Package com.google.cloud.notebooks.v1

Interface ExecutionTemplateOrBuilder

  • All Superinterfaces:
    com.google.protobuf.MessageLiteOrBuilder, com.google.protobuf.MessageOrBuilder
    All Known Implementing Classes:
    ExecutionTemplate, ExecutionTemplate.Builder
    public interface ExecutionTemplateOrBuilder
extends com.google.protobuf.MessageOrBuilder

    • Method Detail

      • getScaleTierValue

        @Deprecated
int getScaleTierValue()
        Deprecated.
        google.cloud.notebooks.v1.ExecutionTemplate.scale_tier is deprecated. See google/cloud/notebooks/v1/execution.proto;l=151
         Required. Scale tier of the hardware used for notebook execution.
 DEPRECATED Will be discontinued. As right now only CUSTOM is supported.
        .google.cloud.notebooks.v1.ExecutionTemplate.ScaleTier scale_tier = 1 [deprecated = true, (.google.api.field_behavior) = REQUIRED];
        Returns:
        The enum numeric value on the wire for scaleTier.

      • getScaleTier

        @Deprecated
ExecutionTemplate.ScaleTier getScaleTier()
        Deprecated.
        google.cloud.notebooks.v1.ExecutionTemplate.scale_tier is deprecated. See google/cloud/notebooks/v1/execution.proto;l=151
         Required. Scale tier of the hardware used for notebook execution.
 DEPRECATED Will be discontinued. As right now only CUSTOM is supported.
        .google.cloud.notebooks.v1.ExecutionTemplate.ScaleTier scale_tier = 1 [deprecated = true, (.google.api.field_behavior) = REQUIRED];
        Returns:
        The scaleTier.

      • getMasterType

        String 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](https://cloud.google.com/ai-platform/training/docs/using-tpus#configuring_a_custom_tpu_machine).
        string master_type = 2;
        Returns:
        The masterType.

      • getMasterTypeBytes

        com.google.protobuf.ByteString getMasterTypeBytes()
         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](https://cloud.google.com/ai-platform/training/docs/using-tpus#configuring_a_custom_tpu_machine).
        string master_type = 2;
        Returns:
        The bytes for masterType.

      • hasAcceleratorConfig

        boolean hasAcceleratorConfig()
         Configuration (count and accelerator type) for hardware running notebook
 execution.
        .google.cloud.notebooks.v1.ExecutionTemplate.SchedulerAcceleratorConfig accelerator_config = 3;
        Returns:
        Whether the acceleratorConfig field is set.

      • getAcceleratorConfig

        ExecutionTemplate.SchedulerAcceleratorConfig getAcceleratorConfig()
         Configuration (count and accelerator type) for hardware running notebook
 execution.
        .google.cloud.notebooks.v1.ExecutionTemplate.SchedulerAcceleratorConfig accelerator_config = 3;
        Returns:
        The acceleratorConfig.

      • getAcceleratorConfigOrBuilder

        ExecutionTemplate.SchedulerAcceleratorConfigOrBuilder getAcceleratorConfigOrBuilder()
         Configuration (count and accelerator type) for hardware running notebook
 execution.
        .google.cloud.notebooks.v1.ExecutionTemplate.SchedulerAcceleratorConfig accelerator_config = 3;

      • getLabelsCount

        int getLabelsCount()
         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.
        map<string, string> labels = 4;

      • containsLabels

        boolean containsLabels​(String key)
         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.
        map<string, string> labels = 4;

      • getLabelsMap

        Map<String,​String> getLabelsMap()
         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.
        map<string, string> labels = 4;

      • getLabelsOrDefault

        String getLabelsOrDefault​(String key,
                          String defaultValue)
         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.
        map<string, string> labels = 4;

      • getLabelsOrThrow

        String getLabelsOrThrow​(String key)
         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.
        map<string, string> labels = 4;

      • getInputNotebookFile

        String 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`
        string input_notebook_file = 5;
        Returns:
        The inputNotebookFile.

      • getInputNotebookFileBytes

        com.google.protobuf.ByteString getInputNotebookFileBytes()
         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`
        string input_notebook_file = 5;
        Returns:
        The bytes for inputNotebookFile.

      • getContainerImageUri

        String 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
        string container_image_uri = 6;
        Returns:
        The containerImageUri.

      • getContainerImageUriBytes

        com.google.protobuf.ByteString getContainerImageUriBytes()
         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
        string container_image_uri = 6;
        Returns:
        The bytes for containerImageUri.

      • getOutputNotebookFolder

        String 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`
        string output_notebook_folder = 7;
        Returns:
        The outputNotebookFolder.

      • getOutputNotebookFolderBytes

        com.google.protobuf.ByteString getOutputNotebookFolderBytes()
         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`
        string output_notebook_folder = 7;
        Returns:
        The bytes for outputNotebookFolder.

      • getParamsYamlFile

        String 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`
        string params_yaml_file = 8;
        Returns:
        The paramsYamlFile.

      • getParamsYamlFileBytes

        com.google.protobuf.ByteString getParamsYamlFileBytes()
         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`
        string params_yaml_file = 8;
        Returns:
        The bytes for paramsYamlFile.

      • getParameters

        String getParameters()
         Parameters used within the 'input_notebook_file' notebook.
        string parameters = 9;
        Returns:
        The parameters.

      • getParametersBytes

        com.google.protobuf.ByteString getParametersBytes()
         Parameters used within the 'input_notebook_file' notebook.
        string parameters = 9;
        Returns:
        The bytes for parameters.

      • getServiceAccount

        String 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.
        string service_account = 10;
        Returns:
        The serviceAccount.

      • getServiceAccountBytes

        com.google.protobuf.ByteString getServiceAccountBytes()
         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.
        string service_account = 10;
        Returns:
        The bytes for serviceAccount.

      • getJobTypeValue

        int getJobTypeValue()
         The type of Job to be used on this execution.
        .google.cloud.notebooks.v1.ExecutionTemplate.JobType job_type = 11;
        Returns:
        The enum numeric value on the wire for jobType.

      • getJobType

        ExecutionTemplate.JobType getJobType()
         The type of Job to be used on this execution.
        .google.cloud.notebooks.v1.ExecutionTemplate.JobType job_type = 11;
        Returns:
        The jobType.

      • hasDataprocParameters

        boolean hasDataprocParameters()
         Parameters used in Dataproc JobType executions.
        .google.cloud.notebooks.v1.ExecutionTemplate.DataprocParameters dataproc_parameters = 12;
        Returns:
        Whether the dataprocParameters field is set.

      • getDataprocParameters

        ExecutionTemplate.DataprocParameters getDataprocParameters()
         Parameters used in Dataproc JobType executions.
        .google.cloud.notebooks.v1.ExecutionTemplate.DataprocParameters dataproc_parameters = 12;
        Returns:
        The dataprocParameters.

      • getDataprocParametersOrBuilder

        ExecutionTemplate.DataprocParametersOrBuilder getDataprocParametersOrBuilder()
         Parameters used in Dataproc JobType executions.
        .google.cloud.notebooks.v1.ExecutionTemplate.DataprocParameters dataproc_parameters = 12;

      • hasVertexAiParameters

        boolean hasVertexAiParameters()
         Parameters used in Vertex AI JobType executions.
        .google.cloud.notebooks.v1.ExecutionTemplate.VertexAIParameters vertex_ai_parameters = 13;
        Returns:
        Whether the vertexAiParameters field is set.

      • getVertexAiParameters

        ExecutionTemplate.VertexAIParameters getVertexAiParameters()
         Parameters used in Vertex AI JobType executions.
        .google.cloud.notebooks.v1.ExecutionTemplate.VertexAIParameters vertex_ai_parameters = 13;
        Returns:
        The vertexAiParameters.

      • getVertexAiParametersOrBuilder

        ExecutionTemplate.VertexAIParametersOrBuilder getVertexAiParametersOrBuilder()
         Parameters used in Vertex AI JobType executions.
        .google.cloud.notebooks.v1.ExecutionTemplate.VertexAIParameters vertex_ai_parameters = 13;

      • getKernelSpec

        String 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.
        string kernel_spec = 14;
        Returns:
        The kernelSpec.

      • getKernelSpecBytes

        com.google.protobuf.ByteString getKernelSpecBytes()
         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.
        string kernel_spec = 14;
        Returns:
        The bytes for kernelSpec.

      • getTensorboard

        String getTensorboard()
         The name of a Vertex AI [Tensorboard] resource to which this execution
 will upload Tensorboard logs.
 Format:
 `projects/{project}/locations/{location}/tensorboards/{tensorboard}`
        string tensorboard = 15 [(.google.api.resource_reference) = { ... }
        Returns:
        The tensorboard.

      • getTensorboardBytes

        com.google.protobuf.ByteString getTensorboardBytes()
         The name of a Vertex AI [Tensorboard] resource to which this execution
 will upload Tensorboard logs.
 Format:
 `projects/{project}/locations/{location}/tensorboards/{tensorboard}`
        string tensorboard = 15 [(.google.api.resource_reference) = { ... }
        Returns:
        The bytes for tensorboard.