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Posted to commits@airflow.apache.org by GitBox <gi...@apache.org> on 2022/02/09 17:10:30 UTC
[GitHub] [airflow] MaksYermak opened a new pull request #21470: Auto ml vertex ai operators
MaksYermak opened a new pull request #21470:
URL: https://github.com/apache/airflow/pull/21470
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Create operators for working with Auto ML for Vertex AI service. Includes operators, hooks, example dags, tests and docs.
Co-authored-by: Wojciech Januszek [januszek@google.com](mailto:januszek@google.com)
Co-authored-by: Lukasz Wyszomirski [wyszomirski@google.com](mailto:wyszomirski@google.com)
Co-authored-by: Maksim Yermakou [maksimy@google.com](mailto:maksimy@google.com)
---
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[GitHub] [airflow] potiuk commented on a change in pull request #21470: Create Auto ML operators for Vertex AI service
Posted by GitBox <gi...@apache.org>.
potiuk commented on a change in pull request #21470:
URL: https://github.com/apache/airflow/pull/21470#discussion_r807032572
##########
File path: airflow/providers/google/cloud/operators/vertex_ai/auto_ml.py
##########
@@ -0,0 +1,707 @@
+#
+# 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 Vertex AI operators."""
+
+from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union
+
+from google.api_core.exceptions import NotFound
+from google.api_core.retry import Retry
+from google.cloud.aiplatform import datasets
+from google.cloud.aiplatform.models import Model
+from google.cloud.aiplatform_v1.types.training_pipeline import TrainingPipeline
+
+from airflow.models import BaseOperator, BaseOperatorLink
+from airflow.models.xcom import XCom
+from airflow.providers.google.cloud.hooks.vertex_ai.auto_ml import AutoMLHook
+
+if TYPE_CHECKING:
+ from airflow.utils.context import Context
+
+VERTEX_AI_BASE_LINK = "https://console.cloud.google.com/vertex-ai"
+VERTEX_AI_MODEL_LINK = (
+ VERTEX_AI_BASE_LINK + "/locations/{region}/models/{model_id}/deploy?project={project_id}"
+)
+VERTEX_AI_TRAINING_PIPELINES_LINK = VERTEX_AI_BASE_LINK + "/training/training-pipelines?project={project_id}"
+
+
+class VertexAIModelLink(BaseOperatorLink):
+ """Helper class for constructing Vertex AI Model link"""
+
+ name = "Vertex AI Model"
+
+ def get_link(self, operator, dttm):
+ model_conf = XCom.get_one(
+ key='model_conf', dag_id=operator.dag.dag_id, task_id=operator.task_id, execution_date=dttm
+ )
+ return (
+ VERTEX_AI_MODEL_LINK.format(
+ region=model_conf["region"],
+ model_id=model_conf["model_id"],
+ project_id=model_conf["project_id"],
+ )
+ if model_conf
+ else ""
+ )
+
+
+class VertexAITrainingPipelinesLink(BaseOperatorLink):
+ """Helper class for constructing Vertex AI Training Pipelines link"""
+
+ name = "Vertex AI Training Pipelines"
+
+ def get_link(self, operator, dttm):
+ project_id = XCom.get_one(
+ key='project_id', dag_id=operator.dag.dag_id, task_id=operator.task_id, execution_date=dttm
+ )
+ return (
+ VERTEX_AI_TRAINING_PIPELINES_LINK.format(
+ project_id=project_id,
+ )
+ if project_id
+ else ""
+ )
+
+
+class AutoMLTrainingJobBaseOperator(BaseOperator):
+ """The base class for operators that launch AutoML jobs on VertexAI."""
+
+ def __init__(
+ self,
+ *,
+ project_id: str,
+ region: str,
+ display_name: str,
+ labels: Optional[Dict[str, str]] = None,
+ training_encryption_spec_key_name: Optional[str] = None,
+ model_encryption_spec_key_name: Optional[str] = None,
+ # RUN
+ training_fraction_split: Optional[float] = None,
+ test_fraction_split: Optional[float] = None,
+ model_display_name: Optional[str] = None,
+ model_labels: Optional[Dict[str, str]] = None,
+ sync: bool = True,
+ gcp_conn_id: str = "google_cloud_default",
+ delegate_to: Optional[str] = None,
+ impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
Review comment:
I am fine with both approaches as long as Google want to maintain it ;)
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[GitHub] [airflow] MaksYermak commented on pull request #21470: Create Auto ML operators for Vertex AI service
Posted by GitBox <gi...@apache.org>.
MaksYermak commented on pull request #21470:
URL: https://github.com/apache/airflow/pull/21470#issuecomment-1041505863
> Please see this [comment](https://github.com/apache/airflow/pull/21470/files#r805326443), let's try to be consistent 👌
@potiuk @turbaszek I have updated links for Vertex AI with new approach. One more thing I with @lwyszomi and @wojsamjan have decided to create a separate package for links. @potiuk @turbaszek what do you think about it?
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[GitHub] [airflow] turbaszek commented on a change in pull request #21470: Create Auto ML operators for Vertex AI service
Posted by GitBox <gi...@apache.org>.
turbaszek commented on a change in pull request #21470:
URL: https://github.com/apache/airflow/pull/21470#discussion_r812154543
##########
File path: airflow/providers/google/cloud/operators/vertex_ai/auto_ml.py
##########
@@ -0,0 +1,707 @@
+#
+# 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 Vertex AI operators."""
+
+from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union
+
+from google.api_core.exceptions import NotFound
+from google.api_core.retry import Retry
+from google.cloud.aiplatform import datasets
+from google.cloud.aiplatform.models import Model
+from google.cloud.aiplatform_v1.types.training_pipeline import TrainingPipeline
+
+from airflow.models import BaseOperator, BaseOperatorLink
+from airflow.models.xcom import XCom
+from airflow.providers.google.cloud.hooks.vertex_ai.auto_ml import AutoMLHook
+
+if TYPE_CHECKING:
+ from airflow.utils.context import Context
+
+VERTEX_AI_BASE_LINK = "https://console.cloud.google.com/vertex-ai"
+VERTEX_AI_MODEL_LINK = (
+ VERTEX_AI_BASE_LINK + "/locations/{region}/models/{model_id}/deploy?project={project_id}"
+)
+VERTEX_AI_TRAINING_PIPELINES_LINK = VERTEX_AI_BASE_LINK + "/training/training-pipelines?project={project_id}"
+
+
+class VertexAIModelLink(BaseOperatorLink):
+ """Helper class for constructing Vertex AI Model link"""
+
+ name = "Vertex AI Model"
+
+ def get_link(self, operator, dttm):
+ model_conf = XCom.get_one(
+ key='model_conf', dag_id=operator.dag.dag_id, task_id=operator.task_id, execution_date=dttm
+ )
+ return (
+ VERTEX_AI_MODEL_LINK.format(
+ region=model_conf["region"],
+ model_id=model_conf["model_id"],
+ project_id=model_conf["project_id"],
+ )
+ if model_conf
+ else ""
+ )
+
+
+class VertexAITrainingPipelinesLink(BaseOperatorLink):
+ """Helper class for constructing Vertex AI Training Pipelines link"""
+
+ name = "Vertex AI Training Pipelines"
+
+ def get_link(self, operator, dttm):
+ project_id = XCom.get_one(
+ key='project_id', dag_id=operator.dag.dag_id, task_id=operator.task_id, execution_date=dttm
+ )
+ return (
+ VERTEX_AI_TRAINING_PIPELINES_LINK.format(
+ project_id=project_id,
+ )
+ if project_id
+ else ""
+ )
+
+
+class AutoMLTrainingJobBaseOperator(BaseOperator):
+ """The base class for operators that launch AutoML jobs on VertexAI."""
+
+ def __init__(
+ self,
+ *,
+ project_id: str,
+ region: str,
+ display_name: str,
+ labels: Optional[Dict[str, str]] = None,
+ training_encryption_spec_key_name: Optional[str] = None,
+ model_encryption_spec_key_name: Optional[str] = None,
+ # RUN
+ training_fraction_split: Optional[float] = None,
+ test_fraction_split: Optional[float] = None,
+ model_display_name: Optional[str] = None,
+ model_labels: Optional[Dict[str, str]] = None,
+ sync: bool = True,
+ gcp_conn_id: str = "google_cloud_default",
+ delegate_to: Optional[str] = None,
+ impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
Review comment:
@MaksYermak @kosteev it's a matter of definition: For example Google AutoML operators accept `model` argument instead of particular elements of model as we do here.
https://github.com/apache/airflow/blob/b2c0a921c155e82d1140029e6495594061945025/airflow/providers/google/cloud/operators/automl.py#L81
Some time ago we had a move that resulted in refactoring Dataproc and BigQuery (as well others) operators to follow "single input" approach because it was considered more generic and easier to follow.
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[GitHub] [airflow] turbaszek merged pull request #21470: Create Auto ML operators for Vertex AI service
Posted by GitBox <gi...@apache.org>.
turbaszek merged pull request #21470:
URL: https://github.com/apache/airflow/pull/21470
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[GitHub] [airflow] potiuk commented on pull request #21470: Create Auto ML operators for Vertex AI service
Posted by GitBox <gi...@apache.org>.
potiuk commented on pull request #21470:
URL: https://github.com/apache/airflow/pull/21470#issuecomment-1040485086
Looks like there are some import errors though :(
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[GitHub] [airflow] turbaszek commented on a change in pull request #21470: Create Auto ML operators for Vertex AI service
Posted by GitBox <gi...@apache.org>.
turbaszek commented on a change in pull request #21470:
URL: https://github.com/apache/airflow/pull/21470#discussion_r805326443
##########
File path: airflow/providers/google/cloud/operators/vertex_ai/auto_ml.py
##########
@@ -0,0 +1,707 @@
+#
+# 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 Vertex AI operators."""
+
+from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union
+
+from google.api_core.exceptions import NotFound
+from google.api_core.retry import Retry
+from google.cloud.aiplatform import datasets
+from google.cloud.aiplatform.models import Model
+from google.cloud.aiplatform_v1.types.training_pipeline import TrainingPipeline
+
+from airflow.models import BaseOperator, BaseOperatorLink
+from airflow.models.xcom import XCom
+from airflow.providers.google.cloud.hooks.vertex_ai.auto_ml import AutoMLHook
+
+if TYPE_CHECKING:
+ from airflow.utils.context import Context
+
+VERTEX_AI_BASE_LINK = "https://console.cloud.google.com/vertex-ai"
+VERTEX_AI_MODEL_LINK = (
+ VERTEX_AI_BASE_LINK + "/locations/{region}/models/{model_id}/deploy?project={project_id}"
+)
+VERTEX_AI_TRAINING_PIPELINES_LINK = VERTEX_AI_BASE_LINK + "/training/training-pipelines?project={project_id}"
+
+
+class VertexAIModelLink(BaseOperatorLink):
+ """Helper class for constructing Vertex AI Model link"""
+
+ name = "Vertex AI Model"
+
+ def get_link(self, operator, dttm):
+ model_conf = XCom.get_one(
+ key='model_conf', dag_id=operator.dag.dag_id, task_id=operator.task_id, execution_date=dttm
+ )
+ return (
+ VERTEX_AI_MODEL_LINK.format(
+ region=model_conf["region"],
+ model_id=model_conf["model_id"],
+ project_id=model_conf["project_id"],
+ )
+ if model_conf
+ else ""
+ )
Review comment:
Same comment as in https://github.com/apache/airflow/pull/21267#discussion_r800086099 , let's try to be consistent
##########
File path: airflow/providers/google/cloud/operators/vertex_ai/auto_ml.py
##########
@@ -0,0 +1,707 @@
+#
+# 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 Vertex AI operators."""
+
+from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union
+
+from google.api_core.exceptions import NotFound
+from google.api_core.retry import Retry
+from google.cloud.aiplatform import datasets
+from google.cloud.aiplatform.models import Model
+from google.cloud.aiplatform_v1.types.training_pipeline import TrainingPipeline
+
+from airflow.models import BaseOperator, BaseOperatorLink
+from airflow.models.xcom import XCom
+from airflow.providers.google.cloud.hooks.vertex_ai.auto_ml import AutoMLHook
+
+if TYPE_CHECKING:
+ from airflow.utils.context import Context
+
+VERTEX_AI_BASE_LINK = "https://console.cloud.google.com/vertex-ai"
+VERTEX_AI_MODEL_LINK = (
+ VERTEX_AI_BASE_LINK + "/locations/{region}/models/{model_id}/deploy?project={project_id}"
+)
+VERTEX_AI_TRAINING_PIPELINES_LINK = VERTEX_AI_BASE_LINK + "/training/training-pipelines?project={project_id}"
+
+
+class VertexAIModelLink(BaseOperatorLink):
+ """Helper class for constructing Vertex AI Model link"""
+
+ name = "Vertex AI Model"
+
+ def get_link(self, operator, dttm):
+ model_conf = XCom.get_one(
+ key='model_conf', dag_id=operator.dag.dag_id, task_id=operator.task_id, execution_date=dttm
+ )
+ return (
+ VERTEX_AI_MODEL_LINK.format(
+ region=model_conf["region"],
+ model_id=model_conf["model_id"],
+ project_id=model_conf["project_id"],
+ )
+ if model_conf
+ else ""
+ )
+
+
+class VertexAITrainingPipelinesLink(BaseOperatorLink):
+ """Helper class for constructing Vertex AI Training Pipelines link"""
+
+ name = "Vertex AI Training Pipelines"
+
+ def get_link(self, operator, dttm):
+ project_id = XCom.get_one(
+ key='project_id', dag_id=operator.dag.dag_id, task_id=operator.task_id, execution_date=dttm
+ )
+ return (
+ VERTEX_AI_TRAINING_PIPELINES_LINK.format(
+ project_id=project_id,
+ )
+ if project_id
+ else ""
+ )
+
+
+class AutoMLTrainingJobBaseOperator(BaseOperator):
+ """The base class for operators that launch AutoML jobs on VertexAI."""
+
+ def __init__(
+ self,
+ *,
+ project_id: str,
+ region: str,
+ display_name: str,
+ labels: Optional[Dict[str, str]] = None,
+ training_encryption_spec_key_name: Optional[str] = None,
+ model_encryption_spec_key_name: Optional[str] = None,
+ # RUN
+ training_fraction_split: Optional[float] = None,
+ test_fraction_split: Optional[float] = None,
+ model_display_name: Optional[str] = None,
+ model_labels: Optional[Dict[str, str]] = None,
+ sync: bool = True,
+ gcp_conn_id: str = "google_cloud_default",
+ delegate_to: Optional[str] = None,
+ impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
Review comment:
I remember that some time ago Google recommended to avoid such interfaces because there was a lot of issues like "can we add foo_bar argument to operator XYZ". Instead it was suggested that an operator should accept a "body" - an object that is accepted by the underlying API.
CC @potiuk @mik-laj @kosteev
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[GitHub] [airflow] potiuk commented on a change in pull request #21470: Create Auto ML operators for Vertex AI service
Posted by GitBox <gi...@apache.org>.
potiuk commented on a change in pull request #21470:
URL: https://github.com/apache/airflow/pull/21470#discussion_r807032572
##########
File path: airflow/providers/google/cloud/operators/vertex_ai/auto_ml.py
##########
@@ -0,0 +1,707 @@
+#
+# 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 Vertex AI operators."""
+
+from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union
+
+from google.api_core.exceptions import NotFound
+from google.api_core.retry import Retry
+from google.cloud.aiplatform import datasets
+from google.cloud.aiplatform.models import Model
+from google.cloud.aiplatform_v1.types.training_pipeline import TrainingPipeline
+
+from airflow.models import BaseOperator, BaseOperatorLink
+from airflow.models.xcom import XCom
+from airflow.providers.google.cloud.hooks.vertex_ai.auto_ml import AutoMLHook
+
+if TYPE_CHECKING:
+ from airflow.utils.context import Context
+
+VERTEX_AI_BASE_LINK = "https://console.cloud.google.com/vertex-ai"
+VERTEX_AI_MODEL_LINK = (
+ VERTEX_AI_BASE_LINK + "/locations/{region}/models/{model_id}/deploy?project={project_id}"
+)
+VERTEX_AI_TRAINING_PIPELINES_LINK = VERTEX_AI_BASE_LINK + "/training/training-pipelines?project={project_id}"
+
+
+class VertexAIModelLink(BaseOperatorLink):
+ """Helper class for constructing Vertex AI Model link"""
+
+ name = "Vertex AI Model"
+
+ def get_link(self, operator, dttm):
+ model_conf = XCom.get_one(
+ key='model_conf', dag_id=operator.dag.dag_id, task_id=operator.task_id, execution_date=dttm
+ )
+ return (
+ VERTEX_AI_MODEL_LINK.format(
+ region=model_conf["region"],
+ model_id=model_conf["model_id"],
+ project_id=model_conf["project_id"],
+ )
+ if model_conf
+ else ""
+ )
+
+
+class VertexAITrainingPipelinesLink(BaseOperatorLink):
+ """Helper class for constructing Vertex AI Training Pipelines link"""
+
+ name = "Vertex AI Training Pipelines"
+
+ def get_link(self, operator, dttm):
+ project_id = XCom.get_one(
+ key='project_id', dag_id=operator.dag.dag_id, task_id=operator.task_id, execution_date=dttm
+ )
+ return (
+ VERTEX_AI_TRAINING_PIPELINES_LINK.format(
+ project_id=project_id,
+ )
+ if project_id
+ else ""
+ )
+
+
+class AutoMLTrainingJobBaseOperator(BaseOperator):
+ """The base class for operators that launch AutoML jobs on VertexAI."""
+
+ def __init__(
+ self,
+ *,
+ project_id: str,
+ region: str,
+ display_name: str,
+ labels: Optional[Dict[str, str]] = None,
+ training_encryption_spec_key_name: Optional[str] = None,
+ model_encryption_spec_key_name: Optional[str] = None,
+ # RUN
+ training_fraction_split: Optional[float] = None,
+ test_fraction_split: Optional[float] = None,
+ model_display_name: Optional[str] = None,
+ model_labels: Optional[Dict[str, str]] = None,
+ sync: bool = True,
+ gcp_conn_id: str = "google_cloud_default",
+ delegate_to: Optional[str] = None,
+ impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
Review comment:
I am fine with both approaches as long as Google want to maintain it ;)
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[GitHub] [airflow] MaksYermak commented on a change in pull request #21470: Create Auto ML operators for Vertex AI service
Posted by GitBox <gi...@apache.org>.
MaksYermak commented on a change in pull request #21470:
URL: https://github.com/apache/airflow/pull/21470#discussion_r811689586
##########
File path: airflow/providers/google/cloud/operators/vertex_ai/auto_ml.py
##########
@@ -0,0 +1,707 @@
+#
+# 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 Vertex AI operators."""
+
+from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union
+
+from google.api_core.exceptions import NotFound
+from google.api_core.retry import Retry
+from google.cloud.aiplatform import datasets
+from google.cloud.aiplatform.models import Model
+from google.cloud.aiplatform_v1.types.training_pipeline import TrainingPipeline
+
+from airflow.models import BaseOperator, BaseOperatorLink
+from airflow.models.xcom import XCom
+from airflow.providers.google.cloud.hooks.vertex_ai.auto_ml import AutoMLHook
+
+if TYPE_CHECKING:
+ from airflow.utils.context import Context
+
+VERTEX_AI_BASE_LINK = "https://console.cloud.google.com/vertex-ai"
+VERTEX_AI_MODEL_LINK = (
+ VERTEX_AI_BASE_LINK + "/locations/{region}/models/{model_id}/deploy?project={project_id}"
+)
+VERTEX_AI_TRAINING_PIPELINES_LINK = VERTEX_AI_BASE_LINK + "/training/training-pipelines?project={project_id}"
+
+
+class VertexAIModelLink(BaseOperatorLink):
+ """Helper class for constructing Vertex AI Model link"""
+
+ name = "Vertex AI Model"
+
+ def get_link(self, operator, dttm):
+ model_conf = XCom.get_one(
+ key='model_conf', dag_id=operator.dag.dag_id, task_id=operator.task_id, execution_date=dttm
+ )
+ return (
+ VERTEX_AI_MODEL_LINK.format(
+ region=model_conf["region"],
+ model_id=model_conf["model_id"],
+ project_id=model_conf["project_id"],
+ )
+ if model_conf
+ else ""
+ )
+
+
+class VertexAITrainingPipelinesLink(BaseOperatorLink):
+ """Helper class for constructing Vertex AI Training Pipelines link"""
+
+ name = "Vertex AI Training Pipelines"
+
+ def get_link(self, operator, dttm):
+ project_id = XCom.get_one(
+ key='project_id', dag_id=operator.dag.dag_id, task_id=operator.task_id, execution_date=dttm
+ )
+ return (
+ VERTEX_AI_TRAINING_PIPELINES_LINK.format(
+ project_id=project_id,
+ )
+ if project_id
+ else ""
+ )
+
+
+class AutoMLTrainingJobBaseOperator(BaseOperator):
+ """The base class for operators that launch AutoML jobs on VertexAI."""
+
+ def __init__(
+ self,
+ *,
+ project_id: str,
+ region: str,
+ display_name: str,
+ labels: Optional[Dict[str, str]] = None,
+ training_encryption_spec_key_name: Optional[str] = None,
+ model_encryption_spec_key_name: Optional[str] = None,
+ # RUN
+ training_fraction_split: Optional[float] = None,
+ test_fraction_split: Optional[float] = None,
+ model_display_name: Optional[str] = None,
+ model_labels: Optional[Dict[str, str]] = None,
+ sync: bool = True,
+ gcp_conn_id: str = "google_cloud_default",
+ delegate_to: Optional[str] = None,
+ impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
Review comment:
@kosteev Yes it is consistent. We have the same approach in other google operators.
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[GitHub] [airflow] turbaszek commented on a change in pull request #21470: Create Auto ML operators for Vertex AI service
Posted by GitBox <gi...@apache.org>.
turbaszek commented on a change in pull request #21470:
URL: https://github.com/apache/airflow/pull/21470#discussion_r805326443
##########
File path: airflow/providers/google/cloud/operators/vertex_ai/auto_ml.py
##########
@@ -0,0 +1,707 @@
+#
+# 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 Vertex AI operators."""
+
+from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union
+
+from google.api_core.exceptions import NotFound
+from google.api_core.retry import Retry
+from google.cloud.aiplatform import datasets
+from google.cloud.aiplatform.models import Model
+from google.cloud.aiplatform_v1.types.training_pipeline import TrainingPipeline
+
+from airflow.models import BaseOperator, BaseOperatorLink
+from airflow.models.xcom import XCom
+from airflow.providers.google.cloud.hooks.vertex_ai.auto_ml import AutoMLHook
+
+if TYPE_CHECKING:
+ from airflow.utils.context import Context
+
+VERTEX_AI_BASE_LINK = "https://console.cloud.google.com/vertex-ai"
+VERTEX_AI_MODEL_LINK = (
+ VERTEX_AI_BASE_LINK + "/locations/{region}/models/{model_id}/deploy?project={project_id}"
+)
+VERTEX_AI_TRAINING_PIPELINES_LINK = VERTEX_AI_BASE_LINK + "/training/training-pipelines?project={project_id}"
+
+
+class VertexAIModelLink(BaseOperatorLink):
+ """Helper class for constructing Vertex AI Model link"""
+
+ name = "Vertex AI Model"
+
+ def get_link(self, operator, dttm):
+ model_conf = XCom.get_one(
+ key='model_conf', dag_id=operator.dag.dag_id, task_id=operator.task_id, execution_date=dttm
+ )
+ return (
+ VERTEX_AI_MODEL_LINK.format(
+ region=model_conf["region"],
+ model_id=model_conf["model_id"],
+ project_id=model_conf["project_id"],
+ )
+ if model_conf
+ else ""
+ )
Review comment:
Same comment as in https://github.com/apache/airflow/pull/21267#discussion_r800086099 , let's try to be consistent
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[GitHub] [airflow] github-actions[bot] commented on pull request #21470: Create Auto ML operators for Vertex AI service
Posted by GitBox <gi...@apache.org>.
github-actions[bot] commented on pull request #21470:
URL: https://github.com/apache/airflow/pull/21470#issuecomment-1040492995
The PR is likely OK to be merged with just subset of tests for default Python and Database versions without running the full matrix of tests, because it does not modify the core of Airflow. If the committers decide that the full tests matrix is needed, they will add the label 'full tests needed'. Then you should rebase to the latest main or amend the last commit of the PR, and push it with --force-with-lease.
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[GitHub] [airflow] kosteev commented on a change in pull request #21470: Create Auto ML operators for Vertex AI service
Posted by GitBox <gi...@apache.org>.
kosteev commented on a change in pull request #21470:
URL: https://github.com/apache/airflow/pull/21470#discussion_r811151561
##########
File path: airflow/providers/google/cloud/operators/vertex_ai/auto_ml.py
##########
@@ -0,0 +1,707 @@
+#
+# 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 Vertex AI operators."""
+
+from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union
+
+from google.api_core.exceptions import NotFound
+from google.api_core.retry import Retry
+from google.cloud.aiplatform import datasets
+from google.cloud.aiplatform.models import Model
+from google.cloud.aiplatform_v1.types.training_pipeline import TrainingPipeline
+
+from airflow.models import BaseOperator, BaseOperatorLink
+from airflow.models.xcom import XCom
+from airflow.providers.google.cloud.hooks.vertex_ai.auto_ml import AutoMLHook
+
+if TYPE_CHECKING:
+ from airflow.utils.context import Context
+
+VERTEX_AI_BASE_LINK = "https://console.cloud.google.com/vertex-ai"
+VERTEX_AI_MODEL_LINK = (
+ VERTEX_AI_BASE_LINK + "/locations/{region}/models/{model_id}/deploy?project={project_id}"
+)
+VERTEX_AI_TRAINING_PIPELINES_LINK = VERTEX_AI_BASE_LINK + "/training/training-pipelines?project={project_id}"
+
+
+class VertexAIModelLink(BaseOperatorLink):
+ """Helper class for constructing Vertex AI Model link"""
+
+ name = "Vertex AI Model"
+
+ def get_link(self, operator, dttm):
+ model_conf = XCom.get_one(
+ key='model_conf', dag_id=operator.dag.dag_id, task_id=operator.task_id, execution_date=dttm
+ )
+ return (
+ VERTEX_AI_MODEL_LINK.format(
+ region=model_conf["region"],
+ model_id=model_conf["model_id"],
+ project_id=model_conf["project_id"],
+ )
+ if model_conf
+ else ""
+ )
+
+
+class VertexAITrainingPipelinesLink(BaseOperatorLink):
+ """Helper class for constructing Vertex AI Training Pipelines link"""
+
+ name = "Vertex AI Training Pipelines"
+
+ def get_link(self, operator, dttm):
+ project_id = XCom.get_one(
+ key='project_id', dag_id=operator.dag.dag_id, task_id=operator.task_id, execution_date=dttm
+ )
+ return (
+ VERTEX_AI_TRAINING_PIPELINES_LINK.format(
+ project_id=project_id,
+ )
+ if project_id
+ else ""
+ )
+
+
+class AutoMLTrainingJobBaseOperator(BaseOperator):
+ """The base class for operators that launch AutoML jobs on VertexAI."""
+
+ def __init__(
+ self,
+ *,
+ project_id: str,
+ region: str,
+ display_name: str,
+ labels: Optional[Dict[str, str]] = None,
+ training_encryption_spec_key_name: Optional[str] = None,
+ model_encryption_spec_key_name: Optional[str] = None,
+ # RUN
+ training_fraction_split: Optional[float] = None,
+ test_fraction_split: Optional[float] = None,
+ model_display_name: Optional[str] = None,
+ model_labels: Optional[Dict[str, str]] = None,
+ sync: bool = True,
+ gcp_conn_id: str = "google_cloud_default",
+ delegate_to: Optional[str] = None,
+ impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
Review comment:
@MaksYermak Is this consistent with approach that we have in other google operators?
I think it should be good as long as we consistent everywhere.
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[GitHub] [airflow] turbaszek commented on a change in pull request #21470: Create Auto ML operators for Vertex AI service
Posted by GitBox <gi...@apache.org>.
turbaszek commented on a change in pull request #21470:
URL: https://github.com/apache/airflow/pull/21470#discussion_r805326734
##########
File path: airflow/providers/google/cloud/operators/vertex_ai/auto_ml.py
##########
@@ -0,0 +1,707 @@
+#
+# 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 Vertex AI operators."""
+
+from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union
+
+from google.api_core.exceptions import NotFound
+from google.api_core.retry import Retry
+from google.cloud.aiplatform import datasets
+from google.cloud.aiplatform.models import Model
+from google.cloud.aiplatform_v1.types.training_pipeline import TrainingPipeline
+
+from airflow.models import BaseOperator, BaseOperatorLink
+from airflow.models.xcom import XCom
+from airflow.providers.google.cloud.hooks.vertex_ai.auto_ml import AutoMLHook
+
+if TYPE_CHECKING:
+ from airflow.utils.context import Context
+
+VERTEX_AI_BASE_LINK = "https://console.cloud.google.com/vertex-ai"
+VERTEX_AI_MODEL_LINK = (
+ VERTEX_AI_BASE_LINK + "/locations/{region}/models/{model_id}/deploy?project={project_id}"
+)
+VERTEX_AI_TRAINING_PIPELINES_LINK = VERTEX_AI_BASE_LINK + "/training/training-pipelines?project={project_id}"
+
+
+class VertexAIModelLink(BaseOperatorLink):
+ """Helper class for constructing Vertex AI Model link"""
+
+ name = "Vertex AI Model"
+
+ def get_link(self, operator, dttm):
+ model_conf = XCom.get_one(
+ key='model_conf', dag_id=operator.dag.dag_id, task_id=operator.task_id, execution_date=dttm
+ )
+ return (
+ VERTEX_AI_MODEL_LINK.format(
+ region=model_conf["region"],
+ model_id=model_conf["model_id"],
+ project_id=model_conf["project_id"],
+ )
+ if model_conf
+ else ""
+ )
+
+
+class VertexAITrainingPipelinesLink(BaseOperatorLink):
+ """Helper class for constructing Vertex AI Training Pipelines link"""
+
+ name = "Vertex AI Training Pipelines"
+
+ def get_link(self, operator, dttm):
+ project_id = XCom.get_one(
+ key='project_id', dag_id=operator.dag.dag_id, task_id=operator.task_id, execution_date=dttm
+ )
+ return (
+ VERTEX_AI_TRAINING_PIPELINES_LINK.format(
+ project_id=project_id,
+ )
+ if project_id
+ else ""
+ )
+
+
+class AutoMLTrainingJobBaseOperator(BaseOperator):
+ """The base class for operators that launch AutoML jobs on VertexAI."""
+
+ def __init__(
+ self,
+ *,
+ project_id: str,
+ region: str,
+ display_name: str,
+ labels: Optional[Dict[str, str]] = None,
+ training_encryption_spec_key_name: Optional[str] = None,
+ model_encryption_spec_key_name: Optional[str] = None,
+ # RUN
+ training_fraction_split: Optional[float] = None,
+ test_fraction_split: Optional[float] = None,
+ model_display_name: Optional[str] = None,
+ model_labels: Optional[Dict[str, str]] = None,
+ sync: bool = True,
+ gcp_conn_id: str = "google_cloud_default",
+ delegate_to: Optional[str] = None,
+ impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
Review comment:
I remember that some time ago Google recommended to avoid such interfaces because there was a lot of issues like "can we add foo_bar argument to operator XYZ". Instead it was suggested that an operator should accept a "body" - an object that is accepted by the underlying API.
CC @potiuk @mik-laj @kosteev
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[GitHub] [airflow] github-actions[bot] commented on pull request #21470: Create Auto ML operators for Vertex AI service
Posted by GitBox <gi...@apache.org>.
github-actions[bot] commented on pull request #21470:
URL: https://github.com/apache/airflow/pull/21470#issuecomment-1040492995
The PR is likely OK to be merged with just subset of tests for default Python and Database versions without running the full matrix of tests, because it does not modify the core of Airflow. If the committers decide that the full tests matrix is needed, they will add the label 'full tests needed'. Then you should rebase to the latest main or amend the last commit of the PR, and push it with --force-with-lease.
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[GitHub] [airflow] potiuk commented on pull request #21470: Create Auto ML operators for Vertex AI service
Posted by GitBox <gi...@apache.org>.
potiuk commented on pull request #21470:
URL: https://github.com/apache/airflow/pull/21470#issuecomment-1040485086
Looks like there are some import errors though :(
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[GitHub] [airflow] kosteev commented on a change in pull request #21470: Create Auto ML operators for Vertex AI service
Posted by GitBox <gi...@apache.org>.
kosteev commented on a change in pull request #21470:
URL: https://github.com/apache/airflow/pull/21470#discussion_r812135001
##########
File path: airflow/providers/google/cloud/operators/vertex_ai/auto_ml.py
##########
@@ -0,0 +1,707 @@
+#
+# 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 Vertex AI operators."""
+
+from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union
+
+from google.api_core.exceptions import NotFound
+from google.api_core.retry import Retry
+from google.cloud.aiplatform import datasets
+from google.cloud.aiplatform.models import Model
+from google.cloud.aiplatform_v1.types.training_pipeline import TrainingPipeline
+
+from airflow.models import BaseOperator, BaseOperatorLink
+from airflow.models.xcom import XCom
+from airflow.providers.google.cloud.hooks.vertex_ai.auto_ml import AutoMLHook
+
+if TYPE_CHECKING:
+ from airflow.utils.context import Context
+
+VERTEX_AI_BASE_LINK = "https://console.cloud.google.com/vertex-ai"
+VERTEX_AI_MODEL_LINK = (
+ VERTEX_AI_BASE_LINK + "/locations/{region}/models/{model_id}/deploy?project={project_id}"
+)
+VERTEX_AI_TRAINING_PIPELINES_LINK = VERTEX_AI_BASE_LINK + "/training/training-pipelines?project={project_id}"
+
+
+class VertexAIModelLink(BaseOperatorLink):
+ """Helper class for constructing Vertex AI Model link"""
+
+ name = "Vertex AI Model"
+
+ def get_link(self, operator, dttm):
+ model_conf = XCom.get_one(
+ key='model_conf', dag_id=operator.dag.dag_id, task_id=operator.task_id, execution_date=dttm
+ )
+ return (
+ VERTEX_AI_MODEL_LINK.format(
+ region=model_conf["region"],
+ model_id=model_conf["model_id"],
+ project_id=model_conf["project_id"],
+ )
+ if model_conf
+ else ""
+ )
+
+
+class VertexAITrainingPipelinesLink(BaseOperatorLink):
+ """Helper class for constructing Vertex AI Training Pipelines link"""
+
+ name = "Vertex AI Training Pipelines"
+
+ def get_link(self, operator, dttm):
+ project_id = XCom.get_one(
+ key='project_id', dag_id=operator.dag.dag_id, task_id=operator.task_id, execution_date=dttm
+ )
+ return (
+ VERTEX_AI_TRAINING_PIPELINES_LINK.format(
+ project_id=project_id,
+ )
+ if project_id
+ else ""
+ )
+
+
+class AutoMLTrainingJobBaseOperator(BaseOperator):
+ """The base class for operators that launch AutoML jobs on VertexAI."""
+
+ def __init__(
+ self,
+ *,
+ project_id: str,
+ region: str,
+ display_name: str,
+ labels: Optional[Dict[str, str]] = None,
+ training_encryption_spec_key_name: Optional[str] = None,
+ model_encryption_spec_key_name: Optional[str] = None,
+ # RUN
+ training_fraction_split: Optional[float] = None,
+ test_fraction_split: Optional[float] = None,
+ model_display_name: Optional[str] = None,
+ model_labels: Optional[Dict[str, str]] = None,
+ sync: bool = True,
+ gcp_conn_id: str = "google_cloud_default",
+ delegate_to: Optional[str] = None,
+ impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
Review comment:
Thx.
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[GitHub] [airflow] turbaszek commented on a change in pull request #21470: Create Auto ML operators for Vertex AI service
Posted by GitBox <gi...@apache.org>.
turbaszek commented on a change in pull request #21470:
URL: https://github.com/apache/airflow/pull/21470#discussion_r812154543
##########
File path: airflow/providers/google/cloud/operators/vertex_ai/auto_ml.py
##########
@@ -0,0 +1,707 @@
+#
+# 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 Vertex AI operators."""
+
+from typing import TYPE_CHECKING, Dict, List, Optional, Sequence, Tuple, Union
+
+from google.api_core.exceptions import NotFound
+from google.api_core.retry import Retry
+from google.cloud.aiplatform import datasets
+from google.cloud.aiplatform.models import Model
+from google.cloud.aiplatform_v1.types.training_pipeline import TrainingPipeline
+
+from airflow.models import BaseOperator, BaseOperatorLink
+from airflow.models.xcom import XCom
+from airflow.providers.google.cloud.hooks.vertex_ai.auto_ml import AutoMLHook
+
+if TYPE_CHECKING:
+ from airflow.utils.context import Context
+
+VERTEX_AI_BASE_LINK = "https://console.cloud.google.com/vertex-ai"
+VERTEX_AI_MODEL_LINK = (
+ VERTEX_AI_BASE_LINK + "/locations/{region}/models/{model_id}/deploy?project={project_id}"
+)
+VERTEX_AI_TRAINING_PIPELINES_LINK = VERTEX_AI_BASE_LINK + "/training/training-pipelines?project={project_id}"
+
+
+class VertexAIModelLink(BaseOperatorLink):
+ """Helper class for constructing Vertex AI Model link"""
+
+ name = "Vertex AI Model"
+
+ def get_link(self, operator, dttm):
+ model_conf = XCom.get_one(
+ key='model_conf', dag_id=operator.dag.dag_id, task_id=operator.task_id, execution_date=dttm
+ )
+ return (
+ VERTEX_AI_MODEL_LINK.format(
+ region=model_conf["region"],
+ model_id=model_conf["model_id"],
+ project_id=model_conf["project_id"],
+ )
+ if model_conf
+ else ""
+ )
+
+
+class VertexAITrainingPipelinesLink(BaseOperatorLink):
+ """Helper class for constructing Vertex AI Training Pipelines link"""
+
+ name = "Vertex AI Training Pipelines"
+
+ def get_link(self, operator, dttm):
+ project_id = XCom.get_one(
+ key='project_id', dag_id=operator.dag.dag_id, task_id=operator.task_id, execution_date=dttm
+ )
+ return (
+ VERTEX_AI_TRAINING_PIPELINES_LINK.format(
+ project_id=project_id,
+ )
+ if project_id
+ else ""
+ )
+
+
+class AutoMLTrainingJobBaseOperator(BaseOperator):
+ """The base class for operators that launch AutoML jobs on VertexAI."""
+
+ def __init__(
+ self,
+ *,
+ project_id: str,
+ region: str,
+ display_name: str,
+ labels: Optional[Dict[str, str]] = None,
+ training_encryption_spec_key_name: Optional[str] = None,
+ model_encryption_spec_key_name: Optional[str] = None,
+ # RUN
+ training_fraction_split: Optional[float] = None,
+ test_fraction_split: Optional[float] = None,
+ model_display_name: Optional[str] = None,
+ model_labels: Optional[Dict[str, str]] = None,
+ sync: bool = True,
+ gcp_conn_id: str = "google_cloud_default",
+ delegate_to: Optional[str] = None,
+ impersonation_chain: Optional[Union[str, Sequence[str]]] = None,
Review comment:
@MaksYermak @kosteev it's a matter of definition: For example Google AutoML operators accept `model` argument instead of particular elements of model as we do here.
https://github.com/apache/airflow/blob/b2c0a921c155e82d1140029e6495594061945025/airflow/providers/google/cloud/operators/automl.py#L81
Some time ago we had a move that resulted in refactoring Dataproc and BigQuery (as well others) operators to follow "single input" approach because it was considered more generic and easier to follow. Also it is more inline with Google change between approach in v1 and v2 clients: https://googleapis.dev/python/dataproc/latest/UPGRADING.html#method-calls
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