[+]deploy-CNN-google-cloud-platform-GCP Implementing and Deploying a Deep Convolutional Neural Network with Google Cloud App Engine

Deploy a CNN on Google Cloud with Keras and Flask

This repository provides a step-by-step guide and code example to deploy a pre-trained InceptionResNetV2 convolutional neural network (CNN) on Google Cloud Platform (GCP) using Flask for serving the model.

Prerequisites

Before getting started, please check, you have the following:

• Google Cloud Platform Account: You'll need an active GCP account. If you don't have one, you can sign up for a free trial here.

• Google Cloud SDK: Download and install the Google Cloud SDK for your operating system from here. This will provide you with the gcloud command-line tool.

• Python 3.6 (Recommended): While other versions may work, this guide uses Python 3.6. Create a virtual environment for this project:

  pip install virtualenv
  virtualenv web_app 
  source web_app/bin/activate 

Project Setup

1. Clone the Repository:

   git clone this_repo_Git
   cd Deploy-CNN-on-google-cloud

2. Install Dependencies:

   pip install -r requirements.txt 

3. Download the Pre-trained Model:

   python download_model.py

   This script downloads the InceptionResNetV2 model weights.

Local Testing

1. Start the Flask Development Server:

   gunicorn -b :8889 app:app -t 120 --graceful-timeout 60

2. Open the Web Interface: Open your web browser and navigate to http://localhost:8889/. You should now be able to interact with the deployed CNN model.

Deploying to Google Cloud

1. Initialize the Google Cloud SDK:

   gcloud init

   Follow the prompts to select your project and default zone.

2. Deploy the Application:

   gcloud app deploy 

3. Access the Deployed App:

   gcloud app browse

   This command will open your deployed web application in your default browser.

Project Structure

• app.py: Contains the Flask application logic for handling requests and predictions.
• download_model.py: Downloads the pre-trained InceptionResNetV2 model weights.
• requirements.txt: Lists the required Python packages for the project.
• templates/index.html: The HTML template for the web interface.

Notes

• Customization: You can modify the code to use different CNN architectures or adjust the web interface to your needs.
• Security: In a production environment, implement appropriate security measures like HTTPS and authentication.
• Error Handling: Implement robust error handling in the Flask application for a smoother user experience.