Define an inference configuration

Sat Apr 09 2022 22:03:48 GMT+0000 (Coordinated Universal Time)

Saved by @wessim

from azureml.core import Environment
from azureml.core.model import InferenceConfig

env = Environment(name="project_environment")
dummy_inference_config = InferenceConfig(
    environment=env,
    source_directory="./source_dir",
    entry_script="./echo_score.py",
)

An inference configuration describes the Docker container and files to use when initializing your web service. All of the files within your source directory, including subdirectories, will be zipped up and uploaded to the cloud when you deploy your web service. The inference configuration below specifies that the machine learning deployment will use the file echo_score.py in the ./source_dir directory to process incoming requests and that it will use the Docker image with the Python packages specified in the project_environment environment. You can use any Azure Machine Learning inference curated environments as the base Docker image when creating your project environment. We will install the required dependencies on top and store the resulting Docker image into the repository that is associated with your workspace.