pipelines/components/gcp/automl/export_model_to_gcs/component.yaml

104 lines
5.3 KiB
YAML

name: Automl export model to gcs
description: |-
Exports a trained model to a user specified Google Cloud Storage location.
Args:
model_path: The resource name of the model to export. Format: 'projects/<project>/locations/<location>/models/<model>'
gcs_output_uri_prefix: The Google Cloud Storage directory where the model should be written to. Must be in the same location as AutoML. Required location: us-central1.
model_format: The format in which the model must be exported. The available, and default, formats depend on the problem and model type. Possible formats: tf_saved_model, tf_js, tflite, core_ml, edgetpu_tflite. See https://cloud.google.com/automl/docs/reference/rest/v1/projects.locations.models/export?hl=en#modelexportoutputconfig
Annotations:
author: Alexey Volkov <alexey.volkov@ark-kun.com>
inputs:
- {name: model_path, type: String}
- {name: gcs_output_uri_prefix, type: String}
- {name: model_format, type: String, default: tf_saved_model, optional: true}
outputs:
- {name: model_directory, type: Uri}
implementation:
container:
image: python:3.8
command:
- sh
- -c
- (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location
'google-cloud-automl==2.0.0' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip
install --quiet --no-warn-script-location 'google-cloud-automl==2.0.0' --user)
&& "$0" "$@"
- python3
- -u
- -c
- |
def automl_export_model_to_gcs(
model_path,
gcs_output_uri_prefix,
model_format = 'tf_saved_model',
):
"""Exports a trained model to a user specified Google Cloud Storage location.
Args:
model_path: The resource name of the model to export. Format: 'projects/<project>/locations/<location>/models/<model>'
gcs_output_uri_prefix: The Google Cloud Storage directory where the model should be written to. Must be in the same location as AutoML. Required location: us-central1.
model_format: The format in which the model must be exported. The available, and default, formats depend on the problem and model type. Possible formats: tf_saved_model, tf_js, tflite, core_ml, edgetpu_tflite. See https://cloud.google.com/automl/docs/reference/rest/v1/projects.locations.models/export?hl=en#modelexportoutputconfig
Annotations:
author: Alexey Volkov <alexey.volkov@ark-kun.com>
"""
from google.cloud import automl
client = automl.AutoMlClient()
response = client.export_model(
name=model_path,
output_config=automl.ModelExportOutputConfig(
model_format=model_format,
gcs_destination=automl.GcsDestination(
output_uri_prefix=gcs_output_uri_prefix,
),
),
)
print('Operation started:')
print(response.operation)
result = response.result()
metadata = response.metadata
print('Operation finished:')
print(metadata)
return (metadata.export_model_details.output_info.gcs_output_directory, )
import argparse
_parser = argparse.ArgumentParser(prog='Automl export model to gcs', description="Exports a trained model to a user specified Google Cloud Storage location.\n\n Args:\n model_path: The resource name of the model to export. Format: 'projects/<project>/locations/<location>/models/<model>'\n gcs_output_uri_prefix: The Google Cloud Storage directory where the model should be written to. Must be in the same location as AutoML. Required location: us-central1.\n model_format: The format in which the model must be exported. The available, and default, formats depend on the problem and model type. Possible formats: tf_saved_model, tf_js, tflite, core_ml, edgetpu_tflite. See https://cloud.google.com/automl/docs/reference/rest/v1/projects.locations.models/export?hl=en#modelexportoutputconfig\n\n Annotations:\n author: Alexey Volkov <alexey.volkov@ark-kun.com>")
_parser.add_argument("--model-path", dest="model_path", type=str, required=True, default=argparse.SUPPRESS)
_parser.add_argument("--gcs-output-uri-prefix", dest="gcs_output_uri_prefix", type=str, required=True, default=argparse.SUPPRESS)
_parser.add_argument("--model-format", dest="model_format", type=str, required=False, default=argparse.SUPPRESS)
_parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1)
_parsed_args = vars(_parser.parse_args())
_output_files = _parsed_args.pop("_output_paths", [])
_outputs = automl_export_model_to_gcs(**_parsed_args)
_output_serializers = [
str,
]
import os
for idx, output_file in enumerate(_output_files):
try:
os.makedirs(os.path.dirname(output_file))
except OSError:
pass
with open(output_file, 'w') as f:
f.write(_output_serializers[idx](_outputs[idx]))
args:
- --model-path
- {inputValue: model_path}
- --gcs-output-uri-prefix
- {inputValue: gcs_output_uri_prefix}
- if:
cond: {isPresent: model_format}
then:
- --model-format
- {inputValue: model_format}
- '----output-paths'
- {outputPath: model_directory}