ExecutionTemplate class

The description a notebook execution workload.

Constructors

ExecutionTemplate({Input<SchedulerAcceleratorConfig>? acceleratorConfig, Input<String>? containerImageUri, Input<DataprocParameters>? dataprocParameters, Input<String>? inputNotebookFile, Input<ExecutionTemplateJobType>? jobType, Input<String>? kernelSpec, Input<Map<String, String>>? labels, Input<String>? masterType, Input<String>? outputNotebookFolder, Input<String>? parameters, Input<String>? paramsYamlFile, required Input<ExecutionTemplateScaleTier> scaleTier, Input<String>? serviceAccount, Input<String>? tensorboard, Input<VertexAIParameters>? vertexAiParameters})
Creates a new ExecutionTemplate. acceleratorConfig Configuration (count and accelerator type) for hardware running notebook execution. containerImageUri 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 dataprocParameters Parameters used in Dataproc JobType executions. inputNotebookFile 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 jobType The type of Job to be used on this execution. kernelSpec 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. labels 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. masterType 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. outputNotebookFolder 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 parameters Parameters used within the 'input_notebook_file' notebook. paramsYamlFile 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 scaleTier Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported. serviceAccount 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. tensorboard The name of a Vertex AI Tensorboard resource to which this execution will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard} vertexAiParameters Parameters used in Vertex AI JobType executions.
const
ExecutionTemplate.fromMap(Map<String, dynamic> map)
factory

Properties

acceleratorConfig → Input<SchedulerAcceleratorConfig>?
Configuration (count and accelerator type) for hardware running notebook execution.
final
containerImageUri → Input<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
final
dataprocParameters → Input<DataprocParameters>?
Parameters used in Dataproc JobType executions.
final
hashCode → int
The hash code for this object.
no setterinherited
inputNotebookFile → Input<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
final
jobType → Input<ExecutionTemplateJobType>?
The type of Job to be used on this execution.
final
kernelSpec → Input<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.
final
labels → Input<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.
final
masterType → Input<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.
final
outputNotebookFolder → Input<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
final
parameters → Input<String>?
Parameters used within the 'input_notebook_file' notebook.
final
paramsYamlFile → Input<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
final
runtimeType → Type
A representation of the runtime type of the object.
no setterinherited
scaleTier → Input<ExecutionTemplateScaleTier>
Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.
final
serviceAccount → Input<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.
final
tensorboard → Input<String>?
The name of a Vertex AI Tensorboard resource to which this execution will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
final
vertexAiParameters → Input<VertexAIParameters>?
Parameters used in Vertex AI JobType executions.
final

Methods

noSuchMethod(Invocation invocation) → dynamic
Invoked when a nonexistent method or property is accessed.
inherited
toMap() → Map<String, dynamic>
toString() → String
A string representation of this object.
inherited

Operators

operator ==(Object other) → bool
The equality operator.
inherited