com/install/jetson/1.6.0/mxnet_cu102-1.6.0-py2.py3-none-linux_aarch64.whl Review comment: Bertrand from the Incubator suggested the following disclaimer: > WARNING: the following links are provided for your convenience but they point to packages that are *not* provided nor endorsed by the Apache Software Foundation. As such, they might contain software components with more restrictive licenses than the Apache License and you'll need to decide whether they are appropriate for your usage. Like all Apache Releases, the official Apache MXNet (incubating) releases consist of source code only and are found at <link>. ---------------------------------------------------------------- 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]
