Test and deploy containerized PyTorch models to Heroku

This tutorial provides detailed instructions on deploying a PyTorch machine learning model as a REST API using Flask, containerized with Docker, and automated for continuous integration and deployment (CI/CD) with CircleCI on the Heroku platform. The guide demonstrates how to set up the necessary development environment, create a Flask application to serve the PyTorch model's predictions, and write unit tests to ensure its functionality. It explains how to build and test the application locally using Docker, then automate the deployment process using CircleCI, which runs tests and deploys the application to Heroku upon successful test completion. It also covers setting up Heroku and CircleCI accounts, configuring environment variables, and selecting appropriate dynos for application scaling. The tutorial aims to streamline the process of moving machine learning models from development to production, leveraging tools like PyTorch, Heroku, and CircleCI to enhance deployment efficiency and reliability.

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