
# Generated by default/object.tt
package Paws::SageMaker::ContainerDefinition;
  use Moose;
  has ContainerHostname => (is => 'ro', isa => 'Str');
  has Environment => (is => 'ro', isa => 'Paws::SageMaker::EnvironmentMap');
  has Image => (is => 'ro', isa => 'Str');
  has ImageConfig => (is => 'ro', isa => 'Paws::SageMaker::ImageConfig');
  has Mode => (is => 'ro', isa => 'Str');
  has ModelDataUrl => (is => 'ro', isa => 'Str');
  has ModelPackageName => (is => 'ro', isa => 'Str');
  has MultiModelConfig => (is => 'ro', isa => 'Paws::SageMaker::MultiModelConfig');

1;

### main pod documentation begin ###

=head1 NAME

Paws::SageMaker::ContainerDefinition

=head1 USAGE

This class represents one of two things:

=head3 Arguments in a call to a service

Use the attributes of this class as arguments to methods. You shouldn't make instances of this class. 
Each attribute should be used as a named argument in the calls that expect this type of object.

As an example, if Att1 is expected to be a Paws::SageMaker::ContainerDefinition object:

  $service_obj->Method(Att1 => { ContainerHostname => $value, ..., MultiModelConfig => $value  });

=head3 Results returned from an API call

Use accessors for each attribute. If Att1 is expected to be an Paws::SageMaker::ContainerDefinition object:

  $result = $service_obj->Method(...);
  $result->Att1->ContainerHostname

=head1 DESCRIPTION

Describes the container, as part of model definition.

=head1 ATTRIBUTES


=head2 ContainerHostname => Str

This parameter is ignored for models that contain only a
C<PrimaryContainer>.

When a C<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
(https://docs.aws.amazon.com/sagemaker/latest/dg/inference-pipeline-logs-metrics.html).
If you don't specify a value for this parameter for a
C<ContainerDefinition> that is part of an inference pipeline, a unique
name is automatically assigned based on the position of the
C<ContainerDefinition> in the pipeline. If you specify a value for the
C<ContainerHostName> for any C<ContainerDefinition> that is part of an
inference pipeline, you must specify a value for the
C<ContainerHostName> parameter of every C<ContainerDefinition> in that
pipeline.


=head2 Environment => L<Paws::SageMaker::EnvironmentMap>

The environment variables to set in the Docker container. Each key and
value in the C<Environment> string to string map can have length of up
to 1024. We support up to 16 entries in the map.


=head2 Image => Str

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 Amazon
SageMaker, the inference code must meet Amazon SageMaker requirements.
Amazon SageMaker supports both C<registry/repository[:tag]> and
C<registry/repository[@digest]> image path formats. For more
information, see Using Your Own Algorithms with Amazon SageMaker
(https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms.html)


=head2 ImageConfig => L<Paws::SageMaker::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
(https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-containers-inference-private.html)


=head2 Mode => Str

Whether the container hosts a single model or multiple models.


=head2 ModelDataUrl => Str

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 Amazon
SageMaker built-in algorithms, but not if you use your own algorithms.
For more information on built-in algorithms, see Common Parameters
(https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html).

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, Amazon 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
(https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_temp_enable-regions.html)
in the I<Amazon Web Services Identity and Access Management User
Guide>.

If you use a built-in algorithm to create a model, Amazon SageMaker
requires that you provide a S3 path to the model artifacts in
C<ModelDataUrl>.


=head2 ModelPackageName => Str

The name or Amazon Resource Name (ARN) of the model package to use to
create the model.


=head2 MultiModelConfig => L<Paws::SageMaker::MultiModelConfig>

Specifies additional configuration for multi-model endpoints.


=head1 SEE ALSO

This class forms part of L<Paws>, describing an object used in L<Paws::SageMaker>

=head1 BUGS and CONTRIBUTIONS

The source code is located here: L<https://github.com/pplu/aws-sdk-perl>

Please report bugs to: L<https://github.com/pplu/aws-sdk-perl/issues>

=cut