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feat: Added deploy_model_with_dedicated_resources_sample, deploy_mode…
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…l_with_automatic_resources_sample, upload_model and get_model samples (#337)

* Added deploy_model and upload_model samples

* Fixed tests

* Fixed tests

* Added get_model sample and fixed errors

* Added get_model sample and fixed errors

* Added get_model sample and fixed errors

* Added deploy_model_custom_trained_model_sample.py

* Small tweaks

* Fixed arguments

* Removed explain args from custom training sample

* Removed comment about AutoML

* Renamed samples

* Added note about explanations

* Fixed test

* Fixed tests

* Ran linter
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ivanmkc committed Apr 30, 2021
1 parent 4241738 commit ef4f6f8
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Showing 11 changed files with 505 additions and 3 deletions.
15 changes: 14 additions & 1 deletion samples/model-builder/conftest.py
Expand Up @@ -220,7 +220,7 @@ def mock_model():


@pytest.fixture
def mock_get_model(mock_model):
def mock_init_model(mock_model):
with patch.object(aiplatform, "Model") as mock:
mock.return_value = mock_model
yield mock
Expand All @@ -232,6 +232,19 @@ def mock_batch_predict_model(mock_model):
yield mock


@pytest.fixture
def mock_upload_model():
with patch.object(aiplatform.models.Model, "upload") as mock:
yield mock


@pytest.fixture
def mock_deploy_model(mock_model, mock_endpoint):
with patch.object(mock_model, "deploy") as mock:
mock.return_value = mock_endpoint
yield mock


"""
----------------------------------------------------------------------------
Job Fixtures
Expand Down
Expand Up @@ -18,7 +18,7 @@


def test_create_batch_prediction_job_sample(
mock_sdk_init, mock_get_model, mock_batch_predict_model
mock_sdk_init, mock_model, mock_init_model, mock_batch_predict_model
):

create_batch_prediction_job_sample.create_batch_prediction_job_sample(
Expand All @@ -33,7 +33,7 @@ def test_create_batch_prediction_job_sample(
mock_sdk_init.assert_called_once_with(
project=constants.PROJECT, location=constants.LOCATION
)
mock_get_model.assert_called_once_with(constants.MODEL_NAME)
mock_init_model.assert_called_once_with(constants.MODEL_NAME)
mock_batch_predict_model.assert_called_once_with(
job_display_name=constants.DISPLAY_NAME,
gcs_source=constants.GCS_SOURCES,
Expand Down
@@ -0,0 +1,57 @@
# Copyright 2021 Google LLC
#
# Licensed 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
#
# https://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.

from typing import Dict, Optional, Sequence, Tuple

from google.cloud import aiplatform


# [START aiplatform_sdk_deploy_model_with_automatic_resources_sample]
def deploy_model_with_automatic_resources_sample(
project,
location,
model_name: str,
endpoint: Optional[aiplatform.Endpoint] = None,
deployed_model_display_name: Optional[str] = None,
traffic_percentage: Optional[int] = 0,
traffic_split: Optional[Dict[str, int]] = None,
min_replica_count: int = 1,
max_replica_count: int = 1,
metadata: Optional[Sequence[Tuple[str, str]]] = (),
sync: bool = True,
):

aiplatform.init(project=project, location=location)

model = aiplatform.Model(model_name=model_name)

model.deploy(
endpoint=endpoint,
deployed_model_display_name=deployed_model_display_name,
traffic_percentage=traffic_percentage,
traffic_split=traffic_split,
min_replica_count=min_replica_count,
max_replica_count=max_replica_count,
metadata=metadata,
sync=sync,
)

model.wait()

print(model.display_name)
print(model.resource_name)
return model


# [END aiplatform_sdk_deploy_model_with_automatic_resources_sample]
@@ -0,0 +1,52 @@
# Copyright 2021 Google LLC
#
# Licensed 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
#
# https://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.


import deploy_model_with_automatic_resources_sample
import test_constants as constants


def test_deploy_model_with_automatic_resources_sample(
mock_sdk_init, mock_model, mock_init_model, mock_deploy_model,
):

deploy_model_with_automatic_resources_sample.deploy_model_with_automatic_resources_sample(
project=constants.PROJECT,
location=constants.LOCATION,
model_name=constants.MODEL_NAME,
endpoint=constants.ENDPOINT_NAME,
deployed_model_display_name=constants.DEPLOYED_MODEL_DISPLAY_NAME,
traffic_percentage=constants.TRAFFIC_PERCENTAGE,
traffic_split=constants.TRAFFIC_SPLIT,
min_replica_count=constants.MIN_REPLICA_COUNT,
max_replica_count=constants.MAX_REPLICA_COUNT,
metadata=constants.ENDPOINT_DEPLOY_METADATA,
)

mock_sdk_init.assert_called_once_with(
project=constants.PROJECT, location=constants.LOCATION
)

mock_init_model.assert_called_once_with(model_name=constants.MODEL_NAME)

mock_deploy_model.assert_called_once_with(
endpoint=constants.ENDPOINT_NAME,
deployed_model_display_name=constants.DEPLOYED_MODEL_DISPLAY_NAME,
traffic_percentage=constants.TRAFFIC_PERCENTAGE,
traffic_split=constants.TRAFFIC_SPLIT,
min_replica_count=constants.MIN_REPLICA_COUNT,
max_replica_count=constants.MAX_REPLICA_COUNT,
metadata=constants.ENDPOINT_DEPLOY_METADATA,
sync=True,
)
@@ -0,0 +1,70 @@
# Copyright 2021 Google LLC
#
# Licensed 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
#
# https://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.

from typing import Dict, Optional, Sequence, Tuple

from google.cloud import aiplatform
from google.cloud.aiplatform import explain


# [START aiplatform_sdk_deploy_model_with_dedicated_resources_sample]
def deploy_model_with_dedicated_resources_sample(
project,
location,
model_name: str,
machine_type: str,
endpoint: Optional[aiplatform.Endpoint] = None,
deployed_model_display_name: Optional[str] = None,
traffic_percentage: Optional[int] = 0,
traffic_split: Optional[Dict[str, int]] = None,
min_replica_count: int = 1,
max_replica_count: int = 1,
accelerator_type: Optional[str] = None,
accelerator_count: Optional[int] = None,
explanation_metadata: Optional[explain.ExplanationMetadata] = None,
explanation_parameters: Optional[explain.ExplanationParameters] = None,
metadata: Optional[Sequence[Tuple[str, str]]] = (),
sync: bool = True,
):

aiplatform.init(project=project, location=location)

model = aiplatform.Model(model_name=model_name)

# The explanation_metadata and explanation_parameters should only be
# provided for a custom trained model and not an AutoML model.
model.deploy(
endpoint=endpoint,
deployed_model_display_name=deployed_model_display_name,
traffic_percentage=traffic_percentage,
traffic_split=traffic_split,
machine_type=machine_type,
min_replica_count=min_replica_count,
max_replica_count=max_replica_count,
accelerator_type=accelerator_type,
accelerator_count=accelerator_count,
explanation_metadata=explanation_metadata,
explanation_parameters=explanation_parameters,
metadata=metadata,
sync=sync,
)

model.wait()

print(model.display_name)
print(model.resource_name)
return model


# [END aiplatform_sdk_deploy_model_with_dedicated_resources_sample]
@@ -0,0 +1,62 @@
# Copyright 2021 Google LLC
#
# Licensed 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
#
# https://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.


import deploy_model_with_dedicated_resources_sample
import test_constants as constants


def test_deploy_model_with_dedicated_resources_sample(
mock_sdk_init, mock_model, mock_init_model, mock_deploy_model
):

deploy_model_with_dedicated_resources_sample.deploy_model_with_dedicated_resources_sample(
project=constants.PROJECT,
location=constants.LOCATION,
machine_type=constants.MACHINE_TYPE,
model_name=constants.MODEL_NAME,
endpoint=constants.ENDPOINT_NAME,
deployed_model_display_name=constants.DEPLOYED_MODEL_DISPLAY_NAME,
traffic_percentage=constants.TRAFFIC_PERCENTAGE,
traffic_split=constants.TRAFFIC_SPLIT,
min_replica_count=constants.MIN_REPLICA_COUNT,
max_replica_count=constants.MAX_REPLICA_COUNT,
accelerator_type=constants.ACCELERATOR_TYPE,
accelerator_count=constants.ACCELERATOR_COUNT,
explanation_metadata=constants.EXPLANATION_METADATA,
explanation_parameters=constants.EXPLANATION_PARAMETERS,
metadata=constants.ENDPOINT_DEPLOY_METADATA,
)

mock_sdk_init.assert_called_once_with(
project=constants.PROJECT, location=constants.LOCATION
)

mock_init_model.assert_called_once_with(model_name=constants.MODEL_NAME)

mock_deploy_model.assert_called_once_with(
endpoint=constants.ENDPOINT_NAME,
deployed_model_display_name=constants.DEPLOYED_MODEL_DISPLAY_NAME,
traffic_percentage=constants.TRAFFIC_PERCENTAGE,
traffic_split=constants.TRAFFIC_SPLIT,
machine_type=constants.MACHINE_TYPE,
min_replica_count=constants.MIN_REPLICA_COUNT,
max_replica_count=constants.MAX_REPLICA_COUNT,
accelerator_type=constants.ACCELERATOR_TYPE,
accelerator_count=constants.ACCELERATOR_COUNT,
explanation_metadata=constants.EXPLANATION_METADATA,
explanation_parameters=constants.EXPLANATION_PARAMETERS,
metadata=constants.ENDPOINT_DEPLOY_METADATA,
sync=True,
)
31 changes: 31 additions & 0 deletions samples/model-builder/get_model_sample.py
@@ -0,0 +1,31 @@
# Copyright 2021 Google LLC
#
# Licensed 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
#
# https://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.


from google.cloud import aiplatform


# [START aiplatform_sdk_get_model_sample]
def get_model_sample(project: str, location: str, model_name: str):

aiplatform.init(project=project, location=location)

model = aiplatform.Model(model_name=model_name)

print(model.display_name)
print(model.resource_name)
return model


# [END aiplatform_sdk_get_model_sample]
32 changes: 32 additions & 0 deletions samples/model-builder/get_model_test.py
@@ -0,0 +1,32 @@
# Copyright 2021 Google LLC
#
# Licensed 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
#
# https://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.


import get_model_sample
import test_constants as constants


def test_get_model_sample(mock_sdk_init, mock_init_model):

get_model_sample.get_model_sample(
project=constants.PROJECT,
location=constants.LOCATION,
model_name=constants.MODEL_NAME,
)

mock_sdk_init.assert_called_once_with(
project=constants.PROJECT, location=constants.LOCATION
)

mock_init_model.assert_called_once_with(model_name=constants.MODEL_NAME)

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