AI Platform Notebooks V1 API - Class Google::Cloud::Notebooks::V1::ExecutionTemplate (v0.1.0)

Reference documentation and code samples for the AI Platform Notebooks V1 API class Google::Cloud::Notebooks::V1::ExecutionTemplate.

The description a notebook execution workload.

Inherits

Object

Extended By

Google::Protobuf::MessageExts::ClassMethods

Includes

Google::Protobuf::MessageExts

Methods

#accelerator_config

def accelerator_config() -> ::Google::Cloud::Notebooks::V1::ExecutionTemplate::SchedulerAcceleratorConfig

Returns

(::Google::Cloud::Notebooks::V1::ExecutionTemplate::SchedulerAcceleratorConfig) — Configuration (count and accelerator type) for hardware running notebook execution.

#accelerator_config=

def accelerator_config=(value) -> ::Google::Cloud::Notebooks::V1::ExecutionTemplate::SchedulerAcceleratorConfig

Parameter

value (::Google::Cloud::Notebooks::V1::ExecutionTemplate::SchedulerAcceleratorConfig) — Configuration (count and accelerator type) for hardware running notebook execution.

Returns

(::Google::Cloud::Notebooks::V1::ExecutionTemplate::SchedulerAcceleratorConfig) — Configuration (count and accelerator type) for hardware running notebook execution.

#container_image_uri

def container_image_uri() -> ::String

Returns

(::String) — 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

#container_image_uri=

def container_image_uri=(value) -> ::String

Parameter

value (::String) — 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

Returns

(::String) — 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

#dataproc_parameters

def dataproc_parameters() -> ::Google::Cloud::Notebooks::V1::ExecutionTemplate::DataprocParameters

Returns

(::Google::Cloud::Notebooks::V1::ExecutionTemplate::DataprocParameters) — Parameters used in Dataproc JobType executions.

#dataproc_parameters=

def dataproc_parameters=(value) -> ::Google::Cloud::Notebooks::V1::ExecutionTemplate::DataprocParameters

Parameter

value (::Google::Cloud::Notebooks::V1::ExecutionTemplate::DataprocParameters) — Parameters used in Dataproc JobType executions.

Returns

(::Google::Cloud::Notebooks::V1::ExecutionTemplate::DataprocParameters) — Parameters used in Dataproc JobType executions.

#input_notebook_file

def input_notebook_file() -> ::String

Returns

(::String) — 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

#input_notebook_file=

def input_notebook_file=(value) -> ::String

Parameter

value (::String) — 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

Returns

(::String) — 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

#job_type

def job_type() -> ::Google::Cloud::Notebooks::V1::ExecutionTemplate::JobType

Returns

(::Google::Cloud::Notebooks::V1::ExecutionTemplate::JobType) — The type of Job to be used on this execution.

#job_type=

def job_type=(value) -> ::Google::Cloud::Notebooks::V1::ExecutionTemplate::JobType

Parameter

value (::Google::Cloud::Notebooks::V1::ExecutionTemplate::JobType) — The type of Job to be used on this execution.

Returns

(::Google::Cloud::Notebooks::V1::ExecutionTemplate::JobType) — The type of Job to be used on this execution.

#labels

def labels() -> ::Google::Protobuf::Map{::String => ::String}

Returns

(::Google::Protobuf::Map{::String => ::String}) — 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.

#labels=

def labels=(value) -> ::Google::Protobuf::Map{::String => ::String}

Parameter

value (::Google::Protobuf::Map{::String => ::String}) — 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.

Returns

(::Google::Protobuf::Map{::String => ::String}) — 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.

#master_type

def master_type() -> ::String

Returns

(::String) — 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.

#master_type=

def master_type=(value) -> ::String

Parameter

value (::String) — 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.

Returns

(::String) — 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.

#output_notebook_folder

def output_notebook_folder() -> ::String

Returns

(::String) — 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

#output_notebook_folder=

def output_notebook_folder=(value) -> ::String

Parameter

value (::String) — 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

Returns

(::String) — 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

#parameters

def parameters() -> ::String

Returns

(::String) — Parameters used within the 'input_notebook_file' notebook.

#parameters=

def parameters=(value) -> ::String

Parameter

value (::String) — Parameters used within the 'input_notebook_file' notebook.

Returns

(::String) — Parameters used within the 'input_notebook_file' notebook.

#params_yaml_file

def params_yaml_file() -> ::String

Returns

(::String) — 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

#params_yaml_file=

def params_yaml_file=(value) -> ::String

Parameter

value (::String) — 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

Returns

(::String) — 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

#scale_tier

def scale_tier() -> ::Google::Cloud::Notebooks::V1::ExecutionTemplate::ScaleTier

Returns

(::Google::Cloud::Notebooks::V1::ExecutionTemplate::ScaleTier) — Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

#scale_tier=

def scale_tier=(value) -> ::Google::Cloud::Notebooks::V1::ExecutionTemplate::ScaleTier

Parameter

value (::Google::Cloud::Notebooks::V1::ExecutionTemplate::ScaleTier) — Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

Returns

(::Google::Cloud::Notebooks::V1::ExecutionTemplate::ScaleTier) — Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

#service_account

def service_account() -> ::String

Returns

(::String) — 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.

#service_account=

def service_account=(value) -> ::String

Parameter

value (::String) — 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.

Returns

(::String) — 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.