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Automl ga vision classification [(#2612)](GoogleCloudPlatform/python-docs-samples#2612)
* automl: add vision classification samples for automl ga * lint * naming * clean up samples and test * lint * Consistently use double quotes * Move test imports to top of file * Fix license year * license year 2020, drop python2 unicode print
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‎packages/google-cloud-automl/samples/snippets/get_model_evaluation_test.py

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import os
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from google.cloud import automl
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import pytest
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import get_model_evaluation
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@pytest.fixture(scope="function")
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def get_evaluation_id():
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from google.cloud import automl
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client = automl.AutoMlClient()
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model_full_id = client.model_path(PROJECT_ID, "us-central1", MODEL_ID)
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evaluation = None

‎packages/google-cloud-automl/samples/snippets/resources/test.png

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‎packages/google-cloud-automl/samples/snippets/vision_classification_create_dataset.py

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# Copyright 2020 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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def create_dataset(project_id, display_name):
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"""Create a dataset."""
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# [START automl_vision_classification_create_dataset]
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from google.cloud import automl
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# TODO(developer): Uncomment and set the following variables
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# project_id = "YOUR_PROJECT_ID"
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# display_name = "your_datasets_display_name"
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client = automl.AutoMlClient()
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# A resource that represents Google Cloud Platform location.
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project_location = client.location_path(project_id, "us-central1")
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# Specify the classification type
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# Types:
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# MultiLabel: Multiple labels are allowed for one example.
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# MultiClass: At most one label is allowed per example.
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metadata = automl.types.ImageClassificationDatasetMetadata(
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classification_type=automl.enums.ClassificationType.MULTILABEL
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)
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dataset = automl.types.Dataset(
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display_name=display_name,
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image_classification_dataset_metadata=metadata,
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)
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# Create a dataset with the dataset metadata in the region.
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response = client.create_dataset(project_location, dataset)
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created_dataset = response.result()
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# Display the dataset information
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print("Dataset name: {}".format(created_dataset.name))
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print("Dataset id: {}".format(created_dataset.name.split("/")[-1]))
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# [END automl_vision_classification_create_dataset]

‎packages/google-cloud-automl/samples/snippets/vision_classification_create_dataset_test.py

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# Copyright 2020 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import datetime
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import os
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from google.cloud import automl
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import pytest
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import vision_classification_create_dataset
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PROJECT_ID = os.environ["GCLOUD_PROJECT"]
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@pytest.mark.slow
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def test_create_dataset(capsys):
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# create dataset
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dataset_name = "test_" + datetime.datetime.now().strftime("%Y%m%d%H%M%S")
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vision_classification_create_dataset.create_dataset(
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PROJECT_ID, dataset_name
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)
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out, _ = capsys.readouterr()
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assert "Dataset id: " in out
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# Delete the created dataset
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dataset_id = out.splitlines()[1].split()[2]
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client = automl.AutoMlClient()
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dataset_full_id = client.dataset_path(
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PROJECT_ID, "us-central1", dataset_id
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)
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response = client.delete_dataset(dataset_full_id)
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response.result()

‎packages/google-cloud-automl/samples/snippets/vision_classification_create_model.py

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# Copyright 2020 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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def create_model(project_id, dataset_id, display_name):
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"""Create a model."""
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# [START automl_vision_classification_create_model]
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from google.cloud import automl
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# TODO(developer): Uncomment and set the following variables
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# project_id = "YOUR_PROJECT_ID"
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# dataset_id = "YOUR_DATASET_ID"
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# display_name = "your_models_display_name"
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client = automl.AutoMlClient()
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# A resource that represents Google Cloud Platform location.
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project_location = client.location_path(project_id, "us-central1")
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# Leave model unset to use the default base model provided by Google
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metadata = automl.types.ImageClassificationModelMetadata(
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train_budget_milli_node_hours=24000
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)
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model = automl.types.Model(
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display_name=display_name,
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dataset_id=dataset_id,
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image_classification_model_metadata=metadata,
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)
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# Create a model with the model metadata in the region.
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response = client.create_model(project_location, model)
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print("Training operation name: {}".format(response.operation.name))
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print("Training started...")
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# [END automl_vision_classification_create_model]

‎packages/google-cloud-automl/samples/snippets/vision_classification_create_model_test.py

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# Copyright 2020 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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from google.cloud import automl
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import pytest
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import vision_classification_create_model
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PROJECT_ID = os.environ["GCLOUD_PROJECT"]
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DATASET_ID = "ICN3946265060617537378"
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@pytest.mark.slow
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def test_vision_classification_create_model(capsys):
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vision_classification_create_model.create_model(
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PROJECT_ID, DATASET_ID, "classification_test_create_model"
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)
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out, _ = capsys.readouterr()
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assert "Training started" in out
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# Cancel the operation
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operation_id = out.split("Training operation name: ")[1].split("\n")[0]
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client = automl.AutoMlClient()
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client.transport._operations_client.cancel_operation(operation_id)

‎packages/google-cloud-automl/samples/snippets/vision_classification_deploy_model_node_count.py

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# Copyright 2020 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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def deploy_model(project_id, model_id):
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"""Deploy a model with a specified node count."""
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# [START automl_vision_classification_deploy_model_node_count]
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from google.cloud import automl
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# TODO(developer): Uncomment and set the following variables
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# project_id = "YOUR_PROJECT_ID"
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# model_id = "YOUR_MODEL_ID"
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client = automl.AutoMlClient()
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# Get the full path of the model.
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model_full_id = client.model_path(project_id, "us-central1", model_id)
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metadata = automl.types.ImageClassificationModelDeploymentMetadata(
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node_count=2
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)
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response = client.deploy_model(
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model_full_id, image_classification_model_deployment_metadata=metadata
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)
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print("Model deployment finished. {}".format(response.result()))
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# [END automl_vision_classification_deploy_model_node_count]

‎packages/google-cloud-automl/samples/snippets/vision_classification_deploy_model_node_count_test.py

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# Copyright 2020 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import pytest
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import vision_classification_deploy_model_node_count
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PROJECT_ID = os.environ["GCLOUD_PROJECT"]
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MODEL_ID = "ICN0000000000000000000"
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@pytest.mark.slow
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def test_classification_deploy_model_with_node_count(capsys):
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# As model deployment can take a long time, instead try to deploy a
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# nonexistent model and confirm that the model was not found, but other
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# elements of the request were valid.
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try:
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vision_classification_deploy_model_node_count.deploy_model(
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PROJECT_ID, MODEL_ID
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)
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out, _ = capsys.readouterr()
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assert "The model does not exist" in out
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except Exception as e:
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assert "The model does not exist" in e.message

‎packages/google-cloud-automl/samples/snippets/vision_classification_predict.py

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# Copyright 2020 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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def predict(project_id, model_id, file_path):
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"""Predict."""
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# [START automl_vision_classification_predict]
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from google.cloud import automl
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# TODO(developer): Uncomment and set the following variables
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# project_id = "YOUR_PROJECT_ID"
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# model_id = "YOUR_MODEL_ID"
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# file_path = "path_to_local_file.jpg"
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prediction_client = automl.PredictionServiceClient()
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# Get the full path of the model.
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model_full_id = prediction_client.model_path(
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project_id, "us-central1", model_id
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)
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# Read the file.
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with open(file_path, "rb") as content_file:
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content = content_file.read()
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image = automl.types.Image(image_bytes=content)
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payload = automl.types.ExamplePayload(image=image)
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# params is additional domain-specific parameters.
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# score_threshold is used to filter the result
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params = {"score_threshold": "0.8"}
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response = prediction_client.predict(model_full_id, payload, params)
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print("Prediction results:")
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for result in response.payload:
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print("Predicted class name: {}".format(result.display_name))
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print("Predicted class score: {}".format(result.classification.score))
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# [END automl_vision_classification_predict]

‎packages/google-cloud-automl/samples/snippets/vision_classification_predict_test.py

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# Copyright 2020 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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from google.cloud import automl
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import pytest
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import vision_classification_predict
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PROJECT_ID = os.environ["GCLOUD_PROJECT"]
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MODEL_ID = "ICN7383667271543079510"
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@pytest.fixture(scope="function")
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def verify_model_state():
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client = automl.AutoMlClient()
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model_full_id = client.model_path(PROJECT_ID, "us-central1", MODEL_ID)
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model = client.get_model(model_full_id)
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if model.deployment_state == automl.enums.Model.DeploymentState.UNDEPLOYED:
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# Deploy model if it is not deployed
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response = client.deploy_model(model_full_id)
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response.result()
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def test_vision_classification_predict(capsys, verify_model_state):
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verify_model_state
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file_path = "resources/test.png"
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vision_classification_predict.predict(PROJECT_ID, MODEL_ID, file_path)
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out, _ = capsys.readouterr()
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assert "Predicted class name:" in out

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