The inference configuration describes how to configure the model to make
predictions. It references your scoring script (entry_script) and is
used to locate all the resources required for the deployment. Inference
configurations use Azure Machine Learning environments (see r_environment())
to define the software dependencies needed for your deployment.
inference_config( entry_script, source_directory = ".", description = NULL, environment = NULL )
| entry_script | A string of the path to the local file that contains the code to run for making predictions. |
|---|---|
| source_directory | A string of the path to the local folder
that contains the files to package and deploy alongside your model, such as
helper files for your scoring script ( |
| description | (Optional) A string of the description to give this configuration. |
| environment | An |
The InferenceConfig object.
To deploy a model, you must provide an entry script that accepts requests, scores the requests by using the model, and returns the results. The entry script is specific to your model. It must understand the format of the incoming request data, the format of the data expected by your model, and the format of the data returned to clients. If the request data is in a format that is not usable by your model, the script can transform it into an acceptable format. It can also transform the response before returning it to the client.
The entry script must contain an init() method that loads your model and
then returns a function that uses the model to make a prediction based on
the input data passed to the function. Azure ML runs the init() method
once, when the Docker container for your web service is started. The
prediction function returned by init() will be run every time the service
is invoked to make a prediction on some input data. The inputs and outputs
of this prediction function typically use JSON for serialization and
deserialization.
To locate the model in your entry script (when you load the model in the
script's init() method), use AZUREML_MODEL_DIR, an environment variable
containing the path to the model location. The environment variable is
created during service deployment, and you can use it to find the location
of your deployed model(s).
To get the path to a file in a model, combine the environment variable with the filename you're looking for. The filenames of the model files are preserved during registration and deployment.
Single model example:
model_path <- file.path(Sys.getenv("AZUREML_MODEL_DIR"), "my_model.rds")
Multiple model example:
model1_path <- file.path(Sys.getenv("AZUREML_MODEL_DIR"), "my_model/1/my_model.rds")
r_environment(), deploy_model()