Launching a Jupyter Notebook with TensorFlow using Docker

This article will walk you through setting up a Jupyter Notebook environment with TensorFlow pre-installed using Docker. Docker allows you to run isolated containerized applications, providing a consistent environment regardless of your underlying operating system.

Prerequisites:

• Docker: Ensure you have Docker installed and running on your system. You can download and install it from the official Docker website (https://www.docker.com/).

Steps:

1. Start Docker: Open your Docker application (Docker Desktop for Windows/macOS or the command line if using Linux).

2. Run the Jupyter Notebook container:

   • For macOS/Linux: Open your terminal application and run the following command:

     docker run -it --rm -p 8888:8888 -v "${PWD}":/home/jovyan/work jupyter/tensorflow-notebook
     

   • For Windows: Open your Command Prompt application and run the following command:

     docker run -it --rm -p 8888:8888 -v "%CD%":/home/jovyan/work jupyter/tensorflow-notebook
     

   Explanation of the command flags:

   • -it: This flag allows you to interact with the container in a terminal window.
   • --rm: This flag removes the container automatically after it exits.
   • -p 8888:8888: This flag maps port 8888 on your host machine to port 8888 inside the container, making the Jupyter Notebook server accessible.
   • -v "${PWD}":/home/jovyan/work (or -v "%CD%":/home/jovyan/work for Windows): This flag mounts your current working directory on the host machine to the /home/jovyan/work directory inside the container. This allows you to access your local files and notebooks within the Jupyter Notebook environment. You can remove this flag if you don't need access to your local files.

3. Access Jupyter Notebook:

   Once the container starts, you will see a message displayed in the terminal with a URL similar to:

   http://127.0.0.1:8888/lab?token=...
   

   Copy this URL and paste it into your web browser's address bar. This will launch the Jupyter Notebook interface in your browser.

Additional Notes:

• By default, the Jupyter Notebook server runs on port 8888. If this port is already in use, you can specify a different port by modifying the -p flag in the command (e.g., -p 8889:8888).
• The jupyter/tensorflow-notebook image used in the command provides a Jupyter Notebook environment with TensorFlow pre-installed. This allows you to start working on your machine learning projects right away.

Further Exploration:

• For more details on using Jupyter Notebook, you can refer to the official documentation (https://docs.jupyter.org/).
• The TensorFlow website offers comprehensive tutorials and resources for getting started with machine learning using TensorFlow (https://www.tensorflow.org/tutorials).

I hope this article helps you in launching a Jupyter Notebook with TensorFlow using Docker!