Running workloads using GPUs

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.

Getting startedGetting started

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

   The folder specified in the CLI profile is used by default. You can specify a different folder 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 it, 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 kubectl and configure it to work with the created cluster.

Create a pod with a GPUCreate a pod with a GPU

1. Save your 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 information about the pod created:

   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 the Managed Service for Kubernetes cluster, delete it.