Copyright | (c) 2013-2023 Brendan Hay |
---|---|
License | Mozilla Public License, v. 2.0. |
Maintainer | Brendan Hay |
Stability | auto-generated |
Portability | non-portable (GHC extensions) |
Safe Haskell | Safe-Inferred |
Language | Haskell2010 |
Synopsis
- data AlgorithmSpecification = AlgorithmSpecification' {}
- newAlgorithmSpecification :: TrainingInputMode -> AlgorithmSpecification
- algorithmSpecification_algorithmName :: Lens' AlgorithmSpecification (Maybe Text)
- algorithmSpecification_containerArguments :: Lens' AlgorithmSpecification (Maybe (NonEmpty Text))
- algorithmSpecification_containerEntrypoint :: Lens' AlgorithmSpecification (Maybe (NonEmpty Text))
- algorithmSpecification_enableSageMakerMetricsTimeSeries :: Lens' AlgorithmSpecification (Maybe Bool)
- algorithmSpecification_metricDefinitions :: Lens' AlgorithmSpecification (Maybe [MetricDefinition])
- algorithmSpecification_trainingImage :: Lens' AlgorithmSpecification (Maybe Text)
- algorithmSpecification_trainingInputMode :: Lens' AlgorithmSpecification TrainingInputMode
Documentation
data AlgorithmSpecification Source #
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.
See: newAlgorithmSpecification
smart constructor.
AlgorithmSpecification' | |
|
Instances
newAlgorithmSpecification Source #
Create a value of AlgorithmSpecification
with all optional fields omitted.
Use generic-lens or optics to modify other optional fields.
The following record fields are available, with the corresponding lenses provided for backwards compatibility:
$sel:algorithmName:AlgorithmSpecification'
, algorithmSpecification_algorithmName
- 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.
You must specify either the algorithm name to the 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.
$sel:containerArguments:AlgorithmSpecification'
, algorithmSpecification_containerArguments
- The arguments for a container used to run a training job. See
How Amazon SageMaker Runs Your Training Image
for additional information.
$sel:containerEntrypoint:AlgorithmSpecification'
, algorithmSpecification_containerEntrypoint
- 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.
$sel:enableSageMakerMetricsTimeSeries:AlgorithmSpecification'
, algorithmSpecification_enableSageMakerMetricsTimeSeries
- 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
$sel:metricDefinitions:AlgorithmSpecification'
, algorithmSpecification_metricDefinitions
- 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.
$sel:trainingImage:AlgorithmSpecification'
, algorithmSpecification_trainingImage
- 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.
You must specify either the algorithm name to the 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.
$sel:trainingInputMode:AlgorithmSpecification'
, algorithmSpecification_trainingInputMode
- Undocumented member.
algorithmSpecification_algorithmName :: Lens' AlgorithmSpecification (Maybe Text) Source #
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.
You must specify either the algorithm name to the 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.
algorithmSpecification_containerArguments :: Lens' AlgorithmSpecification (Maybe (NonEmpty Text)) Source #
The arguments for a container used to run a training job. See How Amazon SageMaker Runs Your Training Image for additional information.
algorithmSpecification_containerEntrypoint :: Lens' AlgorithmSpecification (Maybe (NonEmpty Text)) Source #
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.
algorithmSpecification_enableSageMakerMetricsTimeSeries :: Lens' AlgorithmSpecification (Maybe Bool) Source #
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
algorithmSpecification_metricDefinitions :: Lens' AlgorithmSpecification (Maybe [MetricDefinition]) Source #
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.
algorithmSpecification_trainingImage :: Lens' AlgorithmSpecification (Maybe Text) Source #
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.
You must specify either the algorithm name to the 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.
algorithmSpecification_trainingInputMode :: Lens' AlgorithmSpecification TrainingInputMode Source #
Undocumented member.