Google Cloud Notebooks V1 Client - Class ExecutionTemplate (1.0.2)

Reference documentation and code samples for the Google Cloud Notebooks V1 Client class ExecutionTemplate.

The description a notebook execution workload.

Generated from protobuf message google.cloud.notebooks.v1.ExecutionTemplate

Namespace

Google \ Cloud \ Notebooks \ V1

Methods

__construct

Constructor.

Parameters
Name Description
data array

Optional. Data for populating the Message object.
↳ scale_tier int

Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.
↳ master_type 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.
↳ accelerator_config ExecutionTemplate\SchedulerAcceleratorConfig

Configuration (count and accelerator type) for hardware running notebook execution.
↳ labels array|Google\Protobuf\Internal\MapField

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 string

Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{bucket_name}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
↳ container_image_uri 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
↳ output_notebook_folder string

Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: gs://{bucket_name}/{folder} Ex: gs://notebook_user/scheduled_notebooks
↳ params_yaml_file 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
↳ parameters string

Parameters used within the 'input_notebook_file' notebook.
↳ service_account 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.
↳ job_type int

The type of Job to be used on this execution.
↳ dataproc_parameters ExecutionTemplate\DataprocParameters

Parameters used in Dataproc JobType executions.
↳ vertex_ai_parameters ExecutionTemplate\VertexAIParameters

Parameters used in Vertex AI JobType executions.
↳ kernel_spec string

Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file.
↳ tensorboard string

The name of a Vertex AI [Tensorboard] resource to which this execution will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

getScaleTier

Required. Scale tier of the hardware used for notebook execution.

DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

Returns
Type Description
int

setScaleTier

Required. Scale tier of the hardware used for notebook execution.

DEPRECATED Will be discontinued. As right now only CUSTOM is supported.

Parameter
Name Description
var int
Returns
Type Description
$this

getMasterType

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
Type Description
string

setMasterType

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.
Parameter
Name Description
var string
Returns
Type Description
$this

getAcceleratorConfig

Configuration (count and accelerator type) for hardware running notebook execution.

Returns
Type Description
ExecutionTemplate\SchedulerAcceleratorConfig|null

hasAcceleratorConfig

clearAcceleratorConfig

setAcceleratorConfig

Configuration (count and accelerator type) for hardware running notebook execution.

Parameter
Name Description
var ExecutionTemplate\SchedulerAcceleratorConfig
Returns
Type Description
$this

getLabels

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
Type Description
Google\Protobuf\Internal\MapField

setLabels

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.

Parameter
Name Description
var array|Google\Protobuf\Internal\MapField
Returns
Type Description
$this

getInputNotebookFile

Path to the notebook file to execute.

Must be in a Google Cloud Storage bucket. Format: gs://{bucket_name}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb

Returns
Type Description
string

setInputNotebookFile

Path to the notebook file to execute.

Must be in a Google Cloud Storage bucket. Format: gs://{bucket_name}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb

Parameter
Name Description
var string
Returns
Type Description
$this

getContainerImageUri

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
Type Description
string

setContainerImageUri

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

Parameter
Name Description
var string
Returns
Type Description
$this

getOutputNotebookFolder

Path to the notebook folder to write to.

Must be in a Google Cloud Storage bucket path. Format: gs://{bucket_name}/{folder} Ex: gs://notebook_user/scheduled_notebooks

Returns
Type Description
string

setOutputNotebookFolder

Path to the notebook folder to write to.

Must be in a Google Cloud Storage bucket path. Format: gs://{bucket_name}/{folder} Ex: gs://notebook_user/scheduled_notebooks

Parameter
Name Description
var string
Returns
Type Description
$this

getParamsYamlFile

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
Type Description
string

setParamsYamlFile

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

Parameter
Name Description
var string
Returns
Type Description
$this

getParameters

Parameters used within the 'input_notebook_file' notebook.

Returns
Type Description
string

setParameters

Parameters used within the 'input_notebook_file' notebook.

Parameter
Name Description
var string
Returns
Type Description
$this

getServiceAccount

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
Type Description
string

setServiceAccount

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.

Parameter
Name Description
var string
Returns
Type Description
$this

getJobType

The type of Job to be used on this execution.

Returns
Type Description
int

setJobType

The type of Job to be used on this execution.

Parameter
Name Description
var int
Returns
Type Description
$this

getDataprocParameters

Parameters used in Dataproc JobType executions.

Returns
Type Description
ExecutionTemplate\DataprocParameters|null

hasDataprocParameters

setDataprocParameters

Parameters used in Dataproc JobType executions.

Parameter
Name Description
var ExecutionTemplate\DataprocParameters
Returns
Type Description
$this

getVertexAiParameters

Parameters used in Vertex AI JobType executions.

Returns
Type Description
ExecutionTemplate\VertexAIParameters|null

hasVertexAiParameters

setVertexAiParameters

Parameters used in Vertex AI JobType executions.

Parameter
Name Description
var ExecutionTemplate\VertexAIParameters
Returns
Type Description
$this

getKernelSpec

Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file.

Returns
Type Description
string

setKernelSpec

Name of the kernel spec to use. This must be specified if the kernel spec name on the execution target does not match the name in the input notebook file.

Parameter
Name Description
var string
Returns
Type Description
$this

getTensorboard

The name of a Vertex AI [Tensorboard] resource to which this execution will upload Tensorboard logs.

Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

Returns
Type Description
string

setTensorboard

The name of a Vertex AI [Tensorboard] resource to which this execution will upload Tensorboard logs.

Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}

Parameter
Name Description
var string
Returns
Type Description
$this

getJobParameters

Returns
Type Description
string