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.openpublishing.publish.config.json

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{
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"docset_name": "gamedev",
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"build_source_folder": "visualstudio/gamedev",
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"build_source_folder": "gamedev",
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"build_output_subfolder": "gamedev",
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"locale": "en-us",
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"monikers": [],
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"moniker_ranges": [],
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"open_to_public_contributors": false,
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"open_to_public_contributors": true,
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"type_mapping": {
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"Conceptual": "Content"
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"Conceptual": "Content",
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"ZonePivotGroups": "Toc"
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},
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"build_entry_point": "docs",
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"template_folder": "_themes"

.openpublishing.redirection.json

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.whatsnew.json

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"rootDirectory": "docs/",
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"docLinkSettings": {
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"linkFormat": "relative",
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"relativeLinkPrefix": "/visualstudio/"
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"relativeLinkPrefix": "../"
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},
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"inclusionCriteria": {
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"additionalMicrosoftOrgs": [
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"ASP.NET"
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],
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"minAdditionsToFile": 30
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"labels": [
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"-label:CLA"
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docker/docfx.json

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"author": "jillre",
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"ms.author": "jillfra",
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"ms.topic": "conceptual",
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"audience": "developer",
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"uhfHeaderId": "MSDocsHeader-VisualStudio"
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docker/index.md

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docker/tutorials/persist-your-data.md

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docker volume create todo-db
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```
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1. Stop the todo app container once again in the Dashboard (or with `docker rm -f <id>`), as it is still running without using the persistent volume.
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1. Stop the todo app container once again in the Docker view (or with `docker rm -f <id>`), as it is still running without using the persistent volume.
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1. Start the todo app container, but add the `-v` flag to specify a volume mount. you will use the named volume and mount it to `/etc/todos`, which will capture all files created at the path.
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![Items added to todo list](media/items-added.png)
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1. Remove the container for the todo app. Use the Dashboard or `docker ps` to get the ID and then `docker rm -f <id>` to remove it.
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1. Remove the container for the todo app. Use the Docker view or `docker ps` to get the ID and then `docker rm -f <id>` to remove it.
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1. Start a new container using the same command from above.
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docker/tutorials/share-your-app.md

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docker push <username>/getting-started
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```
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Instead of the command line, you can also right-click on the image tag in the **Images** section of the Docker view, and choose **Push...**, then choose **Connect registry...** and then **Docker Hub**.
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## Run the image on a new instance
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Now that your image has been built and pushed into a registry, try running the app on a brand new instance that has never seen this container image! To do this, you will use Play with Docker.

docker/tutorials/update-your-app.md

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> [!TIP]
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> You can stop and remove a container in a single command by adding the "force" flag to the `docker rm` command. For example: `docker rm -f <the-container-id>`
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### Remove a container using the Docker Dashboard
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### Remove a container using the Docker view
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If you open the VS Code Extension, you can remove a container with two clicks! It's certainly much easier than having to look up the container ID and remove it.
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1. Confirm the removal and you're done!
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![Docker Dashboard - removing a container](media/vs-removing-container.png)
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![Docker view - removing a container](media/vs-removing-container.png)
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### Start the updated app container
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docker/tutorials/your-application.md

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## Get the app
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Before you can run the application, you need to get the application source code onto your machine. For real projects, you will typically clone the repo. But, for this tutorial, you have created a ZIP file containing the application.
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Before you can run the application, you need to get the application source code onto your machine. For real projects, you will typically clone the repo. But, for this tutorial, we have created a ZIP file containing the application.
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1. [Download the ZIP](/assets/app.zip). Open the ZIP file and make sure you extract the contents.
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1. Make sure that you have Docker for Windows or Docker Community Edition installed on the local machine. See [Docker for Windows installation documentation](https://docs.docker.com/docker-for-windows/install/). The install process makes the ZIP file containing the sample available at the localhost address.
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1. [Download the ZIP](http://localhost/assets/app.zip). Open the ZIP file and make sure you extract the contents.
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1. Once extracted, use your favorite code editor to open the project. If you're in need of an editor, you can use [Visual Studio Code](https://code.visualstudio.com/). You should see the `package.json` and two subdirectories (`src` and `spec`).
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```
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Alternatively, you can also right-click on the Dockerfile and choose **Build Image...** and then specify the tag at the prompt.
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This command used the Dockerfile to build a new container image. You might have noticed that a lot of "layers" were downloaded. This is because you instructed the builder that you wanted to start from the `node:12-alpine` image. But, since you didn't have that on your machine, that image needed to be downloaded.
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After the image was downloaded, you copied in your application and used `yarn` to install your application's dependencies. The `CMD` directive specifies the default command to run when starting a container from this image.
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> [!div class="nextstepaction"]
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> [Updating your app](update-your-app.md)
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> [Updating your app](update-your-app.md)

docs/_breadcrumb/toc.yml

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docs/ai/create-project-existing.md

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title: Create an AI project from existing code
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description: Learn how to use Visual Studio Tools for AI to bring existing Python code into a Visual Studio project.
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docs/ai/create-project-repo.md

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title: Clone a repo
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docs/ai/create-project.md

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docs/ai/job-details.md

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description: Learn that once jobs are submitted, you can view the list of jobs to see their status, duration, and more.
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docs/ai/manage-storage.md

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title: Browse storage to upload data
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description: Learn how you can browse all storage on the remote machine or Azure file share to enable uploading data or downloading models and logs.
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docs/ai/monitor-gpu.md

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title: Monitor GPU utilization
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description: Learn about GPU utilization. Specifically, learn how to monitor GPU utilization of remote Linux machines.
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docs/ai/monitor-tensorboard.md

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description: Learn how you can use Visual Studio to visualize your model training progress with TensorBoard.
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docs/ai/overview.md

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title: Overview of Visual Studio Tools for AI
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description: Learn how Visual Studio Tools for AI is an integrated development environment (IDE) that you can use to build, test, and deploy deep learning solutions.
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## 5-minute quickstart
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## 5-minute quickstart
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Learn how to run deep learning solution with [TensorFlow and Python](tensorflow-local.md)
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## See also
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[Visual Studio extensibility](/visualstudio/extensibility/?view=vs-2017)
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[Visual Studio extensibility](../extensibility/index.yml?view=vs-2017&preserve-view=true)

docs/ai/train-model.md

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docs/azure/azure-app-insights-add-connected-service.md

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docs/azure/index.yml

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docs/azure/toc.yml

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docs/azure/vs-azure-tools-access-private-azure-clouds-with-visual-studio.md

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docs/azure/vs-azure-tools-azure-project-create.md

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docs/azure/vs-azure-tools-cloud-service-publish-set-up-required-services-in-visual-studio.md

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docs/azure/vs-azure-tools-cloud-service-retain-a-constant-virtual-ip-address.md

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docs/azure/vs-azure-tools-command-line-build.md

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