ModelBatchDeploymentSettings Class

Note

This is an experimental class, and may change at any time. Please see https://aka.ms/azuremlexperimental for more information.

Model Batch Deployment Settings entity.

Inheritance
builtins.object
ModelBatchDeploymentSettings

Constructor

ModelBatchDeploymentSettings(*, mini_batch_size: int | None, instance_count: int | None = None, max_concurrency_per_instance: int | None = None, output_action: BatchDeploymentOutputAction | None = None, output_file_name: str | None = None, retry_settings: BatchRetrySettings | None = None, environment_variables: Dict[str, str] | None = None, error_threshold: int | None = None, logging_level: str | None = None, **kwargs: Any)

Parameters

Name Description
mini_batch_size
Required
int

Size of the mini-batch passed to each batch invocation, defaults to 10

instance_count
Required
int

Number of instances the interfering will run on. Equivalent to resources.instance_count.

output_action
Required

Indicates how the output will be organized. Possible values include: "summary_only", "append_row". Defaults to "append_row"

output_file_name
Required
str

Customized output file name for append_row output action, defaults to "predictions.csv"

max_concurrency_per_instance
Required
int

Indicates maximum number of parallelism per instance, defaults to 1

retry_settings
Required

Retry settings for a batch inference operation, defaults to None

environment_variables
Required

Environment variables that will be set in deployment.

error_threshold
Required
int

Error threshold, if the error count for the entire input goes above this value, the batch inference will be aborted. Range is [-1, int.MaxValue] -1 value indicates, ignore all failures during batch inference For FileDataset count of file failures For TabularDataset, this is the count of record failures, defaults to -1

logging_level
Required
str

Logging level for batch inference operation, defaults to "info"

Examples

Creating a Model Batch Deployment Settings object.


   from azure.ai.ml.entities._deployment.model_batch_deployment_settings import ModelBatchDeploymentSettings

   modelBatchDeploymentSetting = ModelBatchDeploymentSettings(
       mini_batch_size=256,
       instance_count=5,
       max_concurrency_per_instance=2,
       output_file_name="output-file-name",
       environment_variables={"env1": "value1", "env2": "value2"},
       error_threshold=2,
       logging_level=1,
   )