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fix(sdk): resolve errors in deserialization (kubeflow#2457)
* fix(sdk): import kubernetes.client & make type conversion in swagger.json. Signed-off-by: Electronic-Waste <[email protected]> * fix(sdk): change Union[int, str] to object. Signed-off-by: Electronic-Waste <[email protected]> --------- Signed-off-by: Electronic-Waste <[email protected]>
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‎hack/python-sdk/gen-sdk.sh

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@@ -66,5 +66,6 @@ else
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fi
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# Kubeflow models must have Kubernetes models to perform serialization.
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printf "\n# Import JobSet models for the serialization. It imports the Kubernetes models.\n" >>${SDK_OUTPUT_PATH}/kubeflow/trainer/models/__init__.py
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printf "\n# Import Kubernetes and JobSet models for the serialization. \n" >>${SDK_OUTPUT_PATH}/kubeflow/trainer/models/__init__.py
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printf "from kubernetes.client import *\n" >>${SDK_OUTPUT_PATH}/kubeflow/trainer/models/__init__.py
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printf "from jobset.models import *\n" >>${SDK_OUTPUT_PATH}/kubeflow/trainer/models/__init__.py

‎hack/python-sdk/swagger_config.json

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@@ -1,6 +1,8 @@
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{
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"packageName": "kubeflow.trainer",
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"typeMappings": {
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"K8sIoApiAutoscalingV2MetricSpec": "V2MetricSpec",
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"K8sIoApimachineryPkgUtilIntstrIntOrString": "object",
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"V1Time": "datetime"
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}
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}

‎sdk/docs/TrainerV1alpha1TorchElasticPolicy.md

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@@ -6,7 +6,7 @@ Name | Type | Description | Notes
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------------ | ------------- | ------------- | -------------
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**max_nodes** | **int** | Upper limit for the number of nodes to which training job can scale up. | [optional]
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**max_restarts** | **int** | How many times the training job can be restarted. This value is inserted into the &#x60;--max-restarts&#x60; argument of the &#x60;torchrun&#x60; CLI and the &#x60;.spec.failurePolicy.maxRestarts&#x60; parameter of the training Job. | [optional]
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**metrics** | [**list[K8sIoApiAutoscalingV2MetricSpec]**](K8sIoApiAutoscalingV2MetricSpec.md) | Specification which are used to calculate the desired number of nodes. See the individual metric source types for more information about how each type of metric must respond. The HPA will be created to perform auto-scaling. | [optional]
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**metrics** | [**list[V2MetricSpec]**](K8sIoApiAutoscalingV2MetricSpec.md) | Specification which are used to calculate the desired number of nodes. See the individual metric source types for more information about how each type of metric must respond. The HPA will be created to perform auto-scaling. | [optional]
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**min_nodes** | **int** | Lower limit for the number of nodes to which training job can scale down. | [optional]
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[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md)

‎sdk/docs/TrainerV1alpha1TorchMLPolicySource.md

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@@ -5,7 +5,7 @@ TorchMLPolicySource represents a PyTorch runtime configuration.
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Name | Type | Description | Notes
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------------ | ------------- | ------------- | -------------
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**elastic_policy** | [**TrainerV1alpha1TorchElasticPolicy**](TrainerV1alpha1TorchElasticPolicy.md) | | [optional]
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**num_proc_per_node** | [**K8sIoApimachineryPkgUtilIntstrIntOrString**](K8sIoApimachineryPkgUtilIntstrIntOrString.md) | | [optional]
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**num_proc_per_node** | [**object**](K8sIoApimachineryPkgUtilIntstrIntOrString.md) | | [optional]
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[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md)
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‎sdk/docs/TrainerV1alpha1Trainer.md

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@@ -9,7 +9,7 @@ Name | Type | Description | Notes
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**env** | [**list[V1EnvVar]**](V1EnvVar.md) | List of environment variables to set in the training container. These values will be merged with the TrainingRuntime&#39;s trainer environments. | [optional]
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**image** | **str** | Docker image for the training container. | [optional]
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**num_nodes** | **int** | Number of training nodes. | [optional]
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**num_proc_per_node** | [**K8sIoApimachineryPkgUtilIntstrIntOrString**](K8sIoApimachineryPkgUtilIntstrIntOrString.md) | | [optional]
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**num_proc_per_node** | [**object**](K8sIoApimachineryPkgUtilIntstrIntOrString.md) | | [optional]
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**resources_per_node** | [**V1ResourceRequirements**](V1ResourceRequirements.md) | | [optional]
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[[Back to Model list]](../README.md#documentation-for-models) [[Back to API list]](../README.md#documentation-for-api-endpoints) [[Back to README]](../README.md)

‎sdk/kubeflow/trainer/models/__init__.py

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@@ -43,5 +43,6 @@
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from kubeflow.trainer.models.trainer_v1alpha1_training_runtime_list import TrainerV1alpha1TrainingRuntimeList
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from kubeflow.trainer.models.trainer_v1alpha1_training_runtime_spec import TrainerV1alpha1TrainingRuntimeSpec
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# Import JobSet models for the serialization. It imports the Kubernetes models.
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# Import Kubernetes and JobSet models for the serialization.
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from kubernetes.client import *
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from jobset.models import *

‎sdk/kubeflow/trainer/models/trainer_v1alpha1_torch_elastic_policy.py

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@@ -35,7 +35,7 @@ class TrainerV1alpha1TorchElasticPolicy(object):
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openapi_types = {
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'max_nodes': 'int',
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'max_restarts': 'int',
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'metrics': 'list[K8sIoApiAutoscalingV2MetricSpec]',
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'metrics': 'list[V2MetricSpec]',
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'min_nodes': 'int'
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}
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@@ -120,7 +120,7 @@ def metrics(self):
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Specification which are used to calculate the desired number of nodes. See the individual metric source types for more information about how each type of metric must respond. The HPA will be created to perform auto-scaling. # noqa: E501
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:return: The metrics of this TrainerV1alpha1TorchElasticPolicy. # noqa: E501
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:rtype: list[K8sIoApiAutoscalingV2MetricSpec]
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:rtype: list[V2MetricSpec]
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"""
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return self._metrics
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Specification which are used to calculate the desired number of nodes. See the individual metric source types for more information about how each type of metric must respond. The HPA will be created to perform auto-scaling. # noqa: E501
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:param metrics: The metrics of this TrainerV1alpha1TorchElasticPolicy. # noqa: E501
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:type: list[K8sIoApiAutoscalingV2MetricSpec]
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:type: list[V2MetricSpec]
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"""
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self._metrics = metrics

‎sdk/kubeflow/trainer/models/trainer_v1alpha1_torch_ml_policy_source.py

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@@ -34,7 +34,7 @@ class TrainerV1alpha1TorchMLPolicySource(object):
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"""
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openapi_types = {
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'elastic_policy': 'TrainerV1alpha1TorchElasticPolicy',
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'num_proc_per_node': 'K8sIoApimachineryPkgUtilIntstrIntOrString'
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'num_proc_per_node': 'object'
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}
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attribute_map = {
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:return: The num_proc_per_node of this TrainerV1alpha1TorchMLPolicySource. # noqa: E501
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:rtype: K8sIoApimachineryPkgUtilIntstrIntOrString
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:rtype: object
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"""
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return self._num_proc_per_node
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:param num_proc_per_node: The num_proc_per_node of this TrainerV1alpha1TorchMLPolicySource. # noqa: E501
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:type: K8sIoApimachineryPkgUtilIntstrIntOrString
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:type: object
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"""
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self._num_proc_per_node = num_proc_per_node

‎sdk/kubeflow/trainer/models/trainer_v1alpha1_trainer.py

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@@ -38,7 +38,7 @@ class TrainerV1alpha1Trainer(object):
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'env': 'list[V1EnvVar]',
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'image': 'str',
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'num_nodes': 'int',
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'num_proc_per_node': 'K8sIoApimachineryPkgUtilIntstrIntOrString',
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'num_proc_per_node': 'object',
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'resources_per_node': 'V1ResourceRequirements'
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}
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:return: The num_proc_per_node of this TrainerV1alpha1Trainer. # noqa: E501
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:rtype: K8sIoApimachineryPkgUtilIntstrIntOrString
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:rtype: object
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"""
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return self._num_proc_per_node
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:param num_proc_per_node: The num_proc_per_node of this TrainerV1alpha1Trainer. # noqa: E501
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:type: K8sIoApimachineryPkgUtilIntstrIntOrString
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:type: object
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"""
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self._num_proc_per_node = num_proc_per_node

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