azure.batch.models.BatchJobPreparationTask.yml

### YamlMime:PythonClass
uid: azure.batch.models.BatchJobPreparationTask
name: BatchJobPreparationTask
fullName: azure.batch.models.BatchJobPreparationTask
module: azure.batch.models
summary: 'A Job Preparation Task to run before any Tasks of the Job on any given Compute
  Node.

  You can use Job Preparation to prepare a Node to run Tasks for the Job.

  Activities commonly performed in Job Preparation include: Downloading common

  resource files used by all the Tasks in the Job. The Job Preparation Task can

  download these common resource files to the shared location on the Node.

  (AZ_BATCH_NODE_ROOT_DIRshared), or starting a local service on the Node so

  that all Tasks of that Job can communicate with it. If the Job Preparation Task

  fails (that is, exhausts its retry count before exiting with exit code 0),

  Batch will not run Tasks of this Job on the Node. The Compute Node remains

  ineligible to run Tasks of this Job until it is reimaged. The Compute Node

  remains active and can be used for other Jobs. The Job Preparation Task can run

  multiple times on the same Node. Therefore, you should write the Job

  Preparation Task to handle re-execution. If the Node is rebooted, the Job

  Preparation Task is run again on the Compute Node before scheduling any other

  Task of the Job, if rerunOnNodeRebootAfterSuccess is true or if the Job

  Preparation Task did not previously complete. If the Node is reimaged, the Job

  Preparation Task is run again before scheduling any Task of the Job. Batch will

  retry Tasks when a recovery operation is triggered on a Node. Examples of

  recovery operations include (but are not limited to) when an unhealthy Node is

  rebooted or a Compute Node disappeared due to host failure. Retries due to

  recovery operations are independent of and are not counted against the

  maxTaskRetryCount. Even if the maxTaskRetryCount is 0, an internal retry due to

  a recovery operation may occur. Because of this, all Tasks should be

  idempotent. This means Tasks need to tolerate being interrupted and restarted

  without causing any corruption or duplicate data. The best practice for long

  running Tasks is to use some form of checkpointing.


  All required parameters must be populated in order to send to server.'
constructor:
  syntax: 'BatchJobPreparationTask(*args: Any, **kwargs: Any)'
variables:
- description: 'A string that uniquely identifies the Job Preparation Task within
    the Job. The ID can

    contain any combination of alphanumeric characters including hyphens and underscores
    and cannot

    contain more than 64 characters. If you do not specify this property, the Batch
    service assigns

    a default value of ''jobpreparation''. No other Task in the Job can have the same
    ID as the Job

    Preparation Task. If you try to submit a Task with the same id, the Batch service
    rejects the

    request with error code TaskIdSameAsJobPreparationTask; if you are calling the
    REST API

    directly, the HTTP status code is 409 (Conflict).'
  name: id
  types:
  - <xref:str>
- description: 'The command line of the Job Preparation Task. The command line does
    not run

    under a shell, and therefore cannot take advantage of shell features such as environment

    variable expansion. If you want to take advantage of such features, you should
    invoke the shell

    in the command line, for example using "cmd /c MyCommand" in Windows or "/bin/sh
    -c MyCommand"

    in Linux. If the command line refers to file paths, it should use a relative path
    (relative to

    the Task working directory), or use the Batch provided environment variable

    ([https://docs.microsoft.com/en-us/azure/batch/batch-compute-node-environment-variables](https://docs.microsoft.com/en-us/azure/batch/batch-compute-node-environment-variables)).

    Required.'
  name: command_line
  types:
  - <xref:str>
- description: 'The settings for the container under which the Job Preparation Task

    runs. When this is specified, all directories recursively below the AZ_BATCH_NODE_ROOT_DIR
    (the

    root of Azure Batch directories on the node) are mapped into the container, all
    Task

    environment variables are mapped into the container, and the Task command line
    is executed in

    the container. Files produced in the container outside of AZ_BATCH_NODE_ROOT_DIR
    might not be

    reflected to the host disk, meaning that Batch file APIs will not be able to access
    those

    files.'
  name: container_settings
  types:
  - <xref:azure.batch.models.BatchTaskContainerSettings>
- description: 'A list of files that the Batch service will download to the Compute
    Node

    before running the command line. Files listed under this element are located in
    the Task''s

    working directory.  There is a maximum size for the list of resource files.  When
    the max size

    is exceeded, the request will fail and the response error code will be RequestEntityTooLarge.

    If this occurs, the collection of ResourceFiles must be reduced in size. This
    can be achieved

    using .zip files, Application Packages, or Docker Containers.'
  name: resource_files
  types:
  - <xref:list>[<xref:azure.batch.models.ResourceFile>]
- description: 'A list of environment variable settings for the Job Preparation

    Task.'
  name: environment_settings
  types:
  - <xref:list>[<xref:azure.batch.models.EnvironmentSetting>]
- description: Constraints that apply to the Job Preparation Task.
  name: constraints
  types:
  - <xref:azure.batch.models.BatchTaskConstraints>
- description: 'Whether the Batch service should wait for the Job Preparation Task
    to

    complete successfully before scheduling any other Tasks of the Job on the Compute
    Node. A Job

    Preparation Task has completed successfully if it exits with exit code 0. If true
    and the Job

    Preparation Task fails on a Node, the Batch service retries the Job Preparation
    Task up to its

    maximum retry count (as specified in the constraints element). If the Task has
    still not

    completed successfully after all retries, then the Batch service will not schedule
    Tasks of the

    Job to the Node. The Node remains active and eligible to run Tasks of other Jobs.
    If false, the

    Batch service will not wait for the Job Preparation Task to complete. In this
    case, other Tasks

    of the Job can start executing on the Compute Node while the Job Preparation Task
    is still

    running; and even if the Job Preparation Task fails, new Tasks will continue to
    be scheduled on

    the Compute Node. The default value is true.'
  name: wait_for_success
  types:
  - <xref:bool>
- description: 'The user identity under which the Job Preparation Task runs. If omitted,

    the Task runs as a non-administrative user unique to the Task on Windows Compute
    Nodes, or a

    non-administrative user unique to the Pool on Linux Compute Nodes.'
  name: user_identity
  types:
  - <xref:azure.batch.models.UserIdentity>
- description: 'Whether the Batch service should rerun the Job

    Preparation Task after a Compute Node reboots. The Job Preparation Task is always
    rerun if a

    Compute Node is reimaged, or if the Job Preparation Task did not complete (e.g.
    because the

    reboot occurred while the Task was running). Therefore, you should always write
    a Job

    Preparation Task to be idempotent and to behave correctly if run multiple times.
    The default

    value is true.'
  name: rerun_on_node_reboot_after_success
  types:
  - <xref:bool>
methods:
- uid: azure.batch.models.BatchJobPreparationTask.as_dict
  name: as_dict
  summary: Return a dict that can be JSONify using json.dump.
  signature: 'as_dict(*, exclude_readonly: bool = False) -> Dict[str, Any]'
  keywordOnlyParameters:
  - name: exclude_readonly
    description: Whether to remove the readonly properties.
    types:
    - <xref:bool>
  return:
    description: A dict JSON compatible object
    types:
    - <xref:dict>
- uid: azure.batch.models.BatchJobPreparationTask.clear
  name: clear
  signature: clear() -> None
- uid: azure.batch.models.BatchJobPreparationTask.copy
  name: copy
  signature: copy() -> Model
- uid: azure.batch.models.BatchJobPreparationTask.get
  name: get
  signature: 'get(key: str, default: Any = None) -> Any'
  parameters:
  - name: key
    isRequired: true
  - name: default
    defaultValue: None
- uid: azure.batch.models.BatchJobPreparationTask.items
  name: items
  signature: items() -> ItemsView[str, Any]
- uid: azure.batch.models.BatchJobPreparationTask.keys
  name: keys
  signature: keys() -> KeysView[str]
- uid: azure.batch.models.BatchJobPreparationTask.pop
  name: pop
  signature: 'pop(key: str, default: ~typing.Any = <object object>) -> Any'
  parameters:
  - name: key
    isRequired: true
  - name: default
- uid: azure.batch.models.BatchJobPreparationTask.popitem
  name: popitem
  signature: popitem() -> Tuple[str, Any]
- uid: azure.batch.models.BatchJobPreparationTask.setdefault
  name: setdefault
  signature: 'setdefault(key: str, default: ~typing.Any = <object object>) -> Any'
  parameters:
  - name: key
    isRequired: true
  - name: default
- uid: azure.batch.models.BatchJobPreparationTask.update
  name: update
  signature: 'update(*args: Any, **kwargs: Any) -> None'
- uid: azure.batch.models.BatchJobPreparationTask.values
  name: values
  signature: values() -> ValuesView[Any]
attributes:
- uid: azure.batch.models.BatchJobPreparationTask.command_line
  name: command_line
  summary: 'The command line of the Job Preparation Task. The command line does not
    run under a shell, and

    therefore cannot take advantage of shell features such as environment variable
    expansion. If

    you want to take advantage of such features, you should invoke the shell in the
    command line,

    for example using "cmd /c MyCommand" in Windows or "/bin/sh -c MyCommand" in Linux.
    If the

    command line refers to file paths, it should use a relative path (relative to
    the Task working

    directory), or use the Batch provided environment variable

    ([https://docs.microsoft.com/en-us/azure/batch/batch-compute-node-environment-variables](https://docs.microsoft.com/en-us/azure/batch/batch-compute-node-environment-variables)).

    Required.'
  signature: 'command_line: str'
- uid: azure.batch.models.BatchJobPreparationTask.constraints
  name: constraints
  summary: Constraints that apply to the Job Preparation Task.
  signature: 'constraints: _models.BatchTaskConstraints | None'
- uid: azure.batch.models.BatchJobPreparationTask.container_settings
  name: container_settings
  summary: 'The settings for the container under which the Job Preparation Task runs.
    When this is

    specified, all directories recursively below the AZ_BATCH_NODE_ROOT_DIR (the root
    of Azure

    Batch directories on the node) are mapped into the container, all Task environment
    variables

    are mapped into the container, and the Task command line is executed in the container.
    Files

    produced in the container outside of AZ_BATCH_NODE_ROOT_DIR might not be reflected
    to the host

    disk, meaning that Batch file APIs will not be able to access those files.'
  signature: 'container_settings: _models.BatchTaskContainerSettings | None'
- uid: azure.batch.models.BatchJobPreparationTask.environment_settings
  name: environment_settings
  summary: A list of environment variable settings for the Job Preparation Task.
  signature: 'environment_settings: List[_models.EnvironmentSetting] | None'
- uid: azure.batch.models.BatchJobPreparationTask.id
  name: id
  summary: 'A string that uniquely identifies the Job Preparation Task within the
    Job. The ID can contain

    any combination of alphanumeric characters including hyphens and underscores and
    cannot contain

    more than 64 characters. If you do not specify this property, the Batch service
    assigns a

    default value of ''jobpreparation''. No other Task in the Job can have the same
    ID as the Job

    Preparation Task. If you try to submit a Task with the same id, the Batch service
    rejects the

    request with error code TaskIdSameAsJobPreparationTask; if you are calling the
    REST API

    directly, the HTTP status code is 409 (Conflict).'
  signature: 'id: str | None'
- uid: azure.batch.models.BatchJobPreparationTask.rerun_on_node_reboot_after_success
  name: rerun_on_node_reboot_after_success
  summary: 'Whether the Batch service should rerun the Job Preparation Task after
    a Compute Node reboots.

    The Job Preparation Task is always rerun if a Compute Node is reimaged, or if
    the Job

    Preparation Task did not complete (e.g. because the reboot occurred while the
    Task was

    running). Therefore, you should always write a Job Preparation Task to be idempotent
    and to

    behave correctly if run multiple times. The default value is true.'
  signature: 'rerun_on_node_reboot_after_success: bool | None'
- uid: azure.batch.models.BatchJobPreparationTask.resource_files
  name: resource_files
  summary: 'A list of files that the Batch service will download to the Compute Node
    before running the

    command line. Files listed under this element are located in the Task''s working
    directory.

    There is a maximum size for the list of resource files.  When the max size is
    exceeded, the

    request will fail and the response error code will be RequestEntityTooLarge. If
    this occurs,

    the collection of ResourceFiles must be reduced in size. This can be achieved
    using .zip files,

    Application Packages, or Docker Containers.'
  signature: 'resource_files: List[_models.ResourceFile] | None'
- uid: azure.batch.models.BatchJobPreparationTask.user_identity
  name: user_identity
  summary: 'The user identity under which the Job Preparation Task runs. If omitted,
    the Task runs as a

    non-administrative user unique to the Task on Windows Compute Nodes, or a non-administrative

    user unique to the Pool on Linux Compute Nodes.'
  signature: 'user_identity: _models.UserIdentity | None'
- uid: azure.batch.models.BatchJobPreparationTask.wait_for_success
  name: wait_for_success
  summary: 'Whether the Batch service should wait for the Job Preparation Task to
    complete successfully

    before scheduling any other Tasks of the Job on the Compute Node. A Job Preparation
    Task has

    completed successfully if it exits with exit code 0. If true and the Job Preparation
    Task fails

    on a Node, the Batch service retries the Job Preparation Task up to its maximum
    retry count (as

    specified in the constraints element). If the Task has still not completed successfully
    after

    all retries, then the Batch service will not schedule Tasks of the Job to the
    Node. The Node

    remains active and eligible to run Tasks of other Jobs. If false, the Batch service
    will not

    wait for the Job Preparation Task to complete. In this case, other Tasks of the
    Job can start

    executing on the Compute Node while the Job Preparation Task is still running;
    and even if the

    Job Preparation Task fails, new Tasks will continue to be scheduled on the Compute
    Node. The

    default value is true.'
  signature: 'wait_for_success: bool | None'