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Developers can access Run:ai through various programmatic interfaces.
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## API Support
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The endpoints and fields specified in the [API reference](https://app.run.ai/api/docs) are the ones that are officially supported by Run:ai. Endpoints and fields that are not listed in the API reference are not supported.
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Run:ai does not recommend using API endpoints and fields marked as `deprecated` and will not add functionality to them. Once an API endpoint or field is marked as `deprecated`, Run:ai will stop supporting it after 2 major releases for self-hosted deployments, and after 6 months for SaaS deployments.
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For details, see the [Deprecation notifications](../home/whats-new-2-17.md#deprecation-notifications).
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## API Architecture
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Run:ai is composed of a single, multi-tenant control plane. Each tenant can be connected to one or more GPU clusters. See [Run:ai system components](../home/components.md) for detailed information.
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Below is a diagram of the Run:ai API Architecture. A developer may:
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The following programming interfaces are available:
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| API | Description | Purpose |
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|---------|--------------|--------------|
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|__Control Plane API__| Access the control plane for getting and modifying business objects | This is the API mostly used by system developers. The API is also used by the Run:ai user interface as well as the __new__ command-line interface |
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|__Cluster API__| Submit Workloads directly to the Cluster | A YAML-based API allowing submittion of Workloads directly to the Cluster. With Run:ai 2.18, this API is replaced by a Control-plane API to submit jobs, which is now the recommended method |
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|__Metrics API__ (deprecated) | Get cluster metrics | Get utilization metrics directly from the monitoring agent (Prometheus). This API is in the process of being deprecated and is replaced with metric-specific control plane API |
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1. Access the control plane via the _Control Plane API_.
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2. Access any one of the GPU clusters via _Cluster API_.
Add, delete, modify and list Run:ai meta-data objects such as Projects, Departments, Users, and more.
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Allows you to Add, delete, modify and list Run:ai meta-data objects such as Projects, Departments, Users.
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For Clusters of Run:ai 2.18 and above, allows the submitting of Workloasd.
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The API is provided as REST and is accessible via the control plane endpoint.
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For more information see [Control Plane REST API](admin-rest-api/overview.md).
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!!! Important
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The endpoints and fields specified in the [API reference](https://app.run.ai/api/docs) are the ones that are officially supported by Run:ai. Endpoints and fields that are not listed in the API reference are not supported.
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Run:ai does not recommend using API endpoints and fields marked as `deprecated` and will not add functionality to them. Once an API endpoint or field is marked as `deprecated`, Run:ai will stop supporting it after 2 major releases for self-hosted deployments, and after 6 months for SaaS deployments.
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For details, see the [Deprecation notifications](../home/whats-new-2-17.md#deprecation-notifications).
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## Cluster API
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Submit and delete Workloads.
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The [Cluster API](cluster-api/workload-overview-dev.md) allows you to submit and delete Workloads directly to the cluster itself.
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The API is provided as [Kubernetes API](./cluster-api/submit-yaml.md).
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Cluster API is accessible via the GPU cluster itself. As such, **multiple clusters may have multiple endpoints**.
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!!! Note
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The same functionality is also available via the [Run:ai Command-line interface](../Researcher/cli-reference/Introduction.md). The CLI provides an alternative for automating with shell scripts.
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!!! Important
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* This API is replaced by a Control-plane API to submit jobs, which is now the recommended method for cluster versions of 2.18 and above.
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* If you are looking to automate tasks with older versions of Run:ai, it's best to use the [Run:ai Command-line interface](../Researcher/cli-reference/Introduction.md) which provides forward compatibility.
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