Class ExecutionTemplate (1.1.1)

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

The description a notebook execution workload.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Attributes
Name	Description
`scale_tier`	`google.cloud.notebooks_v1.types.ExecutionTemplate.ScaleTier` 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.
`accelerator_config`	`google.cloud.notebooks_v1.types.ExecutionTemplate.SchedulerAcceleratorConfig` Configuration (count and accelerator type) for hardware running notebook execution.
`labels`	`Sequence[google.cloud.notebooks_v1.types.ExecutionTemplate.LabelsEntry]` 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: 'gcr.io/deeplearning-platform- release/base-cu100' More examples can be found at: https://cloud.google.com/ai-platform/deep- 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 https://papermill.readthedocs.io/en/latest/usage-parameterize.html 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.
`job_type`	`google.cloud.notebooks_v1.types.ExecutionTemplate.JobType` The type of Job to be used on this execution.
`dataproc_parameters`	`google.cloud.notebooks_v1.types.ExecutionTemplate.DataprocParameters` Parameters used in Dataproc JobType executions. This field is a member of `oneof`_ `job_parameters`.

Classes

DataprocParameters

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

Parameters used in Dataproc JobType executions.

JobType

JobType(value)

The backend used for this execution.

LabelsEntry

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

The abstract base class for a message.

Parameters
Name	Description
`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.

ScaleTier

ScaleTier(value)

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

SchedulerAcceleratorConfig

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.

SchedulerAcceleratorType

SchedulerAcceleratorType(value)

Hardware accelerator types for AI Platform Training jobs.