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Class: Aws::SageMaker::Types::TrainingSpecification
- Inherits:
-
Struct
- Object
- Struct
- Aws::SageMaker::Types::TrainingSpecification
- Defined in:
- (unknown)
Overview
When passing TrainingSpecification as input to an Aws::Client method, you can use a vanilla Hash:
{
training_image: "ContainerImage", # required
training_image_digest: "ImageDigest",
supported_hyper_parameters: [
{
name: "ParameterName", # required
description: "EntityDescription",
type: "Integer", # required, accepts Integer, Continuous, Categorical, FreeText
range: {
integer_parameter_range_specification: {
min_value: "ParameterValue", # required
max_value: "ParameterValue", # required
},
continuous_parameter_range_specification: {
min_value: "ParameterValue", # required
max_value: "ParameterValue", # required
},
categorical_parameter_range_specification: {
values: ["ParameterValue"], # required
},
},
is_tunable: false,
is_required: false,
default_value: "HyperParameterValue",
},
],
supported_training_instance_types: ["ml.m4.xlarge"], # required, accepts ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.12xlarge, ml.m5.24xlarge, 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.p3dn.24xlarge, ml.p4d.24xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge
supports_distributed_training: false,
metric_definitions: [
{
name: "MetricName", # required
regex: "MetricRegex", # required
},
],
training_channels: [ # required
{
name: "ChannelName", # required
description: "EntityDescription",
is_required: false,
supported_content_types: ["ContentType"], # required
supported_compression_types: ["None"], # accepts None, Gzip
supported_input_modes: ["Pipe"], # required, accepts Pipe, File
},
],
supported_tuning_job_objective_metrics: [
{
type: "Maximize", # required, accepts Maximize, Minimize
metric_name: "MetricName", # required
},
],
}
Defines how the algorithm is used for a training job.
Returned by:
Instance Attribute Summary collapse
-
#metric_definitions ⇒ Array<Types::MetricDefinition>
A list of
MetricDefinition
objects, which are used for parsing metrics generated by the algorithm. -
#supported_hyper_parameters ⇒ Array<Types::HyperParameterSpecification>
A list of the
HyperParameterSpecification
objects, that define the supported hyperparameters. -
#supported_training_instance_types ⇒ Array<String>
A list of the instance types that this algorithm can use for training.
-
#supported_tuning_job_objective_metrics ⇒ Array<Types::HyperParameterTuningJobObjective>
A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.
-
#supports_distributed_training ⇒ Boolean
Indicates whether the algorithm supports distributed training.
-
#training_channels ⇒ Array<Types::ChannelSpecification>
A list of
ChannelSpecification
objects, which specify the input sources to be used by the algorithm. -
#training_image ⇒ String
The Amazon ECR registry path of the Docker image that contains the training algorithm.
-
#training_image_digest ⇒ String
An MD5 hash of the training algorithm that identifies the Docker image used for training.
Instance Attribute Details
#metric_definitions ⇒ Array<Types::MetricDefinition>
A list of MetricDefinition
objects, which are used for parsing metrics
generated by the algorithm.
#supported_hyper_parameters ⇒ Array<Types::HyperParameterSpecification>
A list of the HyperParameterSpecification
objects, that define the
supported hyperparameters. This is required if the algorithm supports
automatic model tuning.>
#supported_training_instance_types ⇒ Array<String>
A list of the instance types that this algorithm can use for training.
#supported_tuning_job_objective_metrics ⇒ Array<Types::HyperParameterTuningJobObjective>
A list of the metrics that the algorithm emits that can be used as the objective metric in a hyperparameter tuning job.
#supports_distributed_training ⇒ Boolean
Indicates whether the algorithm supports distributed training. If set to false, buyers can\'t request more than one instance during training.
#training_channels ⇒ Array<Types::ChannelSpecification>
A list of ChannelSpecification
objects, which specify the input
sources to be used by the algorithm.
#training_image ⇒ String
The Amazon ECR registry path of the Docker image that contains the training algorithm.
#training_image_digest ⇒ String
An MD5 hash of the training algorithm that identifies the Docker image used for training.