@@ -2777,7 +2777,7 @@ def endpoint_from_job(
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def endpoint_from_model_data (
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self ,
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model_s3_location ,
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- deployment_image_uri ,
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+ image_uri ,
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initial_instance_count ,
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instance_type ,
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name = None ,
@@ -2792,7 +2792,7 @@ def endpoint_from_model_data(
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Args:
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model_s3_location (str): S3 URI of the model artifacts to use for the endpoint.
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- deployment_image_uri (str): The Docker image URI which defines the runtime code to be
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+ image_uri (str): The Docker image URI which defines the runtime code to be
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used as the entry point for accepting prediction requests.
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initial_instance_count (int): Minimum number of EC2 instances to launch. The actual
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number of active instances for an endpoint at any given time varies due to
@@ -2824,7 +2824,7 @@ def endpoint_from_model_data(
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"""
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model_environment_vars = model_environment_vars or {}
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- name = name or name_from_image (deployment_image_uri )
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+ name = name or name_from_image (image_uri )
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model_vpc_config = vpc_utils .sanitize (model_vpc_config )
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if _deployment_entity_exists (
@@ -2838,7 +2838,7 @@ def endpoint_from_model_data(
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lambda : self .sagemaker_client .describe_model (ModelName = name )
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):
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primary_container = container_def (
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- image_uri = deployment_image_uri ,
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+ image_uri = image_uri ,
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model_data_url = model_s3_location ,
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env = model_environment_vars ,
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)
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