AWS SDK for PHP 3.x

Client: Aws\SageMaker\SageMakerClient

Service ID: sagemaker

Version: 2017-07-24

This page describes the parameters and results for the operations of the Amazon SageMaker Service (2017-07-24), and shows how to use the Aws\SageMaker\SageMakerClient object to call the described operations. This documentation is specific to the 2017-07-24 API version of the service.

Operation Summary

Each of the following operations can be created from a client using $client->getCommand('CommandName'), where "CommandName" is the name of one of the following operations. Note: a command is a value that encapsulates an operation and the parameters used to create an HTTP request.

You can also create and send a command immediately using the magic methods available on a client object: $client->commandName(/* parameters */). You can send the command asynchronously (returning a promise) by appending the word "Async" to the operation name: $client->commandNameAsync(/* parameters */).

AddTags ( array $params = [] )
Adds or overwrites one or more tags for the specified Amazon SageMaker resource.
CreateEndpoint ( array $params = [] )
Creates an endpoint using the endpoint configuration specified in the request.
CreateEndpointConfig ( array $params = [] )
Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models.
CreateHyperParameterTuningJob ( array $params = [] )
Starts a hyperparameter tuning job.
CreateModel ( array $params = [] )
Creates a model in Amazon SageMaker.
CreateNotebookInstance ( array $params = [] )
Creates an Amazon SageMaker notebook instance.
CreateNotebookInstanceLifecycleConfig ( array $params = [] )
Creates a lifecycle configuration that you can associate with a notebook instance.
CreatePresignedNotebookInstanceUrl ( array $params = [] )
Returns a URL that you can use to connect to the Jupyter server from a notebook instance.
CreateTrainingJob ( array $params = [] )
Starts a model training job.
CreateTransformJob ( array $params = [] )
Starts a transform job.
DeleteEndpoint ( array $params = [] )
Deletes an endpoint.
DeleteEndpointConfig ( array $params = [] )
Deletes an endpoint configuration.
DeleteModel ( array $params = [] )
Deletes a model.
DeleteNotebookInstance ( array $params = [] )
Deletes an Amazon SageMaker notebook instance.
DeleteNotebookInstanceLifecycleConfig ( array $params = [] )
Deletes a notebook instance lifecycle configuration.
DeleteTags ( array $params = [] )
Deletes the specified tags from an Amazon SageMaker resource.
DescribeEndpoint ( array $params = [] )
Returns the description of an endpoint.
DescribeEndpointConfig ( array $params = [] )
Returns the description of an endpoint configuration created using the CreateEndpointConfig API.
DescribeHyperParameterTuningJob ( array $params = [] )
Gets a description of a hyperparameter tuning job.
DescribeModel ( array $params = [] )
Describes a model that you created using the CreateModel API.
DescribeNotebookInstance ( array $params = [] )
Returns information about a notebook instance.
DescribeNotebookInstanceLifecycleConfig ( array $params = [] )
Returns a description of a notebook instance lifecycle configuration.
DescribeTrainingJob ( array $params = [] )
Returns information about a training job.
DescribeTransformJob ( array $params = [] )
Returns information about a transform job.
ListEndpointConfigs ( array $params = [] )
Lists endpoint configurations.
ListEndpoints ( array $params = [] )
Lists endpoints.
ListHyperParameterTuningJobs ( array $params = [] )
Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.
ListModels ( array $params = [] )
Lists models created with the CreateModel API.
ListNotebookInstanceLifecycleConfigs ( array $params = [] )
Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.
ListNotebookInstances ( array $params = [] )
Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.
ListTags ( array $params = [] )
Returns the tags for the specified Amazon SageMaker resource.
ListTrainingJobs ( array $params = [] )
Lists training jobs.
ListTrainingJobsForHyperParameterTuningJob ( array $params = [] )
Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.
ListTransformJobs ( array $params = [] )
Lists transform jobs.
StartNotebookInstance ( array $params = [] )
Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume.
StopHyperParameterTuningJob ( array $params = [] )
Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.
StopNotebookInstance ( array $params = [] )
Terminates the ML compute instance.
StopTrainingJob ( array $params = [] )
Stops a training job.
StopTransformJob ( array $params = [] )
Stops a transform job.
UpdateEndpoint ( array $params = [] )
Deploys the new EndpointConfig specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous EndpointConfig (there is no availability loss).
UpdateEndpointWeightsAndCapacities ( array $params = [] )
Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint.
UpdateNotebookInstance ( array $params = [] )
Updates a notebook instance.
UpdateNotebookInstanceLifecycleConfig ( array $params = [] )
Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.

Paginators

Paginators handle automatically iterating over paginated API results. Paginators are associated with specific API operations, and they accept the parameters that the corresponding API operation accepts. You can get a paginator from a client class using getPaginator($paginatorName, $operationParameters). This client supports the following paginators:

ListEndpointConfigs
ListEndpoints
ListHyperParameterTuningJobs
ListModels
ListNotebookInstanceLifecycleConfigs
ListNotebookInstances
ListTags
ListTrainingJobs
ListTrainingJobsForHyperParameterTuningJob
ListTransformJobs

Waiters

Waiters allow you to poll a resource until it enters into a desired state. A waiter has a name used to describe what it does, and is associated with an API operation. When creating a waiter, you can provide the API operation parameters associated with the corresponding operation. Waiters can be accessed using the getWaiter($waiterName, $operationParameters) method of a client object. This client supports the following waiters:

Waiter name	API Operation	Delay	Max Attempts
NotebookInstanceInService	DescribeNotebookInstance	30	60
NotebookInstanceStopped	DescribeNotebookInstance	30	60
NotebookInstanceDeleted	DescribeNotebookInstance	30	60
TrainingJobCompletedOrStopped	DescribeTrainingJob	120	180
EndpointInService	DescribeEndpoint	30	120
EndpointDeleted	DescribeEndpoint	30	60

Operations

AddTags

$result = $client->addTags([/* ... */]);
$promise = $client->addTagsAsync([/* ... */]);

Adds or overwrites one or more tags for the specified Amazon SageMaker resource. You can add tags to notebook instances, training jobs, models, endpoint configurations, and endpoints.

Each tag consists of a key and an optional value. Tag keys must be unique per resource. For more information about tags, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

Parameter Syntax

$result = $client->addTags([
    'ResourceArn' => '<string>', // REQUIRED
    'Tags' => [ // REQUIRED
        [
            'Key' => '<string>', // REQUIRED
            'Value' => '<string>', // REQUIRED
        ],
        // ...
    ],
]);

Parameter Details

Members

ResourceArn

Type: string

The Amazon Resource Name (ARN) of the resource that you want to tag.

Tags

Type: Array of Tag structures

An array of Tag objects. Each tag is a key-value pair. Only the key parameter is required. If you don't specify a value, Amazon SageMaker sets the value to an empty string.

Result Syntax

[
    'Tags' => [
        [
            'Key' => '<string>',
            'Value' => '<string>',
        ],
        // ...
    ],
]

Result Details

Members

Tags

Type: Array of Tag structures

A list of tags associated with the Amazon SageMaker resource.

Errors

There are no errors described for this operation.

CreateEndpoint

$result = $client->createEndpoint([/* ... */]);
$promise = $client->createEndpointAsync([/* ... */]);

Creates an endpoint using the endpoint configuration specified in the request. Amazon SageMaker uses the endpoint to provision resources and deploy models. You create the endpoint configuration with the CreateEndpointConfig API.

Use this API only for hosting models using Amazon SageMaker hosting services.

The endpoint name must be unique within an AWS Region in your AWS account.

When it receives the request, Amazon SageMaker creates the endpoint, launches the resources (ML compute instances), and deploys the model(s) on them.

When Amazon SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. Amazon SageMaker can then process incoming requests for inferences. To check the status of an endpoint, use the DescribeEndpoint API.

For an example, see Exercise 1: Using the K-Means Algorithm Provided by Amazon SageMaker.

If any of the models hosted at this endpoint get model data from an Amazon S3 location, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provided. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS i an AWS Region in the AWS Identity and Access Management User Guide.

Parameter Syntax

$result = $client->createEndpoint([
    'EndpointConfigName' => '<string>', // REQUIRED
    'EndpointName' => '<string>', // REQUIRED
    'Tags' => [
        [
            'Key' => '<string>', // REQUIRED
            'Value' => '<string>', // REQUIRED
        ],
        // ...
    ],
]);

Parameter Details

Members

EndpointConfigName

Type: string

The name of an endpoint configuration. For more information, see CreateEndpointConfig.

EndpointName

Type: string

The name of the endpoint. The name must be unique within an AWS Region in your AWS account.

Tags

Type: Array of Tag structures

An array of key-value pairs. For more information, see Using Cost Allocation Tagsin the AWS Billing and Cost Management User Guide.

Result Syntax

[
    'EndpointArn' => '<string>',
]

Result Details

Members

EndpointArn

Type: string

The Amazon Resource Name (ARN) of the endpoint.

Errors

ResourceLimitExceeded:
You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.

CreateEndpointConfig

$result = $client->createEndpointConfig([/* ... */]);
$promise = $client->createEndpointConfigAsync([/* ... */]);

Creates an endpoint configuration that Amazon SageMaker hosting services uses to deploy models. In the configuration, you identify one or more models, created using the CreateModel API, to deploy and the resources that you want Amazon SageMaker to provision. Then you call the CreateEndpoint API.

Use this API only if you want to use Amazon SageMaker hosting services to deploy models into production.

In the request, you define one or more ProductionVariants, each of which identifies a model. Each ProductionVariant parameter also describes the resources that you want Amazon SageMaker to provision. This includes the number and type of ML compute instances to deploy.

If you are hosting multiple models, you also assign a VariantWeight to specify how much traffic you want to allocate to each model. For example, suppose that you want to host two models, A and B, and you assign traffic weight 2 for model A and 1 for model B. Amazon SageMaker distributes two-thirds of the traffic to Model A, and one-third to model B.

Parameter Syntax

$result = $client->createEndpointConfig([
    'EndpointConfigName' => '<string>', // REQUIRED
    'KmsKeyId' => '<string>',
    'ProductionVariants' => [ // REQUIRED
        [
            'InitialInstanceCount' => <integer>, // REQUIRED
            'InitialVariantWeight' => <float>,
            'InstanceType' => 'ml.t2.medium|ml.t2.large|ml.t2.xlarge|ml.t2.2xlarge|ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.c4.large|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.c5.large|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge', // REQUIRED
            'ModelName' => '<string>', // REQUIRED
            'VariantName' => '<string>', // REQUIRED
        ],
        // ...
    ],
    'Tags' => [
        [
            'Key' => '<string>', // REQUIRED
            'Value' => '<string>', // REQUIRED
        ],
        // ...
    ],
]);

Parameter Details

Members

EndpointConfigName

Type: string

The name of the endpoint configuration. You specify this name in a CreateEndpoint request.

KmsKeyId

Type: string

The Amazon Resource Name (ARN) of a AWS Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance that hosts the endpoint.

ProductionVariants

Type: Array of ProductionVariant structures

An array of ProductionVariant objects, one for each model that you want to host at this endpoint.

Tags

Type: Array of Tag structures

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

Result Syntax

[
    'EndpointConfigArn' => '<string>',
]

Result Details

Members

EndpointConfigArn

Type: string

The Amazon Resource Name (ARN) of the endpoint configuration.

Errors

ResourceLimitExceeded:
You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.

CreateHyperParameterTuningJob

$result = $client->createHyperParameterTuningJob([/* ... */]);
$promise = $client->createHyperParameterTuningJobAsync([/* ... */]);

Starts a hyperparameter tuning job.

Parameter Syntax

$result = $client->createHyperParameterTuningJob([
    'HyperParameterTuningJobConfig' => [ // REQUIRED
        'HyperParameterTuningJobObjective' => [ // REQUIRED
            'MetricName' => '<string>', // REQUIRED
            'Type' => 'Maximize|Minimize', // REQUIRED
        ],
        'ParameterRanges' => [ // REQUIRED
            'CategoricalParameterRanges' => [
                [
                    'Name' => '<string>', // REQUIRED
                    'Values' => ['<string>', ...], // REQUIRED
                ],
                // ...
            ],
            'ContinuousParameterRanges' => [
                [
                    'MaxValue' => '<string>', // REQUIRED
                    'MinValue' => '<string>', // REQUIRED
                    'Name' => '<string>', // REQUIRED
                ],
                // ...
            ],
            'IntegerParameterRanges' => [
                [
                    'MaxValue' => '<string>', // REQUIRED
                    'MinValue' => '<string>', // REQUIRED
                    'Name' => '<string>', // REQUIRED
                ],
                // ...
            ],
        ],
        'ResourceLimits' => [ // REQUIRED
            'MaxNumberOfTrainingJobs' => <integer>, // REQUIRED
            'MaxParallelTrainingJobs' => <integer>, // REQUIRED
        ],
        'Strategy' => 'Bayesian', // REQUIRED
    ],
    'HyperParameterTuningJobName' => '<string>', // REQUIRED
    'Tags' => [
        [
            'Key' => '<string>', // REQUIRED
            'Value' => '<string>', // REQUIRED
        ],
        // ...
    ],
    'TrainingJobDefinition' => [ // REQUIRED
        'AlgorithmSpecification' => [ // REQUIRED
            'MetricDefinitions' => [
                [
                    'Name' => '<string>', // REQUIRED
                    'Regex' => '<string>', // REQUIRED
                ],
                // ...
            ],
            'TrainingImage' => '<string>', // REQUIRED
            'TrainingInputMode' => 'Pipe|File', // REQUIRED
        ],
        'InputDataConfig' => [ // REQUIRED
            [
                'ChannelName' => '<string>', // REQUIRED
                'CompressionType' => 'None|Gzip',
                'ContentType' => '<string>',
                'DataSource' => [ // REQUIRED
                    'S3DataSource' => [ // REQUIRED
                        'S3DataDistributionType' => 'FullyReplicated|ShardedByS3Key',
                        'S3DataType' => 'ManifestFile|S3Prefix', // REQUIRED
                        'S3Uri' => '<string>', // REQUIRED
                    ],
                ],
                'RecordWrapperType' => 'None|RecordIO',
            ],
            // ...
        ],
        'OutputDataConfig' => [ // REQUIRED
            'KmsKeyId' => '<string>',
            'S3OutputPath' => '<string>', // REQUIRED
        ],
        'ResourceConfig' => [ // REQUIRED
            'InstanceCount' => <integer>, // REQUIRED
            'InstanceType' => 'ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge', // REQUIRED
            'VolumeKmsKeyId' => '<string>',
            'VolumeSizeInGB' => <integer>, // REQUIRED
        ],
        'RoleArn' => '<string>', // REQUIRED
        'StaticHyperParameters' => ['<string>', ...],
        'StoppingCondition' => [ // REQUIRED
            'MaxRuntimeInSeconds' => <integer>,
        ],
        'VpcConfig' => [
            'SecurityGroupIds' => ['<string>', ...], // REQUIRED
            'Subnets' => ['<string>', ...], // REQUIRED
        ],
    ],
]);

Parameter Details

Members

HyperParameterTuningJobConfig

Required: Yes
Type: HyperParameterTuningJobConfig structure

The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job.

HyperParameterTuningJobName

Type: string

The name of the tuning job. This name is the prefix for the names of all training jobs that this tuning job launches. The name must be unique within the same AWS account and AWS Region. Names are not case sensitive, and must be between 1-32 characters.

Tags

Type: Array of Tag structures

An array of key-value pairs. You can use tags to categorize your AWS resources in different ways, for example, by purpose, owner, or environment. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

TrainingJobDefinition

Required: Yes
Type: HyperParameterTrainingJobDefinition structure

The HyperParameterTrainingJobDefinition object that describes the training jobs that this tuning job launches, including static hyperparameters, input data configuration, output data configuration, resource configuration, and stopping condition.

Result Syntax

[
    'HyperParameterTuningJobArn' => '<string>',
]

Result Details

Members

HyperParameterTuningJobArn

Type: string

The Amazon Resource Name (ARN) of the tuning job.

Errors

ResourceInUse:
Resource being accessed is in use.
ResourceLimitExceeded:
You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.

CreateModel

$result = $client->createModel([/* ... */]);
$promise = $client->createModelAsync([/* ... */]);

Creates a model in Amazon SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the docker image containing inference code, artifacts (from prior training), and custom environment map that the inference code uses when you deploy the model for predictions.

Use this API to create a model if you want to use Amazon SageMaker hosting services or run a batch transform job.

To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then create an endpoint with the CreateEndpoint API. Amazon SageMaker then deploys all of the containers that you defined for the model in the hosting environment.

To run a batch transform using your model, you start a job with the CreateTransformJob API. Amazon SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.

In the CreateModel request, you must define a container with the PrimaryContainer parameter.

In the request, you also provide an IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other AWS resources, you grant necessary permissions via this role.

Parameter Syntax

$result = $client->createModel([
    'ExecutionRoleArn' => '<string>', // REQUIRED
    'ModelName' => '<string>', // REQUIRED
    'PrimaryContainer' => [ // REQUIRED
        'ContainerHostname' => '<string>',
        'Environment' => ['<string>', ...],
        'Image' => '<string>', // REQUIRED
        'ModelDataUrl' => '<string>',
    ],
    'Tags' => [
        [
            'Key' => '<string>', // REQUIRED
            'Value' => '<string>', // REQUIRED
        ],
        // ...
    ],
    'VpcConfig' => [
        'SecurityGroupIds' => ['<string>', ...], // REQUIRED
        'Subnets' => ['<string>', ...], // REQUIRED
    ],
]);

Parameter Details

Members

ExecutionRoleArn

Type: string

The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see Amazon SageMaker Roles.

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

ModelName

Type: string

The name of the new model.

PrimaryContainer

Required: Yes
Type: ContainerDefinition structure

The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.

Tags

Type: Array of Tag structures

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

VpcConfig

Required: Yes
Type: VpcConfig structure

A VpcConfig object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. VpcConfig is currently used in hosting services but not in batch transform. For more information, see host-vpc.

Result Syntax

[
    'ModelArn' => '<string>',
]

Result Details

Members

ModelArn

Type: string

The ARN of the model created in Amazon SageMaker.

Errors

ResourceLimitExceeded:
You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.

CreateNotebookInstance

$result = $client->createNotebookInstance([/* ... */]);
$promise = $client->createNotebookInstanceAsync([/* ... */]);

Creates an Amazon SageMaker notebook instance. A notebook instance is a machine learning (ML) compute instance running on a Jupyter notebook.

In a CreateNotebookInstance request, specify the type of ML compute instance that you want to run. Amazon SageMaker launches the instance, installs common libraries that you can use to explore datasets for model training, and attaches an ML storage volume to the notebook instance.

Amazon SageMaker also provides a set of example notebooks. Each notebook demonstrates how to use Amazon SageMaker with a specific algorithm or with a machine learning framework.

After receiving the request, Amazon SageMaker does the following:

Creates a network interface in the Amazon SageMaker VPC.
(Option) If you specified SubnetId, Amazon SageMaker creates a network interface in your own VPC, which is inferred from the subnet ID that you provide in the input. When creating this network interface, Amazon SageMaker attaches the security group that you specified in the request to the network interface that it creates in your VPC.
Launches an EC2 instance of the type specified in the request in the Amazon SageMaker VPC. If you specified SubnetId of your VPC, Amazon SageMaker specifies both network interfaces when launching this instance. This enables inbound traffic from your own VPC to the notebook instance, assuming that the security groups allow it.

After creating the notebook instance, Amazon SageMaker returns its Amazon Resource Name (ARN).

After Amazon SageMaker creates the notebook instance, you can connect to the Jupyter server and work in Jupyter notebooks. For example, you can write code to explore a dataset that you can use for model training, train a model, host models by creating Amazon SageMaker endpoints, and validate hosted models.

For more information, see How It Works.

Parameter Syntax

$result = $client->createNotebookInstance([
    'DirectInternetAccess' => 'Enabled|Disabled',
    'InstanceType' => 'ml.t2.medium|ml.t2.large|ml.t2.xlarge|ml.t2.2xlarge|ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge', // REQUIRED
    'KmsKeyId' => '<string>',
    'LifecycleConfigName' => '<string>',
    'NotebookInstanceName' => '<string>', // REQUIRED
    'RoleArn' => '<string>', // REQUIRED
    'SecurityGroupIds' => ['<string>', ...],
    'SubnetId' => '<string>',
    'Tags' => [
        [
            'Key' => '<string>', // REQUIRED
            'Value' => '<string>', // REQUIRED
        ],
        // ...
    ],
]);

Parameter Details

Members

DirectInternetAccess

Type: string

Sets whether Amazon SageMaker provides internet access to the notebook instance. If you set this to Disabled this notebook instance will be able to access resources only in your VPC, and will not be able to connect to Amazon SageMaker training and endpoint services unless your configure a NAT Gateway in your VPC.

For more information, see appendix-notebook-and-internet-access. You can set the value of this parameter to Disabled only if you set a value for the SubnetId parameter.

InstanceType

Type: string

The type of ML compute instance to launch for the notebook instance.

KmsKeyId

Type: string

If you provide a AWS KMS key ID, Amazon SageMaker uses it to encrypt data at rest on the ML storage volume that is attached to your notebook instance.

LifecycleConfigName

Type: string

The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see notebook-lifecycle-config.

NotebookInstanceName

Type: string

The name of the new notebook instance.

RoleArn

Type: string

When you send any requests to AWS resources from the notebook instance, Amazon SageMaker assumes this role to perform tasks on your behalf. You must grant this role necessary permissions so Amazon SageMaker can perform these tasks. The policy must allow the Amazon SageMaker service principal (sagemaker.amazonaws.com) permissions to assume this role. For more information, see Amazon SageMaker Roles.

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

SecurityGroupIds

Type: Array of strings

The VPC security group IDs, in the form sg-xxxxxxxx. The security groups must be for the same VPC as specified in the subnet.

SubnetId

Type: string

The ID of the subnet in a VPC to which you would like to have a connectivity from your ML compute instance.

Tags

Type: Array of Tag structures

A list of tags to associate with the notebook instance. You can add tags later by using the CreateTags API.

Result Syntax

[
    'NotebookInstanceArn' => '<string>',
]

Result Details

Members

NotebookInstanceArn

Type: string

The Amazon Resource Name (ARN) of the notebook instance.

Errors

ResourceLimitExceeded:
You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.

CreateNotebookInstanceLifecycleConfig

$result = $client->createNotebookInstanceLifecycleConfig([/* ... */]);
$promise = $client->createNotebookInstanceLifecycleConfigAsync([/* ... */]);

Creates a lifecycle configuration that you can associate with a notebook instance. A lifecycle configuration is a collection of shell scripts that run when you create or start a notebook instance.

Each lifecycle configuration script has a limit of 16384 characters.

The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin.

View CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook].

Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.

For information about notebook instance lifestyle configurations, see notebook-lifecycle-config.

Parameter Syntax

$result = $client->createNotebookInstanceLifecycleConfig([
    'NotebookInstanceLifecycleConfigName' => '<string>', // REQUIRED
    'OnCreate' => [
        [
            'Content' => '<string>',
        ],
        // ...
    ],
    'OnStart' => [
        [
            'Content' => '<string>',
        ],
        // ...
    ],
]);

Parameter Details

Members

NotebookInstanceLifecycleConfigName

Type: string

The name of the lifecycle configuration.

OnCreate

Type: Array of NotebookInstanceLifecycleHook structures

A shell script that runs only once, when you create a notebook instance.

OnStart

Type: Array of NotebookInstanceLifecycleHook structures

A shell script that runs every time you start a notebook instance, including when you create the notebook instance.

Result Syntax

[
    'NotebookInstanceLifecycleConfigArn' => '<string>',
]

Result Details

Members

NotebookInstanceLifecycleConfigArn

Type: string

The Amazon Resource Name (ARN) of the lifecycle configuration.

Errors

ResourceLimitExceeded:
You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.

CreatePresignedNotebookInstanceUrl

$result = $client->createPresignedNotebookInstanceUrl([/* ... */]);
$promise = $client->createPresignedNotebookInstanceUrlAsync([/* ... */]);

Returns a URL that you can use to connect to the Jupyter server from a notebook instance. In the Amazon SageMaker console, when you choose Open next to a notebook instance, Amazon SageMaker opens a new tab showing the Jupyter server home page from the notebook instance. The console uses this API to get the URL and show the page.

Parameter Syntax

$result = $client->createPresignedNotebookInstanceUrl([
    'NotebookInstanceName' => '<string>', // REQUIRED
    'SessionExpirationDurationInSeconds' => <integer>,
]);

Parameter Details

Members

NotebookInstanceName

Type: string

The name of the notebook instance.

SessionExpirationDurationInSeconds

Type: int

The duration of the session, in seconds. The default is 12 hours.

Result Syntax

[
    'AuthorizedUrl' => '<string>',
]

Result Details

Members

AuthorizedUrl

Type: string

A JSON object that contains the URL string.

Errors

There are no errors described for this operation.

CreateTrainingJob

$result = $client->createTrainingJob([/* ... */]);
$promise = $client->createTrainingJobAsync([/* ... */]);

Starts a model training job. After training completes, Amazon SageMaker saves the resulting model artifacts to an Amazon S3 location that you specify.

If you choose to host your model using Amazon SageMaker hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts in a deep learning service other than Amazon SageMaker, provided that you know how to use them for inferences.

In the request body, you provide the following:

AlgorithmSpecification - Identifies the training algorithm to use.
HyperParameters - Specify these algorithm-specific parameters to influence the quality of the final model. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.
InputDataConfig - Describes the training dataset and the Amazon S3 location where it is stored.
OutputDataConfig - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results of model training.
ResourceConfig - Identifies the resources, ML compute instances, and ML storage volumes to deploy for model training. In distributed training, you specify more than one instance.
RoleARN - The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during model training. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete model training.
StoppingCondition - Sets a duration for training. Use this parameter to cap model training costs.

For more information about Amazon SageMaker, see How It Works.

Parameter Syntax

$result = $client->createTrainingJob([
    'AlgorithmSpecification' => [ // REQUIRED
        'TrainingImage' => '<string>', // REQUIRED
        'TrainingInputMode' => 'Pipe|File', // REQUIRED
    ],
    'HyperParameters' => ['<string>', ...],
    'InputDataConfig' => [ // REQUIRED
        [
            'ChannelName' => '<string>', // REQUIRED
            'CompressionType' => 'None|Gzip',
            'ContentType' => '<string>',
            'DataSource' => [ // REQUIRED
                'S3DataSource' => [ // REQUIRED
                    'S3DataDistributionType' => 'FullyReplicated|ShardedByS3Key',
                    'S3DataType' => 'ManifestFile|S3Prefix', // REQUIRED
                    'S3Uri' => '<string>', // REQUIRED
                ],
            ],
            'RecordWrapperType' => 'None|RecordIO',
        ],
        // ...
    ],
    'OutputDataConfig' => [ // REQUIRED
        'KmsKeyId' => '<string>',
        'S3OutputPath' => '<string>', // REQUIRED
    ],
    'ResourceConfig' => [ // REQUIRED
        'InstanceCount' => <integer>, // REQUIRED
        'InstanceType' => 'ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge', // REQUIRED
        'VolumeKmsKeyId' => '<string>',
        'VolumeSizeInGB' => <integer>, // REQUIRED
    ],
    'RoleArn' => '<string>', // REQUIRED
    'StoppingCondition' => [ // REQUIRED
        'MaxRuntimeInSeconds' => <integer>,
    ],
    'Tags' => [
        [
            'Key' => '<string>', // REQUIRED
            'Value' => '<string>', // REQUIRED
        ],
        // ...
    ],
    'TrainingJobName' => '<string>', // REQUIRED
    'VpcConfig' => [
        'SecurityGroupIds' => ['<string>', ...], // REQUIRED
        'Subnets' => ['<string>', ...], // REQUIRED
    ],
]);

Parameter Details

Members

AlgorithmSpecification

Required: Yes
Type: AlgorithmSpecification structure

The registry path of the Docker image that contains the training algorithm and algorithm-specific metadata, including the input mode. For more information about algorithms provided by Amazon SageMaker, see Algorithms. For information about providing your own algorithms, see your-algorithms.

HyperParameters

Type: Associative array of custom strings keys (ParameterKey) to strings

Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.

You can specify a maximum of 100 hyperparameters. Each hyperparameter is a key-value pair. Each key and value is limited to 256 characters, as specified by the Length Constraint.

InputDataConfig

Type: Array of Channel structures

An array of Channel objects. Each channel is a named input source. InputDataConfig describes the input data and its location.

Algorithms can accept input data from one or more channels. For example, an algorithm might have two channels of input data, training_data and validation_data. The configuration for each channel provides the S3 location where the input data is stored. It also provides information about the stored data: the MIME type, compression method, and whether the data is wrapped in RecordIO format.

Depending on the input mode that the algorithm supports, Amazon SageMaker either copies input data files from an S3 bucket to a local directory in the Docker container, or makes it available as input streams.

OutputDataConfig

Required: Yes
Type: OutputDataConfig structure

Specifies the path to the S3 bucket where you want to store model artifacts. Amazon SageMaker creates subfolders for the artifacts.

ResourceConfig

Required: Yes
Type: ResourceConfig structure

The resources, including the ML compute instances and ML storage volumes, to use for model training.

ML storage volumes store model artifacts and incremental states. Training algorithms might also use ML storage volumes for scratch space. If you want Amazon SageMaker to use the ML storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.

RoleArn

Type: string

The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.

During model training, Amazon SageMaker needs your permission to read input data from an S3 bucket, download a Docker image that contains training code, write model artifacts to an S3 bucket, write logs to Amazon CloudWatch Logs, and publish metrics to Amazon CloudWatch. You grant permissions for all of these tasks to an IAM role. For more information, see Amazon SageMaker Roles.

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

StoppingCondition

Type: StoppingCondition structure

Sets a duration for training. Use this parameter to cap model training costs. To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.

When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job. This intermediate data is a valid model artifact. You can use it to create a model using the CreateModel API.

Tags

Type: Array of Tag structures

An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

TrainingJobName

Type: string

The name of the training job. The name must be unique within an AWS Region in an AWS account.

VpcConfig

Required: Yes
Type: VpcConfig structure

A VpcConfig object that specifies the VPC that you want your training job to connect to. Control access to and from your training container by configuring the VPC. For more information, see train-vpc

Result Syntax

[
    'TrainingJobArn' => '<string>',
]

Result Details

Members

TrainingJobArn

Type: string

The Amazon Resource Name (ARN) of the training job.

Errors

ResourceInUse:
Resource being accessed is in use.
ResourceLimitExceeded:
You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.

CreateTransformJob

$result = $client->createTransformJob([/* ... */]);
$promise = $client->createTransformJobAsync([/* ... */]);

Starts a transform job. A transform job uses a trained model to get inferences on a dataset and saves these results to an Amazon S3 location that you specify.

To perform batch transformations, you create a transform job and use the data that you have readily available.

In the request body, you provide the following:

TransformJobName - Identifies the transform job. The name must be unique within an AWS Region in an AWS account.
ModelName - Identifies the model to use. ModelName must be the name of an existing Amazon SageMaker model in the same AWS Region and AWS account. For information on creating a model, see CreateModel.
TransformInput - Describes the dataset to be transformed and the Amazon S3 location where it is stored.
TransformOutput - Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.
TransformResources - Identifies the ML compute instances for the transform job.

For more information about how batch transformation works Amazon SageMaker, see How It Works.

Parameter Syntax

$result = $client->createTransformJob([
    'BatchStrategy' => 'MultiRecord|SingleRecord',
    'Environment' => ['<string>', ...],
    'MaxConcurrentTransforms' => <integer>,
    'MaxPayloadInMB' => <integer>,
    'ModelName' => '<string>', // REQUIRED
    'Tags' => [
        [
            'Key' => '<string>', // REQUIRED
            'Value' => '<string>', // REQUIRED
        ],
        // ...
    ],
    'TransformInput' => [ // REQUIRED
        'CompressionType' => 'None|Gzip',
        'ContentType' => '<string>',
        'DataSource' => [ // REQUIRED
            'S3DataSource' => [ // REQUIRED
                'S3DataType' => 'ManifestFile|S3Prefix', // REQUIRED
                'S3Uri' => '<string>', // REQUIRED
            ],
        ],
        'SplitType' => 'None|Line|RecordIO',
    ],
    'TransformJobName' => '<string>', // REQUIRED
    'TransformOutput' => [ // REQUIRED
        'Accept' => '<string>',
        'AssembleWith' => 'None|Line',
        'KmsKeyId' => '<string>',
        'S3OutputPath' => '<string>', // REQUIRED
    ],
    'TransformResources' => [ // REQUIRED
        'InstanceCount' => <integer>, // REQUIRED
        'InstanceType' => 'ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge', // REQUIRED
    ],
]);

Parameter Details

Members

BatchStrategy

Type: string

Determines the number of records included in a single mini-batch. SingleRecord means only one record is used per mini-batch. MultiRecord means a mini-batch is set to contain as many records that can fit within the MaxPayloadInMB limit.

Batch transform will automatically split your input data into whatever payload size is specified if you set SplitType to Line and BatchStrategy to MultiRecord. There's no need to split the dataset into smaller files or to use larger payload sizes unless the records in your dataset are very large.

Environment

Type: Associative array of custom strings keys (TransformEnvironmentKey) to strings

The environment variables to set in the Docker container. We support up to 16 key and values entries in the map.

MaxConcurrentTransforms

Type: int

The maximum number of parallel requests that can be sent to each instance in a transform job. This is good for algorithms that implement multiple workers on larger instances . The default value is 1. To allow Amazon SageMaker to determine the appropriate number for MaxConcurrentTransforms, set the value to 0.

MaxPayloadInMB

Type: int

The maximum payload size allowed, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater or equal to the size of a single record. You can approximate the size of a record by dividing the size of your dataset by the number of records. Then multiply this value by the number of records you want in a mini-batch. It is recommended to enter a value slightly larger than this to ensure the records fit within the maximum payload size. The default value is 6 MB. For an unlimited payload size, set the value to 0.

ModelName

Type: string

The name of the model that you want to use for the transform job. ModelName must be the name of an existing Amazon SageMaker model within an AWS Region in an AWS account.

Tags

Type: Array of Tag structures

An array of key-value pairs. Adding tags is optional. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

TransformInput

Required: Yes
Type: TransformInput structure

Describes the input source and the way the transform job consumes it.

TransformJobName

Type: string

The name of the transform job. The name must be unique within an AWS Region in an AWS account.

TransformOutput

Required: Yes
Type: TransformOutput structure

Describes the results of the transform job.

TransformResources

Required: Yes
Type: TransformResources structure

Describes the resources, including ML instance types and ML instance count, to use for the transform job.

Result Syntax

[
    'TransformJobArn' => '<string>',
]

Result Details

Members

TransformJobArn

Type: string

The Amazon Resource Name (ARN) of the transform job.

Errors

ResourceInUse:
Resource being accessed is in use.
ResourceLimitExceeded:
You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.

DeleteEndpoint

$result = $client->deleteEndpoint([/* ... */]);
$promise = $client->deleteEndpointAsync([/* ... */]);

Deletes an endpoint. Amazon SageMaker frees up all of the resources that were deployed when the endpoint was created.

Amazon SageMaker retires any custom KMS key grants associated with the endpoint, meaning you don't need to use the RevokeGrant API call.

Parameter Syntax

$result = $client->deleteEndpoint([
    'EndpointName' => '<string>', // REQUIRED
]);

Parameter Details

Members

EndpointName

Type: string

The name of the endpoint that you want to delete.

Result Syntax

[]

Result Details

The results for this operation are always empty.

Errors

There are no errors described for this operation.

DeleteEndpointConfig

$result = $client->deleteEndpointConfig([/* ... */]);
$promise = $client->deleteEndpointConfigAsync([/* ... */]);

Deletes an endpoint configuration. The DeleteEndpointConfig API deletes only the specified configuration. It does not delete endpoints created using the configuration.

Parameter Syntax

$result = $client->deleteEndpointConfig([
    'EndpointConfigName' => '<string>', // REQUIRED
]);

Parameter Details

Members

EndpointConfigName

Type: string

The name of the endpoint configuration that you want to delete.

Result Syntax

[]

Result Details

The results for this operation are always empty.

Errors

There are no errors described for this operation.

DeleteModel

$result = $client->deleteModel([/* ... */]);
$promise = $client->deleteModelAsync([/* ... */]);

Deletes a model. The DeleteModel API deletes only the model entry that was created in Amazon SageMaker when you called the CreateModel API. It does not delete model artifacts, inference code, or the IAM role that you specified when creating the model.

Parameter Syntax

$result = $client->deleteModel([
    'ModelName' => '<string>', // REQUIRED
]);

Parameter Details

Members

ModelName

Type: string

The name of the model to delete.

Result Syntax

[]

Result Details

The results for this operation are always empty.

Errors

There are no errors described for this operation.

DeleteNotebookInstance

$result = $client->deleteNotebookInstance([/* ... */]);
$promise = $client->deleteNotebookInstanceAsync([/* ... */]);

Deletes an Amazon SageMaker notebook instance. Before you can delete a notebook instance, you must call the StopNotebookInstance API.

When you delete a notebook instance, you lose all of your data. Amazon SageMaker removes the ML compute instance, and deletes the ML storage volume and the network interface associated with the notebook instance.

Parameter Syntax

$result = $client->deleteNotebookInstance([
    'NotebookInstanceName' => '<string>', // REQUIRED
]);

Parameter Details

Members

NotebookInstanceName

Type: string

The name of the Amazon SageMaker notebook instance to delete.

Result Syntax

[]

Result Details

The results for this operation are always empty.

Errors

There are no errors described for this operation.

DeleteNotebookInstanceLifecycleConfig

$result = $client->deleteNotebookInstanceLifecycleConfig([/* ... */]);
$promise = $client->deleteNotebookInstanceLifecycleConfigAsync([/* ... */]);

Deletes a notebook instance lifecycle configuration.

Parameter Syntax

$result = $client->deleteNotebookInstanceLifecycleConfig([
    'NotebookInstanceLifecycleConfigName' => '<string>', // REQUIRED
]);

Parameter Details

Members

NotebookInstanceLifecycleConfigName

Type: string

The name of the lifecycle configuration to delete.

Result Syntax

[]

Result Details

The results for this operation are always empty.

Errors

There are no errors described for this operation.

DeleteTags

$result = $client->deleteTags([/* ... */]);
$promise = $client->deleteTagsAsync([/* ... */]);

Deletes the specified tags from an Amazon SageMaker resource.

To list a resource's tags, use the ListTags API.

Parameter Syntax

$result = $client->deleteTags([
    'ResourceArn' => '<string>', // REQUIRED
    'TagKeys' => ['<string>', ...], // REQUIRED
]);

Parameter Details

Members

ResourceArn

Type: string

The Amazon Resource Name (ARN) of the resource whose tags you want to delete.

TagKeys

Type: Array of strings

An array or one or more tag keys to delete.

Result Syntax

[]

Result Details

The results for this operation are always empty.

Errors

There are no errors described for this operation.

DescribeEndpoint

$result = $client->describeEndpoint([/* ... */]);
$promise = $client->describeEndpointAsync([/* ... */]);

Returns the description of an endpoint.

Parameter Syntax

$result = $client->describeEndpoint([
    'EndpointName' => '<string>', // REQUIRED
]);

Parameter Details

Members

EndpointName

Type: string

The name of the endpoint.

Result Syntax

[
    'CreationTime' => <DateTime>,
    'EndpointArn' => '<string>',
    'EndpointConfigName' => '<string>',
    'EndpointName' => '<string>',
    'EndpointStatus' => 'OutOfService|Creating|Updating|RollingBack|InService|Deleting|Failed',
    'FailureReason' => '<string>',
    'LastModifiedTime' => <DateTime>,
    'ProductionVariants' => [
        [
            'CurrentInstanceCount' => <integer>,
            'CurrentWeight' => <float>,
            'DeployedImages' => [
                [
                    'ResolutionTime' => <DateTime>,
                    'ResolvedImage' => '<string>',
                    'SpecifiedImage' => '<string>',
                ],
                // ...
            ],
            'DesiredInstanceCount' => <integer>,
            'DesiredWeight' => <float>,
            'VariantName' => '<string>',
        ],
        // ...
    ],
]

Result Details

Members

CreationTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

A timestamp that shows when the endpoint was created.

EndpointArn

Type: string

The Amazon Resource Name (ARN) of the endpoint.

EndpointConfigName

Type: string

The name of the endpoint configuration associated with this endpoint.

EndpointName

Type: string

Name of the endpoint.

EndpointStatus

Type: string

The status of the endpoint.

FailureReason

Type: string

If the status of the endpoint is Failed, the reason why it failed.

LastModifiedTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

A timestamp that shows when the endpoint was last modified.

ProductionVariants

Type: Array of ProductionVariantSummary structures

An array of ProductionVariantSummary objects, one for each model hosted behind this endpoint.

Errors

There are no errors described for this operation.

DescribeEndpointConfig

$result = $client->describeEndpointConfig([/* ... */]);
$promise = $client->describeEndpointConfigAsync([/* ... */]);

Returns the description of an endpoint configuration created using the CreateEndpointConfig API.

Parameter Syntax

$result = $client->describeEndpointConfig([
    'EndpointConfigName' => '<string>', // REQUIRED
]);

Parameter Details

Members

EndpointConfigName

Type: string

The name of the endpoint configuration.

Result Syntax

[
    'CreationTime' => <DateTime>,
    'EndpointConfigArn' => '<string>',
    'EndpointConfigName' => '<string>',
    'KmsKeyId' => '<string>',
    'ProductionVariants' => [
        [
            'InitialInstanceCount' => <integer>,
            'InitialVariantWeight' => <float>,
            'InstanceType' => 'ml.t2.medium|ml.t2.large|ml.t2.xlarge|ml.t2.2xlarge|ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.c4.large|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.c5.large|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge',
            'ModelName' => '<string>',
            'VariantName' => '<string>',
        ],
        // ...
    ],
]

Result Details

Members

CreationTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

A timestamp that shows when the endpoint configuration was created.

EndpointConfigArn

Type: string

The Amazon Resource Name (ARN) of the endpoint configuration.

EndpointConfigName

Type: string

Name of the Amazon SageMaker endpoint configuration.

KmsKeyId

Type: string

AWS KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.

ProductionVariants

Type: Array of ProductionVariant structures

An array of ProductionVariant objects, one for each model that you want to host at this endpoint.

Errors

There are no errors described for this operation.

DescribeHyperParameterTuningJob

$result = $client->describeHyperParameterTuningJob([/* ... */]);
$promise = $client->describeHyperParameterTuningJobAsync([/* ... */]);

Gets a description of a hyperparameter tuning job.

Parameter Syntax

$result = $client->describeHyperParameterTuningJob([
    'HyperParameterTuningJobName' => '<string>', // REQUIRED
]);

Parameter Details

Members

HyperParameterTuningJobName

Type: string

The name of the tuning job to describe.

Result Syntax

[
    'BestTrainingJob' => [
        'CreationTime' => <DateTime>,
        'FailureReason' => '<string>',
        'FinalHyperParameterTuningJobObjectiveMetric' => [
            'MetricName' => '<string>',
            'Type' => 'Maximize|Minimize',
            'Value' => <float>,
        ],
        'ObjectiveStatus' => 'Succeeded|Pending|Failed',
        'TrainingEndTime' => <DateTime>,
        'TrainingJobArn' => '<string>',
        'TrainingJobName' => '<string>',
        'TrainingJobStatus' => 'InProgress|Completed|Failed|Stopping|Stopped',
        'TrainingStartTime' => <DateTime>,
        'TunedHyperParameters' => ['<string>', ...],
    ],
    'CreationTime' => <DateTime>,
    'FailureReason' => '<string>',
    'HyperParameterTuningEndTime' => <DateTime>,
    'HyperParameterTuningJobArn' => '<string>',
    'HyperParameterTuningJobConfig' => [
        'HyperParameterTuningJobObjective' => [
            'MetricName' => '<string>',
            'Type' => 'Maximize|Minimize',
        ],
        'ParameterRanges' => [
            'CategoricalParameterRanges' => [
                [
                    'Name' => '<string>',
                    'Values' => ['<string>', ...],
                ],
                // ...
            ],
            'ContinuousParameterRanges' => [
                [
                    'MaxValue' => '<string>',
                    'MinValue' => '<string>',
                    'Name' => '<string>',
                ],
                // ...
            ],
            'IntegerParameterRanges' => [
                [
                    'MaxValue' => '<string>',
                    'MinValue' => '<string>',
                    'Name' => '<string>',
                ],
                // ...
            ],
        ],
        'ResourceLimits' => [
            'MaxNumberOfTrainingJobs' => <integer>,
            'MaxParallelTrainingJobs' => <integer>,
        ],
        'Strategy' => 'Bayesian',
    ],
    'HyperParameterTuningJobName' => '<string>',
    'HyperParameterTuningJobStatus' => 'Completed|InProgress|Failed|Stopped|Stopping',
    'LastModifiedTime' => <DateTime>,
    'ObjectiveStatusCounters' => [
        'Failed' => <integer>,
        'Pending' => <integer>,
        'Succeeded' => <integer>,
    ],
    'TrainingJobDefinition' => [
        'AlgorithmSpecification' => [
            'MetricDefinitions' => [
                [
                    'Name' => '<string>',
                    'Regex' => '<string>',
                ],
                // ...
            ],
            'TrainingImage' => '<string>',
            'TrainingInputMode' => 'Pipe|File',
        ],
        'InputDataConfig' => [
            [
                'ChannelName' => '<string>',
                'CompressionType' => 'None|Gzip',
                'ContentType' => '<string>',
                'DataSource' => [
                    'S3DataSource' => [
                        'S3DataDistributionType' => 'FullyReplicated|ShardedByS3Key',
                        'S3DataType' => 'ManifestFile|S3Prefix',
                        'S3Uri' => '<string>',
                    ],
                ],
                'RecordWrapperType' => 'None|RecordIO',
            ],
            // ...
        ],
        'OutputDataConfig' => [
            'KmsKeyId' => '<string>',
            'S3OutputPath' => '<string>',
        ],
        'ResourceConfig' => [
            'InstanceCount' => <integer>,
            'InstanceType' => 'ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge',
            'VolumeKmsKeyId' => '<string>',
            'VolumeSizeInGB' => <integer>,
        ],
        'RoleArn' => '<string>',
        'StaticHyperParameters' => ['<string>', ...],
        'StoppingCondition' => [
            'MaxRuntimeInSeconds' => <integer>,
        ],
        'VpcConfig' => [
            'SecurityGroupIds' => ['<string>', ...],
            'Subnets' => ['<string>', ...],
        ],
    ],
    'TrainingJobStatusCounters' => [
        'Completed' => <integer>,
        'InProgress' => <integer>,
        'NonRetryableError' => <integer>,
        'RetryableError' => <integer>,
        'Stopped' => <integer>,
    ],
]

Result Details

Members

BestTrainingJob

Required: Yes
Type: HyperParameterTrainingJobSummary structure

A TrainingJobSummary object that describes the training job that completed with the best current HyperParameterTuningJobObjective.

CreationTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

The date and time that the tuning job started.

FailureReason

Type: string

If the tuning job failed, the reason it failed.

HyperParameterTuningEndTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

The date and time that the tuning job ended.

HyperParameterTuningJobArn

Type: string

The Amazon Resource Name (ARN) of the tuning job.

HyperParameterTuningJobConfig

Required: Yes
Type: HyperParameterTuningJobConfig structure

The HyperParameterTuningJobConfig object that specifies the configuration of the tuning job.

HyperParameterTuningJobName

Type: string

The name of the tuning job.

HyperParameterTuningJobStatus

Type: string

The status of the tuning job: InProgress, Completed, Failed, Stopping, or Stopped.

LastModifiedTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

The date and time that the status of the tuning job was modified.

ObjectiveStatusCounters

Type: ObjectiveStatusCounters structure

The ObjectiveStatusCounters object that specifies the number of training jobs, categorized by the status of their final objective metric, that this tuning job launched.

TrainingJobDefinition

Required: Yes
Type: HyperParameterTrainingJobDefinition structure

The HyperParameterTrainingJobDefinition object that specifies the definition of the training jobs that this tuning job launches.

TrainingJobStatusCounters

Type: TrainingJobStatusCounters structure

The TrainingJobStatusCounters object that specifies the number of training jobs, categorized by status, that this tuning job launched.

Errors

ResourceNotFound:
Resource being access is not found.

DescribeModel

$result = $client->describeModel([/* ... */]);
$promise = $client->describeModelAsync([/* ... */]);

Describes a model that you created using the CreateModel API.

Parameter Syntax

$result = $client->describeModel([
    'ModelName' => '<string>', // REQUIRED
]);

Parameter Details

Members

ModelName

Type: string

The name of the model.

Result Syntax

[
    'CreationTime' => <DateTime>,
    'ExecutionRoleArn' => '<string>',
    'ModelArn' => '<string>',
    'ModelName' => '<string>',
    'PrimaryContainer' => [
        'ContainerHostname' => '<string>',
        'Environment' => ['<string>', ...],
        'Image' => '<string>',
        'ModelDataUrl' => '<string>',
    ],
    'VpcConfig' => [
        'SecurityGroupIds' => ['<string>', ...],
        'Subnets' => ['<string>', ...],
    ],
]

Result Details

Members

CreationTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

A timestamp that shows when the model was created.

ExecutionRoleArn

Type: string

The Amazon Resource Name (ARN) of the IAM role that you specified for the model.

ModelArn

Type: string

The Amazon Resource Name (ARN) of the model.

ModelName

Type: string

Name of the Amazon SageMaker model.

PrimaryContainer

Required: Yes
Type: ContainerDefinition structure

The location of the primary inference code, associated artifacts, and custom environment map that the inference code uses when it is deployed in production.

VpcConfig

Required: Yes
Type: VpcConfig structure

A VpcConfig object that specifies the VPC that this model has access to. For more information, see host-vpc

Errors

There are no errors described for this operation.

DescribeNotebookInstance

$result = $client->describeNotebookInstance([/* ... */]);
$promise = $client->describeNotebookInstanceAsync([/* ... */]);

Returns information about a notebook instance.

Parameter Syntax

$result = $client->describeNotebookInstance([
    'NotebookInstanceName' => '<string>', // REQUIRED
]);

Parameter Details

Members

NotebookInstanceName

Type: string

The name of the notebook instance that you want information about.

Result Syntax

[
    'CreationTime' => <DateTime>,
    'DirectInternetAccess' => 'Enabled|Disabled',
    'FailureReason' => '<string>',
    'InstanceType' => 'ml.t2.medium|ml.t2.large|ml.t2.xlarge|ml.t2.2xlarge|ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge',
    'KmsKeyId' => '<string>',
    'LastModifiedTime' => <DateTime>,
    'NetworkInterfaceId' => '<string>',
    'NotebookInstanceArn' => '<string>',
    'NotebookInstanceLifecycleConfigName' => '<string>',
    'NotebookInstanceName' => '<string>',
    'NotebookInstanceStatus' => 'Pending|InService|Stopping|Stopped|Failed|Deleting|Updating',
    'RoleArn' => '<string>',
    'SecurityGroups' => ['<string>', ...],
    'SubnetId' => '<string>',
    'Url' => '<string>',
]

Result Details

Members

CreationTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

A timestamp. Use this parameter to return the time when the notebook instance was created

DirectInternetAccess

Type: string

Describes whether Amazon SageMaker provides internet access to the notebook instance. If this value is set to Disabled, he notebook instance does not have internet access, and cannot connect to Amazon SageMaker training and endpoint services.

For more information, see appendix-notebook-and-internet-access.

FailureReason

Type: string

If status is failed, the reason it failed.

InstanceType

Type: string

The type of ML compute instance running on the notebook instance.

KmsKeyId

Type: string

AWS KMS key ID Amazon SageMaker uses to encrypt data when storing it on the ML storage volume attached to the instance.

LastModifiedTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

A timestamp. Use this parameter to retrieve the time when the notebook instance was last modified.

NetworkInterfaceId

Type: string

Network interface IDs that Amazon SageMaker created at the time of creating the instance.

NotebookInstanceArn

Type: string

The Amazon Resource Name (ARN) of the notebook instance.

NotebookInstanceLifecycleConfigName

Type: string

Returns the name of a notebook instance lifecycle configuration.

For information about notebook instance lifestyle configurations, see notebook-lifecycle-config.

NotebookInstanceName

Type: string

Name of the Amazon SageMaker notebook instance.

NotebookInstanceStatus

Type: string

The status of the notebook instance.

RoleArn

Type: string

Amazon Resource Name (ARN) of the IAM role associated with the instance.

SecurityGroups

Type: Array of strings

The IDs of the VPC security groups.

SubnetId

Type: string

The ID of the VPC subnet.

Url

Type: string

The URL that you use to connect to the Jupyter notebook that is running in your notebook instance.

Errors

There are no errors described for this operation.

DescribeNotebookInstanceLifecycleConfig

$result = $client->describeNotebookInstanceLifecycleConfig([/* ... */]);
$promise = $client->describeNotebookInstanceLifecycleConfigAsync([/* ... */]);

Returns a description of a notebook instance lifecycle configuration.

For information about notebook instance lifestyle configurations, see notebook-lifecycle-config.

Parameter Syntax

$result = $client->describeNotebookInstanceLifecycleConfig([
    'NotebookInstanceLifecycleConfigName' => '<string>', // REQUIRED
]);

Parameter Details

Members

NotebookInstanceLifecycleConfigName

Type: string

The name of the lifecycle configuration to describe.

Result Syntax

[
    'CreationTime' => <DateTime>,
    'LastModifiedTime' => <DateTime>,
    'NotebookInstanceLifecycleConfigArn' => '<string>',
    'NotebookInstanceLifecycleConfigName' => '<string>',
    'OnCreate' => [
        [
            'Content' => '<string>',
        ],
        // ...
    ],
    'OnStart' => [
        [
            'Content' => '<string>',
        ],
        // ...
    ],
]

Result Details

Members

CreationTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

A timestamp that tells when the lifecycle configuration was created.

LastModifiedTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

A timestamp that tells when the lifecycle configuration was last modified.

NotebookInstanceLifecycleConfigArn

Type: string

The Amazon Resource Name (ARN) of the lifecycle configuration.

NotebookInstanceLifecycleConfigName

Type: string

The name of the lifecycle configuration.

OnCreate

Type: Array of NotebookInstanceLifecycleHook structures

The shell script that runs only once, when you create a notebook instance.

OnStart

Type: Array of NotebookInstanceLifecycleHook structures

The shell script that runs every time you start a notebook instance, including when you create the notebook instance.

Errors

There are no errors described for this operation.

DescribeTrainingJob

$result = $client->describeTrainingJob([/* ... */]);
$promise = $client->describeTrainingJobAsync([/* ... */]);

Returns information about a training job.

Parameter Syntax

$result = $client->describeTrainingJob([
    'TrainingJobName' => '<string>', // REQUIRED
]);

Parameter Details

Members

TrainingJobName

Type: string

The name of the training job.

Result Syntax

[
    'AlgorithmSpecification' => [
        'TrainingImage' => '<string>',
        'TrainingInputMode' => 'Pipe|File',
    ],
    'CreationTime' => <DateTime>,
    'FailureReason' => '<string>',
    'HyperParameters' => ['<string>', ...],
    'InputDataConfig' => [
        [
            'ChannelName' => '<string>',
            'CompressionType' => 'None|Gzip',
            'ContentType' => '<string>',
            'DataSource' => [
                'S3DataSource' => [
                    'S3DataDistributionType' => 'FullyReplicated|ShardedByS3Key',
                    'S3DataType' => 'ManifestFile|S3Prefix',
                    'S3Uri' => '<string>',
                ],
            ],
            'RecordWrapperType' => 'None|RecordIO',
        ],
        // ...
    ],
    'LastModifiedTime' => <DateTime>,
    'ModelArtifacts' => [
        'S3ModelArtifacts' => '<string>',
    ],
    'OutputDataConfig' => [
        'KmsKeyId' => '<string>',
        'S3OutputPath' => '<string>',
    ],
    'ResourceConfig' => [
        'InstanceCount' => <integer>,
        'InstanceType' => 'ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge',
        'VolumeKmsKeyId' => '<string>',
        'VolumeSizeInGB' => <integer>,
    ],
    'RoleArn' => '<string>',
    'SecondaryStatus' => 'Starting|LaunchingMLInstances|PreparingTrainingStack|Downloading|DownloadingTrainingImage|Training|Uploading|Stopping|Stopped|MaxRuntimeExceeded|Completed|Failed',
    'SecondaryStatusTransitions' => [
        [
            'EndTime' => <DateTime>,
            'StartTime' => <DateTime>,
            'Status' => 'Starting|LaunchingMLInstances|PreparingTrainingStack|Downloading|DownloadingTrainingImage|Training|Uploading|Stopping|Stopped|MaxRuntimeExceeded|Completed|Failed',
            'StatusMessage' => '<string>',
        ],
        // ...
    ],
    'StoppingCondition' => [
        'MaxRuntimeInSeconds' => <integer>,
    ],
    'TrainingEndTime' => <DateTime>,
    'TrainingJobArn' => '<string>',
    'TrainingJobName' => '<string>',
    'TrainingJobStatus' => 'InProgress|Completed|Failed|Stopping|Stopped',
    'TrainingStartTime' => <DateTime>,
    'TuningJobArn' => '<string>',
    'VpcConfig' => [
        'SecurityGroupIds' => ['<string>', ...],
        'Subnets' => ['<string>', ...],
    ],
]

Result Details

Members

AlgorithmSpecification

Required: Yes
Type: AlgorithmSpecification structure

Information about the algorithm used for training, and algorithm metadata.

CreationTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

A timestamp that indicates when the training job was created.

FailureReason

Type: string

If the training job failed, the reason it failed.

HyperParameters

Type: Associative array of custom strings keys (ParameterKey) to strings

Algorithm-specific parameters.

InputDataConfig

Type: Array of Channel structures

An array of Channel objects that describes each data input channel.

LastModifiedTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

A timestamp that indicates when the status of the training job was last modified.

ModelArtifacts

Required: Yes
Type: ModelArtifacts structure

Information about the Amazon S3 location that is configured for storing model artifacts.

OutputDataConfig

Required: Yes
Type: OutputDataConfig structure

The S3 path where model artifacts that you configured when creating the job are stored. Amazon SageMaker creates subfolders for model artifacts.

ResourceConfig

Required: Yes
Type: ResourceConfig structure

Resources, including ML compute instances and ML storage volumes, that are configured for model training.

RoleArn

Type: string

The AWS Identity and Access Management (IAM) role configured for the training job.

SecondaryStatus

Type: string

Provides granular information about the system state. For more information, see TrainingJobStatus.

Starting - starting the training job.
Downloading - downloading the input data.
Training - model training is in progress.
Uploading - uploading the trained model.
Stopping - stopping the training job.
Stopped - the training job has stopped.
MaxRuntimeExceeded - the training job exceeded the specified max run time and has been stopped.
Completed - the training job has completed.
Failed - the training job has failed. The failure reason is stored in the FailureReason field of DescribeTrainingJobResponse.

The valid values for SecondaryStatus are subject to change. They primarily provide information on the progress of the training job.

SecondaryStatusTransitions

Type: Array of SecondaryStatusTransition structures

To give an overview of the training job lifecycle, SecondaryStatusTransitions is a log of time-ordered secondary statuses that a training job has transitioned.

StoppingCondition

Type: StoppingCondition structure

The condition under which to stop the training job.

TrainingEndTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

Indicates the time when the training job ends on training instances. You are billed for the time interval between the value of TrainingStartTime and this time. For successful jobs and stopped jobs, this is the time after model artifacts are uploaded. For failed jobs, this is the time when Amazon SageMaker detects a job failure.

TrainingJobArn

Type: string

The Amazon Resource Name (ARN) of the training job.

TrainingJobName

Type: string

Name of the model training job.

TrainingJobStatus

Type: string

The status of the training job.

For the InProgress status, Amazon SageMaker can return these secondary statuses:

Starting - Preparing for training.
Downloading - Optional stage for algorithms that support File training input mode. It indicates data is being downloaded to ML storage volumes.
Training - Training is in progress.
Uploading - Training is complete and model upload is in progress.

For the Stopped training status, Amazon SageMaker can return these secondary statuses:

MaxRuntimeExceeded - Job stopped as a result of maximum allowed runtime exceeded.

TrainingStartTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

Indicates the time when the training job starts on training instances. You are billed for the time interval between this time and the value of TrainingEndTime. The start time in CloudWatch Logs might be later than this time. The difference is due to the time it takes to download the training data and to the size of the training container.

TuningJobArn

Type: string

The Amazon Resource Name (ARN) of the associated hyperparameter tuning job if the training job was launched by a hyperparameter tuning job.

VpcConfig

Required: Yes
Type: VpcConfig structure

A VpcConfig object that specifies the VPC that this training job has access to. For more information, see train-vpc.

Errors

ResourceNotFound:
Resource being access is not found.

DescribeTransformJob

$result = $client->describeTransformJob([/* ... */]);
$promise = $client->describeTransformJobAsync([/* ... */]);

Returns information about a transform job.

Parameter Syntax

$result = $client->describeTransformJob([
    'TransformJobName' => '<string>', // REQUIRED
]);

Parameter Details

Members

TransformJobName

Type: string

The name of the transform job that you want to view details of.

Result Syntax

[
    'BatchStrategy' => 'MultiRecord|SingleRecord',
    'CreationTime' => <DateTime>,
    'Environment' => ['<string>', ...],
    'FailureReason' => '<string>',
    'MaxConcurrentTransforms' => <integer>,
    'MaxPayloadInMB' => <integer>,
    'ModelName' => '<string>',
    'TransformEndTime' => <DateTime>,
    'TransformInput' => [
        'CompressionType' => 'None|Gzip',
        'ContentType' => '<string>',
        'DataSource' => [
            'S3DataSource' => [
                'S3DataType' => 'ManifestFile|S3Prefix',
                'S3Uri' => '<string>',
            ],
        ],
        'SplitType' => 'None|Line|RecordIO',
    ],
    'TransformJobArn' => '<string>',
    'TransformJobName' => '<string>',
    'TransformJobStatus' => 'InProgress|Completed|Failed|Stopping|Stopped',
    'TransformOutput' => [
        'Accept' => '<string>',
        'AssembleWith' => 'None|Line',
        'KmsKeyId' => '<string>',
        'S3OutputPath' => '<string>',
    ],
    'TransformResources' => [
        'InstanceCount' => <integer>,
        'InstanceType' => 'ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.c4.xlarge|ml.c4.2xlarge|ml.c4.4xlarge|ml.c4.8xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge|ml.c5.xlarge|ml.c5.2xlarge|ml.c5.4xlarge|ml.c5.9xlarge|ml.c5.18xlarge|ml.m5.large|ml.m5.xlarge|ml.m5.2xlarge|ml.m5.4xlarge|ml.m5.12xlarge|ml.m5.24xlarge',
    ],
    'TransformStartTime' => <DateTime>,
]

Result Details

Members

BatchStrategy

Type: string

SingleRecord means only one record was used per a batch. MultiRecord means batches contained as many records that could possibly fit within the MaxPayloadInMB limit.

CreationTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

A timestamp that shows when the transform Job was created.

Environment

Type: Associative array of custom strings keys (TransformEnvironmentKey) to strings

FailureReason

Type: string

If the transform job failed, the reason that it failed.

MaxConcurrentTransforms

Type: int

The maximum number of parallel requests on each instance node that can be launched in a transform job. The default value is 1.

MaxPayloadInMB

Type: int

The maximum payload size , in MB used in the transform job.

ModelName

Type: string

The name of the model used in the transform job.

TransformEndTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

Indicates when the transform job is Completed, Stopped, or Failed. You are billed for the time interval between this time and the value of TransformStartTime.

TransformInput

Required: Yes
Type: TransformInput structure

Describes the dataset to be transformed and the Amazon S3 location where it is stored.

TransformJobArn

Type: string

The Amazon Resource Name (ARN) of the transform job.

TransformJobName

Type: string

The name of the transform job.

TransformJobStatus

Type: string

The status of the transform job. If the transform job failed, the reason is returned in the FailureReason field.

TransformOutput

Required: Yes
Type: TransformOutput structure

Identifies the Amazon S3 location where you want Amazon SageMaker to save the results from the transform job.

TransformResources

Required: Yes
Type: TransformResources structure

Describes the resources, including ML instance types and ML instance count, to use for the transform job.

TransformStartTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

Indicates when the transform job starts on ML instances. You are billed for the time interval between this time and the value of TransformEndTime.

Errors

ResourceNotFound:
Resource being access is not found.

ListEndpointConfigs

$result = $client->listEndpointConfigs([/* ... */]);
$promise = $client->listEndpointConfigsAsync([/* ... */]);

Lists endpoint configurations.

Parameter Syntax

$result = $client->listEndpointConfigs([
    'CreationTimeAfter' => <integer || string || DateTime>,
    'CreationTimeBefore' => <integer || string || DateTime>,
    'MaxResults' => <integer>,
    'NameContains' => '<string>',
    'NextToken' => '<string>',
    'SortBy' => 'Name|CreationTime',
    'SortOrder' => 'Ascending|Descending',
]);

Parameter Details

Members

CreationTimeAfter

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only endpoint configurations created after the specified time (timestamp).

CreationTimeBefore

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only endpoint configurations created before the specified time (timestamp).

MaxResults

Type: int

The maximum number of training jobs to return in the response.

NameContains

Type: string

A string in the endpoint configuration name. This filter returns only endpoint configurations whose name contains the specified string.

NextToken

Type: string

If the result of the previous ListEndpointConfig request was truncated, the response includes a NextToken. To retrieve the next set of endpoint configurations, use the token in the next request.

SortBy

Type: string

The field to sort results by. The default is CreationTime.

SortOrder

Type: string

The sort order for results. The default is Ascending.

Result Syntax

[
    'EndpointConfigs' => [
        [
            'CreationTime' => <DateTime>,
            'EndpointConfigArn' => '<string>',
            'EndpointConfigName' => '<string>',
        ],
        // ...
    ],
    'NextToken' => '<string>',
]

Result Details

Members

EndpointConfigs

Type: Array of EndpointConfigSummary structures

An array of endpoint configurations.

NextToken

Type: string

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of endpoint configurations, use it in the subsequent request

Errors

There are no errors described for this operation.

ListEndpoints

$result = $client->listEndpoints([/* ... */]);
$promise = $client->listEndpointsAsync([/* ... */]);

Lists endpoints.

Parameter Syntax

$result = $client->listEndpoints([
    'CreationTimeAfter' => <integer || string || DateTime>,
    'CreationTimeBefore' => <integer || string || DateTime>,
    'LastModifiedTimeAfter' => <integer || string || DateTime>,
    'LastModifiedTimeBefore' => <integer || string || DateTime>,
    'MaxResults' => <integer>,
    'NameContains' => '<string>',
    'NextToken' => '<string>',
    'SortBy' => 'Name|CreationTime|Status',
    'SortOrder' => 'Ascending|Descending',
    'StatusEquals' => 'OutOfService|Creating|Updating|RollingBack|InService|Deleting|Failed',
]);

Parameter Details

Members

CreationTimeAfter

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only endpoints that were created after the specified time (timestamp).

CreationTimeBefore

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only endpoints that were created before the specified time (timestamp).

LastModifiedTimeAfter

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only endpoints that were modified after the specified timestamp.

LastModifiedTimeBefore

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only endpoints that were modified before the specified timestamp.

MaxResults

Type: int

The maximum number of endpoints to return in the response.

NameContains

Type: string

A string in endpoint names. This filter returns only endpoints whose name contains the specified string.

NextToken

Type: string

If the result of a ListEndpoints request was truncated, the response includes a NextToken. To retrieve the next set of endpoints, use the token in the next request.

SortBy

Type: string

Sorts the list of results. The default is CreationTime.

SortOrder

Type: string

The sort order for results. The default is Ascending.

StatusEquals

Type: string

A filter that returns only endpoints with the specified status.

Result Syntax

[
    'Endpoints' => [
        [
            'CreationTime' => <DateTime>,
            'EndpointArn' => '<string>',
            'EndpointName' => '<string>',
            'EndpointStatus' => 'OutOfService|Creating|Updating|RollingBack|InService|Deleting|Failed',
            'LastModifiedTime' => <DateTime>,
        ],
        // ...
    ],
    'NextToken' => '<string>',
]

Result Details

Members

Endpoints

Type: Array of EndpointSummary structures

An array or endpoint objects.

NextToken

Type: string

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of training jobs, use it in the subsequent request.

Errors

There are no errors described for this operation.

ListHyperParameterTuningJobs

$result = $client->listHyperParameterTuningJobs([/* ... */]);
$promise = $client->listHyperParameterTuningJobsAsync([/* ... */]);

Gets a list of HyperParameterTuningJobSummary objects that describe the hyperparameter tuning jobs launched in your account.

Parameter Syntax

$result = $client->listHyperParameterTuningJobs([
    'CreationTimeAfter' => <integer || string || DateTime>,
    'CreationTimeBefore' => <integer || string || DateTime>,
    'LastModifiedTimeAfter' => <integer || string || DateTime>,
    'LastModifiedTimeBefore' => <integer || string || DateTime>,
    'MaxResults' => <integer>,
    'NameContains' => '<string>',
    'NextToken' => '<string>',
    'SortBy' => 'Name|Status|CreationTime',
    'SortOrder' => 'Ascending|Descending',
    'StatusEquals' => 'Completed|InProgress|Failed|Stopped|Stopping',
]);

Parameter Details

Members

CreationTimeAfter

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only tuning jobs that were created after the specified time.

CreationTimeBefore

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only tuning jobs that were created before the specified time.

LastModifiedTimeAfter

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only tuning jobs that were modified after the specified time.

LastModifiedTimeBefore

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only tuning jobs that were modified before the specified time.

MaxResults

Type: int

The maximum number of tuning jobs to return. The default value is 10.

NameContains

Type: string

A string in the tuning job name. This filter returns only tuning jobs whose name contains the specified string.

NextToken

Type: string

If the result of the previous ListHyperParameterTuningJobs request was truncated, the response includes a NextToken. To retrieve the next set of tuning jobs, use the token in the next request.

SortBy

Type: string

The field to sort results by. The default is Name.

SortOrder

Type: string

The sort order for results. The default is Ascending.

StatusEquals

Type: string

A filter that returns only tuning jobs with the specified status.

Result Syntax

[
    'HyperParameterTuningJobSummaries' => [
        [
            'CreationTime' => <DateTime>,
            'HyperParameterTuningEndTime' => <DateTime>,
            'HyperParameterTuningJobArn' => '<string>',
            'HyperParameterTuningJobName' => '<string>',
            'HyperParameterTuningJobStatus' => 'Completed|InProgress|Failed|Stopped|Stopping',
            'LastModifiedTime' => <DateTime>,
            'ObjectiveStatusCounters' => [
                'Failed' => <integer>,
                'Pending' => <integer>,
                'Succeeded' => <integer>,
            ],
            'ResourceLimits' => [
                'MaxNumberOfTrainingJobs' => <integer>,
                'MaxParallelTrainingJobs' => <integer>,
            ],
            'Strategy' => 'Bayesian',
            'TrainingJobStatusCounters' => [
                'Completed' => <integer>,
                'InProgress' => <integer>,
                'NonRetryableError' => <integer>,
                'RetryableError' => <integer>,
                'Stopped' => <integer>,
            ],
        ],
        // ...
    ],
    'NextToken' => '<string>',
]

Result Details

Members

HyperParameterTuningJobSummaries

Type: Array of HyperParameterTuningJobSummary structures

A list of HyperParameterTuningJobSummary objects that describe the tuning jobs that the ListHyperParameterTuningJobs request returned.

NextToken

Type: string

If the result of this ListHyperParameterTuningJobs request was truncated, the response includes a NextToken. To retrieve the next set of tuning jobs, use the token in the next request.

Errors

There are no errors described for this operation.

ListModels

$result = $client->listModels([/* ... */]);
$promise = $client->listModelsAsync([/* ... */]);

Lists models created with the CreateModel API.

Parameter Syntax

$result = $client->listModels([
    'CreationTimeAfter' => <integer || string || DateTime>,
    'CreationTimeBefore' => <integer || string || DateTime>,
    'MaxResults' => <integer>,
    'NameContains' => '<string>',
    'NextToken' => '<string>',
    'SortBy' => 'Name|CreationTime',
    'SortOrder' => 'Ascending|Descending',
]);

Parameter Details

Members

CreationTimeAfter

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only models created after the specified time (timestamp).

CreationTimeBefore

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only models created before the specified time (timestamp).

MaxResults

Type: int

The maximum number of models to return in the response.

NameContains

Type: string

A string in the training job name. This filter returns only models in the training job whose name contains the specified string.

NextToken

Type: string

If the response to a previous ListModels request was truncated, the response includes a NextToken. To retrieve the next set of models, use the token in the next request.

SortBy

Type: string

Sorts the list of results. The default is CreationTime.

SortOrder

Type: string

The sort order for results. The default is Ascending.

Result Syntax

[
    'Models' => [
        [
            'CreationTime' => <DateTime>,
            'ModelArn' => '<string>',
            'ModelName' => '<string>',
        ],
        // ...
    ],
    'NextToken' => '<string>',
]

Result Details

Members

Models

Type: Array of ModelSummary structures

An array of ModelSummary objects, each of which lists a model.

NextToken

Type: string

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of models, use it in the subsequent request.

Errors

There are no errors described for this operation.

ListNotebookInstanceLifecycleConfigs

$result = $client->listNotebookInstanceLifecycleConfigs([/* ... */]);
$promise = $client->listNotebookInstanceLifecycleConfigsAsync([/* ... */]);

Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.

Parameter Syntax

$result = $client->listNotebookInstanceLifecycleConfigs([
    'CreationTimeAfter' => <integer || string || DateTime>,
    'CreationTimeBefore' => <integer || string || DateTime>,
    'LastModifiedTimeAfter' => <integer || string || DateTime>,
    'LastModifiedTimeBefore' => <integer || string || DateTime>,
    'MaxResults' => <integer>,
    'NameContains' => '<string>',
    'NextToken' => '<string>',
    'SortBy' => 'Name|CreationTime|LastModifiedTime',
    'SortOrder' => 'Ascending|Descending',
]);

Parameter Details

Members

CreationTimeAfter

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only lifecycle configurations that were created after the specified time (timestamp).

CreationTimeBefore

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only lifecycle configurations that were created before the specified time (timestamp).

LastModifiedTimeAfter

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only lifecycle configurations that were modified after the specified time (timestamp).

LastModifiedTimeBefore

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only lifecycle configurations that were modified before the specified time (timestamp).

MaxResults

Type: int

The maximum number of lifecycle configurations to return in the response.

NameContains

Type: string

A string in the lifecycle configuration name. This filter returns only lifecycle configurations whose name contains the specified string.

NextToken

Type: string

If the result of a ListNotebookInstanceLifecycleConfigs request was truncated, the response includes a NextToken. To get the next set of lifecycle configurations, use the token in the next request.

SortBy

Type: string

Sorts the list of results. The default is CreationTime.

SortOrder

Type: string

The sort order for results.

Result Syntax

[
    'NextToken' => '<string>',
    'NotebookInstanceLifecycleConfigs' => [
        [
            'CreationTime' => <DateTime>,
            'LastModifiedTime' => <DateTime>,
            'NotebookInstanceLifecycleConfigArn' => '<string>',
            'NotebookInstanceLifecycleConfigName' => '<string>',
        ],
        // ...
    ],
]

Result Details

Members

NextToken

Type: string

If the response is truncated, Amazon SageMaker returns this token. To get the next set of lifecycle configurations, use it in the next request.

NotebookInstanceLifecycleConfigs

Type: Array of NotebookInstanceLifecycleConfigSummary structures

An array of NotebookInstanceLifecycleConfiguration objects, each listing a lifecycle configuration.

Errors

There are no errors described for this operation.

ListNotebookInstances

$result = $client->listNotebookInstances([/* ... */]);
$promise = $client->listNotebookInstancesAsync([/* ... */]);

Returns a list of the Amazon SageMaker notebook instances in the requester's account in an AWS Region.

Parameter Syntax

$result = $client->listNotebookInstances([
    'CreationTimeAfter' => <integer || string || DateTime>,
    'CreationTimeBefore' => <integer || string || DateTime>,
    'LastModifiedTimeAfter' => <integer || string || DateTime>,
    'LastModifiedTimeBefore' => <integer || string || DateTime>,
    'MaxResults' => <integer>,
    'NameContains' => '<string>',
    'NextToken' => '<string>',
    'NotebookInstanceLifecycleConfigNameContains' => '<string>',
    'SortBy' => 'Name|CreationTime|Status',
    'SortOrder' => 'Ascending|Descending',
    'StatusEquals' => 'Pending|InService|Stopping|Stopped|Failed|Deleting|Updating',
]);

Parameter Details

Members

CreationTimeAfter

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only notebook instances that were created after the specified time (timestamp).

CreationTimeBefore

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only notebook instances that were created before the specified time (timestamp).

LastModifiedTimeAfter

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only notebook instances that were modified after the specified time (timestamp).

LastModifiedTimeBefore

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only notebook instances that were modified before the specified time (timestamp).

MaxResults

Type: int

The maximum number of notebook instances to return.

NameContains

Type: string

A string in the notebook instances' name. This filter returns only notebook instances whose name contains the specified string.

NextToken

Type: string

If the previous call to the ListNotebookInstances is truncated, the response includes a NextToken. You can use this token in your subsequent ListNotebookInstances request to fetch the next set of notebook instances.

You might specify a filter or a sort order in your request. When response is truncated, you must use the same values for the filer and sort order in the next request.

NotebookInstanceLifecycleConfigNameContains

Type: string

A string in the name of a notebook instances lifecycle configuration associated with this notebook instance. This filter returns only notebook instances associated with a lifecycle configuration with a name that contains the specified string.

SortBy

Type: string

The field to sort results by. The default is Name.

SortOrder

Type: string

The sort order for results.

StatusEquals

Type: string

A filter that returns only notebook instances with the specified status.

Result Syntax

[
    'NextToken' => '<string>',
    'NotebookInstances' => [
        [
            'CreationTime' => <DateTime>,
            'InstanceType' => 'ml.t2.medium|ml.t2.large|ml.t2.xlarge|ml.t2.2xlarge|ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge',
            'LastModifiedTime' => <DateTime>,
            'NotebookInstanceArn' => '<string>',
            'NotebookInstanceLifecycleConfigName' => '<string>',
            'NotebookInstanceName' => '<string>',
            'NotebookInstanceStatus' => 'Pending|InService|Stopping|Stopped|Failed|Deleting|Updating',
            'Url' => '<string>',
        ],
        // ...
    ],
]

Result Details

Members

NextToken

Type: string

If the response to the previous ListNotebookInstances request was truncated, Amazon SageMaker returns this token. To retrieve the next set of notebook instances, use the token in the next request.

NotebookInstances

Type: Array of NotebookInstanceSummary structures

An array of NotebookInstanceSummary objects, one for each notebook instance.

Errors

There are no errors described for this operation.

$result = $client->listTags([/* ... */]);
$promise = $client->listTagsAsync([/* ... */]);

Returns the tags for the specified Amazon SageMaker resource.

Parameter Syntax

$result = $client->listTags([
    'MaxResults' => <integer>,
    'NextToken' => '<string>',
    'ResourceArn' => '<string>', // REQUIRED
]);

Parameter Details

Members

MaxResults

Type: int

Maximum number of tags to return.

NextToken

Type: string

If the response to the previous ListTags request is truncated, Amazon SageMaker returns this token. To retrieve the next set of tags, use it in the subsequent request.

ResourceArn

Type: string

The Amazon Resource Name (ARN) of the resource whose tags you want to retrieve.

Result Syntax

[
    'NextToken' => '<string>',
    'Tags' => [
        [
            'Key' => '<string>',
            'Value' => '<string>',
        ],
        // ...
    ],
]

Result Details

Members

NextToken

Type: string

If response is truncated, Amazon SageMaker includes a token in the response. You can use this token in your subsequent request to fetch next set of tokens.

Tags

Type: Array of Tag structures

An array of Tag objects, each with a tag key and a value.

Errors

There are no errors described for this operation.

ListTrainingJobs

$result = $client->listTrainingJobs([/* ... */]);
$promise = $client->listTrainingJobsAsync([/* ... */]);

Lists training jobs.

Parameter Syntax

$result = $client->listTrainingJobs([
    'CreationTimeAfter' => <integer || string || DateTime>,
    'CreationTimeBefore' => <integer || string || DateTime>,
    'LastModifiedTimeAfter' => <integer || string || DateTime>,
    'LastModifiedTimeBefore' => <integer || string || DateTime>,
    'MaxResults' => <integer>,
    'NameContains' => '<string>',
    'NextToken' => '<string>',
    'SortBy' => 'Name|CreationTime|Status',
    'SortOrder' => 'Ascending|Descending',
    'StatusEquals' => 'InProgress|Completed|Failed|Stopping|Stopped',
]);

Parameter Details

Members

CreationTimeAfter

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only training jobs created after the specified time (timestamp).

CreationTimeBefore

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only training jobs created before the specified time (timestamp).

LastModifiedTimeAfter

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only training jobs modified after the specified time (timestamp).

LastModifiedTimeBefore

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only training jobs modified before the specified time (timestamp).

MaxResults

Type: int

The maximum number of training jobs to return in the response.

NameContains

Type: string

A string in the training job name. This filter returns only training jobs whose name contains the specified string.

NextToken

Type: string

If the result of the previous ListTrainingJobs request was truncated, the response includes a NextToken. To retrieve the next set of training jobs, use the token in the next request.

SortBy

Type: string

The field to sort results by. The default is CreationTime.

SortOrder

Type: string

The sort order for results. The default is Ascending.

StatusEquals

Type: string

A filter that retrieves only training jobs with a specific status.

Result Syntax

[
    'NextToken' => '<string>',
    'TrainingJobSummaries' => [
        [
            'CreationTime' => <DateTime>,
            'LastModifiedTime' => <DateTime>,
            'TrainingEndTime' => <DateTime>,
            'TrainingJobArn' => '<string>',
            'TrainingJobName' => '<string>',
            'TrainingJobStatus' => 'InProgress|Completed|Failed|Stopping|Stopped',
        ],
        // ...
    ],
]

Result Details

Members

NextToken

Type: string

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of training jobs, use it in the subsequent request.

TrainingJobSummaries

Type: Array of TrainingJobSummary structures

An array of TrainingJobSummary objects, each listing a training job.

Errors

There are no errors described for this operation.

ListTrainingJobsForHyperParameterTuningJob

$result = $client->listTrainingJobsForHyperParameterTuningJob([/* ... */]);
$promise = $client->listTrainingJobsForHyperParameterTuningJobAsync([/* ... */]);

Gets a list of TrainingJobSummary objects that describe the training jobs that a hyperparameter tuning job launched.

Parameter Syntax

$result = $client->listTrainingJobsForHyperParameterTuningJob([
    'HyperParameterTuningJobName' => '<string>', // REQUIRED
    'MaxResults' => <integer>,
    'NextToken' => '<string>',
    'SortBy' => 'Name|CreationTime|Status|FinalObjectiveMetricValue',
    'SortOrder' => 'Ascending|Descending',
    'StatusEquals' => 'InProgress|Completed|Failed|Stopping|Stopped',
]);

Parameter Details

Members

HyperParameterTuningJobName

Type: string

The name of the tuning job whose training jobs you want to list.

MaxResults

Type: int

The maximum number of training jobs to return. The default value is 10.

NextToken

Type: string

If the result of the previous ListTrainingJobsForHyperParameterTuningJob request was truncated, the response includes a NextToken. To retrieve the next set of training jobs, use the token in the next request.

SortBy

Type: string

The field to sort results by. The default is Name.

If the value of this field is FinalObjectiveMetricValue, any training jobs that did not return an objective metric are not listed.

SortOrder

Type: string

The sort order for results. The default is Ascending.

StatusEquals

Type: string

A filter that returns only training jobs with the specified status.

Result Syntax

[
    'NextToken' => '<string>',
    'TrainingJobSummaries' => [
        [
            'CreationTime' => <DateTime>,
            'FailureReason' => '<string>',
            'FinalHyperParameterTuningJobObjectiveMetric' => [
                'MetricName' => '<string>',
                'Type' => 'Maximize|Minimize',
                'Value' => <float>,
            ],
            'ObjectiveStatus' => 'Succeeded|Pending|Failed',
            'TrainingEndTime' => <DateTime>,
            'TrainingJobArn' => '<string>',
            'TrainingJobName' => '<string>',
            'TrainingJobStatus' => 'InProgress|Completed|Failed|Stopping|Stopped',
            'TrainingStartTime' => <DateTime>,
            'TunedHyperParameters' => ['<string>', ...],
        ],
        // ...
    ],
]

Result Details

Members

NextToken

Type: string

If the result of this ListTrainingJobsForHyperParameterTuningJob request was truncated, the response includes a NextToken. To retrieve the next set of training jobs, use the token in the next request.

TrainingJobSummaries

Type: Array of HyperParameterTrainingJobSummary structures

A list of TrainingJobSummary objects that describe the training jobs that the ListTrainingJobsForHyperParameterTuningJob request returned.

Errors

ResourceNotFound:
Resource being access is not found.

ListTransformJobs

$result = $client->listTransformJobs([/* ... */]);
$promise = $client->listTransformJobsAsync([/* ... */]);

Lists transform jobs.

Parameter Syntax

$result = $client->listTransformJobs([
    'CreationTimeAfter' => <integer || string || DateTime>,
    'CreationTimeBefore' => <integer || string || DateTime>,
    'LastModifiedTimeAfter' => <integer || string || DateTime>,
    'LastModifiedTimeBefore' => <integer || string || DateTime>,
    'MaxResults' => <integer>,
    'NameContains' => '<string>',
    'NextToken' => '<string>',
    'SortBy' => 'Name|CreationTime|Status',
    'SortOrder' => 'Ascending|Descending',
    'StatusEquals' => 'InProgress|Completed|Failed|Stopping|Stopped',
]);

Parameter Details

Members

CreationTimeAfter

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only transform jobs created after the specified time.

CreationTimeBefore

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only transform jobs created before the specified time.

LastModifiedTimeAfter

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only transform jobs modified after the specified time.

LastModifiedTimeBefore

Type: timestamp (string|DateTime or anything parsable by strtotime)

A filter that returns only transform jobs modified before the specified time.

MaxResults

Type: int

The maximum number of transform jobs to return in the response. The default value is 10.

NameContains

Type: string

A string in the transform job name. This filter returns only transform jobs whose name contains the specified string.

NextToken

Type: string

If the result of the previous ListTransformJobs request was truncated, the response includes a NextToken. To retrieve the next set of transform jobs, use the token in the next request.

SortBy

Type: string

The field to sort results by. The default is CreationTime.

SortOrder

Type: string

The sort order for results. The default is Descending.

StatusEquals

Type: string

A filter that retrieves only transform jobs with a specific status.

Result Syntax

[
    'NextToken' => '<string>',
    'TransformJobSummaries' => [
        [
            'CreationTime' => <DateTime>,
            'FailureReason' => '<string>',
            'LastModifiedTime' => <DateTime>,
            'TransformEndTime' => <DateTime>,
            'TransformJobArn' => '<string>',
            'TransformJobName' => '<string>',
            'TransformJobStatus' => 'InProgress|Completed|Failed|Stopping|Stopped',
        ],
        // ...
    ],
]

Result Details

Members

NextToken

Type: string

If the response is truncated, Amazon SageMaker returns this token. To retrieve the next set of transform jobs, use it in the next request.

TransformJobSummaries

Type: Array of TransformJobSummary structures

An array of TransformJobSummary objects.

Errors

There are no errors described for this operation.

StartNotebookInstance

$result = $client->startNotebookInstance([/* ... */]);
$promise = $client->startNotebookInstanceAsync([/* ... */]);

Launches an ML compute instance with the latest version of the libraries and attaches your ML storage volume. After configuring the notebook instance, Amazon SageMaker sets the notebook instance status to InService. A notebook instance's status must be InService before you can connect to your Jupyter notebook.

Parameter Syntax

$result = $client->startNotebookInstance([
    'NotebookInstanceName' => '<string>', // REQUIRED
]);

Parameter Details

Members

NotebookInstanceName

Type: string

The name of the notebook instance to start.

Result Syntax

[]

Result Details

The results for this operation are always empty.

Errors

ResourceLimitExceeded:
You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.

StopHyperParameterTuningJob

$result = $client->stopHyperParameterTuningJob([/* ... */]);
$promise = $client->stopHyperParameterTuningJobAsync([/* ... */]);

Stops a running hyperparameter tuning job and all running training jobs that the tuning job launched.

All model artifacts output from the training jobs are stored in Amazon Simple Storage Service (Amazon S3). All data that the training jobs write to Amazon CloudWatch Logs are still available in CloudWatch. After the tuning job moves to the Stopped state, it releases all reserved resources for the tuning job.

Parameter Syntax

$result = $client->stopHyperParameterTuningJob([
    'HyperParameterTuningJobName' => '<string>', // REQUIRED
]);

Parameter Details

Members

HyperParameterTuningJobName

Type: string

The name of the tuning job to stop.

Result Syntax

[]

Result Details

The results for this operation are always empty.

Errors

ResourceNotFound:
Resource being access is not found.

StopNotebookInstance

$result = $client->stopNotebookInstance([/* ... */]);
$promise = $client->stopNotebookInstanceAsync([/* ... */]);

Terminates the ML compute instance. Before terminating the instance, Amazon SageMaker disconnects the ML storage volume from it. Amazon SageMaker preserves the ML storage volume.

To access data on the ML storage volume for a notebook instance that has been terminated, call the StartNotebookInstance API. StartNotebookInstance launches another ML compute instance, configures it, and attaches the preserved ML storage volume so you can continue your work.

Parameter Syntax

$result = $client->stopNotebookInstance([
    'NotebookInstanceName' => '<string>', // REQUIRED
]);

Parameter Details

Members

NotebookInstanceName

Type: string

The name of the notebook instance to terminate.

Result Syntax

[]

Result Details

The results for this operation are always empty.

Errors

There are no errors described for this operation.

StopTrainingJob

$result = $client->stopTrainingJob([/* ... */]);
$promise = $client->stopTrainingJobAsync([/* ... */]);

Stops a training job. To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of the training is not lost.

Training algorithms provided by Amazon SageMaker save the intermediate results of a model training job. This intermediate data is a valid model artifact. You can use the model artifacts that are saved when Amazon SageMaker stops a training job to create a model.

When it receives a StopTrainingJob request, Amazon SageMaker changes the status of the job to Stopping. After Amazon SageMaker stops the job, it sets the status to Stopped.

Parameter Syntax

$result = $client->stopTrainingJob([
    'TrainingJobName' => '<string>', // REQUIRED
]);

Parameter Details

Members

TrainingJobName

Type: string

The name of the training job to stop.

Result Syntax

[]

Result Details

The results for this operation are always empty.

Errors

ResourceNotFound:
Resource being access is not found.

StopTransformJob

$result = $client->stopTransformJob([/* ... */]);
$promise = $client->stopTransformJobAsync([/* ... */]);

Stops a transform job.

When Amazon SageMaker receives a StopTransformJob request, the status of the job changes to Stopping. After Amazon SageMaker stops the job, the status is set to Stopped. When you stop a transform job before it is completed, Amazon SageMaker doesn't store the job's output in Amazon S3.

Parameter Syntax

$result = $client->stopTransformJob([
    'TransformJobName' => '<string>', // REQUIRED
]);

Parameter Details

Members

TransformJobName

Type: string

The name of the transform job to stop.

Result Syntax

[]

Result Details

The results for this operation are always empty.

Errors

ResourceNotFound:
Resource being access is not found.

UpdateEndpoint

$result = $client->updateEndpoint([/* ... */]);
$promise = $client->updateEndpointAsync([/* ... */]);

Deploys the new EndpointConfig specified in the request, switches to using newly created endpoint, and then deletes resources provisioned for the endpoint using the previous EndpointConfig (there is no availability loss).

When Amazon SageMaker receives the request, it sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API.

You cannot update an endpoint with the current EndpointConfig. To update an endpoint, you must create a new EndpointConfig.

Parameter Syntax

$result = $client->updateEndpoint([
    'EndpointConfigName' => '<string>', // REQUIRED
    'EndpointName' => '<string>', // REQUIRED
]);

Parameter Details

Members

EndpointConfigName

Type: string

The name of the new endpoint configuration.

EndpointName

Type: string

The name of the endpoint whose configuration you want to update.

Result Syntax

[
    'EndpointArn' => '<string>',
]

Result Details

Members

EndpointArn

Type: string

The Amazon Resource Name (ARN) of the endpoint.

Errors

ResourceLimitExceeded:
You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.

UpdateEndpointWeightsAndCapacities

$result = $client->updateEndpointWeightsAndCapacities([/* ... */]);
$promise = $client->updateEndpointWeightsAndCapacitiesAsync([/* ... */]);

Updates variant weight of one or more variants associated with an existing endpoint, or capacity of one variant associated with an existing endpoint. When it receives the request, Amazon SageMaker sets the endpoint status to Updating. After updating the endpoint, it sets the status to InService. To check the status of an endpoint, use the DescribeEndpoint API.

Parameter Syntax

$result = $client->updateEndpointWeightsAndCapacities([
    'DesiredWeightsAndCapacities' => [ // REQUIRED
        [
            'DesiredInstanceCount' => <integer>,
            'DesiredWeight' => <float>,
            'VariantName' => '<string>', // REQUIRED
        ],
        // ...
    ],
    'EndpointName' => '<string>', // REQUIRED
]);

Parameter Details

Members

DesiredWeightsAndCapacities

Type: Array of DesiredWeightAndCapacity structures

An object that provides new capacity and weight values for a variant.

EndpointName

Type: string

The name of an existing Amazon SageMaker endpoint.

Result Syntax

[
    'EndpointArn' => '<string>',
]

Result Details

Members

EndpointArn

Type: string

The Amazon Resource Name (ARN) of the updated endpoint.

Errors

ResourceLimitExceeded:
You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.

UpdateNotebookInstance

$result = $client->updateNotebookInstance([/* ... */]);
$promise = $client->updateNotebookInstanceAsync([/* ... */]);

Updates a notebook instance. NotebookInstance updates include upgrading or downgrading the ML compute instance used for your notebook instance to accommodate changes in your workload requirements. You can also update the VPC security groups.

Parameter Syntax

$result = $client->updateNotebookInstance([
    'DisassociateLifecycleConfig' => true || false,
    'InstanceType' => 'ml.t2.medium|ml.t2.large|ml.t2.xlarge|ml.t2.2xlarge|ml.m4.xlarge|ml.m4.2xlarge|ml.m4.4xlarge|ml.m4.10xlarge|ml.m4.16xlarge|ml.p2.xlarge|ml.p2.8xlarge|ml.p2.16xlarge|ml.p3.2xlarge|ml.p3.8xlarge|ml.p3.16xlarge',
    'LifecycleConfigName' => '<string>',
    'NotebookInstanceName' => '<string>', // REQUIRED
    'RoleArn' => '<string>',
]);

Parameter Details

Members

DisassociateLifecycleConfig

Type: boolean

Set to true to remove the notebook instance lifecycle configuration currently associated with the notebook instance.

InstanceType

Type: string

The Amazon ML compute instance type.

LifecycleConfigName

Type: string

The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see notebook-lifecycle-config.

NotebookInstanceName

Type: string

The name of the notebook instance to update.

RoleArn

Type: string

The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access the notebook instance. For more information, see Amazon SageMaker Roles.

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

Result Syntax

[]

Result Details

The results for this operation are always empty.

Errors

ResourceLimitExceeded:
You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.

UpdateNotebookInstanceLifecycleConfig

$result = $client->updateNotebookInstanceLifecycleConfig([/* ... */]);
$promise = $client->updateNotebookInstanceLifecycleConfigAsync([/* ... */]);

Updates a notebook instance lifecycle configuration created with the CreateNotebookInstanceLifecycleConfig API.

Parameter Syntax

$result = $client->updateNotebookInstanceLifecycleConfig([
    'NotebookInstanceLifecycleConfigName' => '<string>', // REQUIRED
    'OnCreate' => [
        [
            'Content' => '<string>',
        ],
        // ...
    ],
    'OnStart' => [
        [
            'Content' => '<string>',
        ],
        // ...
    ],
]);

Parameter Details

Members

NotebookInstanceLifecycleConfigName

Type: string

The name of the lifecycle configuration.

OnCreate

Type: Array of NotebookInstanceLifecycleHook structures

The shell script that runs only once, when you create a notebook instance

OnStart

Type: Array of NotebookInstanceLifecycleHook structures

The shell script that runs every time you start a notebook instance, including when you create the notebook instance.

Result Syntax

[]

Result Details

The results for this operation are always empty.

Errors

ResourceLimitExceeded:
You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.

Shapes

AlgorithmSpecification

Description

Specifies the training algorithm to use in a CreateTrainingJob request.

For more information about algorithms provided by Amazon SageMaker, see Algorithms. For information about using your own algorithms, see your-algorithms.

Members

TrainingImage

Type: string

The registry path of the Docker image that contains the training algorithm. For information about docker registry paths for built-in algorithms, see sagemaker-algo-docker-registry-paths.

TrainingInputMode

Type: string

The input mode that the algorithm supports. For the input modes that Amazon SageMaker algorithms support, see Algorithms. If an algorithm supports the File input mode, Amazon SageMaker downloads the training data from S3 to the provisioned ML storage Volume, and mounts the directory to docker volume for training container. If an algorithm supports the Pipe input mode, Amazon SageMaker streams data directly from S3 to the container.

In File mode, make sure you provision ML storage volume with sufficient capacity to accommodate the data download from S3. In addition to the training data, the ML storage volume also stores the output model. The algorithm container use ML storage volume to also store intermediate information, if any.

For distributed algorithms using File mode, training data is distributed uniformly, and your training duration is predictable if the input data objects size is approximately same. Amazon 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 where one host in a training cluster is overloaded, thus becoming bottleneck in training.

CategoricalParameterRange

Description

A list of categorical hyperparameters to tune.

Members

Name

Type: string

The name of the categorical hyperparameter to tune.

Values

Type: Array of strings

A list of the categories for the hyperparameter.

Channel

Description

A channel is a named input source that training algorithms can consume.

Members

ChannelName

Type: string

The name of the channel.

CompressionType

Type: string

If training data is compressed, the compression type. The default value is None. CompressionType is used only in Pipe input mode. In File mode, leave this field unset or set it to None.

ContentType

Type: string

The MIME type of the data.

DataSource

Required: Yes
Type: DataSource structure

The location of the channel data.

RecordWrapperType

Type: string

Specify RecordIO as the value when input data is in raw format but the training algorithm requires the RecordIO format, in which case, Amazon SageMaker wraps each individual S3 object in a RecordIO record. If the input data is already in RecordIO format, you don't need to set this attribute. For more information, see Create a Dataset Using RecordIO.

In FILE mode, leave this field unset or set it to None.

ContainerDefinition

Description

Describes the container, as part of model definition.

Members

ContainerHostname

Type: string

The DNS host name for the container after Amazon SageMaker deploys it.

Environment

Type: Associative array of custom strings keys (EnvironmentKey) to strings

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.

Image

Type: string

The Amazon EC2 Container Registry (Amazon ECR) path where inference code is stored. 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 registry/repository[:tag] and registry/repository[@digest] image path formats. For more information, see Using Your Own Algorithms with Amazon SageMaker

ModelDataUrl

Type: string

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).

If you provide a value for this parameter, Amazon SageMaker uses AWS Security Token Service to download model artifacts from the S3 path you provide. AWS STS is activated in your IAM user account by default. If you previously deactivated AWS STS for a region, you need to reactivate AWS STS for that region. For more information, see Activating and Deactivating AWS STS i an AWS Region in the AWS Identity and Access Management User Guide.

ContinuousParameterRange

Description

A list of continuous hyperparameters to tune.

Members

MaxValue

Type: string

The maximum value for the hyperparameter. The tuning job uses floating-point values between MinValue value and this value for tuning.

MinValue

Type: string

The minimum value for the hyperparameter. The tuning job uses floating-point values between this value and MaxValuefor tuning.

Name

Type: string

The name of the continuous hyperparameter to tune.

DataSource

Description

Describes the location of the channel data.

Members

S3DataSource

Required: Yes
Type: S3DataSource structure

The S3 location of the data source that is associated with a channel.

DeployedImage

Description

Gets the Amazon EC2 Container Registry path of the docker image of the model that is hosted in this ProductionVariant.

If you used the registry/repository[:tag] form to to specify the image path of the primary container when you created the model hosted in this ProductionVariant, the path resolves to a path of the form registry/repository[@digest]. A digest is a hash value that identifies a specific version of an image. For information about Amazon ECR paths, see Pulling an Image in the Amazon ECR User Guide.

Members

ResolutionTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

The date and time when the image path for the model resolved to the ResolvedImage

ResolvedImage

Type: string

The specific digest path of the image hosted in this ProductionVariant.

SpecifiedImage

Type: string

The image path you specified when you created the model.

DesiredWeightAndCapacity

Description

Specifies weight and capacity values for a production variant.

Members

DesiredInstanceCount

Type: int

The variant's capacity.

DesiredWeight

Type: float

The variant's weight.

VariantName

Type: string

The name of the variant to update.

EndpointConfigSummary

Description

Provides summary information for an endpoint configuration.

Members

CreationTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

A timestamp that shows when the endpoint configuration was created.

EndpointConfigArn

Type: string

The Amazon Resource Name (ARN) of the endpoint configuration.

EndpointConfigName

Type: string

The name of the endpoint configuration.

EndpointSummary

Description

Provides summary information for an endpoint.

Members

CreationTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

A timestamp that shows when the endpoint was created.

EndpointArn

Type: string

The Amazon Resource Name (ARN) of the endpoint.

EndpointName

Type: string

The name of the endpoint.

EndpointStatus

Type: string

The status of the endpoint.

LastModifiedTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

A timestamp that shows when the endpoint was last modified.

FinalHyperParameterTuningJobObjectiveMetric

Description

Shows the final value for the objective metric for a training job that was launched by a hyperparameter tuning job. You define the objective metric in the HyperParameterTuningJobObjective parameter of HyperParameterTuningJobConfig.

Members

MetricName

Type: string

The name of the objective metric.

Type

Type: string

Whether to minimize or maximize the objective metric. Valid values are Minimize and Maximize.

Value

Type: float

The value of the objective metric.

HyperParameterAlgorithmSpecification

Description

Specifies which training algorithm to use for training jobs that a hyperparameter tuning job launches and the metrics to monitor.

Members

MetricDefinitions

Type: Array of MetricDefinition structures

An array of MetricDefinition objects that specify the metrics that the algorithm emits.

TrainingImage

Type: string

The registry path of the Docker image that contains the training algorithm. For information about Docker registry paths for built-in algorithms, see sagemaker-algo-docker-registry-paths.

TrainingInputMode

Type: string

The input mode that the algorithm supports: File or Pipe. In File input mode, Amazon SageMaker downloads the training data from Amazon S3 to the storage volume that is attached to the training instance and mounts the directory to the Docker volume for the training container. In Pipe input mode, Amazon SageMaker streams data directly from Amazon S3 to the container.

If you specify File mode, make sure that you provision the storage volume that is attached to the training instance with enough capacity to accommodate the training data downloaded from Amazon S3, the model artifacts, and intermediate information.

For more information about input modes, see Algorithms.

HyperParameterTrainingJobDefinition

Description

Defines the training jobs launched by a hyperparameter tuning job.

Members

AlgorithmSpecification

Required: Yes
Type: HyperParameterAlgorithmSpecification structure

The HyperParameterAlgorithmSpecification object that specifies the algorithm to use for the training jobs that the tuning job launches.

InputDataConfig

Type: Array of Channel structures

An array of Channel objects that specify the input for the training jobs that the tuning job launches.

OutputDataConfig

Required: Yes
Type: OutputDataConfig structure

Specifies the path to the Amazon S3 bucket where you store model artifacts from the training jobs that the tuning job launches.

ResourceConfig

Required: Yes
Type: ResourceConfig structure

The resources, including the compute instances and storage volumes, to use for the training jobs that the tuning job launches.

Storage volumes store model artifacts and incremental states. Training algorithms might also use storage volumes for scratch space. If you want Amazon SageMaker to use the storage volume to store the training data, choose File as the TrainingInputMode in the algorithm specification. For distributed training algorithms, specify an instance count greater than 1.

RoleArn

Type: string

The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches.

StaticHyperParameters

Type: Associative array of custom strings keys (ParameterKey) to strings

Specifies the values of hyperparameters that do not change for the tuning job.

StoppingCondition

Type: StoppingCondition structure

Sets a maximum duration for the training jobs that the tuning job launches. Use this parameter to limit model training costs.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This delays job termination for 120 seconds. Algorithms might use this 120-second window to save the model artifacts.

When Amazon SageMaker terminates a job because the stopping condition has been met, training algorithms provided by Amazon SageMaker save the intermediate results of the job.

VpcConfig

Required: Yes
Type: VpcConfig structure

The VpcConfig object that specifies the VPC that you want the training jobs that this hyperparameter tuning job launches to connect to. Control access to and from your training container by configuring the VPC. For more information, see train-vpc.

HyperParameterTrainingJobSummary

Description

Specifies summary information about a training job.

Members

CreationTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

The date and time that the training job was created.

FailureReason

Type: string

The reason that the training job failed.

FinalHyperParameterTuningJobObjectiveMetric

Required: Yes
Type: FinalHyperParameterTuningJobObjectiveMetric structure

The FinalHyperParameterTuningJobObjectiveMetric object that specifies the value of the objective metric of the tuning job that launched this training job.

ObjectiveStatus

Type: string

The status of the objective metric for the training job:

Succeeded: The final objective metric for the training job was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.

Pending: The training job is in progress and evaluation of its final objective metric is pending.

Failed: The final objective metric for the training job was not evaluated, and was not used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.

TrainingEndTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

The date and time that the training job ended.

TrainingJobArn

Type: string

The Amazon Resource Name (ARN) of the training job.

TrainingJobName

Type: string

The name of the training job.

TrainingJobStatus

Type: string

The status of the training job.

TrainingStartTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

The date and time that the training job started.

TunedHyperParameters

Type: Associative array of custom strings keys (ParameterKey) to strings

A list of the hyperparameters for which you specified ranges to search.

HyperParameterTuningJobConfig

Description

Configures a hyperparameter tuning job.

Members

HyperParameterTuningJobObjective

Required: Yes
Type: HyperParameterTuningJobObjective structure

The HyperParameterTuningJobObjective object that specifies the objective metric for this tuning job.

ParameterRanges

Type: ParameterRanges structure

The ParameterRanges object that specifies the ranges of hyperparameters that this tuning job searches.

ResourceLimits

Required: Yes
Type: ResourceLimits structure

The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs for this tuning job.

Strategy

Type: string

Specifies the search strategy for hyperparameters. Currently, the only valid value is Bayesian.

HyperParameterTuningJobObjective

Description

Defines the objective metric for a hyperparameter tuning job. Hyperparameter tuning uses the value of this metric to evaluate the training jobs it launches, and returns the training job that results in either the highest or lowest value for this metric, depending on the value you specify for the Type parameter.

Members

MetricName

Type: string

The name of the metric to use for the objective metric.

Type

Type: string

Whether to minimize or maximize the objective metric.

HyperParameterTuningJobSummary

Description

Provides summary information about a hyperparameter tuning job.

Members

CreationTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

The date and time that the tuning job was created.

HyperParameterTuningEndTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

The date and time that the tuning job ended.

HyperParameterTuningJobArn

Type: string

The Amazon Resource Name (ARN) of the tuning job.

HyperParameterTuningJobName

Type: string

The name of the tuning job.

HyperParameterTuningJobStatus

Type: string

The status of the tuning job.

LastModifiedTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

The date and time that the tuning job was modified.

ObjectiveStatusCounters

Type: ObjectiveStatusCounters structure

The ObjectiveStatusCounters object that specifies the numbers of training jobs, categorized by objective metric status, that this tuning job launched.

ResourceLimits

Required: Yes
Type: ResourceLimits structure

The ResourceLimits object that specifies the maximum number of training jobs and parallel training jobs allowed for this tuning job.

Strategy

Type: string

Specifies the search strategy hyperparameter tuning uses to choose which hyperparameters to use for each iteration. Currently, the only valid value is Bayesian.

TrainingJobStatusCounters

Type: TrainingJobStatusCounters structure

The TrainingJobStatusCounters object that specifies the numbers of training jobs, categorized by status, that this tuning job launched.

IntegerParameterRange

Description

For a hyperparameter of the integer type, specifies the range that a hyperparameter tuning job searches.

Members

MaxValue

Type: string

The maximum value of the hyperparameter to search.

MinValue

Type: string

The minimum value of the hyperparameter to search.

Name

Type: string

The name of the hyperparameter to search.

MetricDefinition

Description

Specifies a metric that the training algorithm writes to stderr or stdout. Amazon SageMakerHyperparamter tuning captures all defined metrics. You specify one metric that a hyperparameter tuning job uses as its objective metric to choose the best training job.

Members

Name

Type: string

The name of the metric.

Regex

Type: string

A regular expression that searches the output of a training job and gets the value of the metric. For more information about using regular expressions to define metrics, see automatic-model-tuning-define-metrics.

ModelArtifacts

Description

Provides information about the location that is configured for storing model artifacts.

Members

S3ModelArtifacts

Type: string

The path of the S3 object that contains the model artifacts. For example, s3://bucket-name/keynameprefix/model.tar.gz.

ModelSummary

Description

Provides summary information about a model.

Members

CreationTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

A timestamp that indicates when the model was created.

ModelArn

Type: string

The Amazon Resource Name (ARN) of the model.

ModelName

Type: string

The name of the model that you want a summary for.

NotebookInstanceLifecycleConfigSummary

Description

Provides a summary of a notebook instance lifecycle configuration.

Members

CreationTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

A timestamp that tells when the lifecycle configuration was created.

LastModifiedTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

A timestamp that tells when the lifecycle configuration was last modified.

NotebookInstanceLifecycleConfigArn

Type: string

The Amazon Resource Name (ARN) of the lifecycle configuration.

NotebookInstanceLifecycleConfigName

Type: string

The name of the lifecycle configuration.

NotebookInstanceLifecycleHook

Description

Contains the notebook instance lifecycle configuration script.

Each lifecycle configuration script has a limit of 16384 characters.

The value of the $PATH environment variable that is available to both scripts is /sbin:bin:/usr/sbin:/usr/bin.

View CloudWatch Logs for notebook instance lifecycle configurations in log group /aws/sagemaker/NotebookInstances in log stream [notebook-instance-name]/[LifecycleConfigHook].

Lifecycle configuration scripts cannot run for longer than 5 minutes. If a script runs for longer than 5 minutes, it fails and the notebook instance is not created or started.

For information about notebook instance lifestyle configurations, see notebook-lifecycle-config.

Members

Content

Type: string

A base64-encoded string that contains a shell script for a notebook instance lifecycle configuration.

NotebookInstanceSummary

Description

Provides summary information for an Amazon SageMaker notebook instance.

Members

CreationTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

A timestamp that shows when the notebook instance was created.

InstanceType

Type: string

The type of ML compute instance that the notebook instance is running on.

LastModifiedTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

A timestamp that shows when the notebook instance was last modified.

NotebookInstanceArn

Type: string

The Amazon Resource Name (ARN) of the notebook instance.

NotebookInstanceLifecycleConfigName

Type: string

The name of a notebook instance lifecycle configuration associated with this notebook instance.

For information about notebook instance lifestyle configurations, see notebook-lifecycle-config.

NotebookInstanceName

Type: string

The name of the notebook instance that you want a summary for.

NotebookInstanceStatus

Type: string

The status of the notebook instance.

Url

Type: string

The URL that you use to connect to the Jupyter instance running in your notebook instance.

ObjectiveStatusCounters

Description

Specifies the number of training jobs that this hyperparameter tuning job launched, categorized by the status of their objective metric. The objective metric status shows whether the final objective metric for the training job has been evaluated by the tuning job and used in the hyperparameter tuning process.

Members

Failed

Type: int

The number of training jobs whose final objective metric was not evaluated and used in the hyperparameter tuning process. This typically occurs when the training job failed or did not emit an objective metric.

Pending

Type: int

The number of training jobs that are in progress and pending evaluation of their final objective metric.

Succeeded

Type: int

The number of training jobs whose final objective metric was evaluated by the hyperparameter tuning job and used in the hyperparameter tuning process.

OutputDataConfig

Description

Provides information about how to store model training results (model artifacts).

Members

KmsKeyId

Type: string

The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt the model artifacts at rest using Amazon S3 server-side encryption.

If you don't provide the KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in Amazon Simple Storage Service developer guide.

The KMS key policy must grant permission to the IAM role you specify in your CreateTrainingJob request. Using Key Policies in AWS KMS in the AWS Key Management Service Developer Guide.

S3OutputPath

Type: string

Identifies the S3 path where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.

ParameterRanges

Description

Specifies ranges of integer, continuous, and categorical hyperparameters that a hyperparameter tuning job searches.

Members

CategoricalParameterRanges

Type: Array of CategoricalParameterRange structures

The array of CategoricalParameterRange objects that specify ranges of categorical hyperparameters that a hyperparameter tuning job searches.

ContinuousParameterRanges

Type: Array of ContinuousParameterRange structures

The array of ContinuousParameterRange objects that specify ranges of continuous hyperparameters that a hyperparameter tuning job searches.

IntegerParameterRanges

Type: Array of IntegerParameterRange structures

The array of IntegerParameterRange objects that specify ranges of integer hyperparameters that a hyperparameter tuning job searches.

ProductionVariant

Description

Identifies a model that you want to host and the resources to deploy for hosting it. If you are deploying multiple models, tell Amazon SageMaker how to distribute traffic among the models by specifying variant weights.

Members

InitialInstanceCount

Type: int

Number of instances to launch initially.

InitialVariantWeight

Type: float

Determines initial traffic distribution among all of the models that you specify in the endpoint configuration. The traffic to a production variant is determined by the ratio of the VariantWeight to the sum of all VariantWeight values across all ProductionVariants. If unspecified, it defaults to 1.0.

InstanceType

Type: string

The ML compute instance type.

ModelName

Type: string

The name of the model that you want to host. This is the name that you specified when creating the model.

VariantName

Type: string

The name of the production variant.

ProductionVariantSummary

Description

Describes weight and capacities for a production variant associated with an endpoint. If you sent a request to the UpdateEndpointWeightsAndCapacities API and the endpoint status is Updating, you get different desired and current values.

Members

CurrentInstanceCount

Type: int

The number of instances associated with the variant.

CurrentWeight

Type: float

The weight associated with the variant.

DeployedImages

Type: Array of DeployedImage structures

An array of DeployedImage objects that specify the Amazon EC2 Container Registry paths of the inference images deployed on instances of this ProductionVariant.

DesiredInstanceCount

Type: int

The number of instances requested in the UpdateEndpointWeightsAndCapacities request.

DesiredWeight

Type: float

The requested weight, as specified in the UpdateEndpointWeightsAndCapacities request.

VariantName

Type: string

The name of the variant.

ResourceConfig

Description

Describes the resources, including ML compute instances and ML storage volumes, to use for model training.

Members

InstanceCount

Type: int

The number of ML compute instances to use. For distributed training, provide a value greater than 1.

InstanceType

Type: string

The ML compute instance type.

VolumeKmsKeyId

Type: string

The Amazon Resource Name (ARN) of a AWS Key Management Service key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.

VolumeSizeInGB

Type: int

The size of the ML storage volume that you want to provision.

ML storage volumes store model artifacts and incremental states. Training algorithms might also use the ML storage volume for scratch space. If you want to store the training data in the ML storage volume, choose File as the TrainingInputMode in the algorithm specification.

You must specify sufficient ML storage for your scenario.

Amazon SageMaker supports only the General Purpose SSD (gp2) ML storage volume type.

ResourceInUse

Description

Resource being accessed is in use.

Members

Message

Type: string

ResourceLimitExceeded

Description

You have exceeded an Amazon SageMaker resource limit. For example, you might have too many training jobs created.

Members

Message

Type: string

ResourceLimits

Description

Specifies the maximum number of training jobs and parallel training jobs that a hyperparameter tuning job can launch.

Members

MaxNumberOfTrainingJobs

Type: int

The maximum number of training jobs that a hyperparameter tuning job can launch.

MaxParallelTrainingJobs

Type: int

The maximum number of concurrent training jobs that a hyperparameter tuning job can launch.

ResourceNotFound

Description

Resource being access is not found.

Members

Message

Type: string

S3DataSource

Description

Describes the S3 data source.

Members

S3DataDistributionType

Type: string

If you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify FullyReplicated.

If you want Amazon SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify ShardedByS3Key. If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data.

Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both FILE and PIPE modes. Keep this in mind when developing algorithms.

In distributed training, where you use multiple ML compute EC2 instances, you might choose ShardedByS3Key. If the algorithm requires copying training data to the ML storage volume (when TrainingInputMode is set to File), this copies 1/n of the number of objects.

S3DataType

Type: string

If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for model training.

If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for model training.

S3Uri

Type: string

Depending on the value specified for the S3DataType, identifies either a key name prefix or a manifest. For example:

A key name prefix might look like this: s3://bucketname/exampleprefix.
A manifest might look like this: s3://bucketname/example.manifest

The manifest is an S3 object which is a JSON file with the following format:

[

{"prefix": "s3://customer_bucket/some/prefix/"},

"relative/path/to/custdata-1",

"relative/path/custdata-2",

...

]

The preceding JSON matches the following s3Uris:

s3://customer_bucket/some/prefix/relative/path/to/custdata-1

s3://customer_bucket/some/prefix/relative/path/custdata-1

...

The complete set of s3uris in this manifest constitutes the input data for the channel for this datasource. The object that each s3uris points to must readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.

SecondaryStatusTransition

Description

Specifies a secondary status the job has transitioned into. It includes a start timestamp and later an end timestamp. The end timestamp is added either after the job transitions to a different secondary status or after the job has ended.

Members

EndTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

A timestamp that shows when the secondary status has ended and the job has transitioned into another secondary status. The EndTime timestamp is also set after the training job has ended.

StartTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

A timestamp that shows when the training job has entered this secondary status.

Status

Type: string

Provides granular information about the system state. For more information, see SecondaryStatus under the DescribeTrainingJob response elements.

StatusMessage

Type: string

Shows a brief description and other information about the secondary status. For example, the LaunchingMLInstances secondary status could show a status message of "Insufficent capacity error while launching instances".

StoppingCondition

Description

Specifies how long model training can run. When model training reaches the limit, Amazon SageMaker ends the training job. Use this API to cap model training cost.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal, which delays job termination for120 seconds. Algorithms might use this 120-second window to save the model artifacts, so the results of training is not lost.

Training algorithms provided by Amazon SageMaker automatically saves the intermediate results of a model training job (it is best effort case, as model might not be ready to save as some stages, for example training just started). This intermediate data is a valid model artifact. You can use it to create a model (CreateModel).

Members

MaxRuntimeInSeconds

Type: int

The maximum length of time, in seconds, that the training job can run. If model training does not complete during this time, Amazon SageMaker ends the job. If value is not specified, default value is 1 day. Maximum value is 5 days.

TrainingJobStatusCounters

Description

The numbers of training jobs launched by a hyperparameter tuning job, categorized by status.

Members

Completed

Type: int

The number of completed training jobs launched by a hyperparameter tuning job.

InProgress

Type: int

The number of in-progress training jobs launched by a hyperparameter tuning job.

NonRetryableError

Type: int

The number of training jobs that failed and can't be retried. A failed training job can't be retried if it failed because a client error occurred.

RetryableError

Type: int

The number of training jobs that failed, but can be retried. A failed training job can be retried only if it failed because an internal service error occurred.

Stopped

Type: int

The number of training jobs launched by a hyperparameter tuning job that were manually stopped.

TrainingJobSummary

Description

Provides summary information about a training job.

Members

CreationTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

A timestamp that shows when the training job was created.

LastModifiedTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

Timestamp when the training job was last modified.

TrainingEndTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

A timestamp that shows when the training job ended. This field is set only if the training job has one of the terminal statuses (Completed, Failed, or Stopped).

TrainingJobArn

Type: string

The Amazon Resource Name (ARN) of the training job.

TrainingJobName

Type: string

The name of the training job that you want a summary for.

TrainingJobStatus

Type: string

The status of the training job.

TransformDataSource

Description

Describes the location of the channel data.

Members

S3DataSource

Required: Yes
Type: TransformS3DataSource structure

The S3 location of the data source that is associated with a channel.

TransformInput

Description

Describes the input source of a transform job and the way the transform job consumes it.

Members

CompressionType

Type: string

Compressing data helps save on storage space. If your transform data is compressed, specify the compression type.and Amazon SageMaker will automatically decompress the data for the transform job accordingly. The default value is None.

ContentType

Type: string

The multipurpose internet mail extension (MIME) type of the data. Amazon SageMaker uses the MIME type with each http call to transfer data to the transform job.

DataSource

Required: Yes
Type: TransformDataSource structure

Describes the location of the channel data, meaning the S3 location of the input data that the model can consume.

SplitType

Type: string

The method to use to split the transform job's data into smaller batches. The default value is None. If you don't want to split the data, specify None. If you want to split records on a newline character boundary, specify Line. To split records according to the RecordIO format, specify RecordIO.

Amazon SageMaker will send maximum number of records per batch in each request up to the MaxPayloadInMB limit. For more information, see RecordIO data format.

For information about the RecordIO format, see Data Format.

TransformJobSummary

Description

Provides a summary information for a transform job. Multiple TransformJobSummary objects are returned as a list after calling ListTransformJobs.

Members

CreationTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

A timestamp that shows when the transform Job was created.

FailureReason

Type: string

If the transform job failed, the reason it failed.

LastModifiedTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

Indicates when the transform job was last modified.

TransformEndTime

Type: timestamp (string|DateTime or anything parsable by strtotime)

Indicates when the transform job ends on compute instances. For successful jobs and stopped jobs, this is the exact time recorded after the results are uploaded. For failed jobs, this is when Amazon SageMaker detected that the job failed.

TransformJobArn

Type: string

The Amazon Resource Name (ARN) of the transform job.

TransformJobName

Type: string

The name of the transform job.

TransformJobStatus

Type: string

The status of the transform job.

TransformOutput

Description

Describes the results of a transform job output.

Members

Accept

Type: string

The MIME type used to specify the output data. Amazon SageMaker uses the MIME type with each http call to transfer data from the transform job.

AssembleWith

Type: string

Defines how to assemble the results of the transform job as a single S3 object. You should select a format that is most convenient to you. To concatenate the results in binary format, specify None. To add a newline character at the end of every transformed record, specify Line. To assemble the output in RecordIO format, specify RecordIO. The default value is None.

For information about the RecordIO format, see Data Format.

KmsKeyId

Type: string

The AWS Key Management Service (AWS KMS) key for Amazon S3 server-side encryption that Amazon SageMaker uses to encrypt the transformed data.

If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.

The KMS key policy must grant permission to the IAM role that you specify in your CreateTramsformJob request. For more information, see Using Key Policies in AWS KMS in the AWS Key Management Service Developer Guide.

S3OutputPath

Type: string

The Amazon S3 path where you want Amazon SageMaker to store the results of the transform job. For example, s3://bucket-name/key-name-prefix.

For every S3 object used as input for the transform job, the transformed data is stored in a corresponding subfolder in the location under the output prefix. For example, the input data s3://bucket-name/input-name-prefix/dataset01/data.csv will have the transformed data stored at s3://bucket-name/key-name-prefix/dataset01/, based on the original name, as a series of .part files (.part0001, part0002, etc).

TransformResources

Description

Describes the resources, including ML instance types and ML instance count, to use for transform job.

Members

InstanceCount

Type: int

The number of ML compute instances to use in the transform job. For distributed transform, provide a value greater than 1. The default value is 1.

InstanceType

Type: string

The ML compute instance type for the transform job. For using built-in algorithms to transform moderately sized datasets, ml.m4.xlarge or ml.m5.large should suffice. There is no default value for InstanceType.

TransformS3DataSource

Description

Describes the S3 data source.

Members

S3DataType

Type: string

If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with the specified key name prefix for batch transform.

If you choose ManifestFile, S3Uri identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for batch transform.

S3Uri

Type: string

Depending on the value specified for the S3DataType, identifies either a key name prefix or a manifest. For example:

A key name prefix might look like this: s3://bucketname/exampleprefix.
A manifest might look like this: s3://bucketname/example.manifest

The manifest is an S3 object which is a JSON file with the following format:

[

{"prefix": "s3://customer_bucket/some/prefix/"},

"relative/path/to/custdata-1",

"relative/path/custdata-2",

...

]

The preceding JSON matches the following S3Uris:

s3://customer_bucket/some/prefix/relative/path/to/custdata-1

s3://customer_bucket/some/prefix/relative/path/custdata-1

...

The complete set of S3Uris in this manifest constitutes the input data for the channel for this datasource. The object that each S3Uris points to must be readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.

VpcConfig

Description

Specifies a VPC that your training jobs and hosted models have access to. Control access to and from your training and model containers by configuring the VPC. For more information, see host-vpc and train-vpc.

Members

SecurityGroupIds

Type: Array of strings

The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

Subnets

Type: Array of strings

The ID of the subnets in the VPC to which you want to connect your training job or model.

Namespaces

Classes

Interfaces

Traits

Exceptions

Functions

Amazon SageMaker Service 2017-07-24

Operation Summary

Paginators

Waiters

Operations

AddTags

Parameter Syntax

Parameter Details

Members

Result Syntax

Result Details

Members

Errors

CreateEndpoint

Parameter Syntax

Parameter Details

Members

Result Syntax

Result Details

Members

Errors

CreateEndpointConfig

Parameter Syntax

Parameter Details

Members

Result Syntax

Result Details

Members

Errors

CreateHyperParameterTuningJob

Parameter Syntax

Parameter Details

Members

Result Syntax

Result Details

Members

Errors

CreateModel

Parameter Syntax

Parameter Details

Members

Result Syntax

Result Details

Members

Errors

CreateNotebookInstance

Parameter Syntax

Parameter Details

Members

Result Syntax

Result Details

Members

Errors

CreateNotebookInstanceLifecycleConfig

Parameter Syntax

Parameter Details

Members

Result Syntax

Result Details

Members

Errors

CreatePresignedNotebookInstanceUrl

Parameter Syntax

Parameter Details

Members

Result Syntax

Result Details

Members

Errors

CreateTrainingJob

Parameter Syntax

Parameter Details

Members

Result Syntax