# Deploy NVIDIA Cloud Functions (NVCF) in NVIDIA Run:ai NVIDIA Cloud Functions (NVCF) is a serverless API platform designed to deploy and manage AI workloads on GPUs. Through its integration with NVIDIA Run:ai, NVCF can be deployed directly onto NVIDIA Run:ai-managed GPU clusters. This allows users to take advantage of NVIDIA Run:ai's scheduling, quota management, and monitoring features. See [Supported features](#supported-features) for more details. This guides provides the required steps for integrating NVIDIA Cloud Functions with the NVIDIA Run:ai platform. ## Workload Priority By default, inference workloads in NVIDIA Run:ai are assigned a priority of `very-high`, which is non-preemptible. This behavior ensures that inference workloads, which often serve real-time or latency-sensitive traffic, are guaranteed the resources they need and will not be disrupted by other workloads. For more details, see [Workload priority control](/self-hosted/2.23/platform-management/runai-scheduler/scheduling/workload-priority-control.md). {% hint style="info" %} **Note** Changing the priority is not supported for NVCF workloads. {% endhint %} ## Setup Follow the official instructions provided in the [NVIDIA Cloud Functions](https://docs.nvidia.com/cloud-functions/user-guide/latest/cloud-function/overview.html) documentation. ### Setting up a Cluster in NVCF Cloud Functions administrators can install the NVIDIA Cluster Agent to enable existing GPU clusters as deployment targets for NVCF functions. Once installed, the cluster appears as a deployment option in the API and Cloud Functions menu, allowing authorized functions to deploy on it. See [Cluster Setup & Management](https://docs.nvidia.com/cloud-functions/user-guide/latest/cloud-function/cluster-management.html) to register, configure and verify the cluster. ### Setting up a Project in NVIDIA Run:ai Once the cluster is registered to NVCF and appears as ready, create a project in the NVIDIA Run:ai UI with an **NVCF namespace**: 1. Follow the instructions detailed in [Projects](/self-hosted/2.23/platform-management/aiinitiatives/organization/projects.md) to create a new project. 2. When setting the **Namespace**, choose **"Enter existing namespace from the cluster"** and enter **`nvcf-backend`**. 3. Assign the necessary resource quotas to the project. {% hint style="info" %} **Note** * This is the designated NVIDIA Run:ai project for all NVCF functions; other projects will not be used. * NVCF is assigned to a specific project and adheres to the project's resource quota. Ensure that your project has sufficient quota allocated to accommodate. {% endhint %} ## Deploying a Function In NVCF, function creation defines the code and resources while deployment registers the function to a GPU cluster, making it available for execution: 1. Create the function as detailed in [Function Creation](https://docs.nvidia.com/cloud-functions/user-guide/latest/cloud-function/function-creation.html#function-creation). 2. Deploy the function as detailed in [Function Deployment](https://docs.nvidia.com/cloud-functions/user-guide/latest/cloud-function/function-deployment.html). {% hint style="info" %} **Note** Using a custom Helm chart when creating a [Cloud Function](https://docs.nvidia.com/cloud-functions/user-guide/latest/cloud-function/function-creation.html) is not supported. {% endhint %} ## Managing and Monitoring After the NVCF function is deployed, it is added to the [Workloads](/self-hosted/2.23/workloads-in-nvidia-run-ai/workloads.md) table, where it can be managed and monitored: * Monitor resource usage, performance, and execution status. * Manage workload lifecycle, including scaling, logging, and troubleshooting. ## Supported Features | NVIDIA Run:ai Functionality | NVCF | | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | :--: | | [Fairness](/self-hosted/2.23/platform-management/runai-scheduler/scheduling/concepts-and-principles.md#fairness-fair-resource-distribution) | v | | [Priority and preemption](/self-hosted/2.23/platform-management/runai-scheduler/scheduling/concepts-and-principles.md#priority-and-preemption) | v | | [Over quota](/self-hosted/2.23/platform-management/runai-scheduler/scheduling/concepts-and-principles.md#over-quota) | v | | [Node pools](/self-hosted/2.23/platform-management/aiinitiatives/resources/node-pools.md) | v | | [Bin packing / Spread](/self-hosted/2.23/platform-management/runai-scheduler/scheduling/concepts-and-principles.md#placement-strategy-bin-pack-and-spread) | v | | [Multi-GPU fractions](/self-hosted/2.23/platform-management/runai-scheduler/resource-optimization/fractions.md) | | | [Multi-GPU dynamic fractions](/self-hosted/2.23/platform-management/runai-scheduler/resource-optimization/dynamic-fractions.md) | | | [Node level scheduler](/self-hosted/2.23/platform-management/runai-scheduler/resource-optimization/node-level-scheduler.md) | v | | [Multi-GPU memory swap](/self-hosted/2.23/platform-management/runai-scheduler/resource-optimization/memory-swap.md) | | | [Gang scheduling](/self-hosted/2.23/platform-management/runai-scheduler/scheduling/concepts-and-principles.md#gang-scheduling) | v | | [Monitoring](/self-hosted/2.23/infrastructure-setup/procedures/system-monitoring.md) | v | | [RBAC](/self-hosted/2.23/infrastructure-setup/authentication/overview.md#role-based-access-control-rbac-in-run-ai) | v | | Workload awareness | v | | [Workload submission](/self-hosted/2.23/workloads-in-nvidia-run-ai/workloads.md) | | | [Workload actions (stop/run)](/self-hosted/2.23/workloads-in-nvidia-run-ai/workloads.md) | | | [Workload Policies](/self-hosted/2.23/platform-management/policies/workload-policies.md) | | | [Scheduling rules](/self-hosted/2.23/platform-management/policies/scheduling-rules.md) | | --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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