#
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
#
"""This module contains Google AutoML operators."""
import ast
from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union
from google.api_core.gapic_v1.method import DEFAULT, _MethodDefault
from google.api_core.retry import Retry
from google.cloud.automl_v1beta1 import (
BatchPredictResult,
ColumnSpec,
Dataset,
Model,
PredictResponse,
TableSpec,
)
from airflow.models import BaseOperator
from airflow.providers.google.cloud.hooks.automl import CloudAutoMLHook
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class AutoMLTrainModelOperator(BaseOperator):
"""
Creates Google Cloud AutoML model.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:AutoMLTrainModelOperator`
:param model: Model definition.
:param project_id: ID of the Google Cloud project where model will be created if None then
default project_id is used.
:param location: The location of the project.
:param retry: A retry object used to retry requests. If `None` is specified, requests will not be
retried.
:param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if
`retry` is specified, the timeout applies to each individual attempt.
:param metadata: Additional metadata that is provided to the method.
:param gcp_conn_id: The connection ID to use to connect to Google Cloud.
:param impersonation_chain: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity, with first
account from the list granting this role to the originating account (templated).
"""
[docs] template_fields: Sequence[str] = (
"model",
"location",
"project_id",
"impersonation_chain",
)
def __init__(
self,
*,
model: dict,
location: str,
project_id: Optional[str] = None,
metadata: MetaData = (),
timeout: Optional[float] = None,
retry: Union[Retry, _MethodDefault] = DEFAULT,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.model = model
self.location = location
self.project_id = project_id
self.metadata = metadata
self.timeout = timeout
self.retry = retry
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: 'Context'):
hook = CloudAutoMLHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
self.log.info("Creating model.")
operation = hook.create_model(
model=self.model,
location=self.location,
project_id=self.project_id,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
result = Model.to_dict(operation.result())
model_id = hook.extract_object_id(result)
self.log.info("Model created: %s", model_id)
self.xcom_push(context, key="model_id", value=model_id)
return result
[docs]class AutoMLPredictOperator(BaseOperator):
"""
Runs prediction operation on Google Cloud AutoML.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:AutoMLPredictOperator`
:param model_id: Name of the model requested to serve the batch prediction.
:param payload: Name od the model used for the prediction.
:param project_id: ID of the Google Cloud project where model is located if None then
default project_id is used.
:param location: The location of the project.
:param operation_params: Additional domain-specific parameters for the predictions.
:param retry: A retry object used to retry requests. If `None` is specified, requests will not be
retried.
:param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if
`retry` is specified, the timeout applies to each individual attempt.
:param metadata: Additional metadata that is provided to the method.
:param gcp_conn_id: The connection ID to use to connect to Google Cloud.
:param impersonation_chain: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity, with first
account from the list granting this role to the originating account (templated).
"""
[docs] template_fields: Sequence[str] = (
"model_id",
"location",
"project_id",
"impersonation_chain",
)
def __init__(
self,
*,
model_id: str,
location: str,
payload: dict,
operation_params: Optional[Dict[str, str]] = None,
project_id: Optional[str] = None,
metadata: MetaData = (),
timeout: Optional[float] = None,
retry: Union[Retry, _MethodDefault] = DEFAULT,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.model_id = model_id
self.operation_params = operation_params # type: ignore
self.location = location
self.project_id = project_id
self.metadata = metadata
self.timeout = timeout
self.retry = retry
self.payload = payload
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: 'Context'):
hook = CloudAutoMLHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
result = hook.predict(
model_id=self.model_id,
payload=self.payload,
location=self.location,
project_id=self.project_id,
params=self.operation_params,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
return PredictResponse.to_dict(result)
[docs]class AutoMLBatchPredictOperator(BaseOperator):
"""
Perform a batch prediction on Google Cloud AutoML.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:AutoMLBatchPredictOperator`
:param project_id: ID of the Google Cloud project where model will be created if None then
default project_id is used.
:param location: The location of the project.
:param model_id: Name of the model_id requested to serve the batch prediction.
:param input_config: Required. The input configuration for batch prediction.
If a dict is provided, it must be of the same form as the protobuf message
`google.cloud.automl_v1beta1.types.BatchPredictInputConfig`
:param output_config: Required. The Configuration specifying where output predictions should be
written. If a dict is provided, it must be of the same form as the protobuf message
`google.cloud.automl_v1beta1.types.BatchPredictOutputConfig`
:param prediction_params: Additional domain-specific parameters for the predictions,
any string must be up to 25000 characters long.
:param project_id: ID of the Google Cloud project where model is located if None then
default project_id is used.
:param location: The location of the project.
:param retry: A retry object used to retry requests. If `None` is specified, requests will not be
retried.
:param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if
`retry` is specified, the timeout applies to each individual attempt.
:param metadata: Additional metadata that is provided to the method.
:param gcp_conn_id: The connection ID to use to connect to Google Cloud.
:param impersonation_chain: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity, with first
account from the list granting this role to the originating account (templated).
"""
[docs] template_fields: Sequence[str] = (
"model_id",
"input_config",
"output_config",
"location",
"project_id",
"impersonation_chain",
)
def __init__(
self,
*,
model_id: str,
input_config: dict,
output_config: dict,
location: str,
project_id: Optional[str] = None,
prediction_params: Optional[Dict[str, str]] = None,
metadata: MetaData = (),
timeout: Optional[float] = None,
retry: Union[Retry, _MethodDefault] = DEFAULT,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.model_id = model_id
self.location = location
self.project_id = project_id
self.prediction_params = prediction_params
self.metadata = metadata
self.timeout = timeout
self.retry = retry
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
self.input_config = input_config
self.output_config = output_config
[docs] def execute(self, context: 'Context'):
hook = CloudAutoMLHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
self.log.info("Fetch batch prediction.")
operation = hook.batch_predict(
model_id=self.model_id,
input_config=self.input_config,
output_config=self.output_config,
project_id=self.project_id,
location=self.location,
params=self.prediction_params,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
result = BatchPredictResult.to_dict(operation.result())
self.log.info("Batch prediction ready.")
return result
[docs]class AutoMLCreateDatasetOperator(BaseOperator):
"""
Creates a Google Cloud AutoML dataset.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:AutoMLCreateDatasetOperator`
:param dataset: The dataset to create. If a dict is provided, it must be of the
same form as the protobuf message Dataset.
:param project_id: ID of the Google Cloud project where dataset is located if None then
default project_id is used.
:param location: The location of the project.
:param params: Additional domain-specific parameters for the predictions.
:param retry: A retry object used to retry requests. If `None` is specified, requests will not be
retried.
:param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if
`retry` is specified, the timeout applies to each individual attempt.
:param metadata: Additional metadata that is provided to the method.
:param gcp_conn_id: The connection ID to use to connect to Google Cloud.
:param impersonation_chain: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity, with first
account from the list granting this role to the originating account (templated).
"""
[docs] template_fields: Sequence[str] = (
"dataset",
"location",
"project_id",
"impersonation_chain",
)
def __init__(
self,
*,
dataset: dict,
location: str,
project_id: Optional[str] = None,
metadata: MetaData = (),
timeout: Optional[float] = None,
retry: Union[Retry, _MethodDefault] = DEFAULT,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.dataset = dataset
self.location = location
self.project_id = project_id
self.metadata = metadata
self.timeout = timeout
self.retry = retry
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: 'Context'):
hook = CloudAutoMLHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
self.log.info("Creating dataset")
result = hook.create_dataset(
dataset=self.dataset,
location=self.location,
project_id=self.project_id,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
result = Dataset.to_dict(result)
dataset_id = hook.extract_object_id(result)
self.log.info("Creating completed. Dataset id: %s", dataset_id)
self.xcom_push(context, key="dataset_id", value=dataset_id)
return result
[docs]class AutoMLImportDataOperator(BaseOperator):
"""
Imports data to a Google Cloud AutoML dataset.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:AutoMLImportDataOperator`
:param dataset_id: ID of dataset to be updated.
:param input_config: The desired input location and its domain specific semantics, if any.
If a dict is provided, it must be of the same form as the protobuf message InputConfig.
:param project_id: ID of the Google Cloud project where dataset is located if None then
default project_id is used.
:param location: The location of the project.
:param params: Additional domain-specific parameters for the predictions.
:param retry: A retry object used to retry requests. If `None` is specified, requests will not be
retried.
:param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if
`retry` is specified, the timeout applies to each individual attempt.
:param metadata: Additional metadata that is provided to the method.
:param gcp_conn_id: The connection ID to use to connect to Google Cloud.
:param impersonation_chain: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity, with first
account from the list granting this role to the originating account (templated).
"""
[docs] template_fields: Sequence[str] = (
"dataset_id",
"input_config",
"location",
"project_id",
"impersonation_chain",
)
def __init__(
self,
*,
dataset_id: str,
location: str,
input_config: dict,
project_id: Optional[str] = None,
metadata: MetaData = (),
timeout: Optional[float] = None,
retry: Union[Retry, _MethodDefault] = DEFAULT,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.dataset_id = dataset_id
self.input_config = input_config
self.location = location
self.project_id = project_id
self.metadata = metadata
self.timeout = timeout
self.retry = retry
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: 'Context'):
hook = CloudAutoMLHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
self.log.info("Importing dataset")
operation = hook.import_data(
dataset_id=self.dataset_id,
input_config=self.input_config,
location=self.location,
project_id=self.project_id,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
operation.result()
self.log.info("Import completed")
[docs]class AutoMLTablesListColumnSpecsOperator(BaseOperator):
"""
Lists column specs in a table.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:AutoMLTablesListColumnSpecsOperator`
:param dataset_id: Name of the dataset.
:param table_spec_id: table_spec_id for path builder.
:param field_mask: Mask specifying which fields to read. If a dict is provided, it must be of the same
form as the protobuf message `google.cloud.automl_v1beta1.types.FieldMask`
:param filter_: Filter expression, see go/filtering.
:param page_size: The maximum number of resources contained in the
underlying API response. If page streaming is performed per
resource, this parameter does not affect the return value. If page
streaming is performed per page, this determines the maximum number
of resources in a page.
:param project_id: ID of the Google Cloud project where dataset is located if None then
default project_id is used.
:param location: The location of the project.
:param retry: A retry object used to retry requests. If `None` is specified, requests will not be
retried.
:param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if
`retry` is specified, the timeout applies to each individual attempt.
:param metadata: Additional metadata that is provided to the method.
:param gcp_conn_id: The connection ID to use to connect to Google Cloud.
:param impersonation_chain: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity, with first
account from the list granting this role to the originating account (templated).
"""
[docs] template_fields: Sequence[str] = (
"dataset_id",
"table_spec_id",
"field_mask",
"filter_",
"location",
"project_id",
"impersonation_chain",
)
def __init__(
self,
*,
dataset_id: str,
table_spec_id: str,
location: str,
field_mask: Optional[dict] = None,
filter_: Optional[str] = None,
page_size: Optional[int] = None,
project_id: Optional[str] = None,
metadata: MetaData = (),
timeout: Optional[float] = None,
retry: Union[Retry, _MethodDefault] = DEFAULT,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.dataset_id = dataset_id
self.table_spec_id = table_spec_id
self.field_mask = field_mask
self.filter_ = filter_
self.page_size = page_size
self.location = location
self.project_id = project_id
self.metadata = metadata
self.timeout = timeout
self.retry = retry
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: 'Context'):
hook = CloudAutoMLHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
self.log.info("Requesting column specs.")
page_iterator = hook.list_column_specs(
dataset_id=self.dataset_id,
table_spec_id=self.table_spec_id,
field_mask=self.field_mask,
filter_=self.filter_,
page_size=self.page_size,
location=self.location,
project_id=self.project_id,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
result = [ColumnSpec.to_dict(spec) for spec in page_iterator]
self.log.info("Columns specs obtained.")
return result
[docs]class AutoMLTablesUpdateDatasetOperator(BaseOperator):
"""
Updates a dataset.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:AutoMLTablesUpdateDatasetOperator`
:param dataset: The dataset which replaces the resource on the server.
If a dict is provided, it must be of the same form as the protobuf message Dataset.
:param update_mask: The update mask applies to the resource. If a dict is provided, it must
be of the same form as the protobuf message FieldMask.
:param location: The location of the project.
:param params: Additional domain-specific parameters for the predictions.
:param retry: A retry object used to retry requests. If `None` is specified, requests will not be
retried.
:param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if
`retry` is specified, the timeout applies to each individual attempt.
:param metadata: Additional metadata that is provided to the method.
:param gcp_conn_id: The connection ID to use to connect to Google Cloud.
:param impersonation_chain: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity, with first
account from the list granting this role to the originating account (templated).
"""
[docs] template_fields: Sequence[str] = (
"dataset",
"update_mask",
"location",
"impersonation_chain",
)
def __init__(
self,
*,
dataset: dict,
location: str,
update_mask: Optional[dict] = None,
metadata: MetaData = (),
timeout: Optional[float] = None,
retry: Union[Retry, _MethodDefault] = DEFAULT,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.dataset = dataset
self.update_mask = update_mask
self.location = location
self.metadata = metadata
self.timeout = timeout
self.retry = retry
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: 'Context'):
hook = CloudAutoMLHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
self.log.info("Updating AutoML dataset %s.", self.dataset["name"])
result = hook.update_dataset(
dataset=self.dataset,
update_mask=self.update_mask,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
self.log.info("Dataset updated.")
return Dataset.to_dict(result)
[docs]class AutoMLGetModelOperator(BaseOperator):
"""
Get Google Cloud AutoML model.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:AutoMLGetModelOperator`
:param model_id: Name of the model requested to serve the prediction.
:param project_id: ID of the Google Cloud project where model is located if None then
default project_id is used.
:param location: The location of the project.
:param params: Additional domain-specific parameters for the predictions.
:param retry: A retry object used to retry requests. If `None` is specified, requests will not be
retried.
:param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if
`retry` is specified, the timeout applies to each individual attempt.
:param metadata: Additional metadata that is provided to the method.
:param gcp_conn_id: The connection ID to use to connect to Google Cloud.
:param impersonation_chain: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity, with first
account from the list granting this role to the originating account (templated).
"""
[docs] template_fields: Sequence[str] = (
"model_id",
"location",
"project_id",
"impersonation_chain",
)
def __init__(
self,
*,
model_id: str,
location: str,
project_id: Optional[str] = None,
metadata: MetaData = (),
timeout: Optional[float] = None,
retry: Union[Retry, _MethodDefault] = DEFAULT,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.model_id = model_id
self.location = location
self.project_id = project_id
self.metadata = metadata
self.timeout = timeout
self.retry = retry
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: 'Context'):
hook = CloudAutoMLHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
result = hook.get_model(
model_id=self.model_id,
location=self.location,
project_id=self.project_id,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
return Model.to_dict(result)
[docs]class AutoMLDeleteModelOperator(BaseOperator):
"""
Delete Google Cloud AutoML model.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:AutoMLDeleteModelOperator`
:param model_id: Name of the model requested to serve the prediction.
:param project_id: ID of the Google Cloud project where model is located if None then
default project_id is used.
:param location: The location of the project.
:param params: Additional domain-specific parameters for the predictions.
:param retry: A retry object used to retry requests. If `None` is specified, requests will not be
retried.
:param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if
`retry` is specified, the timeout applies to each individual attempt.
:param metadata: Additional metadata that is provided to the method.
:param gcp_conn_id: The connection ID to use to connect to Google Cloud.
:param impersonation_chain: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity, with first
account from the list granting this role to the originating account (templated).
"""
[docs] template_fields: Sequence[str] = (
"model_id",
"location",
"project_id",
"impersonation_chain",
)
def __init__(
self,
*,
model_id: str,
location: str,
project_id: Optional[str] = None,
metadata: MetaData = (),
timeout: Optional[float] = None,
retry: Union[Retry, _MethodDefault] = DEFAULT,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.model_id = model_id
self.location = location
self.project_id = project_id
self.metadata = metadata
self.timeout = timeout
self.retry = retry
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: 'Context'):
hook = CloudAutoMLHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
operation = hook.delete_model(
model_id=self.model_id,
location=self.location,
project_id=self.project_id,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
operation.result()
[docs]class AutoMLDeployModelOperator(BaseOperator):
"""
Deploys a model. If a model is already deployed, deploying it with the same parameters
has no effect. Deploying with different parameters (as e.g. changing node_number) will
reset the deployment state without pausing the model_id's availability.
Only applicable for Text Classification, Image Object Detection and Tables; all other
domains manage deployment automatically.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:AutoMLDeployModelOperator`
:param model_id: Name of the model to be deployed.
:param image_detection_metadata: Model deployment metadata specific to Image Object Detection.
If a dict is provided, it must be of the same form as the protobuf message
ImageObjectDetectionModelDeploymentMetadata
:param project_id: ID of the Google Cloud project where model is located if None then
default project_id is used.
:param location: The location of the project.
:param params: Additional domain-specific parameters for the predictions.
:param retry: A retry object used to retry requests. If `None` is specified, requests will not be
retried.
:param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if
`retry` is specified, the timeout applies to each individual attempt.
:param metadata: Additional metadata that is provided to the method.
:param gcp_conn_id: The connection ID to use to connect to Google Cloud.
:param impersonation_chain: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity, with first
account from the list granting this role to the originating account (templated).
"""
[docs] template_fields: Sequence[str] = (
"model_id",
"location",
"project_id",
"impersonation_chain",
)
def __init__(
self,
*,
model_id: str,
location: str,
project_id: Optional[str] = None,
image_detection_metadata: Optional[dict] = None,
metadata: Sequence[Tuple[str, str]] = (),
timeout: Optional[float] = None,
retry: Union[Retry, _MethodDefault] = DEFAULT,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.model_id = model_id
self.image_detection_metadata = image_detection_metadata
self.location = location
self.project_id = project_id
self.metadata = metadata
self.timeout = timeout
self.retry = retry
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: 'Context'):
hook = CloudAutoMLHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
self.log.info("Deploying model_id %s", self.model_id)
operation = hook.deploy_model(
model_id=self.model_id,
location=self.location,
project_id=self.project_id,
image_detection_metadata=self.image_detection_metadata,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
operation.result()
self.log.info("Model deployed.")
[docs]class AutoMLTablesListTableSpecsOperator(BaseOperator):
"""
Lists table specs in a dataset.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:AutoMLTablesListTableSpecsOperator`
:param dataset_id: Name of the dataset.
:param filter_: Filter expression, see go/filtering.
:param page_size: The maximum number of resources contained in the
underlying API response. If page streaming is performed per
resource, this parameter does not affect the return value. If page
streaming is performed per-page, this determines the maximum number
of resources in a page.
:param project_id: ID of the Google Cloud project if None then
default project_id is used.
:param location: The location of the project.
:param retry: A retry object used to retry requests. If `None` is specified, requests will not be
retried.
:param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if
`retry` is specified, the timeout applies to each individual attempt.
:param metadata: Additional metadata that is provided to the method.
:param gcp_conn_id: The connection ID to use to connect to Google Cloud.
:param impersonation_chain: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity, with first
account from the list granting this role to the originating account (templated).
"""
[docs] template_fields: Sequence[str] = (
"dataset_id",
"filter_",
"location",
"project_id",
"impersonation_chain",
)
def __init__(
self,
*,
dataset_id: str,
location: str,
page_size: Optional[int] = None,
filter_: Optional[str] = None,
project_id: Optional[str] = None,
metadata: MetaData = (),
timeout: Optional[float] = None,
retry: Union[Retry, _MethodDefault] = DEFAULT,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.dataset_id = dataset_id
self.filter_ = filter_
self.page_size = page_size
self.location = location
self.project_id = project_id
self.metadata = metadata
self.timeout = timeout
self.retry = retry
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: 'Context'):
hook = CloudAutoMLHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
self.log.info("Requesting table specs for %s.", self.dataset_id)
page_iterator = hook.list_table_specs(
dataset_id=self.dataset_id,
filter_=self.filter_,
page_size=self.page_size,
location=self.location,
project_id=self.project_id,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
result = [TableSpec.to_dict(spec) for spec in page_iterator]
self.log.info(result)
self.log.info("Table specs obtained.")
return result
[docs]class AutoMLListDatasetOperator(BaseOperator):
"""
Lists AutoML Datasets in project.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:AutoMLListDatasetOperator`
:param project_id: ID of the Google Cloud project where datasets are located if None then
default project_id is used.
:param location: The location of the project.
:param retry: A retry object used to retry requests. If `None` is specified, requests will not be
retried.
:param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if
`retry` is specified, the timeout applies to each individual attempt.
:param metadata: Additional metadata that is provided to the method.
:param gcp_conn_id: The connection ID to use to connect to Google Cloud.
:param impersonation_chain: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity, with first
account from the list granting this role to the originating account (templated).
"""
[docs] template_fields: Sequence[str] = (
"location",
"project_id",
"impersonation_chain",
)
def __init__(
self,
*,
location: str,
project_id: Optional[str] = None,
metadata: MetaData = (),
timeout: Optional[float] = None,
retry: Union[Retry, _MethodDefault] = DEFAULT,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.location = location
self.project_id = project_id
self.metadata = metadata
self.timeout = timeout
self.retry = retry
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
[docs] def execute(self, context: 'Context'):
hook = CloudAutoMLHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
self.log.info("Requesting datasets")
page_iterator = hook.list_datasets(
location=self.location,
project_id=self.project_id,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
result = [Dataset.to_dict(dataset) for dataset in page_iterator]
self.log.info("Datasets obtained.")
self.xcom_push(
context,
key="dataset_id_list",
value=[hook.extract_object_id(d) for d in result],
)
return result
[docs]class AutoMLDeleteDatasetOperator(BaseOperator):
"""
Deletes a dataset and all of its contents.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:AutoMLDeleteDatasetOperator`
:param dataset_id: Name of the dataset_id, list of dataset_id or string of dataset_id
coma separated to be deleted.
:param project_id: ID of the Google Cloud project where dataset is located if None then
default project_id is used.
:param location: The location of the project.
:param retry: A retry object used to retry requests. If `None` is specified, requests will not be
retried.
:param timeout: The amount of time, in seconds, to wait for the request to complete. Note that if
`retry` is specified, the timeout applies to each individual attempt.
:param metadata: Additional metadata that is provided to the method.
:param gcp_conn_id: The connection ID to use to connect to Google Cloud.
:param impersonation_chain: Optional service account to impersonate using short-term
credentials, or chained list of accounts required to get the access_token
of the last account in the list, which will be impersonated in the request.
If set as a string, the account must grant the originating account
the Service Account Token Creator IAM role.
If set as a sequence, the identities from the list must grant
Service Account Token Creator IAM role to the directly preceding identity, with first
account from the list granting this role to the originating account (templated).
"""
[docs] template_fields: Sequence[str] = (
"dataset_id",
"location",
"project_id",
"impersonation_chain",
)
def __init__(
self,
*,
dataset_id: Union[str, List[str]],
location: str,
project_id: Optional[str] = None,
metadata: MetaData = (),
timeout: Optional[float] = None,
retry: Union[Retry, _MethodDefault] = DEFAULT,
gcp_conn_id: str = "google_cloud_default",
impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.dataset_id = dataset_id
self.location = location
self.project_id = project_id
self.metadata = metadata
self.timeout = timeout
self.retry = retry
self.gcp_conn_id = gcp_conn_id
self.impersonation_chain = impersonation_chain
@staticmethod
def _parse_dataset_id(dataset_id: Union[str, List[str]]) -> List[str]:
if not isinstance(dataset_id, str):
return dataset_id
try:
return ast.literal_eval(dataset_id)
except (SyntaxError, ValueError):
return dataset_id.split(",")
[docs] def execute(self, context: 'Context'):
hook = CloudAutoMLHook(
gcp_conn_id=self.gcp_conn_id,
impersonation_chain=self.impersonation_chain,
)
dataset_id_list = self._parse_dataset_id(self.dataset_id)
for dataset_id in dataset_id_list:
self.log.info("Deleting dataset %s", dataset_id)
hook.delete_dataset(
dataset_id=dataset_id,
location=self.location,
project_id=self.project_id,
retry=self.retry,
timeout=self.timeout,
metadata=self.metadata,
)
self.log.info("Dataset deleted.")
