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Commit f0706ba

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wuliang229wuliang229
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feat(model_garden): add new samples to list models and deploy a model (GoogleCloudPlatform#13236)
* feat(generative-ai): add samples for the new Model Garden SDK * chore: move Model Garden SDK samples from generative_ai/model_garden/sdk to model_garden/sdk. * refactor: clean up the code to address lint errors * refactor: clean up the code to address more lint errors * feature: remove sdk/ folder, add deploy/ folder and add test with mock. * chore: delete files in sdk/ folder * fix lint issues * fix lint issues * address comments * add some comments * remove trailing whitespace * fixing a few more things * fixing a few more things * fix import names * fix import names * fix import names
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‎model_garden/gemma/gemma3_deploy.py

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# Copyright 2025 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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# https://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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"""Google Cloud Vertex AI sample for deploying Gemma 3 in Model Garden.
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"""
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import os
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from google.cloud import aiplatform
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PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")
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def deploy() -> aiplatform.Endpoint:
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# [START aiplatform_modelgarden_gemma3_deploy]
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import vertexai
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from vertexai.preview import model_garden
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# TODO(developer): Update and un-comment below lines
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# PROJECT_ID = "your-project-id"
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vertexai.init(project=PROJECT_ID, location="us-central1")
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open_model = model_garden.OpenModel("google/gemma3@gemma-3-12b-it")
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endpoint = open_model.deploy(
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machine_type="g2-standard-48",
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accelerator_type="NVIDIA_L4",
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accelerator_count=4,
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accept_eula=True,
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)
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# Optional. Run predictions on the deployed endoint.
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# endpoint.predict(instances=[{"prompt": "What is Generative AI?"}])
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# [END aiplatform_modelgarden_gemma3_deploy]
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return endpoint
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if __name__ == "__main__":
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deploy()

‎model_garden/gemma/models_deploy_options_list.py

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# Copyright 2025 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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# https://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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"""Google Cloud Vertex AI sample for listing verified deploy
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options for models in Model Garden.
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"""
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import os
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from typing import List
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from google.cloud.aiplatform_v1beta1 import types
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PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")
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def list_deploy_options(model : str) -> List[types.PublisherModel.CallToAction.Deploy]:
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# [START aiplatform_modelgarden_models_deployables_options_list]
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import vertexai
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from vertexai.preview import model_garden
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# TODO(developer): Update and un-comment below lines
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# PROJECT_ID = "your-project-id"
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# model = "google/gemma3@gemma-3-1b-it"
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vertexai.init(project=PROJECT_ID, location="us-central1")
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# For Hugging Face modelsm the format is the Hugging Face model name, as in
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# "meta-llama/Llama-3.3-70B-Instruct".
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# Go to https://console.cloud.google.com/vertex-ai/model-garden to find all deployable
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# model names.
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model = model_garden.OpenModel(model)
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deploy_options = model.list_deploy_options()
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print(deploy_options)
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# Example response:
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# [
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# dedicated_resources {
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# machine_spec {
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# machine_type: "g2-standard-12"
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# accelerator_type: NVIDIA_L4
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# accelerator_count: 1
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# }
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# }
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# container_spec {
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# ...
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# }
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# ...
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# ]
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# [END aiplatform_modelgarden_models_deployables_options_list]
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return deploy_options
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if __name__ == "__main__":
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list_deploy_options("google/gemma3@gemma-3-1b-it")

‎model_garden/gemma/models_deployable_list.py

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# Copyright 2025 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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# https://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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"""Google Cloud Vertex AI sample for listing deployable models in
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Model Garden.
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"""
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import os
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from typing import List
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PROJECT_ID = os.getenv("GOOGLE_CLOUD_PROJECT")
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def list_deployable_models() -> List[str]:
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# [START aiplatform_modelgarden_models_deployables_list]
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import vertexai
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from vertexai.preview import model_garden
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# TODO(developer): Update and un-comment below lines
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# PROJECT_ID = "your-project-id"
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vertexai.init(project=PROJECT_ID, location="us-central1")
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# List deployable models, optionally list Hugging Face models only or filter by model name.
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deployable_models = model_garden.list_deployable_models(list_hf_models=False, model_filter="gemma")
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print(deployable_models)
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# Example response:
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# ['google/gemma2@gemma-2-27b','google/gemma2@gemma-2-27b-it', ...]
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# [END aiplatform_modelgarden_models_deployables_list]
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return deployable_models
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if __name__ == "__main__":
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list_deployable_models()

‎model_garden/gemma/noxfile_config.py

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# Copyright 2021 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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# Default TEST_CONFIG_OVERRIDE for python repos.
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# You can copy this file into your directory, then it will be imported from
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# the noxfile.py.
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# The source of truth:
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# https://github.com/GoogleCloudPlatform/python-docs-samples/blob/main/noxfile_config.py
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TEST_CONFIG_OVERRIDE = {
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# You can opt out from the test for specific Python versions.
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"ignored_versions": ["2.7", "3.7", "3.8", "3.10", "3.11", "3.13"],
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# Old samples are opted out of enforcing Python type hints
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# All new samples should feature them
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"enforce_type_hints": True,
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# An envvar key for determining the project id to use. Change it
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# to 'BUILD_SPECIFIC_GCLOUD_PROJECT' if you want to opt in using a
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# build specific Cloud project. You can also use your own string
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# to use your own Cloud project.
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"gcloud_project_env": "GOOGLE_CLOUD_PROJECT",
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# 'gcloud_project_env': 'BUILD_SPECIFIC_GCLOUD_PROJECT',
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# If you need to use a specific version of pip,
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# change pip_version_override to the string representation
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# of the version number, for example, "20.2.4"
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"pip_version_override": None,
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# A dictionary you want to inject into your test. Don't put any
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# secrets here. These values will override predefined values.
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"envs": {},
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}

‎model_garden/gemma/requirements-test.txt

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backoff==2.2.1
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google-api-core==2.19.0
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pytest==8.2.0
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pytest-asyncio==0.23.6

‎model_garden/gemma/requirements.txt

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google-cloud-aiplatform[all]==1.84.0

‎model_garden/gemma/test_model_garden_examples.py

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# Copyright 2025 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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# https://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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from unittest.mock import MagicMock, patch
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from google.cloud import aiplatform
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import gemma3_deploy
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import models_deploy_options_list
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import models_deployable_list
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def test_list_deployable_models() -> None:
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models = models_deployable_list.list_deployable_models()
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assert len(models) > 0
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assert "gemma" in models[0]
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def test_list_deploy_options() -> None:
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deploy_options = models_deploy_options_list.list_deploy_options(
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model="google/gemma3@gemma-3-1b-it"
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)
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assert len(deploy_options) > 0
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@patch("vertexai.preview.model_garden.OpenModel")
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def test_gemma3_deploy(mock_open_model: MagicMock) -> None:
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# Mock the deploy response.
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mock_endpoint = aiplatform.Endpoint(endpoint_name="test-endpoint-name")
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mock_open_model.return_value.deploy.return_value = mock_endpoint
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endpoint = gemma3_deploy.deploy()
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assert endpoint
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mock_open_model.assert_called_once_with("google/gemma3@gemma-3-12b-it")
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mock_open_model.return_value.deploy.assert_called_once_with(
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machine_type="g2-standard-48",
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accelerator_type="NVIDIA_L4",
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accelerator_count=4,
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accept_eula=True,
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)

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