Deploy a Cloud in Under an Hour

Follow these easy steps to get started with Model Designer:

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Step 1: Register for access to MCP Model Designer

Simply submit the form on this page and we’ll send you an email with a personalized link to the Model Designer registration page.

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Step 2: Download ISO file needed for deployment

After logging into Model Designer, you’ll have a download link available for the ISO file that’s used for initial cloud deployment. Since the file is large, we recommend that you start the download before using Model Designer to create your cloud configuration.

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Step 3: Use Model Designer to configure your cloud

Model Designer provides an easy-to-use wizard for cloud configuration. We’ve included several models to help you get started, and you’ll be able to experience the full flexibility and control that’s available from MCP.

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Step 4: Start using your cloud model

After you’ve finalized your model, a Jenkins job will build and verify the model’s YAML file. The model list view in Model Designer will show the progress of your job. Once the job completes successfully, the YAML file will be sent to your email address.
Note: The All-in-One MCP OpenStack cloud can be deployed on a minimum of four physical or virtual nodes with a combined total of 64GB of RAM. Once you’ve downloaded the necessary artifacts for deployment, follow these instructions to deploy your cloud (login required).

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MCP: Flexibility with Predictability

Many commercial open source distributions introduce vendor lock-in and limited options for included technology and architecture. With MCP, our GitOps-based approach to consuming open source software provides increased flexibility while maintaining predictability and control for IT Operations teams.

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DriveTrain: Infrastructure-as-Code

MCP includes a GitOps toolchain called DriveTrain that introduces an Infrastructure-as-Code model for lifecycle management of MCP clouds. Built with open source tooling like Git, Gerrit, Jenkins, Reclass, and SaltStack, DriveTrain ensures that any changes to MCP cloud models are managed just like source code. With pre-built and verified pipelines available for deployment, updates, and upgrades, MCP provides far greater automation and predictability for all phases of cloud lifecycle management.

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