InferenceConfig Class
- Inheritance
-
builtins.objectInferenceConfig
Constructor
InferenceConfig(entry_script, runtime=None, conda_file=None, extra_docker_file_steps=None, source_directory=None, enable_gpu=None, description=None, base_image=None, base_image_registry=None, cuda_version=None, environment=None)Parameters
- runtime
- str
The runtime to use for the image. Current supported runtimes are 'spark-py' and 'python'.
- conda_file
- str
The path to a local file containing a conda environment definition to use for the image.
- extra_docker_file_steps
- str
The path to a local file containing additional Docker steps to run when setting up image.
- source_directory
- str
The path to the folder that contains all files to create the image.
- enable_gpu
- bool
Indicates whether to enable GPU support in the image. The GPU image must be used on Microsoft Azure Services such as Azure Container Instances, Azure Machine Learning Compute, Azure Virtual Machines, and Azure Kubernetes Service. Defaults to False.
- base_image
- str
A custom image to be used as base image. If no base image is given then the base image will be used based off of given runtime parameter.
- base_image_registry
- ContainerRegistry
The image registry that contains the base image.
- cuda_version
- str
The Version of CUDA to install for images that need GPU support. The GPU image must be
used on Microsoft Azure Services such as Azure Container Instances, Azure Machine Learning Compute,
Azure Virtual Machines, and Azure Kubernetes Service. Supported versions are 9.0, 9.1, and 10.0.
If enable_gpu is set, this defaults to '9.1'.
- environment
- Environment
An environment object to use for the deployment. The environment doesn't have to be registered.
Provide either this parameter, or the other parameters, but not both. The individual parameters will
NOT serve as an override for the environment object. Exceptions include entry_script,
source_directory, and description.
- runtime
- str
The runtime to use for the image. Current supported runtimes are 'spark-py' and 'python'.
- conda_file
- str
The path to a local file containing a conda environment definition to use for the image.
- extra_docker_file_steps
- str
The path to a local file containing additional Docker steps to run when setting up image.
- enable_gpu
- bool
Indicates whether to enable GPU support in the image. The GPU image must be used on Microsoft Azure Services such as Azure Container Instances, Azure Machine Learning Compute, Azure Virtual Machines, and Azure Kubernetes Service. Defaults to False.
- base_image
- str
A custom image to be used as base image. If no base image is given then the base image will be used based off of given runtime parameter.
- cuda_version
- str
The Version of CUDA to install for images that need GPU support. The GPU image must be
used on Microsoft Azure Services such as Azure Container Instances, Azure Machine Learning Compute,
Azure Virtual Machines, and Azure Kubernetes Service. Supported versions are 9.0, 9.1, and 10.0.
If enable_gpu is set, this defaults to '9.1'.
- environment
- Environment
An environment object to use for the deployment. The environment doesn't have to be registered.
Provide either this parameter, or the other parameters, but not both. The individual parameters will
NOT serve as an override for the environment object. Exceptions include entry_script,
source_directory, and description.
Remarks
The following sample shows how to create an InferenceConfig object and use it to deploy a model.
from azureml.core.model import InferenceConfig
from azureml.core.webservice import AciWebservice
service_name = 'my-custom-env-service'
inference_config = InferenceConfig(entry_script='score.py', environment=environment)
aci_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)
service = Model.deploy(workspace=ws,
name=service_name,
models=[model],
inference_config=inference_config,
deployment_config=aci_config,
overwrite=True)
service.wait_for_deployment(show_output=True)
Full sample is available from https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/deployment/deploy-to-cloud/model-register-and-deploy.ipynb
Variables
- entry_script
- str
The path to a local file that contains the code to run for the image.
- runtime
- str
The runtime to use for the image. Current supported runtimes are 'spark-py' and 'python'.
- conda_file
- str
The path to a local file containing a conda environment definition to use for the image.
- extra_docker_file_steps
- str
The path to a local file containing additional Docker steps to run when setting up the image.
- source_directory
- str
The path to the folder that contains all files to create the image.
- enable_gpu
- bool
Indicates whether to enable GPU support in the image. The GPU image must be used on Microsoft Azure Services such as Azure Container Instances, Azure Machine Learning Compute, Azure Virtual Machines, and Azure Kubernetes Service.
- azureml.core.model.InferenceConfig.description
A description to give this image.
- base_image
- str
A custom image to be used as base image. If no base image is given then the base image will be used based off of given runtime parameter.
- base_image_registry
- ContainerRegistry
The image registry that contains the base image.
- cuda_version
- str
The version of CUDA to install for images that need GPU support. The GPU image must be
used on Microsoft Azure Services such as Azure Container Instances, Azure Machine Learning Compute,
Azure Virtual Machines, and Azure Kubernetes Service. Supported versions are 9.0, 9.1, and 10.0.
If enable_gpu is set, this defaults to '9.1'.
- azureml.core.model.InferenceConfig.environment
An environment object to use for the deployment. The environment doesn't have to be registered.
Provide either this parameter, or the other parameters, but not both. The individual parameters will
NOT serve as an override for the environment object. Exceptions include entry_script,
source_directory, and description.
Methods
| build_create_payload |
Build the creation payload for the Container image. |
| build_profile_payload |
Build the profiling payload for the Model package. |
| validate_configuration |
Check that the specified configuration values are valid. Raises a WebserviceException if validation fails. |
| validation_script_content |
Check that the syntax of score script is valid with ast.parse. Raises a UserErrorException if validation fails. |
build_create_payload
Build the creation payload for the Container image.
build_create_payload(workspace, name, model_ids)Parameters
Returns
The container image creation payload.
Return type
Exceptions
build_profile_payload
Build the profiling payload for the Model package.
build_profile_payload(profile_name, input_data=None, workspace=None, models=None, dataset_id=None, container_resource_requirements=None, description=None)Parameters
- dataset_id
- str
Id associated with the dataset containing input data for the profiling run.
- container_resource_requirements
- ContainerResourceRequirements
container resource requirements for the largest instance to which the model is to be deployed
Returns
Model profile payload
Return type
Exceptions
validate_configuration
Check that the specified configuration values are valid.
Raises a WebserviceException if validation fails.
validate_configuration()Exceptions
validation_script_content
Check that the syntax of score script is valid with ast.parse.
Raises a UserErrorException if validation fails.
validation_script_content()Exceptions
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