Close httplib2 connections.
create(parent, body=None, executionId=None, x__xgafv=None)
Creates a new Scheduled Notebook in a given project and location.
Deletes execution
Gets details of executions
list(parent, filter=None, orderBy=None, pageSize=None, pageToken=None, x__xgafv=None)
Lists executions in a given project and location
list_next(previous_request, previous_response)
Retrieves the next page of results.
close()
Close httplib2 connections.
create(parent, body=None, executionId=None, x__xgafv=None)
Creates a new Scheduled Notebook in a given project and location.
Args:
parent: string, Required. Format: `parent=projects/{project_id}/locations/{location}` (required)
body: object, The request body.
The object takes the form of:
{ # The definition of a single executed notebook.
"createTime": "A String", # Output only. Time the Execution was instantiated.
"description": "A String", # A brief description of this execution.
"displayName": "A String", # Output only. Name used for UI purposes. Name can only contain alphanumeric characters and underscores '_'.
"executionTemplate": { # The description a notebook execution workload. # execute metadata including name, hardware spec, region, labels, etc.
"acceleratorConfig": { # 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. # Configuration (count and accelerator type) for hardware running notebook execution.
"coreCount": "A String", # Count of cores of this accelerator.
"type": "A String", # Type of this accelerator.
},
"containerImageUri": "A 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
"dataprocParameters": { # Parameters used in Dataproc JobType executions. # Parameters used in Dataproc JobType executions.
"cluster": "A String", # URI for cluster used to run Dataproc execution. Format: 'projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
},
"inputNotebookFile": "A String", # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{project_id}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
"jobType": "A String", # The type of Job to be used on this execution.
"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.
"a_key": "A String",
},
"masterType": "A 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.
"outputNotebookFolder": "A String", # Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: gs://{project_id}/{folder} Ex: gs://notebook_user/scheduled_notebooks
"parameters": "A String", # Parameters used within the 'input_notebook_file' notebook.
"paramsYamlFile": "A 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
"scaleTier": "A String", # Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.
"serviceAccount": "A 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.
},
"jobUri": "A String", # Output only. The URI of the external job used to execute the notebook.
"name": "A String", # Output only. The resource name of the execute. Format: `projects/{project_id}/locations/{location}/execution/{execution_id}
"outputNotebookFile": "A String", # Output notebook file generated by this execution
"state": "A String", # Output only. State of the underlying AI Platform job.
"updateTime": "A String", # Output only. Time the Execution was last updated.
}
executionId: string, Required. User-defined unique ID of this execution.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # This resource represents a long-running operation that is the result of a network API call.
"done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
"error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
"response": { # The normal response of the operation in case of success. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
}
delete(name, x__xgafv=None)
Deletes execution
Args:
name: string, Required. Format: `projects/{project_id}/locations/{location}/executions/{execution_id}` (required)
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # This resource represents a long-running operation that is the result of a network API call.
"done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available.
"error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation.
"code": 42, # The status code, which should be an enum value of google.rpc.Code.
"details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use.
{
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
],
"message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client.
},
"metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
"name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`.
"response": { # The normal response of the operation in case of success. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`.
"a_key": "", # Properties of the object. Contains field @type with type URL.
},
}
get(name, x__xgafv=None)
Gets details of executions
Args:
name: string, Required. Format: `projects/{project_id}/locations/{location}/executions/{execution_id}` (required)
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # The definition of a single executed notebook.
"createTime": "A String", # Output only. Time the Execution was instantiated.
"description": "A String", # A brief description of this execution.
"displayName": "A String", # Output only. Name used for UI purposes. Name can only contain alphanumeric characters and underscores '_'.
"executionTemplate": { # The description a notebook execution workload. # execute metadata including name, hardware spec, region, labels, etc.
"acceleratorConfig": { # 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. # Configuration (count and accelerator type) for hardware running notebook execution.
"coreCount": "A String", # Count of cores of this accelerator.
"type": "A String", # Type of this accelerator.
},
"containerImageUri": "A 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
"dataprocParameters": { # Parameters used in Dataproc JobType executions. # Parameters used in Dataproc JobType executions.
"cluster": "A String", # URI for cluster used to run Dataproc execution. Format: 'projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
},
"inputNotebookFile": "A String", # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{project_id}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
"jobType": "A String", # The type of Job to be used on this execution.
"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.
"a_key": "A String",
},
"masterType": "A 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.
"outputNotebookFolder": "A String", # Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: gs://{project_id}/{folder} Ex: gs://notebook_user/scheduled_notebooks
"parameters": "A String", # Parameters used within the 'input_notebook_file' notebook.
"paramsYamlFile": "A 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
"scaleTier": "A String", # Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.
"serviceAccount": "A 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.
},
"jobUri": "A String", # Output only. The URI of the external job used to execute the notebook.
"name": "A String", # Output only. The resource name of the execute. Format: `projects/{project_id}/locations/{location}/execution/{execution_id}
"outputNotebookFile": "A String", # Output notebook file generated by this execution
"state": "A String", # Output only. State of the underlying AI Platform job.
"updateTime": "A String", # Output only. Time the Execution was last updated.
}
list(parent, filter=None, orderBy=None, pageSize=None, pageToken=None, x__xgafv=None)
Lists executions in a given project and location
Args:
parent: string, Required. Format: `parent=projects/{project_id}/locations/{location}` (required)
filter: string, Filter applied to resulting executions. Currently only supports filtering executions by a specified schedule_id. Format: "schedule_id="
orderBy: string, Sort by field.
pageSize: integer, Maximum return size of the list call.
pageToken: string, A previous returned page token that can be used to continue listing from the last result.
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # Response for listing scheduled notebook executions
"executions": [ # A list of returned instances.
{ # The definition of a single executed notebook.
"createTime": "A String", # Output only. Time the Execution was instantiated.
"description": "A String", # A brief description of this execution.
"displayName": "A String", # Output only. Name used for UI purposes. Name can only contain alphanumeric characters and underscores '_'.
"executionTemplate": { # The description a notebook execution workload. # execute metadata including name, hardware spec, region, labels, etc.
"acceleratorConfig": { # 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. # Configuration (count and accelerator type) for hardware running notebook execution.
"coreCount": "A String", # Count of cores of this accelerator.
"type": "A String", # Type of this accelerator.
},
"containerImageUri": "A 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
"dataprocParameters": { # Parameters used in Dataproc JobType executions. # Parameters used in Dataproc JobType executions.
"cluster": "A String", # URI for cluster used to run Dataproc execution. Format: 'projects/{PROJECT_ID}/regions/{REGION}/clusters/{CLUSTER_NAME}
},
"inputNotebookFile": "A String", # Path to the notebook file to execute. Must be in a Google Cloud Storage bucket. Format: gs://{project_id}/{folder}/{notebook_file_name} Ex: gs://notebook_user/scheduled_notebooks/sentiment_notebook.ipynb
"jobType": "A String", # The type of Job to be used on this execution.
"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.
"a_key": "A String",
},
"masterType": "A 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.
"outputNotebookFolder": "A String", # Path to the notebook folder to write to. Must be in a Google Cloud Storage bucket path. Format: gs://{project_id}/{folder} Ex: gs://notebook_user/scheduled_notebooks
"parameters": "A String", # Parameters used within the 'input_notebook_file' notebook.
"paramsYamlFile": "A 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
"scaleTier": "A String", # Required. Scale tier of the hardware used for notebook execution. DEPRECATED Will be discontinued. As right now only CUSTOM is supported.
"serviceAccount": "A 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.
},
"jobUri": "A String", # Output only. The URI of the external job used to execute the notebook.
"name": "A String", # Output only. The resource name of the execute. Format: `projects/{project_id}/locations/{location}/execution/{execution_id}
"outputNotebookFile": "A String", # Output notebook file generated by this execution
"state": "A String", # Output only. State of the underlying AI Platform job.
"updateTime": "A String", # Output only. Time the Execution was last updated.
},
],
"nextPageToken": "A String", # Page token that can be used to continue listing from the last result in the next list call.
"unreachable": [ # Executions IDs that could not be reached. For example, ['projects/{project_id}/location/{location}/executions/imagenet_test1', 'projects/{project_id}/location/{location}/executions/classifier_train1'].
"A String",
],
}
list_next(previous_request, previous_response)
Retrieves the next page of results.
Args:
previous_request: The request for the previous page. (required)
previous_response: The response from the request for the previous page. (required)
Returns:
A request object that you can call 'execute()' on to request the next
page. Returns None if there are no more items in the collection.