How to set up TensorFlow with Docker in just 5 MINUTES

TensorFlow is an open-source machine learning library developed by Google that is widely used for building and training various machine learning models. Docker is a platform that allows you to package, distribute, and run applications in containers. By combining TensorFlow with Docker, you can easily manage and run TensorFlow applications in a containerized environment. In this tutorial, we will walk you through the process of setting up TensorFlow with Docker in just 5 minutes.

Step 1: Install Docker
Before getting started with TensorFlow, you will need to install Docker on your system. You can download and install Docker from the official Docker website (https://www.docker.com/).

Step 2: Create a Dockerfile
A Dockerfile is a text file that contains the instructions for building a Docker image. In this step, you will create a Dockerfile for your TensorFlow application. Here is a basic example of a Dockerfile for TensorFlow:

FROM tensorflow/tensorflow:latest
WORKDIR /app
COPY . /app

Save this content in a file named "Dockerfile".

Step 3: Build the Docker image
Navigate to the directory containing your Dockerfile and run the following command to build the Docker image:

docker build -t my-tensorflow-app .

This command will build a Docker image named "my-tensorflow-app" based on the instructions provided in the Dockerfile.

Step 4: Run the TensorFlow application in a Docker container
Once the Docker image is built successfully, you can run your TensorFlow application in a Docker container using the following command:

docker run -it my-tensorflow-app

This command will start a Docker container based on the "my-tensorflow-app" image. You can now interact with your TensorFlow application running inside the container.

Step 5: Manage your TensorFlow application with Docker
You can easily stop and start your TensorFlow application by running Docker commands. Here are some useful Docker commands to manage your TensorFlow application:

• Stop a running container:

  docker stop <container_id>

• Start a stopped container:

  docker start <container_id>

• Remove a container:

  docker rm <container_id>

• Remove an image:

  docker rmi my-tensorflow-app

By following these simple steps, you can set up and run your TensorFlow application in a Docker container. Docker makes it easy to manage and deploy TensorFlow applications, allowing you to focus on building and training your machine learning models. Explore more advanced Docker and TensorFlow features to enhance your machine learning workflow. Happy coding!