Class ExecutionTemplate (0.4.3)

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ExecutionTemplate(mapping=None, *, ignore_unknown_fields=False, **kwargs)

The description a notebook execution workload. .. attribute:: scale_tier

Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.



master_type str
Specifies the type of virtual machine to use for your training job's master worker. You must specify this field when ``scaleTier`` is set to ``CUSTOM``. You can use certain Compute Engine machine types directly in this field. The following types are supported: - ``n1-standard-4`` - ``n1-standard-8`` - ``n1-standard-16`` - ``n1-standard-32`` - ``n1-standard-64`` - ``n1-standard-96`` - ``n1-highmem-2`` - ``n1-highmem-4`` - ``n1-highmem-8`` - ``n1-highmem-16`` - ``n1-highmem-32`` - ``n1-highmem-64`` - ``n1-highmem-96`` - ``n1-highcpu-16`` - ``n1-highcpu-32`` - ``n1-highcpu-64`` - ``n1-highcpu-96`` Alternatively, you can use the following legacy machine types: - ``standard`` - ``large_model`` - ``complex_model_s`` - ``complex_model_m`` - ``complex_model_l`` - ``standard_gpu`` - ``complex_model_m_gpu`` - ``complex_model_l_gpu`` - ``standard_p100`` - ``complex_model_m_p100`` - ``standard_v100`` - ``large_model_v100`` - ``complex_model_m_v100`` - ``complex_model_l_v100`` Finally, if you want to use a TPU for training, specify ``cloud_tpu`` in this field. Learn more about the [special configuration options for training with TPU.
Configuration (count and accelerator type) for hardware running notebook execution.
labels Sequence[]
Labels for execution. If execution is scheduled, a field included will be 'nbs-scheduled'. Otherwise, it is an immediate execution, and an included field will be 'nbs-immediate'. Use fields to efficiently index between various types of executions.
input_notebook_file str
Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: ``gs://{project_id}/{folder}/{notebook_file_name}`` Ex: ``gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb``
container_image_uri str
Container Image URI to a DLVM Example: ' release/base-cu100' More examples can be found at: learning-containers/docs/choosing-container
output_notebook_folder str
Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: ``gs://{project_id}/{folder}`` Ex: ``gs://notebook_user/scheduled_notebooks``
params_yaml_file str
Parameters to be overridden in the notebook during execution. Ref on how to specifying parameters in the input notebook and pass them here in an YAML file. Ex: ``gs://notebook_user/scheduled_notebooks/sentiment_notebook_params.yaml``
parameters str
Parameters used within the 'input_notebook_file' notebook.
service_account str
The email address of a service account to use when running the execution. You must have the ``iam.serviceAccounts.actAs`` permission for the specified service account.
The type of Job to be used on this execution.
Parameters used in Dataproc JobType executions.


builtins.object > proto.message.Message > ExecutionTemplate



DataprocParameters(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Parameters used in Dataproc JobType executions. .. attribute:: cluster

URI for cluster used to run Dataproc execution. Format: projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}

:type: str



The backend used for this execution.


LabelsEntry(mapping=None, *, ignore_unknown_fields=False, **kwargs)

The abstract base class for a message.

kwargs dict

Keys and values corresponding to the fields of the message.

mapping Union[dict, `.Message`]

A dictionary or message to be used to determine the values for this message.

ignore_unknown_fields Optional(bool)

If True, do not raise errors for unknown fields. Only applied if mapping is a mapping type or there are keyword parameters.



Required. Specifies the machine types, the number of replicas for workers and parameter servers.


SchedulerAcceleratorConfig(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Definition of a hardware accelerator. Note that not all combinations of type and core_count are valid. Check GPUs on Compute Engine to find a valid combination. TPUs are not supported.



Hardware accelerator types for AI Platform Training jobs.