Policy YAML Reference

A workload policy is an end-to-end solution for AI managers and administrators to control and simplify how workloads are submitted, setting best practices, enforcing limitations, and standardizing processes for AI projects within their organization.

This article explains the policy YAML fields and the possible rules and defaults that can be set for each field.

Policy YAML Fields - Reference Table

The policy fields are structured in a similar format to the workload API fields. The following tables represent a structured guide designed to help you understand and configure policies in a YAML format. It provides the fields, descriptions, defaults and rules for each workload type.

Click the link to view the value type of each field.

Fields	Description	Value type	Supported NVIDIA Run:ai workload type
args	When set, contains the arguments sent along with the command. These override the entry point of the image in the created workload	string	Workspace Standard training Distributed training Inference
command	A command to serve as the entry point of the container running the workload	string	Workspace Standard training Distributed training Inference
createHomeDir	Instructs the system to create a temporary home directory for the user within the container. Data stored in this directory is not saved when the container exists. When the runAsUser flag is set to true, this flag defaults to true as well	boolean	Workspace Standard training Distributed training Inference
environmentVariables	Set of environmentVariables to populate the container running the workload	array	Workspace Standard training Distributed training Inference
image	Specifies the image to use when creating the container running the workload	string	Workspace Standard training Distributed training Inference
imagePullPolicy	Specifies the pull policy of the image when starting t a container running the created workload. Options are: always, ifNotPresent, or never	string	Workspace Standard training Distributed training Inference
imagePullSecrets	Specifies a list of references to Kubernetes secrets in the same namespace used for pulling container images.	array	Workspace Standard training Distributed training Inference
workingDir	Container’s working directory. If not specified, the container runtime default is used, which might be configured in the container image	string	Workspace Standard training Distributed training Inference
nodeType	Nodes (machines) or a group of nodes on which the workload runs	string	Workspace Standard training Distributed training Inference
nodePools	A prioritized list of node pools for the scheduler to run the workload on. The scheduler always tries to use the first node pool before moving to the next one when the first is not available.	array	Workspace Standard training Distributed training Inference
annotations	Set of annotations to populate into the container running the workload	itemized	Workspace Standard training Distributed training Inference
labels	Set of labels to populate into the container running the workload	itemized	Workspace Standard training Distributed training Inference
terminateAfterPreemtpion	Indicates whether the job should be terminated, by the system, after it has been preempted	boolean	Workspace Standard training Distributed training
autoDeletionTimeAfterCompletionSeconds	Specifies the duration after which a finished workload (Completed or Failed) is automatically deleted. If this field is set to zero, the workload becomes eligible to be deleted immediately after it finishes.	integer	Workspace Standard training Distributed training
terminationGracePeriodSeconds	Duration in seconds the pod needs to terminate gracefully upon probe failure. The grace period is the duration in seconds after the processes running in the pod are sent a termination signal and the time when the processes are forcibly halted with a kill signal. Set this value longer than the expected cleanup time for your process. Value must be non-negative integer. The value zero indicates stop immediately via the kill signal (no opportunity to shut down).	integer	Workspace Standard training Distributed training
backoffLimit	Specifies the number of retries before marking a workload as failed	integer	Workspace Standard training Distributed training
restartPolicy	Specify the restart policy of the workload pods. Default is empty, which is determine by the framework default Enum: "Always" "Never" "OnFailure"	string	Workspace Standard training Distributed training
cleanPodPolicy	Specifies which pods will be deleted when the workload reaches a terminal state (completed/failed). The policy can be one of the following values: Running - Only pods still running when a job completes (for example, parameter servers) will be deleted immediately. Completed pods will not be deleted so that the logs will be preserved. (Default). All - All (including completed) pods will be deleted immediately when the job finishes. None - No pods will be deleted when the job completes. It will keep running pods that consume GPU, CPU and memory over time. It is recommended to set to None only for debugging and obtaining logs from running pods.	string	Distributed training
completions	Used with Hyperparameter Optimization. Specifies the number of successful pods the job should reach to be completed. The Job is marked as successful once the specified amount of pods has succeeded.	integer	Standard training
parallelism	Used with Hyperparameters Optimization. Specifies the maximum desired number of pods the workload should run at any given time.	itemized	Standard training
exposedUrls	Specifies a set of exported URLs (e.g. ingress) from the container running the created workload.	itemized	Workspace Standard training Distributed training Inference
relatedUrls	Specifies a set of URLs related to the workload. For example, a URL to an external server providing statistics or logging about the workload.	itemized	Workspace Standard training Distributed training Inference
PodAffinitySchedulingRule	Indicates if we want to use the Pod affinity rule as: the “hard” (required) or the “soft” (preferred) option.	string	Workspace Standard training Distributed training Inference
podAffinityTopology	Specifies the Pod Affinity Topology to be used for scheduling the job.	string	Workspace Standard training Distributed training Inference
category	Specifies the workload category assigned to the workload. Categories are used to classify and monitor different types of workloads within the NVIDIA Run:ai platform.	string	Workspace Standard training Distributed training Inference
sshAuthMountPath	Specifies the directory where SSH keys are mounted	string	Distributed training (MPI only)
mpiLauncherCreationPolicy	Define s whether the MPI Launcher is created in parallel with the workers, or if its creation is postponed until all workers are in Ready state. This prevents failures when the launcher attempts to connect to workers that are not yet ready. Enum: AtStartup, WaitForWorkersReady	string	Distributed training (MPI only)
ports	Specifies a set of ports exposed from the container running the created workload. More information in Ports fields below.	itemized	Workspace Standard training Distributed training Inference
probes	Specifies the ReadinessProbe to use to determine if the container is ready to accept traffic. More information in Probes fields below	-	Workspace Standard training Distributed training Inference
tolerations	Toleration rules which apply to the pods running the workload. Toleration rules guide (but do not require) the system to which node each pod can be scheduled to or evicted from, based on matching between those rules and the set of taints defined for each Kubernetes node.	itemized	Workspace Standard training Distributed training Inference
priorityClass	Specifies the priority class of the workload. The default values are: Workspace - High Training / distributed training - Low Inference - Very high You can change it to any of the following valid values to adjust the workload's scheduling behavior: very-low, low, medium- low, medium, medium-high, high, very-high.	string	Workspace Standard training Distributed training Inference
nodeAffinityRequired	If the affinity requirements specified by this field are not met at scheduling time, the pod will not be scheduled onto the node. If the affinity requirements specified by this field cease to be met at some point during pod execution (e.g. due to an update), the system may or may not try to eventually evict the pod from its node.	array	Workspace Standard training Distributed training Inference
storage	Contains all the fields related to storage configurations. More information in Storage fields below.	-	Workspace Standard training Distributed training Inference
security	Contains all the fields related to security configurations. More information in Security fields below.	-	Workspace Standard training Distributed training Inference
compute	Contains all the fields related to compute configurations. More information in Compute fields below.	-	Workspace Standard training Distributed training Inference
tty	Whether this container should allocate a TTY for itself, also requires 'stdin' to be true	boolean	Workspace Standard training Distributed training
stdin	Whether this container should allocate a buffer for stdin in the container runtime. If this is not set, reads from stdin in the container will always result in EOF	boolean	Workspace Standard training Distributed training
numWorkers	the number of workers that will be allocated for running the workload.	integer	Distributed training
distributedFramework	The distributed training framework used in the workload. Enum: "MPI" "PyTorch" "TF" "XGBoost" "JAX"	string	Distributed training
slotsPerWorker	Specifies the number of slots per worker used in hostfile. Defaults to 1. (applicable only for MPI)	integer	Distributed training (MPI only)
minReplicas	The lower limit for the number of worker pods to which the training job can scale down. (applicable only for PyTorch)	integer	Distributed training (PyTorch only)
maxReplicas	The upper limit for the number of worker pods that can be set by the autoscaler. Cannot be smaller than MinReplicas. (applicable only for PyTorch)	integer	Distributed training (PyTorch only)
servingPort	Specifies the port for accessing the inference service. See Serving Port Fields.	-	Inference
autoscaling	Specifies the minimum and maximum number of replicas to be scaled up and down to meet the changing demands of inference services. See Autoscaling Fields.	-	Inference
servingConfiguration	Specifies the inference workload serving configuration. See Serving Configuration Fields.	-	Inference

Ports Fields

Fields	Description	Value type	Supported NVIDIA Run:ai workload type
container	The port that the container running the workload exposes.	string	Workspace Standard training Distributed training Inference
serviceType	Specifies the default service exposure method for ports. the default shall be used for ports which do not specify service type. Options are: LoadBalancer, NodePort or ClusterIP. For more information see the External Access to Containers guide.	string	Workspace Standard training Distributed training Inference
external	The external port which allows a connection to the container port. If not specified, the port is auto-generated by the system.	integer	Workspace Standard training Distributed training Inference
toolType	The tool type that runs on this port.	string	Workspace Standard training Distributed training Inference
toolName	A name describing the tool that runs on this port.	string	Workspace Standard training Distributed training Inference

Probes Fields

Fields	Description	Value type	Supported NVIDIA Run:ai workload type
readiness	Specifies the Readiness Probe to use to determine if the container is ready to accept traffic.	-	Workspace Standard training Distributed training Inference

Readiness Field Details

• Description: Specifies the Readiness Probe to use to determine if the container is ready to accept traffic

• Value type: itemized

• Example policy snippet:

defaults:
   probes:
     readiness:
         initialDelaySeconds: 2

Spec readiness fields	Description	Value type
initialDelaySeconds	Number of seconds after the container has started before liveness or readiness probes are initiated.	integer
periodSeconds	How often (in seconds) to perform the probe	integer
timeoutSeconds	Number of seconds after which the probe times out	integer
successThreshold	Minimum consecutive successes for the probe to be considered successful after having failed	integer
failureThreshold	When a probe fails, the number of times to try before giving up	integer

Security Fields

Fields	Description	Value type	Supported NVIDIA Run:ai workload type
uidGidSource	Indicates the way to determine the user and group ids of the container. The options are: fromTheImage - user and group IDs are determined by the docker image that the container runs. This is the default option. custom - user and group IDs can be specified in the environment asset and/or the workspace creation request. idpToken - user and group IDs are determined according to the identity provider (idp) access token. This option is intended for internal use of the environment UI form. For more information, see User identity in containers.	string	Workspace Standard training Distributed training Inference
capabilities	The capabilities field allows adding a set of unix capabilities to the container running the workload. Capabilities are Linux distinct privileges traditionally associated with superuser which can be independently enabled and disabled	array	Workspace Standard training Distributed training Inference
seccompProfileType	Indicates which kind of seccomp profile is applied to the container. The options are: RuntimeDefault - the container runtime default profile should be used Unconfined - no profile should be applied	string	Workspace Standard training Distributed training Inference
runAsNonRoot	Indicates that the container must run as a non-root user.	boolean	Workspace Standard training Distributed training Inference
readOnlyRootFilesystem	If true, mounts the container's root filesystem as read-only.	boolean	Workspace Standard training Distributed training Inference
runAsUid	Specifies the Unix user id with which the container running the created workload should run.	integer	Workspace Standard training Distributed training Inference
runasGid	Specifies the Unix Group ID with which the container should run.	integer	Workspace Standard training Distributed training Inference
supplementalGroups	Comma separated list of groups that the user running the container belongs to, in addition to the group indicated by runAsGid.	string	Workspace Standard training Distributed training Inference
allowPrivilegeEscalation	Allows the container running the workload and all launched processes to gain additional privileges after the workload starts	boolean	Workspace Standard training Distributed training
hostIpc	Whether to enable hostIpc. Defaults to false.	boolean	Workspace Standard training Distributed training
hostNetwork	Whether to enable host network.	boolean	Workspace Standard training Distributed training

Compute Fields

Fields	Description	Value type	Supported NVIDIA Run:ai workload type
cpuCoreRequest	CPU units to allocate for the created workload (0.5, 1, .etc). The workload receives at least this amount of CPU. Note that the workload is not scheduled unless the system can guarantee this amount of CPUs to the workload.	number	Workspace Standard training Distributed training Inference
cpuCoreLimit	Limitations on the number of CPUs consumed by the workload (0.5, 1, .etc). The system guarantees that this workload is not able to consume more than this amount of CPUs.	number	Workspace Standard training Distributed training Inference
cpuMemoryRequest	The amount of CPU memory to allocate for this workload (1G, 20M, .etc). The workload receives at least this amount of memory. Note that the workload is not scheduled unless the system can guarantee this amount of memory to the workload	quantity	Workspace Standard training Distributed training Inference
cpuMemoryLimit	Limitations on the CPU memory to allocate for this workload (1G, 20M, .etc). The system guarantees that this workload is not be able to consume more than this amount of memory. The workload receives an error when trying to allocate more memory than this limit.	quantity	Workspace Standard training Distributed training Inference
largeShmRequest	A large /dev/shm device to mount into a container running the created workload (shm is a shared file system mounted on RAM).	boolean	Workspace Standard training Distributed training Inference
gpuRequestType	Sets the unit type for GPU resources requests to either portion or memory. Only if gpuDeviceRequest = 1, the request type can be stated as portion or memory.	string	Workspace Standard training Distributed training Inference
gpuPortionRequest	Specifies the fraction of GPU to be allocated to the workload, between 0 and 1. For backward compatibility, it also supports the number of gpuDevices larger than 1, currently provided using the gpuDevices field.	number	Workspace Standard training Distributed training Inference
gpuDeviceRequest	Specifies the number of GPUs to allocate for the created workload. Only if gpuDeviceRequest = 1, the gpuRequestType can be defined.	integer	Workspace Standard training Distributed training Inference
gpuPortionLimit	When a fraction of a GPU is requested, the GPU limit specifies the portion limit to allocate to the workload. The range of the value is from 0 to 1.	number	Workspace Standard training Distributed training Inference
gpuMemoryRequest	Specifies GPU memory to allocate for the created workload. The workload receives this amount of memory. Note that the workload is not scheduled unless the system can guarantee this amount of GPU memory to the workload.	quantity	Workspace Standard training Distributed training Inference
gpuMemoryLimit	Specifies a limit on the GPU memory to allocate for this workload. Should be no less than the gpuMemory.	quantity	Workspace Standard training Distributed training Inference
extendedResources	Specifies values for extended resources. Extended resources are third-party devices (such as high-performance NICs, FPGAs, or InfiniBand adapters) that you want to allocate to your Job.	itemized	Workspace Standard training Distributed training Inference

Storage Fields

Fields	Description	Value type	Supported NVIDIA Run:ai workload type
dataVolume	Set of data volumes to use in the workload. Each data volume is mapped to a file-system mount point within the container running the workload.	itemized	Workspace Standard training Distributed training Inference
hostPath	Maps a folder to a file-system mount point within the container running the workload.	itemized	Workspace Standard training Distributed training Inference
git	Details of the git repository and items mapped to it.	itemized	Workspace Standard training Distributed training Inference
pvc	Specifies persistent volume claims to mount into a container running the created workload.	itemized	Workspace Standard training Distributed training Inference
nfs	Specifies NFS volume to mount into the container running the workload.	itemized	Workspace Standard training Distributed training Inference
s3	Specifies S3 buckets to mount into the container running the workload.	itemized	Workspace Standard training Distributed training
configMapVolumes	Specifies ConfigMaps to mount as volumes into a container running the created workload.	itemized	Workspace Standard training Distributed training Inference
secretVolume	Set of secret volumes to use in the workload. A secret volume maps a secret resource in the cluster to a file-system mount point within the container running the workload.	itemized	Workspace Standard training Distributed training Inference

Storage Field Examples

hostPath Field Details

• Description: Maps a folder to a file system mount oint within the container running the workload

• Value type: itemized

• Example policy snippet:

defaults:
  storage:
    hostPath:
      instances:
        - path: h3-path-1
          mountPath: h3-mount-1
        - path: h3-path-2
          mountPath: h3-mount-2
      attributes:
        - readOnly: true

hostPath fields	Description	Value type
name	Unique name to identify the instance. Primarily used for policy locked rules.	string
path	Local path within the controller to which the host volume is mapped.	string
readOnly	Force the volume to be mounted with read-only permissions. Defaults to false	boolean
mountPath	The path that the host volume is mounted to when in use. Enum: "None" "HostToContainer"	string
mountPropagation	Share this volume mount with other containers. If set to HostToContainer, this volume mount receives all subsequent mounts that are mounted to this volume or any of its subdirectories. In case of multiple hostPath entries, this field should have the same value for all of them.	string

Git Field Details

• Description: Details of the git repository and items mapped to it

• Value type: itemized

• Example policy snippet:

defaults:
  storage:
    git:
      attributes:
        Repository: https://runai.public.github.com
      instances
        - branch: "master"
          path: /container/my-repository
          passwordSecret: my-password-secret

Git fields	Description	Value type
repository	URL to a remote git repository. The content of this repository is mapped to the container running the workload	string
revision	Specific revision to synchronize the repository from	string
path	Local path within the workspace to which the S3 bucket is mapped	string
secretName	Optional name of Kubernetes secret that holds your git username and password	string
username	If secretName is provided, this field should contain the key, within the provided Kubernetes secret, which holds the value of your git username. Otherwise, this field should specify your git username in plain text (example: myuser).	string

PVC Field Details

• Description: Specifies persistent volume claims to mount into a container running the created workload

• Value type: itemized

• Example policy snippet:

defaults:
  storage:
    pvc:
      instances:
        - claimName: pvc-staging-researcher1-home
          existingPvc: true
          path: /myhome
          readOnly: false
          claimInfo:
            accessModes:
              readWriteMany: true

Spec PVC fields	Description	Value type
claimName (mandatory)	A given name for the PVC. Allowed referencing it across workspaces	string
ephemeral	Use true to set PVC to ephemeral. If set to true, the PVC is deleted when the workspace is stopped.	boolean
path	Local path within the workspace to which the PVC bucket is mapped	string
readonly	Permits read only from the PVC, prevents additions or modifications to its content	boolean
ReadwriteOnce	Requesting claim that can be mounted in read/write mode to exactly 1 host. If none of the modes are specified, the default is readWriteOnce.	boolean
size	Requested size for the PVC. Mandatory when existing PVC is false	string
storageClass	Storage class name to associate with the PVC. This parameter may be omitted if there is a single storage class in the system, or you are using the default storage class. Further details at Kubernetes storage classes.	string
readOnlyMany	Requesting claim that can be mounted in read-only mode to many hosts	boolean
readWriteMany	Requesting claim that can be mounted in read/write mode to many hosts	boolean

NFS Field Details

• Description: Specifies NFS volume to mount into the container running the workload

• Value type: itemized

• Example policy snippet:

defaults:
 storage:
   nfs:
     instances:
       - path: nfs-path
         readOnly: true
         server: nfs-server
         mountPath: nfs-mount
rules:
  storage:
    nfs:
      instances:
        canAdd: false

nfs fields	Description	Value type
mountPath	The path that the NFS volume is mounted to when in use	string
path	Path that is exported by the NFS server	string
readOnly	Whether to force the NFS export to be mounted with read-only permissions	boolean
nfsServer	The hostname or IP address of the NFS server	string

S3 Field Details

• Description: Specifies S3 buckets to mount into the container running the workload

• Value type: itemized

• Example policy snippet:

defaults:
  storage:
    s3:
      instances:
        - bucket: bucket-opt-1
          path: /s3/path
          accessKeySecret: s3-access-key
          secretKeyOfAccessKeyId: s3-secret-id
          secretKeyOfSecretKey: s3-secret-key
      attributes:
        url: https://amazonaws.s3.com

s3 fields	Description	Value type
Bucket	The name of the bucket	string
path	Local path within the workspace to which the S3 bucket is mapped	string
url	The URL of the S3 service provider. The default is the URL of the Amazon AWS S3 service	string

Serving Port Fields

Fields	Description	Value type	Supported NVIDIA Run:ai workload type
container	Specifies the port that the container running the inference service exposes	integer	Inference
protocol	Specifies the protocol used by the port. Defaults to http. Enum: "http", "grpc"	string	Inference
authorizationType	Specifies the authorization type for serving port URL access. Defaults to public, which means no authorization is required. If set to authenticatedUsers, only authenticated NVIDIA Run:ai users are allowed to access the URL. If set to authorizedUsersOrGroups, only users or groups specified in authorizedUsers or authorizedGroups are allowed to access the URL. Supported from cluster version 2.19. Enum: "public", "authenticatedUsers", "authorizedUsersOrGroups"	string	Inference
authorizedUsers	Specifies the list of users that are allowed to access the URL. Note that authorizedUsers and authorizedGroups are mutually exclusive.	array	Inference
authorizedGroups	Specifies the list of groups that are allowed to access the URL. Note that authorizedUsers and authorizedGroups are mutually exclusive.	array	Inference
clusterLocalAccessOnly	Configures the serving port URL to be available only on the cluster-local network, and not externally. Defaults to false.	boolean	Inference

Autoscaling Fields

Fields	Description	Value type	Supported NVIDIA Run:ai workload type
metricThresholdPercentage	Specifies the percentage of metric threshold value to use for autoscaling. Defaults to 70. Applicable only with the 'throughput' and 'concurrency' metrics.	number	Inference
minReplicas	Specifies the minimum number of replicas for autoscaling. Defaults to 1. Use 0 to allow scale-to-zero.	integer	Inference
maxReplicas	Specifies the maximum number of replicas for autoscaling. Defaults to minReplicas, or to 1 if minReplicas is set to 0.	integer	Inference
initialReplicas	Specifies the number of replicas to run when initializing the workload for the first time. Defaults to minReplicas, or to 1 if minReplicas is set to 0.	integer	Inference
activationReplicas	Specifies the number of replicas to run when scaling-up from zero. Defaults to minReplicas, or to 1 if minReplicas is set to 0.	integer	Inference
concurrencyHardLimit	Specifies the maximum number of requests allowed to flow to a single replica at any time. 0 means no limit.	integer	Inference
scaleToZeroRetentionSeconds	Specifies the minimum amount of time (in seconds) that the last replica will remain active after a scale-to-zero decision. Defaults to 0. Available only if minReplicas is set to 0.	integer	Inference
scaleDownDelaySeconds	Specifies the minimum amount of time (in seconds) that a replica will remain active after a scale-down decision	integer	Inference
metric	Specifies the metric to use for autoscaling. Mandatory if minReplicas < maxReplicas, except for the special case where minReplicas is set to 0 and maxReplicas is set to 1, as in this case autoscaling decisions are made according to network activity rather than metrics. Use one of the built-in metrics of 'throughput', 'concurrency' or 'latency', or any other available custom metric. Only the 'throughput' and 'concurrency' metrics support scale-to-zero.	string	Inference
metricThreshold	Specifies the threshold to use with the specified metric for autoscaling. Mandatory if metric is specified.	integer	Inference

Serving Configuration Fields

Fields	Description	Value type	Supported NVIDIA Run:ai workload type
initializationTimeoutSeconds	Specifies the maximum time (in seconds) allowed for a workload to initialize and become ready. If the workload does not start within this time, it will be moved to failed state.	integer	Inference
requestTimeoutSeconds	Specifies the maximum time (in seconds) allowed to process an end-user request. If no response is returned within this time, the request will be ignored.	integer	Inference

Value Types

Each field has a specific value type. The following value types are supported.

Value type	Description	Supported rule type	Defaults
Boolean	A binary value that can be either True or False	canEdit required	true/false
String	A sequence of characters used to represent text. It can include letters, numbers, symbols, and spaces	canEdit required options	abc
Itemized	An ordered collection of items (objects), which can be of different types (all items in the list are of the same type). For further information see the chapter below the table.	canAdd locked	See below
Integer	An Integer is a whole number without a fractional component.	canEdit required min max step defaultFrom	100
Number	Capable of having non-integer values	canEdit required min defaultFrom	10.3
Quantity	Holds a string composed of a number and a unit representing a quantity	canEdit required min max defaultFrom	5M
Array	Set of values that are treated as one, as opposed to Itemized in which each item can be referenced separately.	canEdit required	node-a node-b node-c

Itemized

Workload fields of type itemized have multiple instances, however in comparison to objects, each can be referenced by a key field. The key field is defined for each field.

Consider the following workload spec:

spec:
  image: ubuntu
  compute:
    extendedResources:
      - resource: added/cpu
        quantity: 10
      - resource: added/memory
        quantity: 20M

In this example, extendedResources have two instances, each has two attributes: resource (the key attribute) and quantity.

In policy, the defaults and rules for itemized fields have two sub sections:

• Instances: default items to be added to the policy or rules which apply to an instance as a whole.

• Attributes: defaults for attributes within an item or rules which apply to attributes within each item.

Consider the following example:

defaults:
  compute:
    extendedResources:
      instances: 
        - resource: default/cpu
          quantity: 5
        - resource: default/memory
          quantity: 4M
      attributes:
        quantity: 3
rules:
  compute:
    extendedResources:
      instances:
        locked: 
          - default/cpu
      attributes:
        quantity: 
          required: true

Assume the following workload submission is requested:

spec:
  image: ubuntu
  compute:
    extendedResources:
      - resource: default/memory
        exclude: true
      - resource: added/cpu
      - resource: added/memory
        quantity: 5M

The effective policy for the above mentioned workload has the following extendedResources instances:

Resource	Source of the instance	Quantity	Source of the attribute quantity
default/cpu	Policy defaults	5	The default of this instance in the policy defaults section
added/cpu	Submission request	3	The default of the quantity attribute from the attributes section
added/memory	Submission request	5M	Submission request

Note

The default/memory is not populated to the workload, this is because it has been excluded from the workload using “exclude: true”.

A workload submission request cannot exclude the default/cpu resource, as this key is included in the locked rules under the instances section. {#a-workload-submission-request-cannot-exclude-the-default/cpu-resource,-as-this-key-is-included-in-the-locked-rules-under-the-instances-section.}

Rule Types

Rule types	Description	Supported value types
canAdd	Whether the submission request can add items to an itemized field other than those listed in the policy defaults for this field.	itemized
locked	Set of items that the workload is unable to modify or exclude. In this example, a workload policy default is given to HOME and USER, that the submission request cannot modify or exclude from the workload.	itemized
canEdit	Whether the submission request can modify the policy default for this field. In this example, it is assumed that the policy has default for imagePullPolicy. As canEdit is set to false, submission requests are not able to alter this default.	string boolean integer number quantity array
required	When set to true, the workload must have a value for this field. The value can be obtained from policy defaults. If no value specified in the policy defaults, a value must be specified for this field in the submission request.	string boolean integer number quantity array
min	The minimal value for the field	integer number quantity
max	The maximal value for the field	integer number quantity
step	The allowed gap between values for this field. In this example the allowed values are: 1, 3, 5, 7	integer number
options	Set of allowed values for this field	string
defaultFrom	Set a default value for a field that will be calculated based on the value of another field	integer number quantity

Rule Type Examples

canAdd

storage:
  hostPath:
     instances:
       canAdd: false

locked

storage:
  hostPath:
    Instances:
      locked:
        - HOME
        - USER

canEdit

imagePullPolicy:
    canEdit: false

required

image:
    required: true

min

compute:
  gpuDevicesRequest:
    min: 3

max

compute:
  gpuMemoryRequest:
     max: 2G

step

compute:
  cpuCoreRequest:
    min: 1
    max: 7
    Step: 2

options

image:
  options:
    - value: image-1
    - value: image-2

defaultFrom

cpuCoreRequest:
  defaultFrom:
    field: compute.cpuCoreLimit
    factor: 0.5

Policy Spec Sections

For each field of a specific policy, you can specify both rules and defaults. A policy spec consists of the following sections:

• Rules

• Defaults

• Imposed Assets

Rules

Rules set up constraints on workload policy fields. For example, consider the following policy:

rules:
  compute:
    gpuDevicesRequest: 
      max: 8
  security:
    runAsUid: 
      min: 500

Such a policy restricts the maximum value for gpuDeviceRequests to 8, and the minimal value for runAsUid, provided in the security section to 500.

Defaults

The defaults section is used for providing defaults for various workload fields. For example, consider the following policy:

defaults:
  imagePullPolicy: Always
  security:
    runAsNonRoot: true
    runAsUid: 500

Assume a submission request with the following values:

• Image: ubuntu

• runAsUid: 501

The effective workload that runs has the following set of values:

Field	Value	Source
Image	Ubuntu	Submission request
ImagePullPolicy	Always	Policy defaults
security.runAsNonRoot	true	Policy defaults
security.runAsUid	501	Submission request

Note

It is possible to specify a rule for each field, which states if a submission request is allowed to change the policy default for that given field, for example:

defaults:
imagePullPolicy: Always
security:
    runAsNonRoot: true
    runAsUid: 500
 rules:
 security:
    runAsUid:
    canEdit: false

If this policy is applied, the submission request above fails, as it attempts to change the value of secuirty.runAsUid from 500 (the policy default) to 501 (the value provided in the submission request), which is forbidden due to canEdit rule set to false for this field.

Imposed Assets

Default instances of a storage field can be provided using a datasource containing the details of this storage instance. To add such instances in the policy, specify those asset IDs in the imposedAssets section of the policy.

defaults: null
rules: null
imposedAssets:
  - f12c965b-44e9-4ff6-8b43-01d8f9e630cc

Assets with references to credential assets (for example: private S3, containing reference to an AccessKey asset) cannot be used as imposedAssets.