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 . /appSave 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-appThis 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!
Here are the timestamps!
00:00 Introduction
00:14 What are Docker and TensorFlow?
00:42 Create a Docker container – Overview
01:20 Create a Docker container – Demo
02:09 Reconnect to the container – docker exec
02:23 Install Python packages – TensorFlow and TensorFlow IO
03:21 Check the TensorFlow version
03:52 Call to action
04:12 Start and re-attach to an existing container
05:06 Outro
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How can use gpu from docker
how to run tensorflow with docker in vscode?
top, on point!
AWESOME!!!
This doesn't set it up to run with GPUs does it? When I run print(tf.config.list_physical_devices('GPU')) I get an empty list. That said it should work if you run the install with – – gpus all , right?
Would have been helpful to include how to use tensorflow with gpu.. i.e. including "–gpus all" in run command, installing cuda and cudnn
You really help me. Im stuck when use tensorflow images. Luv your vid <3
underrated channel
Thks NoNo