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@IntegerMan
Created July 19, 2022 03:19
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from azureml.core import Environment
from azureml.core.model import InferenceConfig
# Load the environment from the YAML file downloaded from the best run
env = Environment.from_conda_specification("AutoML-env", "automl-output/outputs/conda_env_v_1_0_0.yml")
# Create an inference config pointing at the files we downloaded. This configuration tells Azure how to handle requests
inference_config = InferenceConfig(environment=env,
source_directory='./automl-output/outputs',
entry_script='./scoring_file_v_2_0_0.py')
# The deployment configuration configures how the endpoint is hosted
deployment_config = AciWebservice.deploy_configuration(
cpu_cores = 1,
memory_gb = 1,
enable_app_insights=True,
auth_enabled=False)
# Deploy the model
service = Model.deploy(ws, "endpoint-name", [automl_model], inference_config, deployment_config, overwrite=True)
service.wait_for_deployment(show_output = True)
# Grab our scoring endpoint for testing
print('Endpoint active at ' + service.scoring_uri)
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