Running workloads with GPUs in Yandex Managed Service for Kubernetes

A Managed Service for Kubernetes cluster allows running workloads on GPUs (GPUs), which may be of use in tasks with special computing requirements.

To run workloads using GPUs on Managed Service for Kubernetes cluster pods:

1. Create a pod with a GPU.
2. Test the pod.

If you no longer need the resources you created, delete them.

Required paid resourcesRequired paid resources

The support cost includes:

• Managed Service for Kubernetes cluster fee: using the master and outgoing traffic (see Managed Service for Kubernetes pricing).
• Cluster nodes (VM) fee: using computing resources, operating system, and storage (see Compute Cloud pricing).
• Fee for a public IP address assigned to cluster nodes (see Virtual Private Cloud pricing).

Getting startedGetting started

1. If you do not have the Yandex Cloud CLI yet, install and initialize it.

   The folder specified when creating the CLI profile is used by default. To change the default folder, use the yc config set folder-id <folder_ID> command. You can also specify a different folder for any command using the --folder-name or --folder-id parameter.

2. Create security groups for the Managed Service for Kubernetes cluster and its node groups.

   Warning

   The configuration of security groups determines the performance and availability of the cluster and the services and applications running in it.

3. Create a Managed Service for Kubernetes cluster with any suitable configuration. When creating them, specify the security groups prepared earlier.

4. Create a Managed Service for Kubernetes node group with the following settings:

   • Platform: Select With GPU → Intel Broadwell with NVIDIA® Tesla v100.
   • GPU: Specify the required number of GPUs.
   • Security groups: Select the security groups created earlier.

5. Install kubect and configure it to work with the new cluster.

Create a pod with a GPUCreate a pod with a GPU

1. Save the GPU pod creation specification to a YAML file named cuda-vector-add.yaml:

   apiVersion: v1
   kind: Pod
   metadata:
     name: cuda-vector-add
   spec:
     restartPolicy: OnFailure
     containers:
       - name: cuda-vector-add
         # https://github.com/kubernetes/kubernetes/blob/v1.7.11/test/images/nvidia-cuda/Dockerfile
         image: "registry.k8s.io/cuda-vector-add:v0.1"
         resources:
           limits:
             nvidia.com/gpu: 1 # Request for 1 GPU.
   

   To learn more about the pod creation specification, see the Kubernetes documentation.

2. Create a pod with a GPU:

   kubectl create -f cuda-vector-add.yaml
   

Test the podTest the pod

1. View the information about the new pod:

   kubectl describe pod cuda-vector-add
   

   Result:

   Name:         cuda-vector-add
   Namespace:    default
   Priority:     0
   ...
     Normal  Pulling    16m   kubelet, cl1i7hcbti99********-ebyq  Successfully pulled image "registry.k8s.io/cuda-vector-add:v0.1"
     Normal  Created    16m   kubelet, cl1i7hcbti99********-ebyq  Created container cuda-vector-add
     Normal  Started    16m   kubelet, cl1i7hcbti99********-ebyq  Created container
   

2. View the pod logs:

   kubectl logs -f cuda-vector-add
   

   Result:

   [Vector addition of 50000 elements]
   Copy input data from the host memory to the CUDA device
   CUDA kernel launch with 196 blocks of 256 threads
   Copy output data from the CUDA device to the host memory
   Test PASSED
   Done
   

Delete the resources you createdDelete the resources you created

Delete the resources you no longer need to avoid paying for them:

1. Delete the Managed Service for Kubernetes cluster.
2. If you reserved a public static IP address for your Managed Service for Kubernetes cluster, delete it.