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feat: Added create_training_pipeline_custom_package_job_sample and cr…
…eate_training_pipeline_custom_container_job_sample and reworked create_training_pipeline_custom_job_sample (#351) * Added create_training_pipeline_custom_container_job_sample * Added create_training_pipeline_custom_package_job_sample * Ran linter * Added managed dataset option to create_training_pipeline_custom_job_sample * Added managed dataset id option to samples/model-builder/create_training_pipeline_custom_container_job_sample.py * Added managed dataset support for samples/model-builder/create_training_pipeline_custom_package_job_sample.py * Removed unneeded samples/model-builder/create_training_pipeline_custom_training_managed_dataset_sample.py * Small fix * Fixed mocks
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samples/model-builder/create_training_pipeline_custom_package_job_sample.py
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# 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. | ||
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from typing import List, Optional, Union | ||
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from google.cloud import aiplatform | ||
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# [START aiplatform_sdk_create_training_pipeline_custom_package_job_sample] | ||
def create_training_pipeline_custom_package_job_sample( | ||
project: str, | ||
location: str, | ||
staging_bucket: str, | ||
display_name: str, | ||
python_package_gcs_uri: str, | ||
python_module_name: str, | ||
container_uri: str, | ||
model_serving_container_image_uri: str, | ||
dataset_id: Optional[str] = None, | ||
model_display_name: Optional[str] = None, | ||
args: Optional[List[Union[str, float, int]]] = None, | ||
replica_count: int = 1, | ||
machine_type: str = "n1-standard-4", | ||
accelerator_type: str = "ACCELERATOR_TYPE_UNSPECIFIED", | ||
accelerator_count: int = 0, | ||
training_fraction_split: float = 0.8, | ||
validation_fraction_split: float = 0.1, | ||
test_fraction_split: float = 0.1, | ||
sync: bool = True, | ||
): | ||
aiplatform.init(project=project, location=location, staging_bucket=staging_bucket) | ||
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job = aiplatform.CustomPythonPackageTrainingJob( | ||
display_name=display_name, | ||
python_package_gcs_uri=python_package_gcs_uri, | ||
python_module_name=python_module_name, | ||
container_uri=container_uri, | ||
model_serving_container_image_uri=model_serving_container_image_uri, | ||
) | ||
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# This example uses an ImageDataset, but you can use another type | ||
dataset = aiplatform.ImageDataset(dataset_id) if dataset_id else None | ||
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model = job.run( | ||
dataset=dataset, | ||
model_display_name=model_display_name, | ||
args=args, | ||
replica_count=replica_count, | ||
machine_type=machine_type, | ||
accelerator_type=accelerator_type, | ||
accelerator_count=accelerator_count, | ||
training_fraction_split=training_fraction_split, | ||
validation_fraction_split=validation_fraction_split, | ||
test_fraction_split=test_fraction_split, | ||
sync=sync, | ||
) | ||
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model.wait() | ||
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print(model.display_name) | ||
print(model.resource_name) | ||
print(model.uri) | ||
return model | ||
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# [END aiplatform_sdk_create_training_pipeline_custom_package_job_sample] |
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