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create_training_pipeline_text_sentiment_analysis_sample.py
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create_training_pipeline_text_sentiment_analysis_sample.py
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# Copyright 2020 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.
# [START aiplatform_create_training_pipeline_text_sentiment_analysis_sample]
from google.cloud import aiplatform
from google.cloud.aiplatform.schema import trainingjob
def create_training_pipeline_text_sentiment_analysis_sample(
project: str,
display_name: str,
dataset_id: str,
model_display_name: str,
location: str = "us-central1",
api_endpoint: str = "us-central1-aiplatform.googleapis.com",
):
# The AI Platform services require regional API endpoints.
client_options = {"api_endpoint": api_endpoint}
# Initialize client that will be used to create and send requests.
# This client only needs to be created once, and can be reused for multiple requests.
client = aiplatform.gapic.PipelineServiceClient(client_options=client_options)
# Use sentiment_max of 4
training_task_inputs_object = trainingjob.definition.AutoMlTextSentimentInputs(
sentiment_max=4
)
training_task_inputs = training_task_inputs_object.to_value()
training_pipeline = {
"display_name": display_name,
"training_task_definition": "gs://google-cloud-aiplatform/schema/trainingjob/definition/automl_text_sentiment_1.0.0.yaml",
"training_task_inputs": training_task_inputs,
"input_data_config": {"dataset_id": dataset_id},
"model_to_upload": {"display_name": model_display_name},
}
parent = f"projects/{project}/locations/{location}"
response = client.create_training_pipeline(
parent=parent, training_pipeline=training_pipeline
)
print("response:", response)
# [END aiplatform_create_training_pipeline_text_sentiment_analysis_sample]