01-deploying-a-deep-learning-model

Deploying a Deep Learning model

A rapid guide to deploying a computer vision model trained to identify common objects in images, utilizing the YOLOv3 model and FastAPI.

Setup

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 playground/01-deploying a machine learning model

This will bring you to the 01-deploying a machine learning model directory. The ls command allows you to list the files and directories. Type ls and let's take a quick look at the content inside 01-deploying a machine learning model directory:

.
└── 01-deploying a machine learning model (this directory)
    ├── images (includes some images from ImageNet)
    ├── server.ipynb (Part 1 of the ungraded lab)
    ├── client.ipynb (Part 2 of the ungraded lab)
    └── requirements.txt (python dependencies)

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 ungraded lab 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 mlep-w1-lab python=3.8

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

conda activate mlep-w1-lab

At this point, you will do all your libraries installation and work in this environment. So, whenever working on this ungraded lab, check the mlep-w1-lab environment is active.

2. Installing dependencies using PIP

Before proceeding, double check that you are currently on the 01-deploying a machine learning model directory, which includes the requirements.txt file. This file lists all the required dependencies and their respective versions.

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/lab 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 the ungraded lab.

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! :)

Based on Machine Learning Engineering for Production (MLOps) Specialization by Andrew Ng, Laurence Moroney, and Robert Crowe (2023).