Ungraded Lab - Deploying a Deep Learning model

Introduction

During this code you will go through the process of deploying an already trained Deep Learning model. To do so, we will take advantage of the user-friendly library fastAPI that provides a nice REST API framework.

This tutorial is specifically designed to run locally on your machine. This can be done via 2 methods: using Python Virtual Environments or using Docker.

Both approaches should yield the same result. If you already have a conda installation available on your computer, we recommend that you use the virtual environment method. If this is not the case, choose the Docker method as it is easier to set up.

As a general note, the commands in this tutorial are meant to be run within a terminal. To begin you need to clone this repo in your local filesystem and cd to the deployment_tutorial directory.

To clone the repo use this command:

git clone https://github.com/youssefHosni/Practical-Machine-Learning.git

or for cloning via SSH use:

git clone git@github.com:youssefHosni/Practical-Machine-Learning.git

If you are unsure which method to use for cloning, use the first one.

The cd command allows you to change directories. Assuming you are at the directory where you issued the cloning command, type the following on your terminal.

cd working_directory/deployment_tutorial

Method 1: Python Virtual Environment with Conda

Prerequisites: Have conda installed on your local machine.

You will use Conda as an environment management system so that all the dependencies you need for this tutorial are stored in an isolated environment.

Conda includes a lot of libraries so if you are only installing it to complete this lab , we suggest using miniconda, which is a minimal version of conda.

1. Creating a virtual Environment

Now we assume that you either successfully installed conda or that it was previously available in your system. The first step is creating a new developing environment. Let's set a new environment with python 3.8 with this command:

conda create --name deployment-tutorial python=3.8

After successfully creating the environment, you need to activate it by issuing this command:

conda activate deployment-tutorial

At this point, you will do all your libraries installation and work in this environment.

2. Installing dependencies using PIP

Now use the following command to install the required dependencies:

pip install -r requirements.txt

This command can take a while to run depending on the speed of your internet connection. Once this step completes you should be ready to spin up jupyter lab and begin working on the ungraded lab.

3. Launching Jupyter Lab

Jupyter lab was installed during the previous step so you can launch it with this command:

jupyter lab

After execution, you will see some information printed on the terminal. Usually you will need to authenticate to use Jupyter lab. For this, copy the token that appears on your terminal, head over to http://localhost:8888/ and paste it there. Your terminal's output should look very similar to the next image, in which the token has been highlighted for reference:

4. Running the notebook

Within Jupyter lab you should be in the same directory where you used the jupyter lab command.

Look for the server.ipynb file and open it to begin to run it.

To stop jupyter lab once you are done with the lab just press Ctrl + C twice.

And... that's it! Have fun deploying a Deep Learning model! :)