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 ContainerDefinition = ContainerDefinition' {}
- newContainerDefinition :: ContainerDefinition
- containerDefinition_containerHostname :: Lens' ContainerDefinition (Maybe Text)
- containerDefinition_environment :: Lens' ContainerDefinition (Maybe (HashMap Text Text))
- containerDefinition_image :: Lens' ContainerDefinition (Maybe Text)
- containerDefinition_imageConfig :: Lens' ContainerDefinition (Maybe ImageConfig)
- containerDefinition_inferenceSpecificationName :: Lens' ContainerDefinition (Maybe Text)
- containerDefinition_mode :: Lens' ContainerDefinition (Maybe ContainerMode)
- containerDefinition_modelDataUrl :: Lens' ContainerDefinition (Maybe Text)
- containerDefinition_modelPackageName :: Lens' ContainerDefinition (Maybe Text)
- containerDefinition_multiModelConfig :: Lens' ContainerDefinition (Maybe MultiModelConfig)
Documentation
data ContainerDefinition Source #
Describes the container, as part of model definition.
See: newContainerDefinition
smart constructor.
ContainerDefinition' | |
|
Instances
newContainerDefinition :: ContainerDefinition Source #
Create a value of ContainerDefinition
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:containerHostname:ContainerDefinition'
, containerDefinition_containerHostname
- This parameter is ignored for models that contain only a
PrimaryContainer
.
When a ContainerDefinition
is part of an inference pipeline, the value
of the parameter uniquely identifies the container for the purposes of
logging and metrics. For information, see
Use Logs and Metrics to Monitor an Inference Pipeline.
If you don't specify a value for this parameter for a
ContainerDefinition
that is part of an inference pipeline, a unique
name is automatically assigned based on the position of the
ContainerDefinition
in the pipeline. If you specify a value for the
ContainerHostName
for any ContainerDefinition
that is part of an
inference pipeline, you must specify a value for the ContainerHostName
parameter of every ContainerDefinition
in that pipeline.
$sel:environment:ContainerDefinition'
, containerDefinition_environment
- The environment variables to set in the Docker container. Each key and
value in the Environment
string to string map can have length of up to
1024. We support up to 16 entries in the map.
$sel:image:ContainerDefinition'
, containerDefinition_image
- The path where inference code is stored. This can be either in Amazon
EC2 Container Registry or in a Docker registry that is accessible from
the same VPC that you configure for your endpoint. If you are using your
own custom algorithm instead of an algorithm provided by SageMaker, the
inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
and registry/repository[@digest]
image
path formats. For more information, see
Using Your Own Algorithms with Amazon SageMaker
$sel:imageConfig:ContainerDefinition'
, containerDefinition_imageConfig
- Specifies whether the model container is in Amazon ECR or a private
Docker registry accessible from your Amazon Virtual Private Cloud (VPC).
For information about storing containers in a private Docker registry,
see
Use a Private Docker Registry for Real-Time Inference Containers
$sel:inferenceSpecificationName:ContainerDefinition'
, containerDefinition_inferenceSpecificationName
- The inference specification name in the model package version.
$sel:mode:ContainerDefinition'
, containerDefinition_mode
- Whether the container hosts a single model or multiple models.
$sel:modelDataUrl:ContainerDefinition'
, containerDefinition_modelDataUrl
- The S3 path where the model artifacts, which result from model training,
are stored. This path must point to a single gzip compressed tar archive
(.tar.gz suffix). The S3 path is required for SageMaker built-in
algorithms, but not if you use your own algorithms. For more information
on built-in algorithms, see
Common Parameters.
The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your IAM user account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
If you use a built-in algorithm to create a model, SageMaker requires
that you provide a S3 path to the model artifacts in ModelDataUrl
.
$sel:modelPackageName:ContainerDefinition'
, containerDefinition_modelPackageName
- The name or Amazon Resource Name (ARN) of the model package to use to
create the model.
$sel:multiModelConfig:ContainerDefinition'
, containerDefinition_multiModelConfig
- Specifies additional configuration for multi-model endpoints.
containerDefinition_containerHostname :: Lens' ContainerDefinition (Maybe Text) Source #
This parameter is ignored for models that contain only a
PrimaryContainer
.
When a ContainerDefinition
is part of an inference pipeline, the value
of the parameter uniquely identifies the container for the purposes of
logging and metrics. For information, see
Use Logs and Metrics to Monitor an Inference Pipeline.
If you don't specify a value for this parameter for a
ContainerDefinition
that is part of an inference pipeline, a unique
name is automatically assigned based on the position of the
ContainerDefinition
in the pipeline. If you specify a value for the
ContainerHostName
for any ContainerDefinition
that is part of an
inference pipeline, you must specify a value for the ContainerHostName
parameter of every ContainerDefinition
in that pipeline.
containerDefinition_environment :: Lens' ContainerDefinition (Maybe (HashMap Text Text)) Source #
The environment variables to set in the Docker container. Each key and
value in the Environment
string to string map can have length of up to
1024. We support up to 16 entries in the map.
containerDefinition_image :: Lens' ContainerDefinition (Maybe Text) Source #
The path where inference code is stored. This can be either in Amazon
EC2 Container Registry or in a Docker registry that is accessible from
the same VPC that you configure for your endpoint. If you are using your
own custom algorithm instead of an algorithm provided by SageMaker, the
inference code must meet SageMaker requirements. SageMaker supports both
registry/repository[:tag]
and registry/repository[@digest]
image
path formats. For more information, see
Using Your Own Algorithms with Amazon SageMaker
containerDefinition_imageConfig :: Lens' ContainerDefinition (Maybe ImageConfig) Source #
Specifies whether the model container is in Amazon ECR or a private Docker registry accessible from your Amazon Virtual Private Cloud (VPC). For information about storing containers in a private Docker registry, see Use a Private Docker Registry for Real-Time Inference Containers
containerDefinition_inferenceSpecificationName :: Lens' ContainerDefinition (Maybe Text) Source #
The inference specification name in the model package version.
containerDefinition_mode :: Lens' ContainerDefinition (Maybe ContainerMode) Source #
Whether the container hosts a single model or multiple models.
containerDefinition_modelDataUrl :: Lens' ContainerDefinition (Maybe Text) Source #
The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). The S3 path is required for SageMaker built-in algorithms, but not if you use your own algorithms. For more information on built-in algorithms, see Common Parameters.
The model artifacts must be in an S3 bucket that is in the same region as the model or endpoint you are creating.
If you provide a value for this parameter, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provide. Amazon Web Services STS is activated in your IAM user account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.
If you use a built-in algorithm to create a model, SageMaker requires
that you provide a S3 path to the model artifacts in ModelDataUrl
.
containerDefinition_modelPackageName :: Lens' ContainerDefinition (Maybe Text) Source #
The name or Amazon Resource Name (ARN) of the model package to use to create the model.
containerDefinition_multiModelConfig :: Lens' ContainerDefinition (Maybe MultiModelConfig) Source #
Specifies additional configuration for multi-model endpoints.