# Custom Inference Templates This section explains how to create custom inference templates for reuse during workload submission. To manage templates, see [Workload Templates](/self-hosted/2.23/workloads-in-nvidia-run-ai/workload-templates.md). {% hint style="info" %} **Note** Flexible workload templates is enabled by default and applies only to flexible workload submission (enabled by default). If unavailable, contact your administrator to enable it under **General settings** → Workloads → Flexible workload templates. {% endhint %} ## Linking Assets When loading an existing asset, environment or compute resource, into a template, you can choose whether to link the asset or use it without linking. Linked assets remain connected to the template. Any updates made to the original environment or compute resource are automatically reflected in the template. While linked, the asset fields in the template cannot be modified. {% hint style="info" %} **Note** Linking data source assets is currently not supported. {% endhint %} ## Adding a New Template 1. To add a new template, go to Workload manager → Templates. 2. Click **+NEW TEMPLATE** and select **Inference** from the dropdown menu**.** 3. Within the new inference template form, select the **scope**. 4. Select **custom** inference from **Inference type** (if applicable) 5. Enter a unique **name** for the inference template. If the name already exists in the project, you will be requested to submit a different name. 6. Click **CONTINUE** ### Setting Up an Environment {% hint style="info" %} **Note** NGC public registry is disabled by default. If unavailable, your administrator must enable it under **General settings** → Workloads → NGC public registry. When the NGC public registry is disabled, only the **Image URL** field is available. {% endhint %} **Load from existing setup** 1. Click the **load** icon. A side pane appears, displaying a list of available environments. Select an environment from the list. 2. Alternatively, click the **➕** icon in the side pane to create a new environment. For step-by-step instructions, see [Environments](/self-hosted/2.23/workloads-in-nvidia-run-ai/assets/environments.md). 3. Choose whether to **link** the environment when applying it to the template. See [Linking assets](#linking-assets) for more details. **Provide your own settings** Manually configure the settings below as needed. **Configure environment** 1. Set the **environment image**: * Select **Custom image** and add the **Image URL** or update the URL of the existing setup * **Select from the NGC public registry** and choose the **image name** and **tag** from the dropdown. 2. Set the condition for pulling the image by selecting the **image pull policy**. It is recommended to pull the image only if it's not already present on the host. 3. Set an inference **serving endpoint**. The connection protocol and the container port are defined within the environment: * Select **HTTP** or **gRPC** and enter a corresponding **container port** * Modify who can access the endpoint. See [Accessing the inference workload](/self-hosted/2.23/workloads-in-nvidia-run-ai/using-inference/custom-inference.md#accessing-the-inference-workload) for more details: * By default, **Public** is selected giving everyone within the network access to the endpoint with no authentication * If you select **All authenticated users and applications**, access is given to everyone within the organization’s account that can log in (to NVIDIA Run:ai or SSO). * For **Specific group(s)**, enter **group names** as they appear in your identity provider. You must be a member of one of the groups listed to have access. * For **Specific user(s) and application(s)**, enter a valid user email or name. If you remove yourself, you will lose access. 4. Set the connection for your **tool(s)**. If you are loading from existing setup, the tools are configured as part of the environment. * Select the connection type - **External URL** or **NodePort:** * **Auto generate** - A unique URL / port is automatically created for each workload using the environment. * **Custom URL** / **Custom port** - Manually define the URL or port. For custom port, make sure to enter a port between `30000` and `32767.` If the node port is already in use, the workload will fail and display an error message. * Modify who can **access** the tool: * By default, **All authenticated users** **and applications** is selected giving access to everyone within the organization’s account. * For **Specific group(s)**, enter **group names** as they appear in your identity provider. You must be a member of one of the groups listed to have access to the tool. * For **Specific user(s) and application(s)**, enter a valid user email or name. If you remove yourself, you will lose access to the tool. 5. Set the **command and arguments** for the container running the workload. If no command is added, the container will use the image’s default command (entry-point). * Modify the existing command or click **+COMMAND & ARGUMENTS** to add a new command. * Set multiple arguments separated by spaces, using the following format (e.g.: `--arg1=val1`). 6. Set the **environment variable(s)**: * Modify the existing environment variable(s) or click **+ENVIRONMENT VARIABLE**. The existing environment variables may include instructions to guide you with entering the correct values. * You can either select **Custom** to define your own variable, or choose from a predefined list of [**Secrets**](/self-hosted/2.23/workloads-in-nvidia-run-ai/assets/credentials.md#creating-secrets-in-advance) or [**ConfigMaps**](/self-hosted/2.23/workloads-in-nvidia-run-ai/assets/datasources.md#creating-configmaps-in-advance). Some environment variables are automatically injected by NVIDIA Run:ai. See [Built-in workload environment variables](/self-hosted/2.23/workloads-in-nvidia-run-ai/workloads.md#built-in-workload-environment-variables) for more details. 7. Enter a path pointing to the **container's working directory** 8. Set where the UID, GID, and supplementary groups for the container should be taken from. If you select **Custom**, you’ll need to manually enter the **UID,** **GID and** **Supplementary groups values**. 9. Select additional Linux capabilities for the container from the drop-down menu. This grants certain privileges to a container without granting all the root user's privileges. ### Setting Up Compute Resources {% hint style="info" %} **Note** * GPU memory limit is disabled by default. If unavailable, your administrator must enable it under **General settings** → Resources → GPU resource optimization. * Replica autoscaling and toleration(s) can still be modified even when the compute resource asset is linked. {% endhint %} **Load from existing setup** 1. Click the **load** icon. A side pane appears, displaying a list of available compute resources. Select a compute resource from the list. 2. Alternatively, click the **➕** icon in the side pane to create a new compute resource. For step-by-step instructions, see [Compute resources](/self-hosted/2.23/workloads-in-nvidia-run-ai/assets/compute-resources.md). 3. Choose whether to **link** the compute resource when applying it to the template. See [Linking assets](#linking-assets) for more details. **Provide your own settings** Manually configure the settings below as needed. **Configure compute resources** 1. Set the number of **GPU devices** per pod (physical GPUs). 2. Enable **GPU fractioning** to set the GPU memory per device using either a fraction of a GPU device’s memory **(% of device)** or a GPU memory unit **(MB/GB)**: * **Request** - The minimum GPU memory allocated per device. Each pod in the workload receives at least this amount per device it uses. * **Limit** - The maximum GPU memory allocated per device. Each pod in the workload receives **at most** this amount of GPU memory for each device(s) the pod utilizes. This is disabled by default, to enable see the above note. 3. Set the **CPU resources** * Set **CPU compute resources** per pod by choosing the unit (**cores** or **millicores**): * **Request** - The minimum amount of CPU compute provisioned per pod. Each running pod receives this amount of CPU compute. * **Limit** - The maximum amount of CPU compute a pod can use. Each pod receives **at most** this amount of CPU compute. By default, the limit is set to **Auto** which means that the pod may consume up to the node's maximum available CPU compute resources. * Set the **CPU memory per pod** by selecting the unit (**MB** or **GB**): * **Request** - The minimum amount of CPU memory provisioned per pod. Each running pod receives this amount of CPU memory. * **Limit** - The maximum amount of CPU memory a pod can use. Each pod receives at most this amount of CPU memory. By default, the limit is set to **Auto** which means that the pod may consume up to the node's maximum available CPU memory resources. 4. Set **extended resource(s)** * Enable **Increase shared memory size** to allow the shared memory size available to the pod to increase from the default 64MB to the node's total available memory or the CPU memory limit, if set above. * Click **+EXTENDED RESOURCES** to add resource/quantity pairs. For more information on how to set extended resources, see the [Extended resources](https://kubernetes.io/docs/tasks/configure-pod-container/extended-resource/) and [Quantity](https://kubernetes.io/docs/reference/kubernetes-api/common-definitions/quantity/) guides. 5. Set the **minimum and maximum** number of replicas to be scaled up and down to meet the changing demands of inference services: * If the number of minimum and maximum replicas are different, autoscaling will be triggered and you'll need to set **conditions for creating a new replica**. A replica will be created every time a condition is met. When a condition is no longer met after a replica was created, the replica will be automatically deleted to save resources. * Select one of the **variables to set** the conditions for creating a new replica. The variable's values will be monitored via the container's port. When you set a **value**, this value is the threshold at which autoscaling is triggered. 6. Set when the replicas should be automatically **scaled down to zero**. This allows compute resources to be freed up when the model is inactive (i.e., there are no requests being sent). Automatic scaling to zero is enabled only when the minimum number of replicas in the previous step is set to 0. 7. Click **+TOLERATION** to allow the workload to be scheduled on a node with a matching taint. Select the **operator** and the **effect**: * If you select **Exists**, the effect will be applied if the key exists on the node. * If you select **Equals,** the effect will be applied if the key and the value set match the value on the node. ### Setting Up Data & Storage {% hint style="info" %} **Note** * Data volumes is enabled by default. If unavailable, contact your administrator to enable it under **General settings** → Workloads → Data volumes. * If Data volumes is not enabled, **Data & storage** appears as **Data sources** only, and no data volumes will be available. * S3 data sources are not supported for inference workloads. {% endhint %} **Load from existing setup** 1. Click the **load** icon. A side pane appears, displaying a list of available data sources/volumes. Select a data source/volume from the list. 2. Optionally, customize any of the data source's predefined fields as shown below. The changes will apply to this template only and will not affect the selected data source: * **Container path** - Enter the **container path** to set the **data target location**. * **ConfigMap** **sub-path** - Specify a **sub-path** (file/key) inside the ConfigMap to mount (for example, `app.properties`). This lets you mount a single file from an existing ConfigMap. 3. Alternatively, click the **➕** icon in the side pane to create a new data source/volume. For step-by-step instructions, see [Data sources](/self-hosted/2.23/workloads-in-nvidia-run-ai/assets/datasources.md) or [Data volumes](/self-hosted/2.23/workloads-in-nvidia-run-ai/assets/data-volumes.md). **Configure data sources for a one-time configuration** {% hint style="info" %} **Note** [PVCs](/self-hosted/2.23/workloads-in-nvidia-run-ai/assets/datasources.md#pvc), [Secrets](/self-hosted/2.23/workloads-in-nvidia-run-ai/assets/datasources.md#secret), [ConfigMaps](/self-hosted/2.23/workloads-in-nvidia-run-ai/assets/datasources.md#configmap) and [Data volumes](/self-hosted/2.23/workloads-in-nvidia-run-ai/assets/data-volumes.md) cannot be added as a one-time configuration. {% endhint %} 1. Click the **➕** icon and choose the data source from the dropdown menu. You can add multiple data sources. 2. Once selected, set the **data origin** according to the required fields and enter the **container path** to set the **data target location**. 3. Select **Volume** to allocate a storage space to your workload that is persistent across restarts: * Set the **Storage class** to **None** or select an existing storage class from the list. To add new storage classes, and for additional information, see [Kubernetes storage classes](/self-hosted/2.23/infrastructure-setup/procedures/shared-storage.md). If the administrator defined the storage class configuration, the rest of the fields will appear accordingly. * Select one or more **access mode(s)** and define the **claim size** and its **units**. * Select the **volume mode.** If you select **Filesystem** (default), the volume will be mounted as a filesystem, enabling the usage of directories and files. If you select **Block**, the volume is exposed as a block storage, which can be formatted or used directly by applications without a filesystem. * Set the **Container path** with the volume target location. ### Setting Up General Settings {% hint style="info" %} **Note** The following general settings are optional. {% endhint %} 1. Set the **workload priority**. Choose the appropriate priority level for the workload. Higher-priority workloads are scheduled before lower-priority ones. See [Workload priority control](/self-hosted/2.23/platform-management/runai-scheduler/scheduling/workload-priority-control.md) for more details. 2. Set the **workload initialization timeout.** This is the maximum amount of time the system will wait for the workload to start and become ready. If the workload does not start within this time, it will automatically fail. Enter a value between 5 seconds and 60 minutes. If you do not set a value, the default is taken from [Knative’s max-revision-timeout-seconds](https://knative.dev/docs/serving/configuration/config-defaults/#max-revision-timeout-seconds). 3. Set the **request timeout.** This defines the maximum time allowed to process an end-user request. If the system does not receive a response within this time, the request will be ignored. Enter a value between 5 seconds and 10 minutes. If you do not set a value, the default is taken from [Knative’s revision-timeout-seconds](https://knative.dev/docs/serving/configuration/config-defaults/#revision-timeout-seconds). 4. Set **annotations(s).** Kubernetes annotations are key-value pairs attached to the workload. They are used for storing additional descriptive metadata to enable documentation, monitoring and automation. 5. Set **labels(s).** Kubernetes labels are key-value pairs attached to the workload. They are used for categorizing to enable querying. ### Completing the Template 1. Before finalizing your template, review your configurations and make any necessary adjustments. 2. Click **CREATE TEMPLATE** ## Using API Go to the [Workload templates](https://run-ai-docs.nvidia.com/api/2.23/workloads/workload-templates) API reference to view the available actions. --- # 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. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://run-ai-docs.nvidia.com/self-hosted/2.23/workloads-in-nvidia-run-ai/workload-templates/inference-templates/custom-inference-templates.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.