Close httplib2 connections.
create(parent, body=None, scheduleId=None, x__xgafv=None)
Creates a new Scheduled Notebook in a given project and location.
Deletes schedule and all underlying jobs
Gets details of schedule
list(parent, filter=None, orderBy=None, pageSize=None, pageToken=None, x__xgafv=None)
Lists schedules in a given project and location.
list_next(previous_request, previous_response)
Retrieves the next page of results.
trigger(name, body=None, x__xgafv=None)
Triggers execution of an existing schedule.
close()
Close httplib2 connections.
create(parent, body=None, scheduleId=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 schedule. "createTime": "A String", # Output only. Time the schedule was created. "cronSchedule": "A String", # Cron-tab formatted schedule by which the job will execute Format: minute, hour, day of month, month, day of week e.g. 0 0 * * WED = every Wednesday More examples: https://crontab.guru/examples.html "description": "A String", # A brief description of this environment. "displayName": "A String", # Output only. Display name used for UI purposes. Name can only contain alphanumeric characters, hyphens '-', and underscores '_'. "executionTemplate": { # The description a notebook execution workload. # Notebook Execution Template corresponding to this schedule. "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 "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 "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. "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. }, "name": "A String", # Output only. The name of this schedule. Format: `projects/{project_id}/locations/{location}/schedules/{schedule_id}` "recentExecutions": [ # Output only. The most recent execution names triggered from this schedule and their corresponding states. { # 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 "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 "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. "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. }, "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. }, ], "state": "A String", "timeZone": "A String", # Timezone on which the cron_schedule. The value of this field must be a time zone name from the tz database. TZ Database: https://en.wikipedia.org/wiki/List_of_tz_database_time_zones Note that some time zones include a provision for daylight savings time. The rules for daylight saving time are determined by the chosen tz. For UTC use the string "utc". If a time zone is not specified, the default will be in UTC (also known as GMT). "updateTime": "A String", # Output only. Time the schedule was last updated. } scheduleId: string, Required. User-defined unique ID of this schedule. 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 schedule and all underlying jobs Args: name: string, Required. Format: `projects/{project_id}/locations/{location}/schedules/{schedule_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 schedule Args: name: string, Required. Format: `projects/{project_id}/locations/{location}/schedules/{schedule_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 schedule. "createTime": "A String", # Output only. Time the schedule was created. "cronSchedule": "A String", # Cron-tab formatted schedule by which the job will execute Format: minute, hour, day of month, month, day of week e.g. 0 0 * * WED = every Wednesday More examples: https://crontab.guru/examples.html "description": "A String", # A brief description of this environment. "displayName": "A String", # Output only. Display name used for UI purposes. Name can only contain alphanumeric characters, hyphens '-', and underscores '_'. "executionTemplate": { # The description a notebook execution workload. # Notebook Execution Template corresponding to this schedule. "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 "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 "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. "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. }, "name": "A String", # Output only. The name of this schedule. Format: `projects/{project_id}/locations/{location}/schedules/{schedule_id}` "recentExecutions": [ # Output only. The most recent execution names triggered from this schedule and their corresponding states. { # 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 "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 "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. "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. }, "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. }, ], "state": "A String", "timeZone": "A String", # Timezone on which the cron_schedule. The value of this field must be a time zone name from the tz database. TZ Database: https://en.wikipedia.org/wiki/List_of_tz_database_time_zones Note that some time zones include a provision for daylight savings time. The rules for daylight saving time are determined by the chosen tz. For UTC use the string "utc". If a time zone is not specified, the default will be in UTC (also known as GMT). "updateTime": "A String", # Output only. Time the schedule was last updated. }
list(parent, filter=None, orderBy=None, pageSize=None, pageToken=None, x__xgafv=None)
Lists schedules in a given project and location. Args: parent: string, Required. Format: `parent=projects/{project_id}/locations/{location}` (required) filter: string, Filter applied to resulting schedules. orderBy: string, Field to order results by. 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 job. "nextPageToken": "A String", # Page token that can be used to continue listing from the last result in the next list call. "schedules": [ # A list of returned instances. { # The definition of a schedule. "createTime": "A String", # Output only. Time the schedule was created. "cronSchedule": "A String", # Cron-tab formatted schedule by which the job will execute Format: minute, hour, day of month, month, day of week e.g. 0 0 * * WED = every Wednesday More examples: https://crontab.guru/examples.html "description": "A String", # A brief description of this environment. "displayName": "A String", # Output only. Display name used for UI purposes. Name can only contain alphanumeric characters, hyphens '-', and underscores '_'. "executionTemplate": { # The description a notebook execution workload. # Notebook Execution Template corresponding to this schedule. "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 "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 "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. "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. }, "name": "A String", # Output only. The name of this schedule. Format: `projects/{project_id}/locations/{location}/schedules/{schedule_id}` "recentExecutions": [ # Output only. The most recent execution names triggered from this schedule and their corresponding states. { # 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 "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 "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. "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. }, "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. }, ], "state": "A String", "timeZone": "A String", # Timezone on which the cron_schedule. The value of this field must be a time zone name from the tz database. TZ Database: https://en.wikipedia.org/wiki/List_of_tz_database_time_zones Note that some time zones include a provision for daylight savings time. The rules for daylight saving time are determined by the chosen tz. For UTC use the string "utc". If a time zone is not specified, the default will be in UTC (also known as GMT). "updateTime": "A String", # Output only. Time the schedule was last updated. }, ], "unreachable": [ # Schedules that could not be reached. For example, ['projects/{project_id}/location/{location}/schedules/monthly_digest', 'projects/{project_id}/location/{location}/schedules/weekly_sentiment']. "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.
trigger(name, body=None, x__xgafv=None)
Triggers execution of an existing schedule. Args: name: string, Required. Format: `parent=projects/{project_id}/locations/{location}/schedules/{schedule_id}` (required) body: object, The request body. The object takes the form of: { # Request for created scheduled notebooks } 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. }, }