Class: Google::Apis::NotebooksV1::ExecutionTemplate

Inherits:
Object
  • Object
show all
Includes:
Core::Hashable, Core::JsonObjectSupport
Defined in:
lib/google/apis/notebooks_v1/classes.rb,
lib/google/apis/notebooks_v1/representations.rb,
lib/google/apis/notebooks_v1/representations.rb

Overview

The description a notebook execution workload.

Instance Attribute Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(**args) ⇒ ExecutionTemplate

Returns a new instance of ExecutionTemplate.



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# File 'lib/google/apis/notebooks_v1/classes.rb', line 620

def initialize(**args)
   update!(**args)
end

Instance Attribute Details

#accelerator_config ⇒ Google::Apis::NotebooksV1::SchedulerAcceleratorConfig

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. Corresponds to the JSON property acceleratorConfig

Returns:



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# File 'lib/google/apis/notebooks_v1/classes.rb', line 515

def accelerator_config
  @accelerator_config
end

#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 Corresponds to the JSON property containerImageUri

Returns:

  • (String) 


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# File 'lib/google/apis/notebooks_v1/classes.rb', line 522

def container_image_uri
  @container_image_uri
end

#dataproc_parameters ⇒ Google::Apis::NotebooksV1::DataprocParameters

Parameters used in Dataproc JobType executions. Corresponds to the JSON property dataprocParameters

Returns:



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# File 'lib/google/apis/notebooks_v1/classes.rb', line 527

def dataproc_parameters
  @dataproc_parameters
end

#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 Corresponds to the JSON propertyinputNotebookFile`

Returns:

  • (String) 


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# File 'lib/google/apis/notebooks_v1/classes.rb', line 534

def input_notebook_file
  @input_notebook_file
end

#job_type ⇒ String

The type of Job to be used on this execution. Corresponds to the JSON property jobType

Returns:

  • (String) 


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# File 'lib/google/apis/notebooks_v1/classes.rb', line 539

def job_type
  @job_type
end

#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. Corresponds to the JSON property kernelSpec

Returns:

  • (String) 


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# File 'lib/google/apis/notebooks_v1/classes.rb', line 545

def kernel_spec
  @kernel_spec
end

#labels ⇒ Hash<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. Corresponds to the JSON property labels

Returns:

  • (Hash<String,String>) 


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# File 'lib/google/apis/notebooks_v1/classes.rb', line 553

def labels
  @labels
end

#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. Corresponds to the JSON property masterType

Returns:

  • (String) 


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# File 'lib/google/apis/notebooks_v1/classes.rb', line 573

def master_type
  @master_type
end

#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 Corresponds to the JSON propertyoutputNotebookFolder`

Returns:

  • (String) 


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# File 'lib/google/apis/notebooks_v1/classes.rb', line 580

def output_notebook_folder
  @output_notebook_folder
end

#parameters ⇒ String

Parameters used within the 'input_notebook_file' notebook. Corresponds to the JSON property parameters

Returns:

  • (String) 


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# File 'lib/google/apis/notebooks_v1/classes.rb', line 585

def parameters
  @parameters
end

#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 Corresponds to the JSON property paramsYamlFile

Returns:

  • (String) 


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# File 'lib/google/apis/notebooks_v1/classes.rb', line 593

def params_yaml_file
  @params_yaml_file
end

#scale_tier ⇒ String

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

Returns:

  • (String) 


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# File 'lib/google/apis/notebooks_v1/classes.rb', line 599

def scale_tier
  @scale_tier
end

#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. Corresponds to the JSON property serviceAccount

Returns:

  • (String) 


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# File 'lib/google/apis/notebooks_v1/classes.rb', line 606

def 
  @service_account
end

#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` Corresponds to the JSON propertytensorboard`

Returns:

  • (String) 


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# File 'lib/google/apis/notebooks_v1/classes.rb', line 613

def tensorboard
  @tensorboard
end

#vertex_ai_parameters ⇒ Google::Apis::NotebooksV1::VertexAiParameters

Parameters used in Vertex AI JobType executions. Corresponds to the JSON property vertexAiParameters

Returns:



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# File 'lib/google/apis/notebooks_v1/classes.rb', line 618

def vertex_ai_parameters
  @vertex_ai_parameters
end

Instance Method Details

#update!(**args) ⇒ Object

Update properties of this object



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# File 'lib/google/apis/notebooks_v1/classes.rb', line 625

def update!(**args)
  @accelerator_config = args[:accelerator_config] if args.key?(:accelerator_config)
  @container_image_uri = args[:container_image_uri] if args.key?(:container_image_uri)
  @dataproc_parameters = args[:dataproc_parameters] if args.key?(:dataproc_parameters)
  @input_notebook_file = args[:input_notebook_file] if args.key?(:input_notebook_file)
  @job_type = args[:job_type] if args.key?(:job_type)
  @kernel_spec = args[:kernel_spec] if args.key?(:kernel_spec)
  @labels = args[:labels] if args.key?(:labels)
  @master_type = args[:master_type] if args.key?(:master_type)
  @output_notebook_folder = args[:output_notebook_folder] if args.key?(:output_notebook_folder)
  @parameters = args[:parameters] if args.key?(:parameters)
  @params_yaml_file = args[:params_yaml_file] if args.key?(:params_yaml_file)
  @scale_tier = args[:scale_tier] if args.key?(:scale_tier)
  @service_account = args[:service_account] if args.key?(:service_account)
  @tensorboard = args[:tensorboard] if args.key?(:tensorboard)
  @vertex_ai_parameters = args[:vertex_ai_parameters] if args.key?(:vertex_ai_parameters)
end