Training

• HyperPodPytorchJob

• HyperPodPytorchJob Configs

HyperPodPytorchJob

class sagemaker.hyperpod.training.hyperpod_pytorch_job.HyperPodPytorchJob

Bases: _HyperPodPytorchJob

HyperPod PyTorch job for distributed training on Amazon SageMaker HyperPod clusters.

This class provides methods to create, manage, and monitor PyTorch training jobs on SageMaker HyperPod clusters orchestrated by Amazon EKS.

classmethod allocate_quotas_if_applicable(spec)

create(debug=False)

Create and submit the HyperPod PyTorch job to the Kubernetes cluster.

Parameters:

Parameter	Type	Description
debug	bool, optional	Enable debug logging. Defaults to False.

Raises:

Exception: If the job creation fails or Kubernetes API call fails

Usage Examples

>>> job = HyperPodPytorchJob(metadata=Metadata(name="my-job"), ...)
>>> job.create()
>>>
>>> # Create with debug logging
>>> job.create(debug=True)

classmethod list(namespace=None) → List[HyperPodPytorchJob]

List all HyperPod PyTorch jobs in the specified namespace.

Parameters:

Parameter	Type	Description
namespace	str, optional	The Kubernetes namespace to list jobs from. If None, uses the default namespace from current context.

Returns:

List[HyperPodPytorchJob]: List of HyperPodPytorchJob instances found in the namespace

Raises:

Exception: If the Kubernetes API call fails or jobs cannot be retrieved

Notes

This method requires a valid kubeconfig to be available and will automatically load it if not already loaded.

Usage Examples

>>> jobs = HyperPodPytorchJob.list()
>>> print(f"Found {len(jobs)} jobs")
>>>
>>> # List jobs in specific namespace
>>> jobs = HyperPodPytorchJob.list(namespace="my-namespace")

delete()

Delete the HyperPod PyTorch job from the Kubernetes cluster.

Raises:

Exception: If the job deletion fails or Kubernetes API call fails

Usage Examples

>>> job = HyperPodPytorchJob.get("my-job")
>>> job.delete()

exec_command(command: List[str], pod: str | None = None, all_pods: bool = False, container: str | None = None)

Execute a command in one or all pods associated with this job.

classmethod get(name, namespace=None) → HyperPodPytorchJob

Get a specific HyperPod PyTorch job by name.

Parameters:

Parameter	Type	Description
name	str	The name of the HyperPod PyTorch job to retrieve
namespace	str, optional	The Kubernetes namespace to search in. If None, uses the default namespace from current context.

Returns:

HyperPodPytorchJob: The requested HyperPod PyTorch job instance

Raises:

Exception: If the job is not found or Kubernetes API call fails

Usage Examples

>>> job = HyperPodPytorchJob.get("my-job")
>>> print(job.metadata.name)
>>>
>>> # Get job from specific namespace
>>> job = HyperPodPytorchJob.get("my-job", namespace="my-namespace")

refresh() → HyperPodPytorchJob

Refresh the job status by fetching the latest state from the Kubernetes cluster.

Returns:

HyperPodPytorchJob: The updated job instance with refreshed status

Raises:

Exception: If the refresh operation fails or Kubernetes API call fails

Usage Examples

>>> job = HyperPodPytorchJob.get("my-job")
>>> updated_job = job.refresh()
>>> print(updated_job.status)

list_pods() → List[str]

List all pods associated with this HyperPod PyTorch job.

Returns:

List[str]: List of pod names associated with this job

Raises:

Exception: If listing pods fails or Kubernetes API call fails

Usage Examples

>>> job = HyperPodPytorchJob.get("my-job")
>>> pods = job.list_pods()
>>> print(f"Found {len(pods)} pods: {pods}")

get_logs_from_pod(pod_name: str, container: str | None = None) → str

Get logs from a specific pod associated with this HyperPod PyTorch job.

Parameters:

Parameter	Type	Description
pod_name	str	The name of the pod to get logs from
container	str, optional	The container name within the pod. If None, uses the first container.

Returns:

str: The log output from the specified pod and container

Raises:

Exception: If getting logs fails or Kubernetes API call fails

Usage Examples

>>> job = HyperPodPytorchJob.get("my-job")
>>> pods = job.list_pods()
>>> logs = job.get_logs_from_pod(pods[0])
>>> print(logs)
>>>
>>> # Get logs from specific container
>>> logs = job.get_logs_from_pod(pods[0], container="pytorch")

classmethod get_operator_logs(since_hours: float)

HyperPodPytorchJob Configs

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Conditions

Bases: BaseModel

JobCondition describes current state of a job.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

lastProbeTime: str | None

lastTransitionTime: str | None

message: str | None

reason: str | None

status: str

type: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.JobPods

Bases: BaseModel

ObjectReference contains enough information to let you inspect or modify the referred object.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

apiVersion: str | None

fieldPath: str | None

kind: str | None

name: str | None

namespace: str | None

resourceVersion: str | None

uid: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.ManagerPods

Bases: BaseModel

Pod Manager pods

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

apiVersion: str | None

fieldPath: str | None

kind: str | None

name: str | None

namespace: str | None

resourceVersion: str | None

uid: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.PodManagerStatuses

Bases: BaseModel

ObjectReference contains enough information to let you inspect or modify the referred object.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

apiVersion: str | None

fieldPath: str | None

kind: str | None

name: str | None

namespace: str | None

resourceVersion: str | None

uid: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Tolerations

Bases: BaseModel

The pod this Toleration is attached to tolerates any taint that matches the triple <key,value,effect> using the matching operator <operator>.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

effect: str | None

key: str | None

operator: str | None

tolerationSeconds: int | None

value: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.PodSetInfo

Bases: BaseModel

DEPRECATED podSetInfo to include pod set information provided by Kueue in podSetInfos PodSetInformation assigned to the HyperPodPytorchJob’s PodSet by Kueue podSetInfo is retained here to support operator upgrade

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

annotations: Dict[str, str] | None

labels: Dict[str, str] | None

nodeSelector: Dict[str, str] | None

tolerations: List[Tolerations] | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.PodSetInfos

Bases: BaseModel

PodSetInformation contains the data that Kueue wants to inject into an admitted PodSpecTemplate

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

annotations: Dict[str, str] | None

labels: Dict[str, str] | None

nodeSelector: Dict[str, str] | None

tolerations: List[Tolerations] | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Metadata

Bases: BaseModel

Standard object’s metadata. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#metadata

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

annotations: Dict[str, str] | None

finalizers: List[str] | None

labels: Dict[str, str] | None

name: str | None

namespace: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.MatchExpressions

Bases: BaseModel

A node selector requirement is a selector that contains values, a key, and an operator that relates the key and values.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

key: str

operator: str

values: List[str] | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.MatchFields

Bases: BaseModel

A node selector requirement is a selector that contains values, a key, and an operator that relates the key and values.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

key: str

operator: str

values: List[str] | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Preference

Bases: BaseModel

A node selector term, associated with the corresponding weight.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

matchExpressions: List[MatchExpressions] | None

matchFields: List[MatchFields] | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.PreferredDuringSchedulingIgnoredDuringExecution

Bases: BaseModel

An empty preferred scheduling term matches all objects with implicit weight 0 (i.e. it’s a no-op). A null preferred scheduling term matches no objects (i.e. is also a no-op).

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

preference: Preference

weight: int

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.NodeSelectorTerms

Bases: BaseModel

A null or empty node selector term matches no objects. The requirements of them are ANDed. The TopologySelectorTerm type implements a subset of the NodeSelectorTerm.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

matchExpressions: List[MatchExpressions] | None

matchFields: List[MatchFields] | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.RequiredDuringSchedulingIgnoredDuringExecution

Bases: BaseModel

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.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

nodeSelectorTerms: List[NodeSelectorTerms]

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.NodeAffinity

Bases: BaseModel

Describes node affinity scheduling rules for the pod.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

preferredDuringSchedulingIgnoredDuringExecution: List[PreferredDuringSchedulingIgnoredDuringExecution] | None

requiredDuringSchedulingIgnoredDuringExecution: RequiredDuringSchedulingIgnoredDuringExecution | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.PodAffinity

Bases: BaseModel

Describes pod affinity scheduling rules (e.g. co-locate this pod in the same node, zone, etc. as some other pod(s)).

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

preferredDuringSchedulingIgnoredDuringExecution: List[PreferredDuringSchedulingIgnoredDuringExecution] | None

requiredDuringSchedulingIgnoredDuringExecution: List[RequiredDuringSchedulingIgnoredDuringExecution] | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.PodAntiAffinity

Bases: BaseModel

Describes pod anti-affinity scheduling rules (e.g. avoid putting this pod in the same node, zone, etc. as some other pod(s)).

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

preferredDuringSchedulingIgnoredDuringExecution: List[PreferredDuringSchedulingIgnoredDuringExecution] | None

requiredDuringSchedulingIgnoredDuringExecution: List[RequiredDuringSchedulingIgnoredDuringExecution] | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Affinity

Bases: BaseModel

If specified, the pod’s scheduling constraints

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

nodeAffinity: NodeAffinity | None

podAffinity: PodAffinity | None

podAntiAffinity: PodAntiAffinity | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.ConfigMapKeyRef

Bases: BaseModel

Selects a key of a ConfigMap.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

key: str

name: str | None

optional: bool | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.FieldRef

Bases: BaseModel

Selects a field of the pod: supports metadata.name, metadata.namespace, metadata.labels['<KEY>'], metadata.annotations['<KEY>'], spec.nodeName, spec.serviceAccountName, status.hostIP, status.podIP, status.podIPs.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

apiVersion: str | None

fieldPath: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.ResourceFieldRef

Bases: BaseModel

Selects a resource of the container: only resources limits and requests (limits.cpu, limits.memory, limits.ephemeral-storage, requests.cpu, requests.memory and requests.ephemeral-storage) are currently supported.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

containerName: str | None

divisor: int | str | None

resource: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.SecretKeyRef

Bases: BaseModel

Selects a key of a secret in the pod’s namespace

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

key: str

name: str | None

optional: bool | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.ValueFrom

Bases: BaseModel

Source for the environment variable’s value. Cannot be used if value is not empty.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

configMapKeyRef: ConfigMapKeyRef | None

fieldRef: FieldRef | None

resourceFieldRef: ResourceFieldRef | None

secretKeyRef: SecretKeyRef | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Env

Bases: BaseModel

EnvVar represents an environment variable present in a Container.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str

value: str | None

valueFrom: ValueFrom | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.ConfigMapRef

Bases: BaseModel

The ConfigMap to select from

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

optional: bool | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.SecretRef

Bases: BaseModel

The Secret to select from

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

optional: bool | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.EnvFrom

Bases: BaseModel

EnvFromSource represents the source of a set of ConfigMaps

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

configMapRef: ConfigMapRef | None

prefix: str | None

secretRef: SecretRef | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Exec

Bases: BaseModel

Exec specifies the action to take.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

command: List[str] | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.HttpHeaders

Bases: BaseModel

HTTPHeader describes a custom header to be used in HTTP probes

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str

value: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.HttpGet

Bases: BaseModel

HTTPGet specifies the http request to perform.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

host: str | None

httpHeaders: List[HttpHeaders] | None

path: str | None

port: int | str

scheme: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Sleep

Bases: BaseModel

Sleep represents the duration that the container should sleep before being terminated.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

seconds: int

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.TcpSocket

Bases: BaseModel

Deprecated. TCPSocket is NOT supported as a LifecycleHandler and kept for the backward compatibility. There are no validation of this field and lifecycle hooks will fail in runtime when tcp handler is specified.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

host: str | None

port: int | str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.PostStart

Bases: BaseModel

PostStart is called immediately after a container is created. If the handler fails, the container is terminated and restarted according to its restart policy. Other management of the container blocks until the hook completes. More info: https://kubernetes.io/docs/concepts/containers/container-lifecycle-hooks/#container-hooks

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

exec: Exec | None

httpGet: HttpGet | None

sleep: Sleep | None

tcpSocket: TcpSocket | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.PreStop

Bases: BaseModel

PreStop is called immediately before a container is terminated due to an API request or management event such as liveness/startup probe failure, preemption, resource contention, etc. The handler is not called if the container crashes or exits. The Pod’s termination grace period countdown begins before the PreStop hook is executed. Regardless of the outcome of the handler, the container will eventually terminate within the Pod’s termination grace period (unless delayed by finalizers). Other management of the container blocks until the hook completes or until the termination grace period is reached. More info: https://kubernetes.io/docs/concepts/containers/container-lifecycle-hooks/#container-hooks

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

exec: Exec | None

httpGet: HttpGet | None

sleep: Sleep | None

tcpSocket: TcpSocket | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Lifecycle

Bases: BaseModel

Actions that the management system should take in response to container lifecycle events. Cannot be updated.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

postStart: PostStart | None

preStop: PreStop | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Grpc

Bases: BaseModel

GRPC specifies an action involving a GRPC port.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

port: int

service: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.LivenessProbe

Bases: BaseModel

Periodic probe of container liveness. Container will be restarted if the probe fails. Cannot be updated. More info: https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle#container-probes

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

exec: Exec | None

failureThreshold: int | None

grpc: Grpc | None

httpGet: HttpGet | None

initialDelaySeconds: int | None

periodSeconds: int | None

successThreshold: int | None

tcpSocket: TcpSocket | None

terminationGracePeriodSeconds: int | None

timeoutSeconds: int | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Ports

Bases: BaseModel

ContainerPort represents a network port in a single container.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

containerPort: int

hostIP: str | None

hostPort: int | None

name: str | None

protocol: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.ReadinessProbe

Bases: BaseModel

Periodic probe of container service readiness. Container will be removed from service endpoints if the probe fails. Cannot be updated. More info: https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle#container-probes

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

exec: Exec | None

failureThreshold: int | None

grpc: Grpc | None

httpGet: HttpGet | None

initialDelaySeconds: int | None

periodSeconds: int | None

successThreshold: int | None

tcpSocket: TcpSocket | None

terminationGracePeriodSeconds: int | None

timeoutSeconds: int | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.ResizePolicy

Bases: BaseModel

ContainerResizePolicy represents resource resize policy for the container.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

resourceName: str

restartPolicy: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Claims

Bases: BaseModel

ResourceClaim references one entry in PodSpec.ResourceClaims.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str

request: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Resources

Bases: BaseModel

Compute Resources required by this container. Cannot be updated. More info: https://kubernetes.io/docs/concepts/configuration/manage-resources-containers/

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

claims: List[Claims] | None

limits: Dict[str, int | str] | None

requests: Dict[str, int | str] | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.AppArmorProfile

Bases: BaseModel

appArmorProfile is the AppArmor options to use by this container. If set, this profile overrides the pod’s appArmorProfile. Note that this field cannot be set when spec.os.name is windows.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

localhostProfile: str | None

type: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Capabilities

Bases: BaseModel

The capabilities to add/drop when running containers. Defaults to the default set of capabilities granted by the container runtime. Note that this field cannot be set when spec.os.name is windows.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

add: List[str] | None

drop: List[str] | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.SeLinuxOptions

Bases: BaseModel

The SELinux context to be applied to the container. If unspecified, the container runtime will allocate a random SELinux context for each container. May also be set in PodSecurityContext. If set in both SecurityContext and PodSecurityContext, the value specified in SecurityContext takes precedence. Note that this field cannot be set when spec.os.name is windows.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

level: str | None

role: str | None

type: str | None

user: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.SeccompProfile

Bases: BaseModel

The seccomp options to use by this container. If seccomp options are provided at both the pod & container level, the container options override the pod options. Note that this field cannot be set when spec.os.name is windows.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

localhostProfile: str | None

type: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.WindowsOptions

Bases: BaseModel

The Windows specific settings applied to all containers. If unspecified, the options from the PodSecurityContext will be used. If set in both SecurityContext and PodSecurityContext, the value specified in SecurityContext takes precedence. Note that this field cannot be set when spec.os.name is linux.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

gmsaCredentialSpec: str | None

gmsaCredentialSpecName: str | None

hostProcess: bool | None

runAsUserName: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.SecurityContext

Bases: BaseModel

SecurityContext defines the security options the container should be run with. If set, the fields of SecurityContext override the equivalent fields of PodSecurityContext. More info: https://kubernetes.io/docs/tasks/configure-pod-container/security-context/

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

allowPrivilegeEscalation: bool | None

appArmorProfile: AppArmorProfile | None

capabilities: Capabilities | None

privileged: bool | None

procMount: str | None

readOnlyRootFilesystem: bool | None

runAsGroup: int | None

runAsNonRoot: bool | None

runAsUser: int | None

seLinuxOptions: SeLinuxOptions | None

seccompProfile: SeccompProfile | None

windowsOptions: WindowsOptions | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.StartupProbe

Bases: BaseModel

StartupProbe indicates that the Pod has successfully initialized. If specified, no other probes are executed until this completes successfully. If this probe fails, the Pod will be restarted, just as if the livenessProbe failed. This can be used to provide different probe parameters at the beginning of a Pod’s lifecycle, when it might take a long time to load data or warm a cache, than during steady-state operation. This cannot be updated. More info: https://kubernetes.io/docs/concepts/workloads/pods/pod-lifecycle#container-probes

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

exec: Exec | None

failureThreshold: int | None

grpc: Grpc | None

httpGet: HttpGet | None

initialDelaySeconds: int | None

periodSeconds: int | None

successThreshold: int | None

tcpSocket: TcpSocket | None

terminationGracePeriodSeconds: int | None

timeoutSeconds: int | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.VolumeDevices

Bases: BaseModel

volumeDevice describes a mapping of a raw block device within a container.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

devicePath: str

name: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.VolumeMounts

Bases: BaseModel

VolumeMount describes a mounting of a Volume within a container.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

mountPath: str

mountPropagation: str | None

name: str

readOnly: bool | None

recursiveReadOnly: str | None

subPath: str | None

subPathExpr: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Containers

Bases: BaseModel

A single application container that you want to run within a pod.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

args: List[str] | None

command: List[str] | None

env: List[Env] | None

envFrom: List[EnvFrom] | None

image: str | None

imagePullPolicy: str | None

lifecycle: Lifecycle | None

livenessProbe: LivenessProbe | None

name: str

ports: List[Ports] | None

readinessProbe: ReadinessProbe | None

resizePolicy: List[ResizePolicy] | None

resources: Resources | None

restartPolicy: str | None

securityContext: SecurityContext | None

startupProbe: StartupProbe | None

stdin: bool | None

stdinOnce: bool | None

terminationMessagePath: str | None

terminationMessagePolicy: str | None

tty: bool | None

volumeDevices: List[VolumeDevices] | None

volumeMounts: List[VolumeMounts] | None

workingDir: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Options

Bases: BaseModel

PodDNSConfigOption defines DNS resolver options of a pod.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

value: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.DnsConfig

Bases: BaseModel

Specifies the DNS parameters of a pod. Parameters specified here will be merged to the generated DNS configuration based on DNSPolicy.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

nameservers: List[str] | None

options: List[Options] | None

searches: List[str] | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.EphemeralContainers

Bases: BaseModel

An EphemeralContainer is a temporary container that you may add to an existing Pod for user-initiated activities such as debugging. Ephemeral containers have no resource or scheduling guarantees, and they will not be restarted when they exit or when a Pod is removed or restarted. The kubelet may evict a Pod if an ephemeral container causes the Pod to exceed its resource allocation. To add an ephemeral container, use the ephemeralcontainers subresource of an existing Pod. Ephemeral containers may not be removed or restarted.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

args: List[str] | None

command: List[str] | None

env: List[Env] | None

envFrom: List[EnvFrom] | None

image: str | None

imagePullPolicy: str | None

lifecycle: Lifecycle | None

livenessProbe: LivenessProbe | None

name: str

ports: List[Ports] | None

readinessProbe: ReadinessProbe | None

resizePolicy: List[ResizePolicy] | None

resources: Resources | None

restartPolicy: str | None

securityContext: SecurityContext | None

startupProbe: StartupProbe | None

stdin: bool | None

stdinOnce: bool | None

targetContainerName: str | None

terminationMessagePath: str | None

terminationMessagePolicy: str | None

tty: bool | None

volumeDevices: List[VolumeDevices] | None

volumeMounts: List[VolumeMounts] | None

workingDir: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.HostAliases

Bases: BaseModel

HostAlias holds the mapping between IP and hostnames that will be injected as an entry in the pod’s hosts file.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

hostnames: List[str] | None

ip: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.ImagePullSecrets

Bases: BaseModel

LocalObjectReference contains enough information to let you locate the referenced object inside the same namespace.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.InitContainers

Bases: BaseModel

A single application container that you want to run within a pod.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

args: List[str] | None

command: List[str] | None

env: List[Env] | None

envFrom: List[EnvFrom] | None

image: str | None

imagePullPolicy: str | None

lifecycle: Lifecycle | None

livenessProbe: LivenessProbe | None

name: str

ports: List[Ports] | None

readinessProbe: ReadinessProbe | None

resizePolicy: List[ResizePolicy] | None

resources: Resources | None

restartPolicy: str | None

securityContext: SecurityContext | None

startupProbe: StartupProbe | None

stdin: bool | None

stdinOnce: bool | None

terminationMessagePath: str | None

terminationMessagePolicy: str | None

tty: bool | None

volumeDevices: List[VolumeDevices] | None

volumeMounts: List[VolumeMounts] | None

workingDir: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Os

Bases: BaseModel

Specifies the OS of the containers in the pod. Some pod and container fields are restricted if this is set. If the OS field is set to linux, the following fields must be unset: -securityContext.windowsOptions If the OS field is set to windows, following fields must be unset: - spec.hostPID - spec.hostIPC - spec.hostUsers - spec.securityContext.appArmorProfile - spec.securityContext.seLinuxOptions - spec.securityContext.seccompProfile - spec.securityContext.fsGroup - spec.securityContext.fsGroupChangePolicy - spec.securityContext.sysctls - spec.shareProcessNamespace - spec.securityContext.runAsUser - spec.securityContext.runAsGroup - spec.securityContext.supplementalGroups - spec.securityContext.supplementalGroupsPolicy - spec.containers[*].securityContext.appArmorProfile - spec.containers[*].securityContext.seLinuxOptions - spec.containers[*].securityContext.seccompProfile - spec.containers[*].securityContext.capabilities - spec.containers[*].securityContext.readOnlyRootFilesystem - spec.containers[*].securityContext.privileged - spec.containers[*].securityContext.allowPrivilegeEscalation - spec.containers[*].securityContext.procMount - spec.containers[*].securityContext.runAsUser - spec.containers[*].securityContext.runAsGroup

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.ReadinessGates

Bases: BaseModel

PodReadinessGate contains the reference to a pod condition

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

conditionType: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.ResourceClaims

Bases: BaseModel

PodResourceClaim references exactly one ResourceClaim, either directly or by naming a ResourceClaimTemplate which is then turned into a ResourceClaim for the pod. It adds a name to it that uniquely identifies the ResourceClaim inside the Pod. Containers that need access to the ResourceClaim reference it with this name.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str

resourceClaimName: str | None

resourceClaimTemplateName: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.SchedulingGates

Bases: BaseModel

PodSchedulingGate is associated to a Pod to guard its scheduling.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.LabelSelector

Bases: BaseModel

LabelSelector is used to find matching pods. Pods that match this label selector are counted to determine the number of pods in their corresponding topology domain.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

matchExpressions: List[MatchExpressions] | None

matchLabels: Dict[str, str] | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.NamespaceSelector

Bases: BaseModel

A label query over the set of namespaces that the term applies to. The term is applied to the union of the namespaces selected by this field and the ones listed in the namespaces field. null selector and null or empty namespaces list means “this pod’s namespace”. An empty selector ({}) matches all namespaces.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

matchExpressions: List[MatchExpressions] | None

matchLabels: Dict[str, str] | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.TopologySpreadConstraints

Bases: BaseModel

TopologySpreadConstraint specifies how to spread matching pods among the given topology.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

labelSelector: LabelSelector | None

matchLabelKeys: List[str] | None

maxSkew: int

minDomains: int | None

nodeAffinityPolicy: str | None

nodeTaintsPolicy: str | None

topologyKey: str

whenUnsatisfiable: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.AwsElasticBlockStore

Bases: BaseModel

awsElasticBlockStore represents an AWS Disk resource that is attached to a kubelet’s host machine and then exposed to the pod. More info: https://kubernetes.io/docs/concepts/storage/volumes#awselasticblockstore

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

fsType: str | None

partition: int | None

readOnly: bool | None

volumeID: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.AzureDisk

Bases: BaseModel

azureDisk represents an Azure Data Disk mount on the host and bind mount to the pod.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

cachingMode: str | None

diskName: str

diskURI: str

fsType: str | None

kind: str | None

readOnly: bool | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.AzureFile

Bases: BaseModel

azureFile represents an Azure File Service mount on the host and bind mount to the pod.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

readOnly: bool | None

secretName: str

shareName: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Cephfs

Bases: BaseModel

cephFS represents a Ceph FS mount on the host that shares a pod’s lifetime

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

monitors: List[str]

path: str | None

readOnly: bool | None

secretFile: str | None

secretRef: SecretRef | None

user: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Cinder

Bases: BaseModel

cinder represents a cinder volume attached and mounted on kubelets host machine. More info: https://examples.k8s.io/mysql-cinder-pd/README.md

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

fsType: str | None

readOnly: bool | None

secretRef: SecretRef | None

volumeID: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Items

Bases: BaseModel

Maps a string key to a path within a volume.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

key: str

mode: int | None

path: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.ConfigMap

Bases: BaseModel

configMap represents a configMap that should populate this volume

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

defaultMode: int | None

items: List[Items] | None

name: str | None

optional: bool | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.NodePublishSecretRef

Bases: BaseModel

nodePublishSecretRef is a reference to the secret object containing sensitive information to pass to the CSI driver to complete the CSI NodePublishVolume and NodeUnpublishVolume calls. This field is optional, and may be empty if no secret is required. If the secret object contains more than one secret, all secret references are passed.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Csi

Bases: BaseModel

csi (Container Storage Interface) represents ephemeral storage that is handled by certain external CSI drivers (Beta feature).

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

driver: str

fsType: str | None

nodePublishSecretRef: NodePublishSecretRef | None

readOnly: bool | None

volumeAttributes: Dict[str, str] | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.DownwardApi

Bases: BaseModel

downwardAPI represents downward API about the pod that should populate this volume

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

defaultMode: int | None

items: List[Items] | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.EmptyDir

Bases: BaseModel

emptyDir represents a temporary directory that shares a pod’s lifetime. More info: https://kubernetes.io/docs/concepts/storage/volumes#emptydir

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

medium: str | None

sizeLimit: int | str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.DataSource

Bases: BaseModel

dataSource field can be used to specify either: * An existing VolumeSnapshot object (snapshot.storage.k8s.io/VolumeSnapshot) * An existing PVC (PersistentVolumeClaim) If the provisioner or an external controller can support the specified data source, it will create a new volume based on the contents of the specified data source. When the AnyVolumeDataSource feature gate is enabled, dataSource contents will be copied to dataSourceRef, and dataSourceRef contents will be copied to dataSource when dataSourceRef.namespace is not specified. If the namespace is specified, then dataSourceRef will not be copied to dataSource.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

apiGroup: str | None

kind: str

name: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.DataSourceRef

Bases: BaseModel

dataSourceRef specifies the object from which to populate the volume with data, if a non-empty volume is desired. This may be any object from a non-empty API group (non core object) or a PersistentVolumeClaim object. When this field is specified, volume binding will only succeed if the type of the specified object matches some installed volume populator or dynamic provisioner. This field will replace the functionality of the dataSource field and as such if both fields are non-empty, they must have the same value. For backwards compatibility, when namespace isn’t specified in dataSourceRef, both fields (dataSource and dataSourceRef) will be set to the same value automatically if one of them is empty and the other is non-empty. When namespace is specified in dataSourceRef, dataSource isn’t set to the same value and must be empty. There are three important differences between dataSource and dataSourceRef: * While dataSource only allows two specific types of objects, dataSourceRef allows any non-core object, as well as PersistentVolumeClaim objects. * While dataSource ignores disallowed values (dropping them), dataSourceRef preserves all values, and generates an error if a disallowed value is specified. * While dataSource only allows local objects, dataSourceRef allows objects in any namespaces. (Beta) Using this field requires the AnyVolumeDataSource feature gate to be enabled. (Alpha) Using the namespace field of dataSourceRef requires the CrossNamespaceVolumeDataSource feature gate to be enabled.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

apiGroup: str | None

kind: str

name: str

namespace: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Selector

Bases: BaseModel

selector is a label query over volumes to consider for binding.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

matchExpressions: List[MatchExpressions] | None

matchLabels: Dict[str, str] | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.VolumeClaimTemplateSpec

Bases: BaseModel

The specification for the PersistentVolumeClaim. The entire content is copied unchanged into the PVC that gets created from this template. The same fields as in a PersistentVolumeClaim are also valid here.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

accessModes: List[str] | None

dataSource: DataSource | None

dataSourceRef: DataSourceRef | None

resources: Resources | None

selector: Selector | None

storageClassName: str | None

volumeAttributesClassName: str | None

volumeMode: str | None

volumeName: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.VolumeClaimTemplate

Bases: BaseModel

Will be used to create a stand-alone PVC to provision the volume. The pod in which this EphemeralVolumeSource is embedded will be the owner of the PVC, i.e. the PVC will be deleted together with the pod. The name of the PVC will be <pod name>- where <volume name> is the name from the PodSpec.Volumes array entry. Pod validation will reject the pod if the concatenated name is not valid for a PVC (for example, too long). An existing PVC with that name that is not owned by the pod will not be used for the pod to avoid using an unrelated volume by mistake. Starting the pod is then blocked until the unrelated PVC is removed. If such a pre-created PVC is meant to be used by the pod, the PVC has to updated with an owner reference to the pod once the pod exists. Normally this should not be necessary, but it may be useful when manually reconstructing a broken cluster. This field is read-only and no changes will be made by Kubernetes to the PVC after it has been created. Required, must not be nil.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

metadata: Metadata | None

volumeClaimTemplateSpec: VolumeClaimTemplateSpec

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Ephemeral

Bases: BaseModel

ephemeral represents a volume that is handled by a cluster storage driver. The volume’s lifecycle is tied to the pod that defines it - it will be created before the pod starts, and deleted when the pod is removed. Use this if: a) the volume is only needed while the pod runs, b) features of normal volumes like restoring from snapshot or capacity tracking are needed, c) the storage driver is specified through a storage class, and d) the storage driver supports dynamic volume provisioning through a PersistentVolumeClaim (see EphemeralVolumeSource for more information on the connection between this volume type and PersistentVolumeClaim). Use PersistentVolumeClaim or one of the vendor-specific APIs for volumes that persist for longer than the lifecycle of an individual pod. Use CSI for light-weight local ephemeral volumes if the CSI driver is meant to be used that way - see the documentation of the driver for more information. A pod can use both types of ephemeral volumes and persistent volumes at the same time.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

volumeClaimTemplate: VolumeClaimTemplate | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Fc

Bases: BaseModel

fc represents a Fibre Channel resource that is attached to a kubelet’s host machine and then exposed to the pod.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

fsType: str | None

lun: int | None

readOnly: bool | None

targetWWNs: List[str] | None

wwids: List[str] | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.FlexVolume

Bases: BaseModel

flexVolume represents a generic volume resource that is provisioned/attached using an exec based plugin.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

driver: str

fsType: str | None

options: Dict[str, str] | None

readOnly: bool | None

secretRef: SecretRef | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Flocker

Bases: BaseModel

flocker represents a Flocker volume attached to a kubelet’s host machine. This depends on the Flocker control service being running

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

datasetName: str | None

datasetUUID: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.GcePersistentDisk

Bases: BaseModel

gcePersistentDisk represents a GCE Disk resource that is attached to a kubelet’s host machine and then exposed to the pod. More info: https://kubernetes.io/docs/concepts/storage/volumes#gcepersistentdisk

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

fsType: str | None

partition: int | None

pdName: str

readOnly: bool | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.GitRepo

Bases: BaseModel

gitRepo represents a git repository at a particular revision. DEPRECATED: GitRepo is deprecated. To provision a container with a git repo, mount an EmptyDir into an InitContainer that clones the repo using git, then mount the EmptyDir into the Pod’s container.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

directory: str | None

repository: str

revision: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Glusterfs

Bases: BaseModel

glusterfs represents a Glusterfs mount on the host that shares a pod’s lifetime. More info: https://examples.k8s.io/volumes/glusterfs/README.md

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

endpoints: str

path: str

readOnly: bool | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.HostPath

Bases: BaseModel

hostPath represents a pre-existing file or directory on the host machine that is directly exposed to the container. This is generally used for system agents or other privileged things that are allowed to see the host machine. Most containers will NOT need this. More info: https://kubernetes.io/docs/concepts/storage/volumes#hostpath

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

path: str

type: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Image

Bases: BaseModel

image represents an OCI object (a container image or artifact) pulled and mounted on the kubelet’s host machine. The volume is resolved at pod startup depending on which PullPolicy value is provided: - Always: the kubelet always attempts to pull the reference. Container creation will fail If the pull fails. - Never: the kubelet never pulls the reference and only uses a local image or artifact. Container creation will fail if the reference isn’t present. - IfNotPresent: the kubelet pulls if the reference isn’t already present on disk. Container creation will fail if the reference isn’t present and the pull fails. The volume gets re-resolved if the pod gets deleted and recreated, which means that new remote content will become available on pod recreation. A failure to resolve or pull the image during pod startup will block containers from starting and may add significant latency. Failures will be retried using normal volume backoff and will be reported on the pod reason and message. The types of objects that may be mounted by this volume are defined by the container runtime implementation on a host machine and at minimum must include all valid types supported by the container image field. The OCI object gets mounted in a single directory (spec.containers[*].volumeMounts.mountPath) by merging the manifest layers in the same way as for container images. The volume will be mounted read-only (ro) and non-executable files (noexec). Sub path mounts for containers are not supported (spec.containers[*].volumeMounts.subpath). The field spec.securityContext.fsGroupChangePolicy has no effect on this volume type.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

pullPolicy: str | None

reference: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Iscsi

Bases: BaseModel

iscsi represents an ISCSI Disk resource that is attached to a kubelet’s host machine and then exposed to the pod. More info: https://examples.k8s.io/volumes/iscsi/README.md

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

chapAuthDiscovery: bool | None

chapAuthSession: bool | None

fsType: str | None

initiatorName: str | None

iqn: str

iscsiInterface: str | None

lun: int

portals: List[str] | None

readOnly: bool | None

secretRef: SecretRef | None

targetPortal: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Nfs

Bases: BaseModel

nfs represents an NFS mount on the host that shares a pod’s lifetime More info: https://kubernetes.io/docs/concepts/storage/volumes#nfs

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

path: str

readOnly: bool | None

server: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.PersistentVolumeClaim

Bases: BaseModel

persistentVolumeClaimVolumeSource represents a reference to a PersistentVolumeClaim in the same namespace. More info: https://kubernetes.io/docs/concepts/storage/persistent-volumes#persistentvolumeclaims

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

claimName: str

readOnly: bool | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.PhotonPersistentDisk

Bases: BaseModel

photonPersistentDisk represents a PhotonController persistent disk attached and mounted on kubelets host machine

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

fsType: str | None

pdID: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.PortworxVolume

Bases: BaseModel

portworxVolume represents a portworx volume attached and mounted on kubelets host machine

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

fsType: str | None

readOnly: bool | None

volumeID: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.ClusterTrustBundle

Bases: BaseModel

ClusterTrustBundle allows a pod to access the spec.trustBundle field of ClusterTrustBundle objects in an auto-updating file. Alpha, gated by the ClusterTrustBundleProjection feature gate. ClusterTrustBundle objects can either be selected by name, or by the combination of signer name and a label selector. Kubelet performs aggressive normalization of the PEM contents written into the pod filesystem. Esoteric PEM features such as inter-block comments and block headers are stripped. Certificates are deduplicated. The ordering of certificates within the file is arbitrary, and Kubelet may change the order over time.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

labelSelector: LabelSelector | None

name: str | None

optional: bool | None

path: str

signerName: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Secret

Bases: BaseModel

secret information about the secret data to project

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

items: List[Items] | None

name: str | None

optional: bool | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.ServiceAccountToken

Bases: BaseModel

serviceAccountToken is information about the serviceAccountToken data to project

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

audience: str | None

expirationSeconds: int | None

path: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Sources

Bases: BaseModel

Projection that may be projected along with other supported volume types. Exactly one of these fields must be set.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

clusterTrustBundle: ClusterTrustBundle | None

configMap: ConfigMap | None

downwardAPI: DownwardApi | None

secret: Secret | None

serviceAccountToken: ServiceAccountToken | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Projected

Bases: BaseModel

projected items for all in one resources secrets, configmaps, and downward API

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

defaultMode: int | None

sources: List[Sources] | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Quobyte

Bases: BaseModel

quobyte represents a Quobyte mount on the host that shares a pod’s lifetime

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

group: str | None

readOnly: bool | None

registry: str

tenant: str | None

user: str | None

volume: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Rbd

Bases: BaseModel

rbd represents a Rados Block Device mount on the host that shares a pod’s lifetime. More info: https://examples.k8s.io/volumes/rbd/README.md

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

fsType: str | None

image: str

keyring: str | None

monitors: List[str]

pool: str | None

readOnly: bool | None

secretRef: SecretRef | None

user: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.ScaleIo

Bases: BaseModel

scaleIO represents a ScaleIO persistent volume attached and mounted on Kubernetes nodes.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

fsType: str | None

gateway: str

protectionDomain: str | None

readOnly: bool | None

secretRef: SecretRef

sslEnabled: bool | None

storageMode: str | None

storagePool: str | None

system: str

volumeName: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Storageos

Bases: BaseModel

storageOS represents a StorageOS volume attached and mounted on Kubernetes nodes.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

fsType: str | None

readOnly: bool | None

secretRef: SecretRef | None

volumeName: str | None

volumeNamespace: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.VsphereVolume

Bases: BaseModel

vsphereVolume represents a vSphere volume attached and mounted on kubelets host machine

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

fsType: str | None

storagePolicyID: str | None

storagePolicyName: str | None

volumePath: str

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Volumes

Bases: BaseModel

Volume represents a named volume in a pod that may be accessed by any container in the pod.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

awsElasticBlockStore: AwsElasticBlockStore | None

azureDisk: AzureDisk | None

azureFile: AzureFile | None

cephfs: Cephfs | None

cinder: Cinder | None

configMap: ConfigMap | None

csi: Csi | None

downwardAPI: DownwardApi | None

emptyDir: EmptyDir | None

ephemeral: Ephemeral | None

fc: Fc | None

flexVolume: FlexVolume | None

flocker: Flocker | None

gcePersistentDisk: GcePersistentDisk | None

gitRepo: GitRepo | None

glusterfs: Glusterfs | None

hostPath: HostPath | None

image: Image | None

iscsi: Iscsi | None

name: str

nfs: Nfs | None

persistentVolumeClaim: PersistentVolumeClaim | None

photonPersistentDisk: PhotonPersistentDisk | None

portworxVolume: PortworxVolume | None

projected: Projected | None

quobyte: Quobyte | None

rbd: Rbd | None

scaleIO: ScaleIo | None

secret: Secret | None

storageos: Storageos | None

vsphereVolume: VsphereVolume | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Spec

Bases: BaseModel

Specification of the desired behavior of the pod. More info: https://git.k8s.io/community/contributors/devel/sig-architecture/api-conventions.md#spec-and-status

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

activeDeadlineSeconds: int | None

affinity: Affinity | None

automountServiceAccountToken: bool | None

containers: List[Containers]

dnsConfig: DnsConfig | None

dnsPolicy: str | None

enableServiceLinks: bool | None

ephemeralContainers: List[EphemeralContainers] | None

hostAliases: List[HostAliases] | None

hostIPC: bool | None

hostNetwork: bool | None

hostPID: bool | None

hostUsers: bool | None

hostname: str | None

imagePullSecrets: List[ImagePullSecrets] | None

initContainers: List[InitContainers] | None

nodeName: str | None

nodeSelector: Dict[str, str] | None

os: Os | None

overhead: Dict[str, int | str] | None

preemptionPolicy: str | None

priority: int | None

priorityClassName: str | None

readinessGates: List[ReadinessGates] | None

resourceClaims: List[ResourceClaims] | None

restartPolicy: str | None

runtimeClassName: str | None

schedulerName: str | None

schedulingGates: List[SchedulingGates] | None

securityContext: SecurityContext | None

serviceAccount: str | None

serviceAccountName: str | None

setHostnameAsFQDN: bool | None

shareProcessNamespace: bool | None

subdomain: str | None

terminationGracePeriodSeconds: int | None

tolerations: List[Tolerations] | None

topologySpreadConstraints: List[TopologySpreadConstraints] | None

volumes: List[Volumes] | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Template

Bases: BaseModel

template is the Pod template. The only allowed fields in template.metadata are labels and annotations. If requests are omitted for a container or initContainer, they default to the limits if they are explicitly specified for the container or initContainer. During admission, the rules in nodeSelector and nodeAffinity.requiredDuringSchedulingIgnoredDuringExecution that match the keys in the nodeLabels from the ResourceFlavors considered for this Workload are used to filter the ResourceFlavors that can be assigned to this podSet.

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

metadata: Metadata | None

spec: Spec | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.ReplicaSpec

Bases: BaseModel

ReplicaSpec is a description of the replica

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str

replicas: int | None

maxReplicas: int | None

spares: int | None

template: Template | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.ElasticPolicy

Bases: BaseModel

ElasticPolicy defines the elastic training policy

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

minReplicas: int | None

maxReplicas: int | None

replicaIncrementStep: int | None

replicaDiscreteValues: List[int] | None

scalingTimeoutInSeconds: int | None

gracefulShutdownTimeoutInSeconds: int | None

faultyScaleDownTimeoutInSeconds: int | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.LogMonitoringConfiguration

Bases: BaseModel

LogMonitoringRule defines the criteria used to detect a SLOW or HANGING job

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

expectedRecurringFrequencyInSeconds: int | None

expectedStartCutOffInSeconds: int | None

logPattern: str

metricEvaluationDataPoints: int | None

metricThreshold: int | None

name: str

operator: str | None

stopPattern: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.RestartPolicy

Bases: BaseModel

Additional restart limiting configurations

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

evalPeriodSeconds: int

maxFullJobRestarts: int | None

numRestartBeforeFullJobRestart: int | None

maxNumRepeatOffendersToAvoid: int | None

scaleUpSnoozeTimeInSeconds: int | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.RunPolicy

Bases: BaseModel

RunPolicy

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

activeDeadlineSeconds: int | None

cleanPodPolicy: str | None

faultDeadlineSeconds: int | None

jobMaxRetryCount: int | None

logMonitoringConfiguration: List[LogMonitoringConfiguration] | None

restartPolicy: RestartPolicy | None

startupDeadlineSeconds: int | None

suspend: bool | None

ttlSecondsAfterFinished: int | None

workloadMode: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.PodSets

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

count: int

minCount: int | None

name: str | None

template: Template

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.Pods

Bases: BaseModel

DEPRECATED pods to include job pods status in jobPods associated with replicaSpecs pods is retained here to support operator upgrade

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

apiVersion: str | None

fieldPath: str | None

kind: str | None

name: str | None

namespace: str | None

resourceVersion: str | None

uid: str | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.ElasticScalingStatus

Bases: BaseModel

ElasticScalingStatus represents the current state of elastic scaling operations

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

targetReplicas: Dict[str, int] | None

lastUpdated: str | None

lastScalingTime: str | None

lastRestartTime: str | None

podsScaled: bool | None

isFaultyPodScaleDown: bool | None

consecutiveScalingFailures: int | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.RestartStatus

Bases: BaseModel

Additional restart limiting status

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

currentEvalPeriod: int

fullJobRestartCount: int

restartCount: int

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.FaultyPodInstanceList

Bases: BaseModel

FaultyPodInstanceRecord tracks faulty pod/instances for each restart

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

restartType: str | None

faultyInstanceIdList: List[str] | None

faultyPodList: List[str] | None

faultyRankList: List[str] | None

class sagemaker.hyperpod.training.config.hyperpod_pytorch_job_unified_config.HyperPodPytorchJobStatus

Bases: BaseModel

HyperPodPytorchJobStatus defines the observed state of HyperPodPytorchJob

model_config: ClassVar[ConfigDict] = {'extra': 'forbid'}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

completionTime: str | None

conditions: List[Conditions] | None

jobPods: List[JobPods] | None

latestFaultyPodInstanceList: FaultyPodInstanceList | None

managerPods: ManagerPods | None

masterAddr: str | None

masterPort: str | None

podManagerStatuses: List[PodManagerStatuses] | None

podSetInfos: List[PodSetInfos] | None

podSets: List[PodSets] | None

restartCount: int | None

restartStatus: RestartStatus | None

elasticScalingStatus: ElasticScalingStatus | None

elasticWorkloadRef: Dict[str, str] | None

startTime: str | None