python-tensorflowHow can I use Docker to deploy a Python TensorFlow application?

To deploy a Python TensorFlow application using Docker, first you need to create a Dockerfile. A Dockerfile is a text document that contains all the commands a user could call on the command line to assemble an image. In this Dockerfile, you will need to include commands to install TensorFlow and any other dependencies your application requires.

For example, the following Dockerfile will install TensorFlow and any other necessary packages:

FROM python:3.7
RUN pip install tensorflow
COPY . .
CMD ["python", "app.py"]

Once you have written your Dockerfile, you can build a Docker image using the following command:

docker build -t <image_name>:<tag> .

This command will create a Docker image with the name and tag specified. You can then run the image using the following command:

docker run -it <image_name>:<tag>

This will start your application in a container. You can then access your application by pointing your browser to the IP address of the container.

Code explanation

• FROM python:3.7 - This line specifies the base image from which the Docker image will be built
• RUN pip install tensorflow - This line installs TensorFlow
• COPY . . - This line copies all the files from the current directory to the Docker image
• CMD ["python", "app.py"] - This line specifies the command to run when the Docker image is started
• docker build -t : . - This command builds the Docker image
• docker run -it : - This command runs the Docker image

Helpful links

• Dockerfile Reference
• Docker Build Reference
• Docker Run Reference

Edit this code on GitHub