Copyright | (c) 2013-2021 Brendan Hay |
---|---|
License | Mozilla Public License, v. 2.0. |
Maintainer | Brendan Hay <brendan.g.hay+amazonka@gmail.com> |
Stability | auto-generated |
Portability | non-portable (GHC extensions) |
Safe Haskell | None |
Create a machine learning algorithm that you can use in Amazon SageMaker and list in the Amazon Web Services Marketplace.
Synopsis
- data CreateAlgorithm = CreateAlgorithm' {}
- newCreateAlgorithm :: Text -> TrainingSpecification -> CreateAlgorithm
- createAlgorithm_validationSpecification :: Lens' CreateAlgorithm (Maybe AlgorithmValidationSpecification)
- createAlgorithm_inferenceSpecification :: Lens' CreateAlgorithm (Maybe InferenceSpecification)
- createAlgorithm_algorithmDescription :: Lens' CreateAlgorithm (Maybe Text)
- createAlgorithm_certifyForMarketplace :: Lens' CreateAlgorithm (Maybe Bool)
- createAlgorithm_tags :: Lens' CreateAlgorithm (Maybe [Tag])
- createAlgorithm_algorithmName :: Lens' CreateAlgorithm Text
- createAlgorithm_trainingSpecification :: Lens' CreateAlgorithm TrainingSpecification
- data CreateAlgorithmResponse = CreateAlgorithmResponse' {
- httpStatus :: Int
- algorithmArn :: Text
- newCreateAlgorithmResponse :: Int -> Text -> CreateAlgorithmResponse
- createAlgorithmResponse_httpStatus :: Lens' CreateAlgorithmResponse Int
- createAlgorithmResponse_algorithmArn :: Lens' CreateAlgorithmResponse Text
Creating a Request
data CreateAlgorithm Source #
See: newCreateAlgorithm
smart constructor.
CreateAlgorithm' | |
|
Instances
Create a value of CreateAlgorithm
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:validationSpecification:CreateAlgorithm'
, createAlgorithm_validationSpecification
- Specifies configurations for one or more training jobs and that Amazon
SageMaker runs to test the algorithm's training code and, optionally,
one or more batch transform jobs that Amazon SageMaker runs to test the
algorithm's inference code.
$sel:inferenceSpecification:CreateAlgorithm'
, createAlgorithm_inferenceSpecification
- Specifies details about inference jobs that the algorithm runs,
including the following:
- The Amazon ECR paths of containers that contain the inference code and model artifacts.
- The instance types that the algorithm supports for transform jobs and real-time endpoints used for inference.
- The input and output content formats that the algorithm supports for inference.
$sel:algorithmDescription:CreateAlgorithm'
, createAlgorithm_algorithmDescription
- A description of the algorithm.
$sel:certifyForMarketplace:CreateAlgorithm'
, createAlgorithm_certifyForMarketplace
- Whether to certify the algorithm so that it can be listed in Amazon Web
Services Marketplace.
$sel:tags:CreateAlgorithm'
, createAlgorithm_tags
- An array of key-value pairs. You can use tags to categorize your Amazon
Web Services resources in different ways, for example, by purpose,
owner, or environment. For more information, see
Tagging Amazon Web Services Resources.
$sel:algorithmName:CreateAlgorithm'
, createAlgorithm_algorithmName
- The name of the algorithm.
$sel:trainingSpecification:CreateAlgorithm'
, createAlgorithm_trainingSpecification
- Specifies details about training jobs run by this algorithm, including
the following:
- The Amazon ECR path of the container and the version digest of the algorithm.
- The hyperparameters that the algorithm supports.
- The instance types that the algorithm supports for training.
- Whether the algorithm supports distributed training.
- The metrics that the algorithm emits to Amazon CloudWatch.
- Which metrics that the algorithm emits can be used as the objective metric for hyperparameter tuning jobs.
- The input channels that the algorithm supports for training data.
For example, an algorithm might support
train
,validation
, andtest
channels.
Request Lenses
createAlgorithm_validationSpecification :: Lens' CreateAlgorithm (Maybe AlgorithmValidationSpecification) Source #
Specifies configurations for one or more training jobs and that Amazon SageMaker runs to test the algorithm's training code and, optionally, one or more batch transform jobs that Amazon SageMaker runs to test the algorithm's inference code.
createAlgorithm_inferenceSpecification :: Lens' CreateAlgorithm (Maybe InferenceSpecification) Source #
Specifies details about inference jobs that the algorithm runs, including the following:
- The Amazon ECR paths of containers that contain the inference code and model artifacts.
- The instance types that the algorithm supports for transform jobs and real-time endpoints used for inference.
- The input and output content formats that the algorithm supports for inference.
createAlgorithm_algorithmDescription :: Lens' CreateAlgorithm (Maybe Text) Source #
A description of the algorithm.
createAlgorithm_certifyForMarketplace :: Lens' CreateAlgorithm (Maybe Bool) Source #
Whether to certify the algorithm so that it can be listed in Amazon Web Services Marketplace.
createAlgorithm_tags :: Lens' CreateAlgorithm (Maybe [Tag]) Source #
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.
createAlgorithm_algorithmName :: Lens' CreateAlgorithm Text Source #
The name of the algorithm.
createAlgorithm_trainingSpecification :: Lens' CreateAlgorithm TrainingSpecification Source #
Specifies details about training jobs run by this algorithm, including the following:
- The Amazon ECR path of the container and the version digest of the algorithm.
- The hyperparameters that the algorithm supports.
- The instance types that the algorithm supports for training.
- Whether the algorithm supports distributed training.
- The metrics that the algorithm emits to Amazon CloudWatch.
- Which metrics that the algorithm emits can be used as the objective metric for hyperparameter tuning jobs.
- The input channels that the algorithm supports for training data.
For example, an algorithm might support
train
,validation
, andtest
channels.
Destructuring the Response
data CreateAlgorithmResponse Source #
See: newCreateAlgorithmResponse
smart constructor.
CreateAlgorithmResponse' | |
|
Instances
newCreateAlgorithmResponse Source #
:: Int | |
-> Text | |
-> CreateAlgorithmResponse |
Create a value of CreateAlgorithmResponse
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:httpStatus:CreateAlgorithmResponse'
, createAlgorithmResponse_httpStatus
- The response's http status code.
$sel:algorithmArn:CreateAlgorithmResponse'
, createAlgorithmResponse_algorithmArn
- The Amazon Resource Name (ARN) of the new algorithm.
Response Lenses
createAlgorithmResponse_httpStatus :: Lens' CreateAlgorithmResponse Int Source #
The response's http status code.
createAlgorithmResponse_algorithmArn :: Lens' CreateAlgorithmResponse Text Source #
The Amazon Resource Name (ARN) of the new algorithm.