Creates a new Execution in a given project and location.

Scopes

You will need authorization for the https://www.googleapis.com/auth/cloud-platform scope to make a valid call.

If unset, the scope for this method defaults to https://www.googleapis.com/auth/cloud-platform. You can set the scope for this method like this: notebooks1 --scope <scope> projects locations-executions-create ...

Required Scalar Argument

• <parent> (string)
  • Required. Format: parent=projects/{project_id}/locations/{location}

Required Request Value

The request value is a data-structure with various fields. Each field may be a simple scalar or another data-structure. In the latter case it is advised to set the field-cursor to the data-structure's field to specify values more concisely.

For example, a structure like this:

Execution:
  create-time: string
  description: string
  display-name: string
  execution-template:
    accelerator-config:
      core-count: int64
      type: string
    container-image-uri: string
    dataproc-parameters:
      cluster: string
    input-notebook-file: string
    job-type: string
    kernel-spec: string
    labels: { string: string }
    master-type: string
    output-notebook-folder: string
    parameters: string
    params-yaml-file: string
    scale-tier: string
    service-account: string
    tensorboard: string
    vertex-ai-parameters:
      env: { string: string }
      network: string
  job-uri: string
  name: string
  output-notebook-file: string
  state: string
  update-time: string

can be set completely with the following arguments which are assumed to be executed in the given order. Note how the cursor position is adjusted to the respective structures, allowing simple field names to be used most of the time.

• -r . create-time=amet.
  • Output only. Time the Execution was instantiated.
• description=duo
  • A brief description of this execution.
• display-name=ipsum
  • Output only. Name used for UI purposes. Name can only contain alphanumeric characters and underscores '_'.
• execution-template.accelerator-config core-count=-62
  • Count of cores of this accelerator.

• type=lorem

  • Type of this accelerator.

• .. container-image-uri=gubergren

  • 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 cluster=eos

  • URI for cluster used to run Dataproc execution. Format: projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}

• .. input-notebook-file=dolor

  • 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
• job-type=ea
  • The type of Job to be used on this execution.
• kernel-spec=ipsum
  • 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=key=invidunt
  • 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.
  • the value will be associated with the given key
• master-type=amet
  • 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=duo
  • 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=ipsum
  • Parameters used within the 'input_notebook_file' notebook.
• params-yaml-file=sed
  • 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=ut
  • Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.
• service-account=gubergren
  • 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=rebum.
  • The name of a Vertex AI [Tensorboard] resource to which this execution will upload Tensorboard logs. Format: projects/{project}/locations/{location}/tensorboards/{tensorboard}
• vertex-ai-parameters env=key=est
  • Environment variables. At most 100 environment variables can be specified and unique. Example: GCP_BUCKET=gs://my-bucket/samples/
  • the value will be associated with the given key

• network=ipsum

  • The full name of the Compute Engine network to which the Job should be peered. For example, projects/12345/global/networks/myVPC. Format is of the form projects/{project}/global/networks/{network}. Where {project} is a project number, as in 12345, and {network} is a network name. Private services access must already be configured for the network. If left unspecified, the job is not peered with any network.

• ... job-uri=ipsum

  • Output only. The URI of the external job used to execute the notebook.
• name=est
  • Output only. The resource name of the execute. Format: projects/{project_id}/locations/{location}/executions/{execution_id}
• output-notebook-file=gubergren
  • Output notebook file generated by this execution
• state=ea
  • Output only. State of the underlying AI Platform job.
• update-time=dolor
  • Output only. Time the Execution was last updated.

About Cursors

The cursor position is key to comfortably set complex nested structures. The following rules apply:

• The cursor position is always set relative to the current one, unless the field name starts with the . character. Fields can be nested such as in -r f.s.o .
• The cursor position is set relative to the top-level structure if it starts with ., e.g. -r .s.s
• You can also set nested fields without setting the cursor explicitly. For example, to set a value relative to the current cursor position, you would specify -r struct.sub_struct=bar.
• You can move the cursor one level up by using ... Each additional . moves it up one additional level. E.g. ... would go three levels up.

Optional Output Flags

The method's return value a JSON encoded structure, which will be written to standard output by default.

• -o out
  • out specifies the destination to which to write the server's result to. It will be a JSON-encoded structure. The destination may be - to indicate standard output, or a filepath that is to contain the received bytes. If unset, it defaults to standard output.

Optional Method Properties

You may set the following properties to further configure the call. Please note that -p is followed by one or more key-value-pairs, and is called like this -p k1=v1 k2=v2 even though the listing below repeats the -p for completeness.

• -p execution-id=string
  • Required. User-defined unique ID of this execution.

Optional General Properties

The following properties can configure any call, and are not specific to this method.

• -p $-xgafv=string

  • V1 error format.

• -p access-token=string

  • OAuth access token.

• -p alt=string

  • Data format for response.

• -p callback=string

  • JSONP

• -p fields=string

  • Selector specifying which fields to include in a partial response.

• -p key=string

  • API key. Your API key identifies your project and provides you with API access, quota, and reports. Required unless you provide an OAuth 2.0 token.

• -p oauth-token=string

  • OAuth 2.0 token for the current user.

• -p pretty-print=boolean

  • Returns response with indentations and line breaks.

• -p quota-user=string

  • Available to use for quota purposes for server-side applications. Can be any arbitrary string assigned to a user, but should not exceed 40 characters.

• -p upload-type=string

  • Legacy upload protocol for media (e.g. "media", "multipart").

• -p upload-protocol=string

  • Upload protocol for media (e.g. "raw", "multipart").