Close httplib2 connections.
create(parent, body=None, executionId=None, x__xgafv=None)
Creates a new Execution 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
Retrieves the next page of results.
close()
Close httplib2 connections.
create(parent, body=None, executionId=None, x__xgafv=None)
Creates a new Execution 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. See [GPUs on Compute Engine](https://cloud.google.com/compute/docs/gpus) 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://{bucket_name}/{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.
"kernelSpec": "A 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.
"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](https://cloud.google.com/ai-platform/training/docs/using-tpus#configuring_a_custom_tpu_machine).
"outputNotebookFolder": "A 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`
"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.
"tensorboard": "A String", # 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. # Parameters used in Vertex AI JobType executions.
"env": { # Environment variables. At most 100 environment variables can be specified and unique. Example: `GCP_BUCKET=gs://my-bucket/samples/`
"a_key": "A String",
},
"network": "A String", # The full name of the Compute Engine [network](https://cloud.google.com/compute/docs/networks-and-firewalls#networks) to which the Job should be peered. For example, `projects/12345/global/networks/myVPC`. [Format](https://cloud.google.com/compute/docs/reference/rest/v1/networks/insert) 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.
},
},
"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}/executions/{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, successful response of the operation. 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, successful response of the operation. 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. See [GPUs on Compute Engine](https://cloud.google.com/compute/docs/gpus) 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://{bucket_name}/{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.
"kernelSpec": "A 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.
"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](https://cloud.google.com/ai-platform/training/docs/using-tpus#configuring_a_custom_tpu_machine).
"outputNotebookFolder": "A 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`
"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.
"tensorboard": "A String", # 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. # Parameters used in Vertex AI JobType executions.
"env": { # Environment variables. At most 100 environment variables can be specified and unique. Example: `GCP_BUCKET=gs://my-bucket/samples/`
"a_key": "A String",
},
"network": "A String", # The full name of the Compute Engine [network](https://cloud.google.com/compute/docs/networks-and-firewalls#networks) to which the Job should be peered. For example, `projects/12345/global/networks/myVPC`. [Format](https://cloud.google.com/compute/docs/reference/rest/v1/networks/insert) 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.
},
},
"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}/executions/{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. See [GPUs on Compute Engine](https://cloud.google.com/compute/docs/gpus) 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://{bucket_name}/{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.
"kernelSpec": "A 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.
"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](https://cloud.google.com/ai-platform/training/docs/using-tpus#configuring_a_custom_tpu_machine).
"outputNotebookFolder": "A 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`
"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.
"tensorboard": "A String", # 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. # Parameters used in Vertex AI JobType executions.
"env": { # Environment variables. At most 100 environment variables can be specified and unique. Example: `GCP_BUCKET=gs://my-bucket/samples/`
"a_key": "A String",
},
"network": "A String", # The full name of the Compute Engine [network](https://cloud.google.com/compute/docs/networks-and-firewalls#networks) to which the Job should be peered. For example, `projects/12345/global/networks/myVPC`. [Format](https://cloud.google.com/compute/docs/reference/rest/v1/networks/insert) 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.
},
},
"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}/executions/{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()
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.