From 73450c24ee8cbfd7224347591bef9ba79754e0db Mon Sep 17 00:00:00 2001 From: Danielle Dean Date: Mon, 29 Oct 2018 15:16:39 -0400 Subject: [PATCH] Update readme based on standardization proposal --- README.md | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index bd86d13..067365e 100644 --- a/README.md +++ b/README.md @@ -19,11 +19,18 @@ In this repository there are a number of tutorials in Jupyter notebooks that hav * Testing the throughput of our model * Cleaning up resources -The application we will develop is a simple image classification service, where we will submit an image and get back what class the image belongs to. +## Design +![alt text](static/Design.png "Design") + +The application we will develop is a simple image classification service, where we will submit an image and get back what class the image belongs to. The application flow for the deep learning model is as follows: +1) The client sends a HTTP POST request with the encoded image data. +2) The Flask app extracts the image from the request. +3) The image is then appropriately preprocessed and sent to the model for scoring. +4) The scoring result is then piped into a JSON object and returned to the client. If you already have a Docker image that you would like to deploy you can skip the first four notebooks. -**NOTE**: The tutorial goes through step by step how to deploy a deep learning model on Azure it **does** **not** include enterprise best practices such as securing the endpoints and setting up remote logging etc. +**NOTE**: The tutorial goes through step by step how to deploy a deep learning model on Azure; it **does** **not** include enterprise best practices such as securing the endpoints and setting up remote logging etc. ## Prerequisites * Linux(Ubuntu). The tutorial was developed on an Azure Linux DSVM @@ -31,7 +38,7 @@ If you already have a Docker image that you would like to deploy you can skip th * [Dockerhub account](https://hub.docker.com/) * Port 9999 open: Jupyter notebook will use port 9999 so please ensure that it is open. For instructions on how to do that on Azure see [here](https://blogs.msdn.microsoft.com/pkirchner/2016/02/02/allow-incoming-web-traffic-to-web-server-in-azure-vm/) -## Setting Up +## Setup 1. Clone the repo: ```bash git clone @@ -55,8 +62,11 @@ jupyter notebook ``` 7. Start the first notebook and make sure the kernel corresponding to the above environment is selected. +## Steps +After following the setup instructions above, run the Jupyter notebooks in order. The same basic steps are followed for each deep learning framework. + ## Cleaning up -To remove the conda environment created see [here](https://conda.io/docs/commands/env/conda-env-remove.html) +To remove the conda environment created see [here](https://conda.io/docs/commands/env/conda-env-remove.html). The last Jupyter notebook within each folder also gives details on deleting Azure resources associated with this repo. # Contributing