> ## Documentation Index
> Fetch the complete documentation index at: https://docs.domino.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Use Intel Habana accelerators

Gaudi accelerators from Habana Labs (an Intel company) deliver low-cost-to-train deep learning models for natural language processing, object detection, and image recognition use cases.

AWS currently offers a dl1.24xlarge EC2 image with eight Gaudi accelerators available. See [Amazon EC2 DL1 Instances](https://aws.amazon.com/ec2/instance-types/dl1/).

## 1. Add node pool and hardware tiers

1. Create or add a Gaudi-enabled node to an existing node-pool in your Domino cluster.

2. Create a [hardware tier](/cloud/admin/infrastructure-and-compute/manage-compute-resources/hardware-tiers/create-hardware-tier) so users can use this resource in Domino.

   1. Select the **Use custom GPU resource name** checkbox.

   2. In **GPU Resource Name** enter `habana.ai/gaudi`.

## 2. Install the Habana Device Plugin for Kubernetes

**Prerequisite:** Admin `kubectl` permissions to your cluster

1. Use the kubectl command to add the [Habana device plugin for Kubernetes](https://docs.habana.ai/en/latest/Installation_Guide/Additional_Installation/Kubernetes_Installation/Intel_Gaudi_Kubernetes_Device_Plugin.html).

   ```shell theme={null}
   kubectl create -f
   https://vault.habana.ai/artifactory/docker-k8s-device-plugin/habana-k8s-device-plugin.yaml
   ```

2. Run the following command to verify the plugin is running:

   ```shell theme={null}
   kubectl get pods -n habana-system
   ```

## 3. Use Gaudi-enabled containers

Many Intel Habana environment containers work natively in Domino. To use a custom image to create a new environment, paste the Docker registry path into the **FROM** field.\
For instance:

```
vault.habana.ai/gaudi-docker/1.5.0/ubuntu20.04/habanalabs/tensorflow-installer-tf-cpu-2.9.1
```

See the [Habana Developer Catalog](https://www.intel.com/content/www/us/en/developer/platform/gaudi/models/catalog.html) for more pre-built container images that can be used in Domino.