Sharing the Docker Image and Summary of TensorFlow.js Face Detection with Docker

has a `develop` subsection with the `watch` attribute that defines a list of rules that control automatic service updates based on local file changes. For more details about the Compose instructions, see the [Compose file reference](/reference/compose-file/_index.md). Save the changes to your `compose.yaml` file and then run the following command to run the application. ```console $ docker compose watch ``` Once the application is running, open a web browser and access the application at [http://localhost:80](http://localhost:80). You may need to grant access to your webcam for the application. Now you can make changes to the source code and see the changes automatically reflected in the container without having to rebuild and rerun the container. Open the `index.js` file and update the landmark points to be green instead of blue on line 83. ```diff - ctx.fillStyle = "blue"; + ctx.fillStyle = "green"; ``` Save the changes to the `index.js` file and then refresh the browser page. The landmark points should now appear green. To stop the application, press `ctrl`+`c` in the terminal. ## Share your image Publishing your Docker image on Docker Hub streamlines deployment processes for others, enabling seamless integration into diverse projects. It also promotes the adoption of your containerized solutions, broadening their impact across the developer ecosystem. To share your image: 1. [Sign up](https://www.docker.com/pricing?utm_source=docker&utm_medium=webreferral&utm_campaign=docs_driven_upgrade) or sign in to [Docker Hub](https://hub.docker.com). 2. Rebuild your image to include the changes to your application. This time, prefix the image name with your Docker ID. Docker uses the name to determine which repository to push it to. Open a terminal and run the following command in the `TensorJS-Face-Detection` directory. Replace `YOUR-USER-NAME` with your Docker ID. ```console $ docker build -t YOUR-USER-NAME/face-detection-tensorjs . ``` 3. Run the following `docker push` command to push the image to Docker Hub. Replace `YOUR-USER-NAME` with your Docker ID. ```console $ docker push YOUR-USER-NAME/face-detection-tensorjs ``` 4. Verify that you pushed the image to Docker Hub. 1. Go to [Docker Hub](https://hub.docker.com). 2. Select **My Hub** > **Repositories**. 3. View the **Last pushed** time for your repository. Other users can now download and run your image using the `docker run` command. They need to replace `YOUR-USER-NAME` with your Docker ID. ```console $ docker run -p 80:80 YOUR-USER-NAME/face-detection-tensorjs ``` ## Summary This guide demonstrated leveraging TensorFlow.js and Docker for face detection in web applications. It highlighted the ease of running containerized TensorFlow.js applications, and developing with Docker Compose for real-time code changes. Additionally, it covered how sharing your Docker image on Docker Hub can streamline deployment for others, enhancing the application's reach within the developer community. Related information: - [TensorFlow.js website](https://www.tensorflow.org/js) - [MediaPipe website](https://developers.google.com/mediapipe/) - [Dockerfile reference](/reference/dockerfile/) - [Compose file reference](/reference/compose-file/_index.md) - [Docker CLI reference](/reference/cli/docker/) - [Docker Blog: Accelerating Machine Learning with TensorFlow.js](https://www.docker.com/blog/accelerating-machine-learning-with-tensorflow-js-using-pretrained-models-and-docker/)

This section details how to share a built Docker image on Docker Hub, enabling others to easily deploy and use the application. It outlines the steps to rebuild the image with the Docker ID, push it to Docker Hub, and verify the upload. It also provides the command for others to run the shared image. Furthermore, the section summarizes the guide's content, highlighting the use of TensorFlow.js and Docker for face detection in web applications, development with Docker Compose, and the benefits of sharing the Docker image.