Time-Slicing GPUs in Kubernetes

Understanding Time-Slicing GPUs

The NVIDIA GPU Operator enables oversubscription of GPUs through a set of extended options for the NVIDIA Kubernetes Device Plugin. GPU time-slicing enables workloads that are scheduled on oversubscribed GPUs to interleave with one another.

This mechanism for enabling time-slicing of GPUs in Kubernetes enables a system administrator to define a set of replicas for a GPU, each of which can be handed out independently to a pod to run workloads on. Unlike Multi-Instance GPU (MIG), there is no memory or fault-isolation between replicas, but for some workloads this is better than not being able to share at all. Internally, GPU time-slicing is used to multiplex workloads from replicas of the same underlying GPU.

Note

A typical resource request provides exclusive access to GPUs. A request for a time-sliced GPU provides shared access. A request for more than one time-sliced GPU does not guarantee that the pod receives access to a proportional amount of GPU compute power.

A request for more than one time-sliced GPU only specifies that the pod receives access to a GPU that is shared by other pods. Each pod can run as many processes on the underlying GPU without a limit. The GPU simply provides an equal share of time to all GPU processes, across all of the pods.

You can apply a cluster-wide default time-slicing configuration. You can also apply node-specific configurations. For example, you can apply a time-slicing configuration to nodes with Tesla-T4 GPUs only and not modify nodes with other GPU models.

You can combine the two approaches by applying a cluster-wide default configuration and then label nodes so that those nodes receive a node-specific configuration.

Comparison: Time-Slicing and Multi-Instance GPU

The latest generations of NVIDIA GPUs provide an operation mode called Multi-Instance GPU (MIG). MIG allows you to partition a GPU into several smaller, predefined instances, each of which looks like a mini-GPU that provides memory and fault isolation at the hardware layer. You can share access to a GPU by running workloads on one of these predefined instances instead of the full native GPU.

MIG support was added to Kubernetes in 2020. Refer to Supporting MIG in Kubernetes for details on how this works.

Time-slicing trades the memory and fault-isolation that is provided by MIG for the ability to share a GPU by a larger number of users. Time-slicing also provides a way to provide shared access to a GPU for older generation GPUs that do not support MIG. However, you can combine MIG and time-slicing to provide shared access to MIG instances.

Support Platforms and Resource Types

GPU time-slicing can be used with bare-metal applications, virtual machines with GPU passthrough, and virtual machines with NVIDIA vGPU.

Currently, the only supported resource types are nvidia.com/gpu and any of the resource types that emerge from configuring a node with the mixed MIG strategy.

Limitations

  • DCGM-Exporter does not support associating metrics to containers when GPU time-slicing is enabled with the NVIDIA Kubernetes Device Plugin.

  • The Operator does not monitor changes to a time-slicing config map. Refer to Updating a Time-Slicing Config Map.

Changes to Node Labels

In addition to the standard node labels that GPU Feature Discovery (GFD) applies to nodes, the following label is also applied after you configure GPU time-slicing for a node:

nvidia.com/<resource-name>.replicas = <replicas-count>

Where <replicas-count> is the factor by which each resource of <resource-name> is oversubscribed.

Additionally, by default, the nvidia.com/<resource-name>.product label is modified:

nvidia.com/<resource-name>.product = <product-name>-SHARED

For example, on an NVIDIA DGX A100 machine, depending on the time-slicing configuration, the labels can be similar to the following example:

nvidia.com/gpu.replicas = 8
nvidia.com/gpu.product = A100-SXM4-40GB-SHARED

Using these labels, you can request time-sliced access to a GPU or exclusive access to a GPU in the same way that you traditionally specify a node selector to request one GPU model over another. That is, the -SHARED product name suffix ensures that you can specify a node selector to assign pods to nodes with time-sliced GPUs.

The migStrategy configuration option has an effect on the node label for the product name. When renameByDefault=false, the default value, and migStrategy=single, both the MIG profile name and the -SHARED suffix are appended to the product name, such as the following example:

nvidia.com/gpu.product = A100-SXM4-40GB-MIG-1g.5gb-SHARED

If you set renameByDefault=true, then the value of the nvidia.com/gpu.product node label is not modified.

Configuration

About Configuring GPU Time-Slicing

You configure GPU time-slicing by performing the following high-level steps:

  • Add a config map to the namespace that is used by the GPU operator.

  • Configure the cluster policy so that the device plugin uses the config map.

  • Apply a label to the nodes that you want to configure for GPU time-slicing.

On a machine with one GPU, the following config map configures Kubernetes so that the node advertises four GPU resources. A machine with two GPUs advertises eight GPUs, and so on.

Sample Config Map

apiVersion: v1
kind: ConfigMap
metadata:
  name: time-slicing-config
data:
  any: |-
    version: v1
    flags:
      migStrategy: none
    sharing:
      timeSlicing:
        renameByDefault: false
        failRequestsGreaterThanOne: false
        resources:
          - name: nvidia.com/gpu
            replicas: 4

The following table describes the key fields in the config map.

Field

Type

Description

data.<key>

string

Specifies the time-slicing configuration name.

You can specify multiple configurations if you want to assign node-specific configurations. In the preceding example, the value for key is any.

flags.migStrategy

string

Specifies how to label MIG devices for the nodes that receive the time-slicing configuration. Specify one of none, single, or mixed.

The default value is none.

renameByDefault

boolean

When set to true, each resource is advertised under the name <resource-name>.shared instead of <resource-name>.

For example, if this field is set to true and the resource is typically nvidia.com/gpu, the nodes that are configured for time-sliced GPU access then advertise the resource as nvidia.com/gpu.shared. Setting this field to true can be helpful if you want to schedule pods on GPUs with shared access by specifying <resource-name>.shared in the resource request.

When this field is set to false, the advertised resource name, such as nvidia.com/gpu, is not modified. However, label for the product name is suffixed with -SHARED. For example, if the output of kubectl describe node shows the node label nvidia.com/gpu.product=Tesla-T4, then after the node is configured for time-sliced GPU access, the label becomes nvidia.com/gpu.product=Tesla-T4-SHARED. In this case, you can specify a node selector that includes the -SHARED suffix to schedule pods on GPUs with shared access.

The default value is false.

failRequestsGreaterThanOne

boolean

The purpose of this field is to enforce awareness that requesting more than one GPU replica does not result in receiving more proportional access to the GPU.

For example, if 4 GPU replicas are available and two pods request 1 GPU each and a third pod requests 2 GPUs, the applications in the three pods have an equal share of GPU compute time. Specifically, the pod that requests 2 GPUs does not receive twice as much compute time as the pods that request 1 GPU.

When set to true, a resource request for more than one GPU fails with an UnexpectedAdmissionError. In this case, you must manually delete the pod, update the resource request, and redeploy.

resources.name

string

Specifies the resource type to make available with time-sliced access, such as nvidia.com/gpu, nvidia.com/mig-1g.5gb, and so on.

resources.replicas

integer

Specifies the number of time-sliced GPU replicas to make available for shared access to GPUs of the specified resource type.

Applying One Cluster-Wide Configuration

Perform the following steps to configure GPU time-slicing if you already installed the GPU operator and want to apply the same time-slicing configuration on all nodes in the cluster.

  1. Create a file, such as time-slicing-config-all.yaml, with contents like the following example:

    apiVersion: v1
kind: ConfigMap
metadata:
  name: time-slicing-config-all
data:
  any: |-
    version: v1
    flags:
      migStrategy: none
    sharing:
      timeSlicing:
        resources:
        - name: nvidia.com/gpu
          replicas: 4

  2. Add the config map to the same namespace as the GPU operator:

    $ kubectl create -n gpu-operator -f time-slicing-config-all.yaml

  3. Configure the device plugin with the config map and set the default time-slicing configuration:

    $ kubectl patch clusterpolicies.nvidia.com/cluster-policy \
    -n gpu-operator --type merge \
    -p '{"spec": {"devicePlugin": {"config": {"name": "time-slicing-config-all", "default": "any"}}}}'

  4. Optional: Confirm that the gpu-feature-discovery and nvidia-device-plugin-daemonset pods restart.

    $ kubectl get events -n gpu-operator --sort-by='.lastTimestamp'

    Example Output

    LAST SEEN   TYPE      REASON             OBJECT                                     MESSAGE
33s         Normal    Created            pod/nvidia-device-plugin-daemonset-cffds   Created container toolkit-validation
33s         Normal    Started            pod/nvidia-device-plugin-daemonset-cffds   Started container toolkit-validation
33s         Normal    Started            pod/gpu-feature-discovery-rvlg9            Started container toolkit-validation
33s         Normal    Created            pod/gpu-feature-discovery-rvlg9            Created container toolkit-validation
33s         Normal    Pulled             pod/gpu-feature-discovery-rvlg9            Container image "nvcr.io/nvidia/cloud-native/gpu-operator-validator:v22.9.1" already present on machine
33s         Normal    Pulled             pod/nvidia-device-plugin-daemonset-cffds   Container image "nvcr.io/nvidia/cloud-native/gpu-operator-validator:v22.9.1" already present on machine
32s         Normal    Created            pod/nvidia-device-plugin-daemonset-cffds   Created container config-manager-init
32s         Normal    Pulled             pod/nvidia-device-plugin-daemonset-cffds   Container image "nvcr.io/nvidia/k8s-device-plugin:v0.13.0-ubi8" already present on machine
32s         Normal    Pulled             pod/gpu-feature-discovery-rvlg9            Container image "nvcr.io/nvidia/k8s-device-plugin:v0.13.0-ubi8" already present on machine
32s         Normal    Created            pod/gpu-feature-discovery-rvlg9            Created container config-manager-init
32s         Normal    Started            pod/gpu-feature-discovery-rvlg9            Started container config-manager-init
32s         Normal    Started            pod/nvidia-device-plugin-daemonset-cffds   Started container config-manager-init
31s         Normal    Created            pod/gpu-feature-discovery-rvlg9            Created container config-manager
31s         Normal    Started            pod/gpu-feature-discovery-rvlg9            Started container gpu-feature-discovery
31s         Normal    Created            pod/gpu-feature-discovery-rvlg9            Created container gpu-feature-discovery
31s         Normal    Pulled             pod/gpu-feature-discovery-rvlg9            Container image "nvcr.io/nvidia/gpu-feature-discovery:v0.7.0-ubi8" already present on machine
31s         Normal    Started            pod/nvidia-device-plugin-daemonset-cffds   Started container config-manager
31s         Normal    Created            pod/nvidia-device-plugin-daemonset-cffds   Created container config-manager
31s         Normal    Pulled             pod/nvidia-device-plugin-daemonset-cffds   Container image "nvcr.io/nvidia/k8s-device-plugin:v0.13.0-ubi8" already present on machine
31s         Normal    Started            pod/nvidia-device-plugin-daemonset-cffds   Started container nvidia-device-plugin
31s         Normal    Created            pod/nvidia-device-plugin-daemonset-cffds   Created container nvidia-device-plugin
31s         Normal    Pulled             pod/nvidia-device-plugin-daemonset-cffds   Container image "nvcr.io/nvidia/k8s-device-plugin:v0.13.0-ubi8" already present on machine
31s         Normal    Pulled             pod/gpu-feature-discovery-rvlg9            Container image "nvcr.io/nvidia/k8s-device-plugin:v0.13.0-ubi8" already present on machine
31s         Normal    Started            pod/gpu-feature-discovery-rvlg9            Started container config-manager

Refer to Verifying the GPU Time-Slicing Configuration.

Applying Multiple Node-Specific Configurations

An alternative to applying one cluster-wide configuration is to specify multiple time-slicing configurations in the config map and to apply labels node-by-node to control which configuration is applied to which nodes.

  1. Create a file, such as time-slicing-config-fine.yaml, with contents like the following example:

    apiVersion: v1
kind: ConfigMap
metadata:
  name: time-slicing-config-fine
data:
  a100-40gb: |-
    version: v1
    flags:
      migStrategy: mixed
    sharing:
      timeSlicing:
        resources:
        - name: nvidia.com/gpu
          replicas: 8
        - name: nvidia.com/mig-1g.5gb
          replicas: 2
        - name: nvidia.com/mig-2g.10gb
          replicas: 2
        - name: nvidia.com/mig-3g.20gb
          replicas: 3
        - name: nvidia.com/mig-7g.40gb
          replicas: 7
  tesla-t4: |-
    version: v1
    flags:
      migStrategy: none
    sharing:
      timeSlicing:
        resources:
        - name: nvidia.com/gpu
          replicas: 4

  2. Add the config map to the same namespace as the GPU operator:

    $ kubectl create -n gpu-operator -f time-slicing-config-fine.yaml

  3. Configure the device plugin with the config map and set the default time-slicing configuration:

    $ kubectl patch clusterpolicies.nvidia.com/cluster-policy \
    -n gpu-operator --type merge \
    -p '{"spec": {"devicePlugin": {"config": {"name": "time-slicing-config-fine"}}}}'

    Because the specification does not include the devicePlugin.config.default field, when the device plugin pods redeploy, they do not automatically apply the time-slicing configuration to all nodes.

  4. Optional: Confirm that the gpu-feature-discovery and nvidia-device-plugin-daemonset pods restart.

    $ kubectl get events -n gpu-operator --sort-by='.lastTimestamp'

    Example Output

    LAST SEEN   TYPE      REASON             OBJECT                                     MESSAGE
33s         Normal    Created            pod/nvidia-device-plugin-daemonset-cffds   Created container toolkit-validation
33s         Normal    Started            pod/nvidia-device-plugin-daemonset-cffds   Started container toolkit-validation
33s         Normal    Started            pod/gpu-feature-discovery-rvlg9            Started container toolkit-validation
33s         Normal    Created            pod/gpu-feature-discovery-rvlg9            Created container toolkit-validation
33s         Normal    Pulled             pod/gpu-feature-discovery-rvlg9            Container image "nvcr.io/nvidia/cloud-native/gpu-operator-validator:v22.9.1" already present on machine
33s         Normal    Pulled             pod/nvidia-device-plugin-daemonset-cffds   Container image "nvcr.io/nvidia/cloud-native/gpu-operator-validator:v22.9.1" already present on machine
32s         Normal    Created            pod/nvidia-device-plugin-daemonset-cffds   Created container config-manager-init
32s         Normal    Pulled             pod/nvidia-device-plugin-daemonset-cffds   Container image "nvcr.io/nvidia/k8s-device-plugin:v0.13.0-ubi8" already present on machine
32s         Normal    Pulled             pod/gpu-feature-discovery-rvlg9            Container image "nvcr.io/nvidia/k8s-device-plugin:v0.13.0-ubi8" already present on machine
32s         Normal    Created            pod/gpu-feature-discovery-rvlg9            Created container config-manager-init
32s         Normal    Started            pod/gpu-feature-discovery-rvlg9            Started container config-manager-init
32s         Normal    Started            pod/nvidia-device-plugin-daemonset-cffds   Started container config-manager-init
31s         Normal    Created            pod/gpu-feature-discovery-rvlg9            Created container config-manager
31s         Normal    Started            pod/gpu-feature-discovery-rvlg9            Started container gpu-feature-discovery
31s         Normal    Created            pod/gpu-feature-discovery-rvlg9            Created container gpu-feature-discovery
31s         Normal    Pulled             pod/gpu-feature-discovery-rvlg9            Container image "nvcr.io/nvidia/gpu-feature-discovery:v0.7.0-ubi8" already present on machine
31s         Normal    Started            pod/nvidia-device-plugin-daemonset-cffds   Started container config-manager
31s         Normal    Created            pod/nvidia-device-plugin-daemonset-cffds   Created container config-manager
31s         Normal    Pulled             pod/nvidia-device-plugin-daemonset-cffds   Container image "nvcr.io/nvidia/k8s-device-plugin:v0.13.0-ubi8" already present on machine
31s         Normal    Started            pod/nvidia-device-plugin-daemonset-cffds   Started container nvidia-device-plugin
31s         Normal    Created            pod/nvidia-device-plugin-daemonset-cffds   Created container nvidia-device-plugin
31s         Normal    Pulled             pod/nvidia-device-plugin-daemonset-cffds   Container image "nvcr.io/nvidia/k8s-device-plugin:v0.13.0-ubi8" already present on machine
31s         Normal    Pulled             pod/gpu-feature-discovery-rvlg9            Container image "nvcr.io/nvidia/k8s-device-plugin:v0.13.0-ubi8" already present on machine
31s         Normal    Started            pod/gpu-feature-discovery-rvlg9            Started container config-manager

  5. Apply a label to the nodes by running one or more of the following commands:

    • Apply a label to nodes one-by-one by specifying the node name:

      $ kubectl label node <node-name> nvidia.com/device-plugin.config=tesla-t4

    • Apply a label to several nodes at one time by specifying a label selector:

      $ kubectl label node \
    --selector=nvidia.com/gpu.product=Tesla-T4 \
    nvidia.com/device-plugin.config=tesla-t4

Refer to Verifying the GPU Time-Slicing Configuration.

Configuring Time-Slicing Before Installing the NVIDIA GPU Operator

You can enable time-slicing with the NVIDIA GPU Operator by passing the devicePlugin.config.name=<config-map-name> parameter during installation.

Perform the following steps to configure time-slicing before installing the operator:

  1. Create the namespace for the operator:

    $ kubectl create namespace gpu-operator

  2. Create a file, such as time-slicing-config.yaml, with the config map contents.

    Refer to the Applying One Cluster-Wide Configuration or Applying Multiple Node-Specific Configurations sections.

  3. Add the config map to the same namespace as the GPU operator:

    $ kubectl create -f time-slicing-config.yaml

  4. Install the operator with Helm:

    $ helm install gpu-operator nvidia/gpu-operator \
    -n gpu-operator \
    --set devicePlugin.config.name=time-slicing-config

  5. Refer to either Applying One Cluster-Wide Configuration or Applying Multiple Node-Specific Configurations and perform the following tasks:

    • Configure the device plugin by running the kubectl patch command.

    • Apply labels to nodes if you added a config map with node-specific configurations.

After installation, refer to Verifying the GPU Time-Slicing Configuration.

Updating a Time-Slicing Config Map

The Operator does not monitor the time-slicing config maps. As a result, if you modify a config map, the device plugin pods do not restart and do not apply the modified configuration.

To apply the modified config map, manually restart the device plugin pods:

$ kubectl rollout restart -n gpu-operator daemonset/nvidia-device-plugin-daemonset

Currently running workloads are not affected and continue to run, though NVIDIA recommends performing the restart during a maintenance period.

Verifying the GPU Time-Slicing Configuration

Perform the following steps to verify that the time-slicing configuration is applied successfully:

  1. Confirm that the node advertises additional GPU resources:

    $ kubectl describe node <node-name>

    Example Output

    The example output varies according to the GPU in your node and the configuration that you apply.

    The following output applies when renameByDefault is set to false, the default value. The key considerations are as follows:

    • The nvidia.com/gpu.count label reports the number of physical GPUs in the machine.

    • The nvidia.com/gpu.product label includes a -SHARED suffix to the product name.

    • The nvidia.com/gpu.replicas label matches the reported capacity.

    ...
Labels:
                  nvidia.com/gpu.count=4
                  nvidia.com/gpu.product=Tesla-T4-SHARED
                  nvidia.com/gpu.replicas=4
Capacity:
  nvidia.com/gpu: 16
  ...
Allocatable:
  nvidia.com/gpu: 16
  ...

    The following output applies when renameByDefault is set to true. The key considerations are as follows:

    • The nvidia.com/gpu.count label reports the number of physical GPUs in the machine.

    • The nvidia.com/gpu capacity reports 0.

    • The nvidia.com/gpu.shared capacity equals the number of physical GPUs multiplied by the specified number of GPU replicas to create.

    ...
Labels:
                  nvidia.com/gpu.count=4
                  nvidia.com/gpu.product=Tesla-T4
                  nvidia.com/gpu.replicas=4
Capacity:
  nvidia.com/gpu:        0
  nvidia.com/gpu.shared: 16
  ...
Allocatable:
  nvidia.com/gpu:        0
  nvidia.com/gpu.shared: 16
  ...

  2. Optional: Deploy a workload to validate GPU time-slicing:

    • Create a file, such as time-slicing-verification.yaml, with contents like the following:

      apiVersion: apps/v1
kind: Deployment
metadata:
  name: time-slicing-verification
  labels:
    app: time-slicing-verification
spec:
  replicas: 5
  selector:
    matchLabels:
      app: time-slicing-verification
  template:
    metadata:
      labels:
        app: time-slicing-verification
    spec:
      tolerations:
        - key: nvidia.com/gpu
          operator: Exists
          effect: NoSchedule
      hostPID: true
      containers:
        - name: cuda-sample-vector-add
          image: "nvcr.io/nvidia/k8s/cuda-sample:vectoradd-cuda11.7.1-ubuntu20.04"
          command: ["/bin/bash", "-c", "--"]
          args:
            - while true; do /cuda-samples/vectorAdd; done
          resources:
           limits:
             nvidia.com/gpu: 1

    • Create the deployment with multiple replicas:

      $ kubectl apply -f time-slicing-verification.yaml

    • Verify that all five replicas are running:

      $ kubectl get pods

      Example Output

      NAME                                         READY   STATUS    RESTARTS   AGE
time-slicing-verification-7cdc7f87c5-lkd9d   1/1     Running   0          23s
time-slicing-verification-7cdc7f87c5-rrzq7   1/1     Running   0          23s
time-slicing-verification-7cdc7f87c5-s8qwk   1/1     Running   0          23s
time-slicing-verification-7cdc7f87c5-xhmb7   1/1     Running   0          23s
time-slicing-verification-7cdc7f87c5-zsncp   1/1     Running   0          23s

    • View the logs from one of the pods:

      $ kubectl logs deploy/time-slicing-verification

      Example Output

      Found 5 pods, using pod/time-slicing-verification-7cdc7f87c5-s8qwk
[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
[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
...

    • Stop the deployment:

      $ kubectl delete -f time-slicing-verification.yaml

    Example Output

    deployment.apps "time-slicing-verification" deleted

References