Class: Aws::SageMaker::Types::AlgorithmSpecification
- Inherits:
-
Struct
- Object
- Struct
- Aws::SageMaker::Types::AlgorithmSpecification
- Defined in:
- gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb
Overview
Specifies the training algorithm to use in a CreateTrainingJob request.
For more information about algorithms provided by SageMaker, see Algorithms. For information about using your own algorithms, see Using Your Own Algorithms with Amazon SageMaker.
Constant Summary collapse
- SENSITIVE =
[]
Instance Attribute Summary collapse
-
#algorithm_name ⇒ String
The name of the algorithm resource to use for the training job.
-
#container_arguments ⇒ Array<String>
The arguments for a container used to run a training job.
-
#container_entrypoint ⇒ Array<String>
The [entrypoint script for a Docker container][1] used to run a training job.
-
#enable_sage_maker_metrics_time_series ⇒ Boolean
To generate and save time-series metrics during training, set to
true
. -
#metric_definitions ⇒ Array<Types::MetricDefinition>
A list of metric definition objects.
-
#training_image ⇒ String
The registry path of the Docker image that contains the training algorithm.
-
#training_image_config ⇒ Types::TrainingImageConfig
The configuration to use an image from a private Docker registry for a training job.
-
#training_input_mode ⇒ String
The training input mode that the algorithm supports.
Instance Attribute Details
#algorithm_name ⇒ String
The name of the algorithm resource to use for the training job. This must be an algorithm resource that you created or subscribe to on Amazon Web Services Marketplace.
AlgorithmName
parameter or the image URI of the algorithm container to the
TrainingImage
parameter.
Note that the AlgorithmName
parameter is mutually exclusive with
the TrainingImage
parameter. If you specify a value for the
AlgorithmName
parameter, you can't specify a value for
TrainingImage
, and vice versa.
If you specify values for both parameters, the training job might
break; if you don't specify any value for both parameters, the
training job might raise a null
error.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 473 class AlgorithmSpecification < Struct.new( :training_image, :algorithm_name, :training_input_mode, :metric_definitions, :enable_sage_maker_metrics_time_series, :container_entrypoint, :container_arguments, :training_image_config) SENSITIVE = [] include Aws::Structure end |
#container_arguments ⇒ Array<String>
The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 473 class AlgorithmSpecification < Struct.new( :training_image, :algorithm_name, :training_input_mode, :metric_definitions, :enable_sage_maker_metrics_time_series, :container_entrypoint, :container_arguments, :training_image_config) SENSITIVE = [] include Aws::Structure end |
#container_entrypoint ⇒ Array<String>
The entrypoint script for a Docker container used to run a training job. This script takes precedence over the default train processing instructions. See How Amazon SageMaker Runs Your Training Image for more information.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 473 class AlgorithmSpecification < Struct.new( :training_image, :algorithm_name, :training_input_mode, :metric_definitions, :enable_sage_maker_metrics_time_series, :container_entrypoint, :container_arguments, :training_image_config) SENSITIVE = [] include Aws::Structure end |
#enable_sage_maker_metrics_time_series ⇒ Boolean
To generate and save time-series metrics during training, set to
true
. The default is false
and time-series metrics aren't
generated except in the following cases:
You use one of the SageMaker built-in algorithms
You use one of the following Prebuilt SageMaker Docker Images:
Tensorflow (version >= 1.15)
MXNet (version >= 1.6)
PyTorch (version >= 1.3)
You specify at least one MetricDefinition
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 473 class AlgorithmSpecification < Struct.new( :training_image, :algorithm_name, :training_input_mode, :metric_definitions, :enable_sage_maker_metrics_time_series, :container_entrypoint, :container_arguments, :training_image_config) SENSITIVE = [] include Aws::Structure end |
#metric_definitions ⇒ Array<Types::MetricDefinition>
A list of metric definition objects. Each object specifies the metric name and regular expressions used to parse algorithm logs. SageMaker publishes each metric to Amazon CloudWatch.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 473 class AlgorithmSpecification < Struct.new( :training_image, :algorithm_name, :training_input_mode, :metric_definitions, :enable_sage_maker_metrics_time_series, :container_entrypoint, :container_arguments, :training_image_config) SENSITIVE = [] include Aws::Structure end |
#training_image ⇒ String
The registry path of the Docker image that contains the training
algorithm. For information about docker registry paths for SageMaker
built-in algorithms, see Docker Registry Paths and Example Code
in the Amazon SageMaker developer guide. SageMaker supports both
registry/repository[:tag]
and registry/repository[@digest]
image
path formats. For more information about using your custom training
container, see Using Your Own Algorithms with Amazon SageMaker.
AlgorithmName
parameter or the image URI of the algorithm container to the
TrainingImage
parameter.
For more information, see the note in the AlgorithmName
parameter
description.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 473 class AlgorithmSpecification < Struct.new( :training_image, :algorithm_name, :training_input_mode, :metric_definitions, :enable_sage_maker_metrics_time_series, :container_entrypoint, :container_arguments, :training_image_config) SENSITIVE = [] include Aws::Structure end |
#training_image_config ⇒ Types::TrainingImageConfig
The configuration to use an image from a private Docker registry for a training job.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 473 class AlgorithmSpecification < Struct.new( :training_image, :algorithm_name, :training_input_mode, :metric_definitions, :enable_sage_maker_metrics_time_series, :container_entrypoint, :container_arguments, :training_image_config) SENSITIVE = [] include Aws::Structure end |
#training_input_mode ⇒ String
The training input mode that the algorithm supports. For more information about input modes, see Algorithms.
Pipe mode
If an algorithm supports Pipe
mode, Amazon SageMaker streams data
directly from Amazon S3 to the container.
File mode
If an algorithm supports File
mode, SageMaker downloads the
training data from S3 to the provisioned ML storage volume, and
mounts the directory to the Docker volume for the training
container.
You must provision the ML storage volume with sufficient capacity to accommodate the data downloaded from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container uses the ML storage volume to also store intermediate information, if any.
For distributed algorithms, training data is distributed uniformly. Your training duration is predictable if the input data objects sizes are approximately the same. SageMaker does not split the files any further for model training. If the object sizes are skewed, training won't be optimal as the data distribution is also skewed when one host in a training cluster is overloaded, thus becoming a bottleneck in training.
FastFile mode
If an algorithm supports FastFile
mode, SageMaker streams data
directly from S3 to the container with no code changes, and provides
file system access to the data. Users can author their training
script to interact with these files as if they were stored on disk.
FastFile
mode works best when the data is read sequentially.
Augmented manifest files aren't supported. The startup time is
lower when there are fewer files in the S3 bucket provided.
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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 473 class AlgorithmSpecification < Struct.new( :training_image, :algorithm_name, :training_input_mode, :metric_definitions, :enable_sage_maker_metrics_time_series, :container_entrypoint, :container_arguments, :training_image_config) SENSITIVE = [] include Aws::Structure end |