--- title: "Registries - Use & manage your registries" description: "Learn how to use and manage your public and private registries through ovhai command line interface (AI CLI), API, and OVHcloud Control Panel. This will help yo" url: https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/manage-registries lang: en --- > For AI agents: the complete documentation index is available at https://docs.us.ovhcloud.com/en/llms.txt, the full documentation bundle is available at https://docs.us.ovhcloud.com/en/llms-full.txt. # Registries - Use & manage your registries **Learn how to use and manage your public and private registries through `ovhai` command line interface ([AI CLI](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/cli-install-client.md)), API, and [OVHcloud Control Panel](https://manager.us.ovhcloud.com/).** This will help you when working with OVHcloud AI Tools, especially with AI Training and AI Deploy.   ## Requirements - A [Public Cloud project](https://us.ovhcloud.com/public-cloud/) - Access to the [OVHcloud Control Panel](https://manager.us.ovhcloud.com/)   ## Instructions This documentation is divided into the following parts: - Manage existing registries through - OVHcloud Control Panel (UI) - OVHcloud AI CLI `ovhai` - Create, configure, and use new registries - Private Harbor registry - Private Docker registry - GitHub registry (packages)   ## Manage existing registries The Public Cloud provides a default registry called `shared registry`, where users of the same Public Cloud project can push their custom images and use them through our AI Tools. **Warning:** **NOTE:** This `shared registry` can help you perform your tests but **should not be used in production**, as we reserve the right to delete its content if deemed necessary. The images pushed to this registry are for AI Tools' workloads only and will not be accessible for external uses. This is why it can be interesting to add and manage other registries. We can either do this by using the OVHcloud Control Panel (UI) or the `ovhai` CLI. **Tip:** Click each tab across the top to view all content. ### Via OVHcloud Control Panel First, log in to the [OVHcloud Control Panel](https://manager.us.ovhcloud.com/) and go to the `AI Dashboard` section via the `Public Cloud` menu. By clicking the `Docker registries` tab, you should be able to see and manage (add, delete) your different private registries. Note that the `shared registry` will not appear here. This one is displayed in the **Home** panel of AI Training. ### Via CLI If you prefer, you can also use the OVHcloud `ovhai` CLI: List all your available registries with: ```console ovhai registry list ``` Add a registry by using: ```console ovhai registry add ``` Update one of your registries with the following command: ```console ovhai registry update ``` Delete a registry by using: ```console ovhai registry delete ``` _If you do not have our `ovhai` CLI yet, learn how to install it [here](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/cli-install-client.md)._   ## Create, configure, and use new registries Using your own and private registries is the best way to use your images without exposing them to everyone. There are three main possibilities: - Private Harbor registry - Private Docker registry - GitHub registry / packages **Warning:** **Port restriction:** Only port **443** is supported for registry connections. If you attempt to add a registry URL with a non-**443** port (e.g., `8443`), the system will enforce port 443. This may lead to errors or connection timeouts if the registry is not configured to accept HTTPS on port 443. For self-hosted registries using non-443 ports, consider configuring a **reverse proxy** (e.g., Nginx, HAProxy) to map traffic from **443** to your desired port (e.g., 8443). We will see how to implement each of these registries and use them through AI Tools. **Note:** In any case, note that the default `shared registry` will remain available even if you add new registries. **Tip:** Click each tab across the top to view all content. ### Harbor private registry [OVHcloud Managed Private Registry service](https://us.ovhcloud.com/public-cloud/managed-private-registry/) allows you to spawn your own authenticated Docker registry where you can privately store your Docker images. This registry is an instance of [Harbor](https://goharbor.io/), an open source trusted cloud native registry project that stores, signs, and scans Docker images.   #### Creation To create your new private registry, we advise you to read and follow the steps mentioned in the [Creating a private registry documentation](https://docs.us.ovhcloud.com/en/public-cloud/managed-private-registry/create-private-registry.md). At the end of this step, you should have your **registry identification details**.   #### Configuration Then, you will need to use these credentials to **connect to the Harbor UI**. This will allow you to manage this private registry. This simple step is explained in the [Connecting to the UI documentation](https://docs.us.ovhcloud.com/en/public-cloud/managed-private-registry/connecting-harbor-user-interface.md). To finish setting up your private Harbor registry, you will need to **create a project on Harbor**, and associate a user to it (Administrator user is added by default, but you can create a new one for this project). You will find all the information you need in this [documentation](https://docs.us.ovhcloud.com/en/public-cloud/managed-private-registry/managing-users-projects-harbor.md).   #### Get your OVHcloud Managed Private Registry API URL To add this registry to AI Tools, you will need to retrieve its URL. To do this, go to the `Managed Private Registry` section on the OVHcloud Public Cloud Manager, click the _more options_ `...` button to the right, and choose `Harbor API`: Then, copy the URL of the Harbor API, which is the URL of your private registry:   #### Add the Harbor registry To add your registry, you can either use the OVHcloud Control Panel (UI) or the `ovhai` CLI. During this step, you will be asked for your user's credentials (user ID and password). You can use the default user (administrator) credentials, which were obtained during the registry creation step, or those of another user if you have created one.   ##### Using UI To add your private registry via UI, log in to the [OVHcloud Control Panel](https://manager.us.ovhcloud.com/), go to the **AI Dashboard** section. From the dashboard, you can add your private Harbor registry by clicking `Docker registries` > `+ Add`. Here, you will need to provide the credentials of your registry along with its URL. When you have finished filling in the form, click `Add`.   ##### Using CLI To add your private Harbor registry via CLI, use: ```console ovhai registry add ``` Once your registry is added, you should see it in your registries list. You can then use any images pushed on this registry for your AI Training jobs and AI Deploy apps.   #### Push an image to your registry **Warning:** **NOTE:** Be careful for the following, we will use the displayed URL. This version does not contain the `https://` part, which is present in the URL given by the Public Cloud. Log in to your private registry, using a user with write rights to your registry project, with the following command: ```console docker login -u -p ``` Once connected, all you have to do is build, tag, and push your image. Here is an example: Build the image from the current directory's Dockerfile: ```console docker build . -t : ``` Tag the compiled image: ```console docker tag : //: ``` Push the image to your private registry's project: ```console docker push //: ```   #### Use your private registry images with OVHcloud AI Tools Now that your private registry has been created, added to your Public Cloud account, you can use its image(s) in AI Training and AI Deploy. Here is a basic example with AI Training, when using the CLI: ```console ovhai job run //: ``` You can also use the OVHcloud Control Panel if you prefer. In this case, you will have to indicate your custom Docker image during Step 2 of your job/app creation. This path will be `//:`. ##### Docker private registry If you prefer to use a private registry from your Docker account, it is also possible. Let's say we have created a private repository on our Docker account. We build, tag, and push our Docker image in this private repository: ```console docker build . -t : docker tag : /: docker push /: ```   #### Add the private Docker registry Once your image is pushed to your Docker account, you will need to add your private Docker registry to the registries of your Public Cloud project. As with Harbor, this can either be done with the UI or CLI. This time, your private registry's URL will be `index.docker.io` and the requested username and password correspond to those of your Docker account. During this step, you will be asked for your Docker credentials.   ##### Using UI Log in to the [OVHcloud Control Panel](https://manager.us.ovhcloud.com/), go to the **AI Dashboard** section. From the dashboard, you can add your private Docker registry by clicking `Docker registries` > `Add` button. Here, you will need to provide your Docker credentials along with its URL. When you have finished filling in the form, click `Add`.   ##### Using CLI To add your private Docker registry via CLI, use: ```console ovhai registry add index.docker.io ``` Once your registry is added, you should see it in your registries list.   #### Use your private registry images with OVHcloud AI Tools You can now use the images contained in this private Docker registry in AI Tools by specifying `index.docker.io`. Here is an example to deploy a Streamlit application on AI Deploy: ```console ovhai app run index.docker.io//: \ --default-http-port 8501 ``` You can also use the Control Panel to deploy a job or an app from your private Docker registry. You will just need to specify your Docker image path, during Step 2 of your job/app creation. This path will be `index.docker.io//:`. ##### GitHub registry You can also use a GitHub registry, also known as [GitHub Packages](https://github.com/features/packages). As indicated in their documentation, you can build and push a package image by using the following commands: ```console docker login ghcr.io --username --password docker tag ghcr.io//: docker push ghcr.io//: ```   #### Add a GitHub registry Once your image is pushed on your GitHub account, you will need to add your GitHub registry to the registries of your Public Cloud project. As with Harbor and Docker, this can either be done with UI or CLI. This time, your registry's URL will be `ghcr.io`. You will be asked for your GitHub credentials.   ##### Using UI Log in to the [OVHcloud Control Panel](https://manager.us.ovhcloud.com/), go to the **AI Dashboard** section. From the dashboard, you can add your private Docker registry by clicking `Docker registries` > `Add` button. Here, you will need to provide your GitHub credentials along with the registry URL. When you have finished filling in the form, click `Add`.   ##### Using CLI ```console ovhai registry add ghcr.io ``` Once your registry is added, you should see it in your registries list.   #### Use your GitHub registry images with OVHcloud AI Tools You can now use the packages/images contained in this GitHub registry in AI Tools by specifying `ghcr.io`. Here is an example to deploy a Streamlit application on AI Deploy: ```console ovhai app run ghcr.io//: \ --default-http-port 8501 ``` You can also use the Control Panel to deploy a job or an app from a GitHub registry. You will just need to specify your GitHub image path, during Step 2 of your job/app creation. This path will be `ghcr.io//:`. Once you have pushed your image, you can access it via `ghcr.io//`.   ## Go further - You can check the official documentation about [how to submit a job](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-training-submit-job.md). - Learn [how to deploy an app](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/ai-deploy-getting-started.md). - You can check out the documentation about the [ovhai CLI](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/cli-overview.md). Browse all the [AI & Machine Learning guides](https://docs.us.ovhcloud.com/en/public-cloud/ai-machine-learning/landing-page-ai-machine-learning.md). Browse the rest of the [OVHcloud US documentation](https://docs.us.ovhcloud.com/en/index.md) for guides on our other products and services. Learn more about [AI & Machine Learning](https://us.ovhcloud.com/public-cloud/ai-machine-learning/) on the OVHcloud US website. If you need training or technical assistance to implement our solutions, contact your sales representative or click on [this link](https://us.ovhcloud.com/professional-services/) to get a quote and ask our Professional Services experts for a custom analysis of your project.