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feat: Added tabular forecasting sample (#156)
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Co-authored-by: Ivan Cheung <ivanmkc@google.com>
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ivanmkc and Ivan Cheung committed Jan 6, 2021
1 parent e2577fe commit a23857b
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def make_parent(parent: str) -> str:
parent = parent

return parent


def make_batch_prediction_job(
display_name: str,
model_name: str,
gcs_source_uri: str,
gcs_destination_output_uri_prefix: str,
predictions_format: str,
) -> google.cloud.aiplatform_v1beta1.types.batch_prediction_job.BatchPredictionJob:
batch_prediction_job = {
"display_name": display_name,
# Format: 'projects/{project}/locations/{location}/models/{model_id}'
"model": model_name,
"input_config": {
"instances_format": predictions_format,
"gcs_source": {"uris": [gcs_source_uri]},
},
"output_config": {
"predictions_format": predictions_format,
"gcs_destination": {"output_uri_prefix": gcs_destination_output_uri_prefix},
},
}
return batch_prediction_job

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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_batch_prediction_job_tabular_forecasting_sample]
from google.cloud import aiplatform


def create_batch_prediction_job_tabular_forecasting_sample(
project: str,
display_name: str,
model_name: str,
gcs_source_uri: str,
gcs_destination_output_uri_prefix: str,
predictions_format: 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.JobServiceClient(client_options=client_options)
batch_prediction_job = {
"display_name": display_name,
# Format: 'projects/{project}/locations/{location}/models/{model_id}'
"model": model_name,
"input_config": {
"instances_format": predictions_format,
"gcs_source": {"uris": [gcs_source_uri]},
},
"output_config": {
"predictions_format": predictions_format,
"gcs_destination": {"output_uri_prefix": gcs_destination_output_uri_prefix},
},
}
parent = f"projects/{project}/locations/{location}"
response = client.create_batch_prediction_job(
parent=parent, batch_prediction_job=batch_prediction_job
)
print("response:", response)


# [END aiplatform_create_batch_prediction_job_tabular_forecasting_sample]
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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.

import os
from uuid import uuid4

import pytest

import create_batch_prediction_job_tabular_forecasting_sample
import helpers

PROJECT_ID = os.getenv("BUILD_SPECIFIC_GCLOUD_PROJECT")
LOCATION = "us-central1"
MODEL_ID = "8531330622239539200" # Permanent restaurant rating model
DISPLAY_NAME = f"temp_create_batch_prediction_tabular_forecasting_test_{uuid4()}"
GCS_SOURCE_URI = "gs://cloud-samples-data/ai-platform/covid/bigquery-public-covid-nyt-us-counties-train.csv"
GCS_OUTPUT_URI = "gs://ucaip-samples-test-output/"
PREDICTIONS_FORMAT = "csv"


@pytest.fixture(scope="function", autouse=True)
def teardown(teardown_batch_prediction_job):
yield


# Creating AutoML Tabular Forecasting Classification batch prediction job
def test_create_batch_prediction_job_tabular_forecasting_sample(capsys, shared_state):

model_name = f"projects/{PROJECT_ID}/locations/{LOCATION}/models/{MODEL_ID}"

create_batch_prediction_job_tabular_forecasting_sample.create_batch_prediction_job_tabular_forecasting_sample(
project=PROJECT_ID,
display_name=DISPLAY_NAME,
model_name=model_name,
gcs_source_uri=GCS_SOURCE_URI,
gcs_destination_output_uri_prefix=GCS_OUTPUT_URI,
predictions_format=PREDICTIONS_FORMAT,
)

out, _ = capsys.readouterr()

# Save resource name of the newly created batch prediction job
shared_state["batch_prediction_job_name"] = helpers.get_name(out)

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