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Class: Aws::SageMaker::Types::ProductionVariant

Inherits:
Struct
  • Object
show all
Defined in:
(unknown)

Overview

Note:

When passing ProductionVariant as input to an Aws::Client method, you can use a vanilla Hash:

{
  variant_name: "VariantName", # required
  model_name: "ModelName", # required
  initial_instance_count: 1, # required
  instance_type: "ml.t2.medium", # required, accepts ml.t2.medium, ml.t2.large, ml.t2.xlarge, ml.t2.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, ml.c4.large, ml.c4.xlarge, ml.c4.2xlarge, ml.c4.4xlarge, ml.c4.8xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge
  initial_variant_weight: 1.0,
  accelerator_type: "ml.eia1.medium", # accepts ml.eia1.medium, ml.eia1.large, ml.eia1.xlarge
}

Identifies a model that you want to host and the resources to deploy for hosting it. If you are deploying multiple models, tell Amazon SageMaker how to distribute traffic among the models by specifying variant weights.

Instance Attribute Summary collapse

Instance Attribute Details

#accelerator_typeString

The size of the Elastic Inference (EI) instance to use for the production variant. EI instances provide on-demand GPU computing for inference. For more information, see Using Elastic Inference in Amazon SageMaker. For more information, see Using Elastic Inference in Amazon SageMaker.

Returns:

  • (String)

    The size of the Elastic Inference (EI) instance to use for the production variant.

#initial_instance_countInteger

Number of instances to launch initially.

Returns:

  • (Integer)

    Number of instances to launch initially.

#initial_variant_weightFloat

Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0.

Returns:

  • (Float)

    Determines initial traffic distribution among all of the models that you specify in the endpoint configuration.

#instance_typeString

The ML compute instance type.

Possible values:

  • ml.t2.medium
  • ml.t2.large
  • ml.t2.xlarge
  • ml.t2.2xlarge
  • ml.m4.xlarge
  • ml.m4.2xlarge
  • ml.m4.4xlarge
  • ml.m4.10xlarge
  • ml.m4.16xlarge
  • ml.m5.large
  • ml.m5.xlarge
  • ml.m5.2xlarge
  • ml.m5.4xlarge
  • ml.m5.12xlarge
  • ml.m5.24xlarge
  • ml.c4.large
  • ml.c4.xlarge
  • ml.c4.2xlarge
  • ml.c4.4xlarge
  • ml.c4.8xlarge
  • ml.p2.xlarge
  • ml.p2.8xlarge
  • ml.p2.16xlarge
  • ml.p3.2xlarge
  • ml.p3.8xlarge
  • ml.p3.16xlarge
  • ml.c5.large
  • ml.c5.xlarge
  • ml.c5.2xlarge
  • ml.c5.4xlarge
  • ml.c5.9xlarge
  • ml.c5.18xlarge

Returns:

  • (String)

    The ML compute instance type.

#model_nameString

The name of the model that you want to host. This is the name that you specified when creating the model.

Returns:

  • (String)

    The name of the model that you want to host.

#variant_nameString

The name of the production variant.

Returns:

  • (String)

    The name of the production variant.