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# coding=utf-8
# --------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License. See License.txt in the project root for
# license information.
#
# Code generated by Microsoft (R) AutoRest Code Generator.
# Changes may cause incorrect behavior and will be lost if the code is
# regenerated.
# --------------------------------------------------------------------------
from .proxy_resource_py3 import ProxyResource
class Job(ProxyResource):
"""Contains information about a Job.
Variables are only populated by the server, and will be ignored when
sending a request.
:ivar id: The ID of the resource.
:vartype id: str
:ivar name: The name of the resource.
:vartype name: str
:ivar type: The type of the resource.
:vartype type: str
:param scheduling_priority: Scheduling priority associated with the job.
Scheduling priority associated with the job. Possible values include:
'low', 'normal', 'high'. Default value: "normal" .
:type scheduling_priority: str or ~azure.mgmt.batchai.models.JobPriority
:param cluster: Specifies the Id of the cluster on which this job will
run.
:type cluster: ~azure.mgmt.batchai.models.ResourceId
:param mount_volumes: Information on mount volumes to be used by the job.
These volumes will be mounted before the job execution and will be
unmouted after the job completion. The volumes will be mounted at location
specified by $AZ_BATCHAI_JOB_MOUNT_ROOT environment variable.
:type mount_volumes: ~azure.mgmt.batchai.models.MountVolumes
:ivar job_output_directory_path_segment: A segment of job's output
directories path created by BatchAI. Batch AI creates job's output
directories under an unique path to avoid conflicts between jobs. This
value contains a path segment generated by Batch AI to make the path
unique and can be used to find the output directory on the node or mounted
filesystem.
:vartype job_output_directory_path_segment: str
:param node_count: Number of compute nodes to run the job on. The job will
be gang scheduled on that many compute nodes
:type node_count: int
:param container_settings: If provided the job will run in the specified
container. If the container was downloaded as part of cluster setup then
the same container image will be used. If not provided, the job will run
on the VM.
:type container_settings: ~azure.mgmt.batchai.models.ContainerSettings
:param tool_type: The toolkit type of this job. Possible values are: cntk,
tensorflow, caffe, caffe2, chainer, pytorch, custom, mpi, horovod.
Possible values include: 'cntk', 'tensorflow', 'caffe', 'caffe2',
'chainer', 'horovod', 'mpi', 'custom'
:type tool_type: str or ~azure.mgmt.batchai.models.ToolType
:param cntk_settings: Specifies the settings for CNTK (aka Microsoft
Cognitive Toolkit) job.
:type cntk_settings: ~azure.mgmt.batchai.models.CNTKsettings
:param py_torch_settings: Specifies the settings for pyTorch job.
:type py_torch_settings: ~azure.mgmt.batchai.models.PyTorchSettings
:param tensor_flow_settings: Specifies the settings for Tensor Flow job.
:type tensor_flow_settings: ~azure.mgmt.batchai.models.TensorFlowSettings
:param caffe_settings: Specifies the settings for Caffe job.
:type caffe_settings: ~azure.mgmt.batchai.models.CaffeSettings
:param caffe2_settings: Specifies the settings for Caffe2 job.
:type caffe2_settings: ~azure.mgmt.batchai.models.Caffe2Settings
:param chainer_settings: Specifies the settings for Chainer job.
:type chainer_settings: ~azure.mgmt.batchai.models.ChainerSettings
:param custom_toolkit_settings: Specifies the settings for custom tool kit
job.
:type custom_toolkit_settings:
~azure.mgmt.batchai.models.CustomToolkitSettings
:param custom_mpi_settings: Specifies the settings for custom MPI job.
:type custom_mpi_settings: ~azure.mgmt.batchai.models.CustomMpiSettings
:param horovod_settings: Specifies the settings for Horovod job.
:type horovod_settings: ~azure.mgmt.batchai.models.HorovodSettings
:param job_preparation: Specifies the actions to be performed before tool
kit is launched. The specified actions will run on all the nodes that are
part of the job
:type job_preparation: ~azure.mgmt.batchai.models.JobPreparation
:param std_out_err_path_prefix: The path where the Batch AI service will
upload stdout and stderror of the job.
:type std_out_err_path_prefix: str
:param input_directories: Specifies the list of input directories for the
Job.
:type input_directories: list[~azure.mgmt.batchai.models.InputDirectory]
:param output_directories: Specifies the list of output directories where
the models will be created.
:type output_directories: list[~azure.mgmt.batchai.models.OutputDirectory]
:param environment_variables: Additional environment variables to set on
the job. Batch AI will setup these additional environment variables for
the job.
:type environment_variables:
list[~azure.mgmt.batchai.models.EnvironmentVariable]
:param secrets: Additional environment variables with secret values to set
on the job. Batch AI will setup these additional environment variables for
the job. Server will never report values of these variables back.
:type secrets:
list[~azure.mgmt.batchai.models.EnvironmentVariableWithSecretValue]
:param constraints: Constraints associated with the Job.
:type constraints: ~azure.mgmt.batchai.models.JobPropertiesConstraints
:ivar creation_time: The job creation time. The creation time of the job.
:vartype creation_time: datetime
:ivar provisioning_state: The provisioned state of the Batch AI job.
Possible values include: 'creating', 'succeeded', 'failed', 'deleting'
:vartype provisioning_state: str or
~azure.mgmt.batchai.models.ProvisioningState
:ivar provisioning_state_transition_time: The time at which the job
entered its current provisioning state. The time at which the job entered
its current provisioning state.
:vartype provisioning_state_transition_time: datetime
:ivar execution_state: The current state of the job. The current state of
the job. Possible values are: queued - The job is queued and able to run.
A job enters this state when it is created, or when it is awaiting a retry
after a failed run. running - The job is running on a compute cluster.
This includes job-level preparation such as downloading resource files or
set up container specified on the job - it does not necessarily mean that
the job command line has started executing. terminating - The job is
terminated by the user, the terminate operation is in progress. succeeded
- The job has completed running succesfully and exited with exit code 0.
failed - The job has finished unsuccessfully (failed with a non-zero exit
code) and has exhausted its retry limit. A job is also marked as failed if
an error occurred launching the job. Possible values include: 'queued',
'running', 'terminating', 'succeeded', 'failed'
:vartype execution_state: str or ~azure.mgmt.batchai.models.ExecutionState
:ivar execution_state_transition_time: The time at which the job entered
its current execution state. The time at which the job entered its current
execution state.
:vartype execution_state_transition_time: datetime
:param execution_info: Contains information about the execution of a job
in the Azure Batch service.
:type execution_info:
~azure.mgmt.batchai.models.JobPropertiesExecutionInfo
"""
_validation = {
'id': {'readonly': True},
'name': {'readonly': True},
'type': {'readonly': True},
'job_output_directory_path_segment': {'readonly': True},
'creation_time': {'readonly': True},
'provisioning_state': {'readonly': True},
'provisioning_state_transition_time': {'readonly': True},
'execution_state': {'readonly': True},
'execution_state_transition_time': {'readonly': True},
}
_attribute_map = {
'id': {'key': 'id', 'type': 'str'},
'name': {'key': 'name', 'type': 'str'},
'type': {'key': 'type', 'type': 'str'},
'scheduling_priority': {'key': 'properties.schedulingPriority', 'type': 'str'},
'cluster': {'key': 'properties.cluster', 'type': 'ResourceId'},
'mount_volumes': {'key': 'properties.mountVolumes', 'type': 'MountVolumes'},
'job_output_directory_path_segment': {'key': 'properties.jobOutputDirectoryPathSegment', 'type': 'str'},
'node_count': {'key': 'properties.nodeCount', 'type': 'int'},
'container_settings': {'key': 'properties.containerSettings', 'type': 'ContainerSettings'},
'tool_type': {'key': 'properties.toolType', 'type': 'str'},
'cntk_settings': {'key': 'properties.cntkSettings', 'type': 'CNTKsettings'},
'py_torch_settings': {'key': 'properties.pyTorchSettings', 'type': 'PyTorchSettings'},
'tensor_flow_settings': {'key': 'properties.tensorFlowSettings', 'type': 'TensorFlowSettings'},
'caffe_settings': {'key': 'properties.caffeSettings', 'type': 'CaffeSettings'},
'caffe2_settings': {'key': 'properties.caffe2Settings', 'type': 'Caffe2Settings'},
'chainer_settings': {'key': 'properties.chainerSettings', 'type': 'ChainerSettings'},
'custom_toolkit_settings': {'key': 'properties.customToolkitSettings', 'type': 'CustomToolkitSettings'},
'custom_mpi_settings': {'key': 'properties.customMpiSettings', 'type': 'CustomMpiSettings'},
'horovod_settings': {'key': 'properties.horovodSettings', 'type': 'HorovodSettings'},
'job_preparation': {'key': 'properties.jobPreparation', 'type': 'JobPreparation'},
'std_out_err_path_prefix': {'key': 'properties.stdOutErrPathPrefix', 'type': 'str'},
'input_directories': {'key': 'properties.inputDirectories', 'type': '[InputDirectory]'},
'output_directories': {'key': 'properties.outputDirectories', 'type': '[OutputDirectory]'},
'environment_variables': {'key': 'properties.environmentVariables', 'type': '[EnvironmentVariable]'},
'secrets': {'key': 'properties.secrets', 'type': '[EnvironmentVariableWithSecretValue]'},
'constraints': {'key': 'properties.constraints', 'type': 'JobPropertiesConstraints'},
'creation_time': {'key': 'properties.creationTime', 'type': 'iso-8601'},
'provisioning_state': {'key': 'properties.provisioningState', 'type': 'str'},
'provisioning_state_transition_time': {'key': 'properties.provisioningStateTransitionTime', 'type': 'iso-8601'},
'execution_state': {'key': 'properties.executionState', 'type': 'str'},
'execution_state_transition_time': {'key': 'properties.executionStateTransitionTime', 'type': 'iso-8601'},
'execution_info': {'key': 'properties.executionInfo', 'type': 'JobPropertiesExecutionInfo'},
}
def __init__(self, *, scheduling_priority="normal", cluster=None, mount_volumes=None, node_count: int=None, container_settings=None, tool_type=None, cntk_settings=None, py_torch_settings=None, tensor_flow_settings=None, caffe_settings=None, caffe2_settings=None, chainer_settings=None, custom_toolkit_settings=None, custom_mpi_settings=None, horovod_settings=None, job_preparation=None, std_out_err_path_prefix: str=None, input_directories=None, output_directories=None, environment_variables=None, secrets=None, constraints=None, execution_info=None, **kwargs) -> None:
super(Job, self).__init__(**kwargs)
self.scheduling_priority = scheduling_priority
self.cluster = cluster
self.mount_volumes = mount_volumes
self.job_output_directory_path_segment = None
self.node_count = node_count
self.container_settings = container_settings
self.tool_type = tool_type
self.cntk_settings = cntk_settings
self.py_torch_settings = py_torch_settings
self.tensor_flow_settings = tensor_flow_settings
self.caffe_settings = caffe_settings
self.caffe2_settings = caffe2_settings
self.chainer_settings = chainer_settings
self.custom_toolkit_settings = custom_toolkit_settings
self.custom_mpi_settings = custom_mpi_settings
self.horovod_settings = horovod_settings
self.job_preparation = job_preparation
self.std_out_err_path_prefix = std_out_err_path_prefix
self.input_directories = input_directories
self.output_directories = output_directories
self.environment_variables = environment_variables
self.secrets = secrets
self.constraints = constraints
self.creation_time = None
self.provisioning_state = None
self.provisioning_state_transition_time = None
self.execution_state = None
self.execution_state_transition_time = None
self.execution_info = execution_info
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