:py:mod:`airflow.providers.microsoft.azure.operators.container_instances`
=========================================================================

.. py:module:: airflow.providers.microsoft.azure.operators.container_instances


Module Contents
---------------

Classes
~~~~~~~

.. autoapisummary::

   airflow.providers.microsoft.azure.operators.container_instances.AzureContainerInstancesOperator


Attributes
~~~~~~~~~~

.. autoapisummary::

   airflow.providers.microsoft.azure.operators.container_instances.Volume
   airflow.providers.microsoft.azure.operators.container_instances.DEFAULT_ENVIRONMENT_VARIABLES
   airflow.providers.microsoft.azure.operators.container_instances.DEFAULT_SECURED_VARIABLES
   airflow.providers.microsoft.azure.operators.container_instances.DEFAULT_VOLUMES
   airflow.providers.microsoft.azure.operators.container_instances.DEFAULT_MEMORY_IN_GB
   airflow.providers.microsoft.azure.operators.container_instances.DEFAULT_CPU


.. py:data:: Volume
   

.. py:data:: DEFAULT_ENVIRONMENT_VARIABLES
   :annotation: :Dict[str, str]

   
.. py:data:: DEFAULT_SECURED_VARIABLES
   :annotation: :Sequence[str] = []

   
.. py:data:: DEFAULT_VOLUMES
   :annotation: :Sequence[Volume] = []

   
.. py:data:: DEFAULT_MEMORY_IN_GB
   :annotation: = 2.0

   
.. py:data:: DEFAULT_CPU
   :annotation: = 1.0

   
.. py:class:: AzureContainerInstancesOperator(*, ci_conn_id, registry_conn_id, resource_group, name, image, region, environment_variables = None, secured_variables = None, volumes = None, memory_in_gb = None, cpu = None, gpu = None, command = None, remove_on_error = True, fail_if_exists = True, tags = None, os_type = 'Linux', restart_policy = 'Never', ip_address = None, ports = None, **kwargs)

   Bases: :py:obj:`airflow.models.BaseOperator`

   Start a container on Azure Container Instances

   :param ci_conn_id: connection id of a service principal which will be used
       to start the container instance
   :param registry_conn_id: connection id of a user which can login to a
       private docker registry. For Azure use :ref:`Azure connection id<howto/connection:azure>`
   :param resource_group: name of the resource group wherein this container
       instance should be started
   :param name: name of this container instance. Please note this name has
       to be unique in order to run containers in parallel.
   :param image: the docker image to be used
   :param region: the region wherein this container instance should be started
   :param environment_variables: key,value pairs containing environment
       variables which will be passed to the running container
   :param secured_variables: names of environmental variables that should not
       be exposed outside the container (typically passwords).
   :param volumes: list of ``Volume`` tuples to be mounted to the container.
       Currently only Azure Fileshares are supported.
   :param memory_in_gb: the amount of memory to allocate to this container
   :param cpu: the number of cpus to allocate to this container
   :param gpu: GPU Resource for the container.
   :param command: the command to run inside the container
   :param container_timeout: max time allowed for the execution of
       the container instance.
   :param tags: azure tags as dict of str:str
   :param os_type: The operating system type required by the containers
       in the container group. Possible values include: 'Windows', 'Linux'
   :param restart_policy: Restart policy for all containers within the container group.
       Possible values include: 'Always', 'OnFailure', 'Never'
   :param ip_address: The IP address type of the container group.

   **Example**::

               AzureContainerInstancesOperator(
                   ci_conn_id = "azure_service_principal",
                   registry_conn_id = "azure_registry_user",
                   resource_group = "my-resource-group",
                   name = "my-container-name-{{ ds }}",
                   image = "myprivateregistry.azurecr.io/my_container:latest",
                   region = "westeurope",
                   environment_variables = {"MODEL_PATH":  "my_value",
                    "POSTGRES_LOGIN": "{{ macros.connection('postgres_default').login }}",
                    "POSTGRES_PASSWORD": "{{ macros.connection('postgres_default').password }}",
                    "JOB_GUID": "{{ ti.xcom_pull(task_ids='task1', key='guid') }}" },
                   secured_variables = ['POSTGRES_PASSWORD'],
                   volumes = [("azure_container_instance_conn_id",
                           "my_storage_container",
                           "my_fileshare",
                           "/input-data",
                       True),],
                   memory_in_gb=14.0,
                   cpu=4.0,
                   gpu=GpuResource(count=1, sku='K80'),
                   command=["/bin/echo", "world"],
                   task_id="start_container"
               )

   .. py:attribute:: template_fields
      :annotation: :Sequence[str] = ['name', 'image', 'command', 'environment_variables']

      
   .. py:attribute:: template_fields_renderers
      

   .. py:method:: execute(self, context)

      This is the main method to derive when creating an operator.
      Context is the same dictionary used as when rendering jinja templates.

      Refer to get_template_context for more context.


   .. py:method:: on_kill(self)

      Override this method to cleanup subprocesses when a task instance
      gets killed. Any use of the threading, subprocess or multiprocessing
      module within an operator needs to be cleaned up or it will leave
      ghost processes behind.