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Class: Aws::SageMaker::Client

Inherits:
Seahorse::Client::Base show all
Defined in:
(unknown)

Overview

An API client for Amazon SageMaker Service. To construct a client, you need to configure a :region and :credentials.

sagemaker = Aws::SageMaker::Client.new(
  region: region_name,
  credentials: credentials,
  # ...
)

See #initialize for a full list of supported configuration options.

Region

You can configure a default region in the following locations:

  • ENV['AWS_REGION']
  • Aws.config[:region]

Go here for a list of supported regions.

Credentials

Default credentials are loaded automatically from the following locations:

  • ENV['AWS_ACCESS_KEY_ID'] and ENV['AWS_SECRET_ACCESS_KEY']
  • Aws.config[:credentials]
  • The shared credentials ini file at ~/.aws/credentials (more information)
  • From an instance profile when running on EC2

You can also construct a credentials object from one of the following classes:

Alternatively, you configure credentials with :access_key_id and :secret_access_key:

# load credentials from disk
creds = YAML.load(File.read('/path/to/secrets'))

Aws::SageMaker::Client.new(
  access_key_id: creds['access_key_id'],
  secret_access_key: creds['secret_access_key']
)

Always load your credentials from outside your application. Avoid configuring credentials statically and never commit them to source control.

Instance Attribute Summary

Attributes inherited from Seahorse::Client::Base

#config, #handlers

Constructor collapse

API Operations collapse

Instance Method Summary collapse

Methods inherited from Seahorse::Client::Base

add_plugin, api, #build_request, clear_plugins, define, new, #operation, #operation_names, plugins, remove_plugin, set_api, set_plugins

Methods included from Seahorse::Client::HandlerBuilder

#handle, #handle_request, #handle_response

Constructor Details

#initialize(options = {}) ⇒ Aws::SageMaker::Client

Constructs an API client.

Options Hash (options):

  • :access_key_id (String)

    Used to set credentials statically. See Plugins::RequestSigner for more details.

  • :active_endpoint_cache (Boolean)

    When set to true, a thread polling for endpoints will be running in the background every 60 secs (default). Defaults to false. See Plugins::EndpointDiscovery for more details.

  • :convert_params (Boolean) — default: true

    When true, an attempt is made to coerce request parameters into the required types. See Plugins::ParamConverter for more details.

  • :credentials (required, Credentials)

    Your AWS credentials. The following locations will be searched in order for credentials:

    • :access_key_id, :secret_access_key, and :session_token options
    • ENV['AWS_ACCESS_KEY_ID'], ENV['AWS_SECRET_ACCESS_KEY']
    • HOME/.aws/credentials shared credentials file
    • EC2 instance profile credentials See Plugins::RequestSigner for more details.
  • :disable_host_prefix_injection (Boolean)

    Set to true to disable SDK automatically adding host prefix to default service endpoint when available. See Plugins::EndpointPattern for more details.

  • :endpoint (String)

    A default endpoint is constructed from the :region. See Plugins::RegionalEndpoint for more details.

  • :endpoint_cache_max_entries (Integer)

    Used for the maximum size limit of the LRU cache storing endpoints data for endpoint discovery enabled operations. Defaults to 1000. See Plugins::EndpointDiscovery for more details.

  • :endpoint_cache_max_threads (Integer)

    Used for the maximum threads in use for polling endpoints to be cached, defaults to 10. See Plugins::EndpointDiscovery for more details.

  • :endpoint_cache_poll_interval (Integer)

    When :endpoint_discovery and :active_endpoint_cache is enabled, Use this option to config the time interval in seconds for making requests fetching endpoints information. Defaults to 60 sec. See Plugins::EndpointDiscovery for more details.

  • :endpoint_discovery (Boolean)

    When set to true, endpoint discovery will be enabled for operations when available. Defaults to false. See Plugins::EndpointDiscovery for more details.

  • :http_continue_timeout (Float) — default: 1

    See Seahorse::Client::Plugins::NetHttp for more details.

  • :http_idle_timeout (Integer) — default: 5

    See Seahorse::Client::Plugins::NetHttp for more details.

  • :http_open_timeout (Integer) — default: 15

    See Seahorse::Client::Plugins::NetHttp for more details.

  • :http_proxy (String)

    See Seahorse::Client::Plugins::NetHttp for more details.

  • :http_read_timeout (Integer) — default: 60

    See Seahorse::Client::Plugins::NetHttp for more details.

  • :http_wire_trace (Boolean) — default: false

    See Seahorse::Client::Plugins::NetHttp for more details.

  • :log_level (Symbol) — default: :info

    The log level to send messages to the logger at. See Plugins::Logging for more details.

  • :log_formatter (Logging::LogFormatter)

    The log formatter. Defaults to Seahorse::Client::Logging::Formatter.default. See Plugins::Logging for more details.

  • :logger (Logger) — default: nil

    The Logger instance to send log messages to. If this option is not set, logging will be disabled. See Plugins::Logging for more details.

  • :profile (String)

    Used when loading credentials from the shared credentials file at HOME/.aws/credentials. When not specified, 'default' is used. See Plugins::RequestSigner for more details.

  • :raise_response_errors (Boolean) — default: true

    When true, response errors are raised. See Seahorse::Client::Plugins::RaiseResponseErrors for more details.

  • :region (required, String)

    The AWS region to connect to. The region is used to construct the client endpoint. Defaults to ENV['AWS_REGION']. Also checks AMAZON_REGION and AWS_DEFAULT_REGION. See Plugins::RegionalEndpoint for more details.

  • :retry_limit (Integer) — default: 3

    The maximum number of times to retry failed requests. Only ~ 500 level server errors and certain ~ 400 level client errors are retried. Generally, these are throttling errors, data checksum errors, networking errors, timeout errors and auth errors from expired credentials. See Plugins::RetryErrors for more details.

  • :secret_access_key (String)

    Used to set credentials statically. See Plugins::RequestSigner for more details.

  • :session_token (String)

    Used to set credentials statically. See Plugins::RequestSigner for more details.

  • :simple_json (Boolean) — default: false

    Disables request parameter conversion, validation, and formatting. Also disable response data type conversions. This option is useful when you want to ensure the highest level of performance by avoiding overhead of walking request parameters and response data structures.

    When :simple_json is enabled, the request parameters hash must be formatted exactly as the DynamoDB API expects. See Plugins::Protocols::JsonRpc for more details.

  • :ssl_ca_bundle (String)

    See Seahorse::Client::Plugins::NetHttp for more details.

  • :ssl_ca_directory (String)

    See Seahorse::Client::Plugins::NetHttp for more details.

  • :ssl_ca_store (String)

    See Seahorse::Client::Plugins::NetHttp for more details.

  • :ssl_verify_peer (Boolean) — default: true

    See Seahorse::Client::Plugins::NetHttp for more details.

  • :stub_responses (Boolean) — default: false

    Causes the client to return stubbed responses. By default fake responses are generated and returned. You can specify the response data to return or errors to raise by calling ClientStubs#stub_responses. See ClientStubs for more information.

    Please note When response stubbing is enabled, no HTTP requests are made, and retries are disabled. See Plugins::StubResponses for more details.

  • :validate_params (Boolean) — default: true

    When true, request parameters are validated before sending the request. See Plugins::ParamValidator for more details.

Instance Method Details

#add_tags(options = {}) ⇒ Types::AddTagsOutput

Adds or overwrites one or more tags for the specified Amazon SageMaker resource. You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, 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 For more information, see AWS Tagging Strategies.

Tags that you add to a hyperparameter tuning job by calling this API are also added to any training jobs that the hyperparameter tuning job launches after you call this API, but not to training jobs that the hyperparameter tuning job launched before you called this API. To make sure that the tags associated with a hyperparameter tuning job are also added to all training jobs that the hyperparameter tuning job launches, add the tags when you first create the tuning job by specifying them in the Tags parameter of CreateHyperParameterTuningJob

Examples:

Request syntax with placeholder values


resp = client.add_tags({
  resource_arn: "ResourceArn", # required
  tags: [ # required
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.tags #=> Array
resp.tags[0].key #=> String
resp.tags[0].value #=> String

Options Hash (options):

  • :resource_arn (required, String)

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

  • :tags (required, Array<Types::Tag>)

    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.

Returns:

See Also:

#create_algorithm(options = {}) ⇒ Types::CreateAlgorithmOutput

Create a machine learning algorithm that you can use in Amazon SageMaker and list in the AWS Marketplace.

Examples:

Request syntax with placeholder values


resp = client.create_algorithm({
  algorithm_name: "EntityName", # required
  algorithm_description: "EntityDescription",
  training_specification: { # required
    training_image: "Image", # required
    training_image_digest: "ImageDigest",
    supported_hyper_parameters: [
      {
        name: "ParameterName", # required
        description: "EntityDescription",
        type: "Integer", # required, accepts Integer, Continuous, Categorical, FreeText
        range: {
          integer_parameter_range_specification: {
            min_value: "ParameterValue", # required
            max_value: "ParameterValue", # required
          },
          continuous_parameter_range_specification: {
            min_value: "ParameterValue", # required
            max_value: "ParameterValue", # required
          },
          categorical_parameter_range_specification: {
            values: ["ParameterValue"], # required
          },
        },
        is_tunable: false,
        is_required: false,
        default_value: "ParameterValue",
      },
    ],
    supported_training_instance_types: ["ml.m4.xlarge"], # required, accepts 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.p3dn.24xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge
    supports_distributed_training: false,
    metric_definitions: [
      {
        name: "MetricName", # required
        regex: "MetricRegex", # required
      },
    ],
    training_channels: [ # required
      {
        name: "ChannelName", # required
        description: "EntityDescription",
        is_required: false,
        supported_content_types: ["ContentType"], # required
        supported_compression_types: ["None"], # accepts None, Gzip
        supported_input_modes: ["Pipe"], # required, accepts Pipe, File
      },
    ],
    supported_tuning_job_objective_metrics: [
      {
        type: "Maximize", # required, accepts Maximize, Minimize
        metric_name: "MetricName", # required
      },
    ],
  },
  inference_specification: {
    containers: [ # required
      {
        container_hostname: "ContainerHostname",
        image: "Image", # required
        image_digest: "ImageDigest",
        model_data_url: "Url",
        product_id: "ProductId",
      },
    ],
    supported_transform_instance_types: ["ml.m4.xlarge"], # required, accepts 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
    supported_realtime_inference_instance_types: ["ml.t2.medium"], # required, accepts 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.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.12xlarge, ml.m5d.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, ml.c5d.large, ml.c5d.xlarge, ml.c5d.2xlarge, ml.c5d.4xlarge, ml.c5d.9xlarge, ml.c5d.18xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.12xlarge, ml.r5.24xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.12xlarge, ml.r5d.24xlarge
    supported_content_types: ["ContentType"], # required
    supported_response_mime_types: ["ResponseMIMEType"], # required
  },
  validation_specification: {
    validation_role: "RoleArn", # required
    validation_profiles: [ # required
      {
        profile_name: "EntityName", # required
        training_job_definition: { # required
          training_input_mode: "Pipe", # required, accepts Pipe, File
          hyper_parameters: {
            "ParameterKey" => "ParameterValue",
          },
          input_data_config: [ # required
            {
              channel_name: "ChannelName", # required
              data_source: { # required
                s3_data_source: {
                  s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile
                  s3_uri: "S3Uri", # required
                  s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
                  attribute_names: ["AttributeName"],
                },
                file_system_data_source: {
                  file_system_id: "FileSystemId", # required
                  file_system_access_mode: "rw", # required, accepts rw, ro
                  file_system_type: "EFS", # required, accepts EFS, FSxLustre
                  directory_path: "DirectoryPath", # required
                },
              },
              content_type: "ContentType",
              compression_type: "None", # accepts None, Gzip
              record_wrapper_type: "None", # accepts None, RecordIO
              input_mode: "Pipe", # accepts Pipe, File
              shuffle_config: {
                seed: 1, # required
              },
            },
          ],
          output_data_config: { # required
            kms_key_id: "KmsKeyId",
            s3_output_path: "S3Uri", # required
          },
          resource_config: { # required
            instance_type: "ml.m4.xlarge", # required, accepts 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.p3dn.24xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge
            instance_count: 1, # required
            volume_size_in_gb: 1, # required
            volume_kms_key_id: "KmsKeyId",
          },
          stopping_condition: { # required
            max_runtime_in_seconds: 1,
            max_wait_time_in_seconds: 1,
          },
        },
        transform_job_definition: {
          max_concurrent_transforms: 1,
          max_payload_in_mb: 1,
          batch_strategy: "MultiRecord", # accepts MultiRecord, SingleRecord
          environment: {
            "TransformEnvironmentKey" => "TransformEnvironmentValue",
          },
          transform_input: { # required
            data_source: { # required
              s3_data_source: { # required
                s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile
                s3_uri: "S3Uri", # required
              },
            },
            content_type: "ContentType",
            compression_type: "None", # accepts None, Gzip
            split_type: "None", # accepts None, Line, RecordIO, TFRecord
          },
          transform_output: { # required
            s3_output_path: "S3Uri", # required
            accept: "Accept",
            assemble_with: "None", # accepts None, Line
            kms_key_id: "KmsKeyId",
          },
          transform_resources: { # required
            instance_type: "ml.m4.xlarge", # required, accepts 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
            instance_count: 1, # required
            volume_kms_key_id: "KmsKeyId",
          },
        },
      },
    ],
  },
  certify_for_marketplace: false,
})

Response structure


resp.algorithm_arn #=> String

Options Hash (options):

  • :algorithm_name (required, String)

    The name of the algorithm.

  • :algorithm_description (String)

    A description of the algorithm.

  • :training_specification (required, Types::TrainingSpecification)

    Specifies details about training jobs run by this algorithm, including the following:

    • The Amazon ECR path of the container and the version digest of the algorithm.

    • The hyperparameters that the algorithm supports.

    • The instance types that the algorithm supports for training.

    • Whether the algorithm supports distributed training.

    • The metrics that the algorithm emits to Amazon CloudWatch.

    • Which metrics that the algorithm emits can be used as the objective metric for hyperparameter tuning jobs.

    • The input channels that the algorithm supports for training data. For example, an algorithm might support train, validation, and test channels.

  • :inference_specification (Types::InferenceSpecification)

    Specifies details about inference jobs that the algorithm runs, including the following:

    • The Amazon ECR paths of containers that contain the inference code and model artifacts.

    • The instance types that the algorithm supports for transform jobs and real-time endpoints used for inference.

    • The input and output content formats that the algorithm supports for inference.

  • :validation_specification (Types::AlgorithmValidationSpecification)

    Specifies configurations for one or more training jobs and that Amazon SageMaker runs to test the algorithm\'s training code and, optionally, one or more batch transform jobs that Amazon SageMaker runs to test the algorithm\'s inference code.

  • :certify_for_marketplace (Boolean)

    Whether to certify the algorithm so that it can be listed in AWS Marketplace.

Returns:

See Also:

#create_code_repository(options = {}) ⇒ Types::CreateCodeRepositoryOutput

Creates a Git repository as a resource in your Amazon SageMaker account. You can associate the repository with notebook instances so that you can use Git source control for the notebooks you create. The Git repository is a resource in your Amazon SageMaker account, so it can be associated with more than one notebook instance, and it persists independently from the lifecycle of any notebook instances it is associated with.

The repository can be hosted either in AWS CodeCommit or in any other Git repository.

Examples:

Request syntax with placeholder values


resp = client.create_code_repository({
  code_repository_name: "EntityName", # required
  git_config: { # required
    repository_url: "GitConfigUrl", # required
    branch: "Branch",
    secret_arn: "SecretArn",
  },
})

Response structure


resp.code_repository_arn #=> String

Options Hash (options):

  • :code_repository_name (required, String)

    The name of the Git repository. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

  • :git_config (required, Types::GitConfig)

    Specifies details about the repository, including the URL where the repository is located, the default branch, and credentials to use to access the repository.

Returns:

See Also:

#create_compilation_job(options = {}) ⇒ Types::CreateCompilationJobResponse

Starts a model compilation job. After the model has been compiled, Amazon SageMaker saves the resulting model artifacts to an Amazon Simple Storage Service (Amazon S3) bucket 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 with AWS IoT Greengrass. In that case, deploy them as an ML resource.

In the request body, you provide the following:

  • A name for the compilation job

  • Information about the input model artifacts

  • The output location for the compiled model and the device (target) that the model runs on

  • The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker assumes to perform the model compilation job

You can also provide a Tag to track the model compilation job's resource use and costs. The response body contains the CompilationJobArn for the compiled job.

To stop a model compilation job, use StopCompilationJob. To get information about a particular model compilation job, use DescribeCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.

Examples:

Request syntax with placeholder values


resp = client.create_compilation_job({
  compilation_job_name: "EntityName", # required
  role_arn: "RoleArn", # required
  input_config: { # required
    s3_uri: "S3Uri", # required
    data_input_config: "DataInputConfig", # required
    framework: "TENSORFLOW", # required, accepts TENSORFLOW, MXNET, ONNX, PYTORCH, XGBOOST
  },
  output_config: { # required
    s3_output_location: "S3Uri", # required
    target_device: "lambda", # required, accepts lambda, ml_m4, ml_m5, ml_c4, ml_c5, ml_p2, ml_p3, jetson_tx1, jetson_tx2, jetson_nano, rasp3b, deeplens, rk3399, rk3288, aisage, sbe_c, qcs605, qcs603
  },
  stopping_condition: { # required
    max_runtime_in_seconds: 1,
    max_wait_time_in_seconds: 1,
  },
})

Response structure


resp.compilation_job_arn #=> String

Options Hash (options):

  • :compilation_job_name (required, String)

    A name for the model compilation job. The name must be unique within the AWS Region and within your AWS account.

  • :role_arn (required, String)

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

    During model compilation, Amazon SageMaker needs your permission to:

    • Read input data from an S3 bucket

    • Write model artifacts to an S3 bucket

    • Write logs to Amazon CloudWatch Logs

    • Publish metrics to Amazon CloudWatch

    You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker Roles.

  • :input_config (required, Types::InputConfig)

    Provides information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.

  • :output_config (required, Types::OutputConfig)

    Provides information about the output location for the compiled model and the target device the model runs on.

  • :stopping_condition (required, Types::StoppingCondition)

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

Returns:

See Also:

#create_endpoint(options = {}) ⇒ Types::CreateEndpointOutput

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.

You must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig.

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.

Examples:

Request syntax with placeholder values


resp = client.create_endpoint({
  endpoint_name: "EndpointName", # required
  endpoint_config_name: "EndpointConfigName", # required
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.endpoint_arn #=> String

Options Hash (options):

  • :endpoint_name (required, String)

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

  • :endpoint_config_name (required, String)

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

  • :tags (Array<Types::Tag>)

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

Returns:

See Also:

#create_endpoint_config(options = {}) ⇒ Types::CreateEndpointConfigOutput

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.

Examples:

Request syntax with placeholder values


resp = client.create_endpoint_config({
  endpoint_config_name: "EndpointConfigName", # required
  production_variants: [ # required
    {
      variant_name: "VariantName", # required
      model_name: "ModelName", # required
      initial_instance_count: 1, # required
      instance_type: "ml.t2.medium", # required, accepts 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.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.12xlarge, ml.m5d.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, ml.c5d.large, ml.c5d.xlarge, ml.c5d.2xlarge, ml.c5d.4xlarge, ml.c5d.9xlarge, ml.c5d.18xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.12xlarge, ml.r5.24xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.12xlarge, ml.r5d.24xlarge
      initial_variant_weight: 1.0,
      accelerator_type: "ml.eia1.medium", # accepts ml.eia1.medium, ml.eia1.large, ml.eia1.xlarge, ml.eia2.medium, ml.eia2.large, ml.eia2.xlarge
    },
  ],
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  kms_key_id: "KmsKeyId",
})

Response structure


resp.endpoint_config_arn #=> String

Options Hash (options):

  • :endpoint_config_name (required, String)

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

  • :production_variants (required, Array<Types::ProductionVariant>)

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

  • :tags (Array<Types::Tag>)

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

  • :kms_key_id (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.

    Nitro-based instances do not support encryption with AWS KMS. If any of the models that you specify in the ProductionVariants parameter use nitro-based instances, do not specify a value for the KmsKeyId parameter. If you specify a value for KmsKeyId when using any nitro-based instances, the call to CreateEndpointConfig fails.

    For a list of nitro-based instances, see Nitro-based Instances in the Amazon Elastic Compute Cloud User Guide for Linux Instances.

    For more information about storage volumes on nitro-based instances, see Amazon EBS and NVMe on Linux Instances.

Returns:

See Also:

#create_hyper_parameter_tuning_job(options = {}) ⇒ Types::CreateHyperParameterTuningJobResponse

Starts a hyperparameter tuning job. A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose.

Examples:

Request syntax with placeholder values


resp = client.create_hyper_parameter_tuning_job({
  hyper_parameter_tuning_job_name: "HyperParameterTuningJobName", # required
  hyper_parameter_tuning_job_config: { # required
    strategy: "Bayesian", # required, accepts Bayesian, Random
    hyper_parameter_tuning_job_objective: {
      type: "Maximize", # required, accepts Maximize, Minimize
      metric_name: "MetricName", # required
    },
    resource_limits: { # required
      max_number_of_training_jobs: 1, # required
      max_parallel_training_jobs: 1, # required
    },
    parameter_ranges: {
      integer_parameter_ranges: [
        {
          name: "ParameterKey", # required
          min_value: "ParameterValue", # required
          max_value: "ParameterValue", # required
          scaling_type: "Auto", # accepts Auto, Linear, Logarithmic, ReverseLogarithmic
        },
      ],
      continuous_parameter_ranges: [
        {
          name: "ParameterKey", # required
          min_value: "ParameterValue", # required
          max_value: "ParameterValue", # required
          scaling_type: "Auto", # accepts Auto, Linear, Logarithmic, ReverseLogarithmic
        },
      ],
      categorical_parameter_ranges: [
        {
          name: "ParameterKey", # required
          values: ["ParameterValue"], # required
        },
      ],
    },
    training_job_early_stopping_type: "Off", # accepts Off, Auto
  },
  training_job_definition: {
    static_hyper_parameters: {
      "ParameterKey" => "ParameterValue",
    },
    algorithm_specification: { # required
      training_image: "AlgorithmImage",
      training_input_mode: "Pipe", # required, accepts Pipe, File
      algorithm_name: "ArnOrName",
      metric_definitions: [
        {
          name: "MetricName", # required
          regex: "MetricRegex", # required
        },
      ],
    },
    role_arn: "RoleArn", # required
    input_data_config: [
      {
        channel_name: "ChannelName", # required
        data_source: { # required
          s3_data_source: {
            s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile
            s3_uri: "S3Uri", # required
            s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
            attribute_names: ["AttributeName"],
          },
          file_system_data_source: {
            file_system_id: "FileSystemId", # required
            file_system_access_mode: "rw", # required, accepts rw, ro
            file_system_type: "EFS", # required, accepts EFS, FSxLustre
            directory_path: "DirectoryPath", # required
          },
        },
        content_type: "ContentType",
        compression_type: "None", # accepts None, Gzip
        record_wrapper_type: "None", # accepts None, RecordIO
        input_mode: "Pipe", # accepts Pipe, File
        shuffle_config: {
          seed: 1, # required
        },
      },
    ],
    vpc_config: {
      security_group_ids: ["SecurityGroupId"], # required
      subnets: ["SubnetId"], # required
    },
    output_data_config: { # required
      kms_key_id: "KmsKeyId",
      s3_output_path: "S3Uri", # required
    },
    resource_config: { # required
      instance_type: "ml.m4.xlarge", # required, accepts 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.p3dn.24xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge
      instance_count: 1, # required
      volume_size_in_gb: 1, # required
      volume_kms_key_id: "KmsKeyId",
    },
    stopping_condition: { # required
      max_runtime_in_seconds: 1,
      max_wait_time_in_seconds: 1,
    },
    enable_network_isolation: false,
    enable_inter_container_traffic_encryption: false,
    enable_managed_spot_training: false,
    checkpoint_config: {
      s3_uri: "S3Uri", # required
      local_path: "DirectoryPath",
    },
  },
  warm_start_config: {
    parent_hyper_parameter_tuning_jobs: [ # required
      {
        hyper_parameter_tuning_job_name: "HyperParameterTuningJobName",
      },
    ],
    warm_start_type: "IdenticalDataAndAlgorithm", # required, accepts IdenticalDataAndAlgorithm, TransferLearning
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.hyper_parameter_tuning_job_arn #=> String

Options Hash (options):

  • :hyper_parameter_tuning_job_name (required, 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. The name must have { } to { } characters. Valid characters are a-z, A-Z, 0-9, and : + = @ _ % - (hyphen). The name is not case sensitive.

  • :hyper_parameter_tuning_job_config (required, Types::HyperParameterTuningJobConfig)

    The HyperParameterTuningJobConfig object that describes the tuning job, including the search strategy, the objective metric used to evaluate training jobs, ranges of parameters to search, and resource limits for the tuning job. For more information, see automatic-model-tuning

  • :training_job_definition (Types::HyperParameterTrainingJobDefinition)

    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.

  • :warm_start_config (Types::HyperParameterTuningJobWarmStartConfig)

    Specifies the configuration for starting the hyperparameter tuning job using one or more previous tuning jobs as a starting point. The results of previous tuning jobs are used to inform which combinations of hyperparameters to search over in the new tuning job.

    All training jobs launched by the new hyperparameter tuning job are evaluated by using the objective metric. If you specify IDENTICAL_DATA_AND_ALGORITHM as the WarmStartType value for the warm start configuration, the training job that performs the best in the new tuning job is compared to the best training jobs from the parent tuning jobs. From these, the training job that performs the best as measured by the objective metric is returned as the overall best training job.

    All training jobs launched by parent hyperparameter tuning jobs and the new hyperparameter tuning jobs count against the limit of training jobs for the tuning job.

  • :tags (Array<Types::Tag>)

    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 AWS Tagging Strategies.

    Tags that you specify for the tuning job are also added to all training jobs that the tuning job launches.

Returns:

See Also:

#create_labeling_job(options = {}) ⇒ Types::CreateLabelingJobResponse

Creates a job that uses workers to label the data objects in your input dataset. You can use the labeled data to train machine learning models.

You can select your workforce from one of three providers:

  • A private workforce that you create. It can include employees, contractors, and outside experts. Use a private workforce when want the data to stay within your organization or when a specific set of skills is required.

  • One or more vendors that you select from the AWS Marketplace. Vendors provide expertise in specific areas.

  • The Amazon Mechanical Turk workforce. This is the largest workforce, but it should only be used for public data or data that has been stripped of any personally identifiable information.

You can also use automated data labeling to reduce the number of data objects that need to be labeled by a human. Automated data labeling uses active learning to determine if a data object can be labeled by machine or if it needs to be sent to a human worker. For more information, see Using Automated Data Labeling.

The data objects to be labeled are contained in an Amazon S3 bucket. You create a manifest file that describes the location of each object. For more information, see Using Input and Output Data.

The output can be used as the manifest file for another labeling job or as training data for your machine learning models.

Examples:

Request syntax with placeholder values


resp = client.create_labeling_job({
  labeling_job_name: "LabelingJobName", # required
  label_attribute_name: "LabelAttributeName", # required
  input_config: { # required
    data_source: { # required
      s3_data_source: { # required
        manifest_s3_uri: "S3Uri", # required
      },
    },
    data_attributes: {
      content_classifiers: ["FreeOfPersonallyIdentifiableInformation"], # accepts FreeOfPersonallyIdentifiableInformation, FreeOfAdultContent
    },
  },
  output_config: { # required
    s3_output_path: "S3Uri", # required
    kms_key_id: "KmsKeyId",
  },
  role_arn: "RoleArn", # required
  label_category_config_s3_uri: "S3Uri",
  stopping_conditions: {
    max_human_labeled_object_count: 1,
    max_percentage_of_input_dataset_labeled: 1,
  },
  labeling_job_algorithms_config: {
    labeling_job_algorithm_specification_arn: "LabelingJobAlgorithmSpecificationArn", # required
    initial_active_learning_model_arn: "ModelArn",
    labeling_job_resource_config: {
      volume_kms_key_id: "KmsKeyId",
    },
  },
  human_task_config: { # required
    workteam_arn: "WorkteamArn", # required
    ui_config: { # required
      ui_template_s3_uri: "S3Uri", # required
    },
    pre_human_task_lambda_arn: "LambdaFunctionArn", # required
    task_keywords: ["TaskKeyword"],
    task_title: "TaskTitle", # required
    task_description: "TaskDescription", # required
    number_of_human_workers_per_data_object: 1, # required
    task_time_limit_in_seconds: 1, # required
    task_availability_lifetime_in_seconds: 1,
    max_concurrent_task_count: 1,
    annotation_consolidation_config: { # required
      annotation_consolidation_lambda_arn: "LambdaFunctionArn", # required
    },
    public_workforce_task_price: {
      amount_in_usd: {
        dollars: 1,
        cents: 1,
        tenth_fractions_of_a_cent: 1,
      },
    },
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.labeling_job_arn #=> String

Options Hash (options):

  • :labeling_job_name (required, String)

    The name of the labeling job. This name is used to identify the job in a list of labeling jobs.

  • :label_attribute_name (required, String)

    The attribute name to use for the label in the output manifest file. This is the key for the key/value pair formed with the label that a worker assigns to the object. The name can\'t end with \"-metadata\". If you are running a semantic segmentation labeling job, the attribute name must end with \"-ref\". If you are running any other kind of labeling job, the attribute name must not end with \"-ref\".

  • :input_config (required, Types::LabelingJobInputConfig)

    Input data for the labeling job, such as the Amazon S3 location of the data objects and the location of the manifest file that describes the data objects.

  • :output_config (required, Types::LabelingJobOutputConfig)

    The location of the output data and the AWS Key Management Service key ID for the key used to encrypt the output data, if any.

  • :role_arn (required, String)

    The Amazon Resource Number (ARN) that Amazon SageMaker assumes to perform tasks on your behalf during data labeling. You must grant this role the necessary permissions so that Amazon SageMaker can successfully complete data labeling.

  • :label_category_config_s3_uri (String)

    The S3 URL of the file that defines the categories used to label the data objects.

    The file is a JSON structure in the following format:

    {

    "document-version": "2018-11-28"

    "labels": [

    {

    "label": "label 1"

    },

    {

    "label": "label 2"

    },

    ...

    {

    "label": "label n"

    }

    ]

    }

  • :stopping_conditions (Types::LabelingJobStoppingConditions)

    A set of conditions for stopping the labeling job. If any of the conditions are met, the job is automatically stopped. You can use these conditions to control the cost of data labeling.

  • :labeling_job_algorithms_config (Types::LabelingJobAlgorithmsConfig)

    Configures the information required to perform automated data labeling.

  • :human_task_config (required, Types::HumanTaskConfig)

    Configures the information required for human workers to complete a labeling task.

  • :tags (Array<Types::Tag>)

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

Returns:

See Also:

#create_model(options = {}) ⇒ Types::CreateModelOutput

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.

Examples:

Request syntax with placeholder values


resp = client.create_model({
  model_name: "ModelName", # required
  primary_container: {
    container_hostname: "ContainerHostname",
    image: "Image",
    model_data_url: "Url",
    environment: {
      "EnvironmentKey" => "EnvironmentValue",
    },
    model_package_name: "ArnOrName",
  },
  containers: [
    {
      container_hostname: "ContainerHostname",
      image: "Image",
      model_data_url: "Url",
      environment: {
        "EnvironmentKey" => "EnvironmentValue",
      },
      model_package_name: "ArnOrName",
    },
  ],
  execution_role_arn: "RoleArn", # required
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  vpc_config: {
    security_group_ids: ["SecurityGroupId"], # required
    subnets: ["SubnetId"], # required
  },
  enable_network_isolation: false,
})

Response structure


resp.model_arn #=> String

Options Hash (options):

  • :model_name (required, String)

    The name of the new model.

  • :primary_container (Types::ContainerDefinition)

    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.

  • :containers (Array<Types::ContainerDefinition>)

    Specifies the containers in the inference pipeline.

  • :execution_role_arn (required, 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.

  • :tags (Array<Types::Tag>)

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

  • :vpc_config (Types::VpcConfig)

    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 used in hosting services and in batch transform. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud.

  • :enable_network_isolation (Boolean)

    Isolates the model container. No inbound or outbound network calls can be made to or from the model container.

    The Semantic Segmentation built-in algorithm does not support network isolation.

Returns:

See Also:

#create_model_package(options = {}) ⇒ Types::CreateModelPackageOutput

Creates a model package that you can use to create Amazon SageMaker models or list on AWS Marketplace. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.

To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification. To create a model from an algorithm resource that you created or subscribed to in AWS Marketplace, provide a value for SourceAlgorithmSpecification.

Examples:

Request syntax with placeholder values


resp = client.create_model_package({
  model_package_name: "EntityName", # required
  model_package_description: "EntityDescription",
  inference_specification: {
    containers: [ # required
      {
        container_hostname: "ContainerHostname",
        image: "Image", # required
        image_digest: "ImageDigest",
        model_data_url: "Url",
        product_id: "ProductId",
      },
    ],
    supported_transform_instance_types: ["ml.m4.xlarge"], # required, accepts 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
    supported_realtime_inference_instance_types: ["ml.t2.medium"], # required, accepts 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.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.12xlarge, ml.m5d.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, ml.c5d.large, ml.c5d.xlarge, ml.c5d.2xlarge, ml.c5d.4xlarge, ml.c5d.9xlarge, ml.c5d.18xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.12xlarge, ml.r5.24xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.12xlarge, ml.r5d.24xlarge
    supported_content_types: ["ContentType"], # required
    supported_response_mime_types: ["ResponseMIMEType"], # required
  },
  validation_specification: {
    validation_role: "RoleArn", # required
    validation_profiles: [ # required
      {
        profile_name: "EntityName", # required
        transform_job_definition: { # required
          max_concurrent_transforms: 1,
          max_payload_in_mb: 1,
          batch_strategy: "MultiRecord", # accepts MultiRecord, SingleRecord
          environment: {
            "TransformEnvironmentKey" => "TransformEnvironmentValue",
          },
          transform_input: { # required
            data_source: { # required
              s3_data_source: { # required
                s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile
                s3_uri: "S3Uri", # required
              },
            },
            content_type: "ContentType",
            compression_type: "None", # accepts None, Gzip
            split_type: "None", # accepts None, Line, RecordIO, TFRecord
          },
          transform_output: { # required
            s3_output_path: "S3Uri", # required
            accept: "Accept",
            assemble_with: "None", # accepts None, Line
            kms_key_id: "KmsKeyId",
          },
          transform_resources: { # required
            instance_type: "ml.m4.xlarge", # required, accepts 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
            instance_count: 1, # required
            volume_kms_key_id: "KmsKeyId",
          },
        },
      },
    ],
  },
  source_algorithm_specification: {
    source_algorithms: [ # required
      {
        model_data_url: "Url",
        algorithm_name: "ArnOrName", # required
      },
    ],
  },
  certify_for_marketplace: false,
})

Response structure


resp.model_package_arn #=> String

Options Hash (options):

  • :model_package_name (required, String)

    The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

  • :model_package_description (String)

    A description of the model package.

  • :inference_specification (Types::InferenceSpecification)

    Specifies details about inference jobs that can be run with models based on this model package, including the following:

    • The Amazon ECR paths of containers that contain the inference code and model artifacts.

    • The instance types that the model package supports for transform jobs and real-time endpoints used for inference.

    • The input and output content formats that the model package supports for inference.

  • :validation_specification (Types::ModelPackageValidationSpecification)

    Specifies configurations for one or more transform jobs that Amazon SageMaker runs to test the model package.

  • :source_algorithm_specification (Types::SourceAlgorithmSpecification)

    Details about the algorithm that was used to create the model package.

  • :certify_for_marketplace (Boolean)

    Whether to certify the model package for listing on AWS Marketplace.

Returns:

See Also:

#create_notebook_instance(options = {}) ⇒ Types::CreateNotebookInstanceOutput

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:

  1. Creates a network interface in the Amazon SageMaker VPC.

  2. (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.

  3. 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). You can't change the name of a notebook instance after you create it.

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.

Examples:

Request syntax with placeholder values


resp = client.create_notebook_instance({
  notebook_instance_name: "NotebookInstanceName", # required
  instance_type: "ml.t2.medium", # required, accepts ml.t2.medium, ml.t2.large, ml.t2.xlarge, ml.t2.2xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, 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.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5d.xlarge, ml.c5d.2xlarge, ml.c5d.4xlarge, ml.c5d.9xlarge, ml.c5d.18xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge
  subnet_id: "SubnetId",
  security_group_ids: ["SecurityGroupId"],
  role_arn: "RoleArn", # required
  kms_key_id: "KmsKeyId",
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  lifecycle_config_name: "NotebookInstanceLifecycleConfigName",
  direct_internet_access: "Enabled", # accepts Enabled, Disabled
  volume_size_in_gb: 1,
  accelerator_types: ["ml.eia1.medium"], # accepts ml.eia1.medium, ml.eia1.large, ml.eia1.xlarge, ml.eia2.medium, ml.eia2.large, ml.eia2.xlarge
  default_code_repository: "CodeRepositoryNameOrUrl",
  additional_code_repositories: ["CodeRepositoryNameOrUrl"],
  root_access: "Enabled", # accepts Enabled, Disabled
})

Response structure


resp.notebook_instance_arn #=> String

Options Hash (options):

  • :notebook_instance_name (required, String)

    The name of the new notebook instance.

  • :instance_type (required, String)

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

  • :subnet_id (String)

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

  • :security_group_ids (Array<String>)

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

  • :role_arn (required, 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) permissionsto 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.

  • :kms_key_id (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 your notebook instance. The KMS key you provide must be enabled. For information, see Enabling and Disabling Keys in the AWS Key Management Service Developer Guide.

  • :tags (Array<Types::Tag>)

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

  • :lifecycle_config_name (String)

    The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

  • :direct_internet_access (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 Notebook Instances Are Internet-Enabled by Default. You can set the value of this parameter to Disabled only if you set a value for the SubnetId parameter.

  • :volume_size_in_gb (Integer)

    The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB.

  • :accelerator_types (Array<String>)

    A list of Elastic Inference (EI) instance types to associate with this notebook instance. Currently, only one instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.

  • :default_code_repository (String)

    A Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in AWS CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

  • :additional_code_repositories (Array<String>)

    An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in AWS CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

  • :root_access (String)

    Whether root access is enabled or disabled for users of the notebook instance. The default value is Enabled.

    Lifecycle configurations need root access to be able to set up a notebook instance. Because of this, lifecycle configurations associated with a notebook instance always run with root access even if you disable root access for users.

Returns:

See Also:

#create_notebook_instance_lifecycle_config(options = {}) ⇒ Types::CreateNotebookInstanceLifecycleConfigOutput

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 Step 2.1: (Optional) Customize a Notebook Instance.

Examples:

Request syntax with placeholder values


resp = client.create_notebook_instance_lifecycle_config({
  notebook_instance_lifecycle_config_name: "NotebookInstanceLifecycleConfigName", # required
  on_create: [
    {
      content: "NotebookInstanceLifecycleConfigContent",
    },
  ],
  on_start: [
    {
      content: "NotebookInstanceLifecycleConfigContent",
    },
  ],
})

Response structure


resp.notebook_instance_lifecycle_config_arn #=> String

Options Hash (options):

  • :notebook_instance_lifecycle_config_name (required, String)

    The name of the lifecycle configuration.

  • :on_create (Array<Types::NotebookInstanceLifecycleHook>)

    A shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.

  • :on_start (Array<Types::NotebookInstanceLifecycleHook>)

    A shell script that runs every time you start a notebook instance, including when you create the notebook instance. The shell script must be a base64-encoded string.

Returns:

See Also:

#create_presigned_notebook_instance_url(options = {}) ⇒ Types::CreatePresignedNotebookInstanceUrlOutput

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.

IAM authorization policies for this API are also enforced for every HTTP request and WebSocket frame that attempts to connect to the notebook instance.For example, you can restrict access to this API and to the URL that it returns to a list of IP addresses that you specify. Use the NotIpAddress condition operator and the aws:SourceIP condition context key to specify the list of IP addresses that you want to have access to the notebook instance. For more information, see Limit Access to a Notebook Instance by IP Address.

The URL that you get from a call to is valid only for 5 minutes. If you try to use the URL after the 5-minute limit expires, you are directed to the AWS console sign-in page.

Examples:

Request syntax with placeholder values


resp = client.create_presigned_notebook_instance_url({
  notebook_instance_name: "NotebookInstanceName", # required
  session_expiration_duration_in_seconds: 1,
})

Response structure


resp.authorized_url #=> String

Options Hash (options):

  • :notebook_instance_name (required, String)

    The name of the notebook instance.

  • :session_expiration_duration_in_seconds (Integer)

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

Returns:

See Also:

#create_training_job(options = {}) ⇒ Types::CreateTrainingJobResponse

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 machine 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 enable the estimation of model parameters during training. Hyperparameters can be tuned to optimize this learning process. For a list of hyperparameters for each training algorithm provided by Amazon SageMaker, see Algorithms.

  • InputDataConfig - Describes the training dataset and the Amazon S3, EFS, or FSx location where it is stored.

  • OutputDataConfig - Identifies the Amazon S3 bucket 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.

  • EnableManagedSpotTraining - Optimize the cost of training machine learning models by up to 80% by using Amazon EC2 Spot instances. For more information, see Managed Spot Training.

  • 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 - To help cap training costs, use MaxRuntimeInSeconds to set a time limit for training. Use MaxWaitTimeInSeconds to specify how long you are willing to to wait for a managed spot training job to complete.

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

Examples:

Request syntax with placeholder values


resp = client.create_training_job({
  training_job_name: "TrainingJobName", # required
  hyper_parameters: {
    "ParameterKey" => "ParameterValue",
  },
  algorithm_specification: { # required
    training_image: "AlgorithmImage",
    algorithm_name: "ArnOrName",
    training_input_mode: "Pipe", # required, accepts Pipe, File
    metric_definitions: [
      {
        name: "MetricName", # required
        regex: "MetricRegex", # required
      },
    ],
  },
  role_arn: "RoleArn", # required
  input_data_config: [
    {
      channel_name: "ChannelName", # required
      data_source: { # required
        s3_data_source: {
          s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile
          s3_uri: "S3Uri", # required
          s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
          attribute_names: ["AttributeName"],
        },
        file_system_data_source: {
          file_system_id: "FileSystemId", # required
          file_system_access_mode: "rw", # required, accepts rw, ro
          file_system_type: "EFS", # required, accepts EFS, FSxLustre
          directory_path: "DirectoryPath", # required
        },
      },
      content_type: "ContentType",
      compression_type: "None", # accepts None, Gzip
      record_wrapper_type: "None", # accepts None, RecordIO
      input_mode: "Pipe", # accepts Pipe, File
      shuffle_config: {
        seed: 1, # required
      },
    },
  ],
  output_data_config: { # required
    kms_key_id: "KmsKeyId",
    s3_output_path: "S3Uri", # required
  },
  resource_config: { # required
    instance_type: "ml.m4.xlarge", # required, accepts 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.p3dn.24xlarge, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge
    instance_count: 1, # required
    volume_size_in_gb: 1, # required
    volume_kms_key_id: "KmsKeyId",
  },
  vpc_config: {
    security_group_ids: ["SecurityGroupId"], # required
    subnets: ["SubnetId"], # required
  },
  stopping_condition: { # required
    max_runtime_in_seconds: 1,
    max_wait_time_in_seconds: 1,
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  enable_network_isolation: false,
  enable_inter_container_traffic_encryption: false,
  enable_managed_spot_training: false,
  checkpoint_config: {
    s3_uri: "S3Uri", # required
    local_path: "DirectoryPath",
  },
})

Response structure


resp.training_job_arn #=> String

Options Hash (options):

  • :training_job_name (required, String)

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

  • :hyper_parameters (Hash<String,String>)

    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.

  • :algorithm_specification (required, Types::AlgorithmSpecification)

    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 Using Your Own Algorithms with Amazon SageMaker.

  • :role_arn (required, 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.

  • :input_data_config (Array<Types::Channel>)

    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, EFS, or FSx 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. For example, if you specify an EFS location, input data files will be made available as input streams. They do not need to be downloaded.

  • :output_data_config (required, Types::OutputDataConfig)

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

  • :resource_config (required, Types::ResourceConfig)

    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.

  • :vpc_config (Types::VpcConfig)

    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 Protect Training Jobs by Using an Amazon Virtual Private Cloud.

  • :stopping_condition (required, Types::StoppingCondition)

    Specifies a limit to how long a model training job can run. When the job reaches the time limit, Amazon SageMaker ends the training job. Use this API 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 can use this 120-second window to save the model artifacts, so the results of training are not lost.

  • :tags (Array<Types::Tag>)

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

  • :enable_network_isolation (Boolean)

    Isolates the training container. No inbound or outbound network calls can be made, except for calls between peers within a training cluster for distributed training. If you enable network isolation for training jobs that are configured to use a VPC, Amazon SageMaker downloads and uploads customer data and model artifacts through the specified VPC, but the training container does not have network access.

    The Semantic Segmentation built-in algorithm does not support network isolation.

  • :enable_inter_container_traffic_encryption (Boolean)

    To encrypt all communications between ML compute instances in distributed training, choose True. Encryption provides greater security for distributed training, but training might take longer. How long it takes depends on the amount of communication between compute instances, especially if you use a deep learning algorithm in distributed training. For more information, see Protect Communications Between ML Compute Instances in a Distributed Training Job.

  • :enable_managed_spot_training (Boolean)

    To train models using managed spot training, choose True. Managed spot training provides a fully managed and scalable infrastructure for training machine learning models. this option is useful when training jobs can be interrupted and when there is flexibility when the training job is run.

    The complete and intermediate results of jobs are stored in an Amazon S3 bucket, and can be used as a starting point to train models incrementally. Amazon SageMaker provides metrics and logs in CloudWatch. They can be used to see when managed spot training jobs are running, interrupted, resumed, or completed.

  • :checkpoint_config (Types::CheckpointConfig)

    Contains information about the output location for managed spot training checkpoint data.

Returns:

See Also:

#create_transform_job(options = {}) ⇒ Types::CreateTransformJobResponse

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.

Examples:

Request syntax with placeholder values


resp = client.create_transform_job({
  transform_job_name: "TransformJobName", # required
  model_name: "ModelName", # required
  max_concurrent_transforms: 1,
  max_payload_in_mb: 1,
  batch_strategy: "MultiRecord", # accepts MultiRecord, SingleRecord
  environment: {
    "TransformEnvironmentKey" => "TransformEnvironmentValue",
  },
  transform_input: { # required
    data_source: { # required
      s3_data_source: { # required
        s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile
        s3_uri: "S3Uri", # required
      },
    },
    content_type: "ContentType",
    compression_type: "None", # accepts None, Gzip
    split_type: "None", # accepts None, Line, RecordIO, TFRecord
  },
  transform_output: { # required
    s3_output_path: "S3Uri", # required
    accept: "Accept",
    assemble_with: "None", # accepts None, Line
    kms_key_id: "KmsKeyId",
  },
  transform_resources: { # required
    instance_type: "ml.m4.xlarge", # required, accepts 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
    instance_count: 1, # required
    volume_kms_key_id: "KmsKeyId",
  },
  data_processing: {
    input_filter: "JsonPath",
    output_filter: "JsonPath",
    join_source: "Input", # accepts Input, None
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.transform_job_arn #=> String

Options Hash (options):

  • :transform_job_name (required, String)

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

  • :model_name (required, 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.

  • :max_concurrent_transforms (Integer)

    The maximum number of parallel requests that can be sent to each instance in a transform job. If MaxConcurrentTransforms is set to 0 or left unset, Amazon SageMaker checks the optional execution-parameters to determine the optimal settings for your chosen algorithm. If the execution-parameters endpoint is not enabled, the default value is 1. For more information on execution-parameters, see How Containers Serve Requests. For built-in algorithms, you don\'t need to set a value for MaxConcurrentTransforms.

  • :max_payload_in_mb (Integer)

    The maximum allowed size of the payload, in MB. A payload is the data portion of a record (without metadata). The value in MaxPayloadInMB must be greater than, or equal to, the size of a single record. To estimate the size of a record in MB, divide the size of your dataset by the number of records. To ensure that the records fit within the maximum payload size, we recommend using a slightly larger value. The default value is 6 MB.

    For cases where the payload might be arbitrarily large and is transmitted using HTTP chunked encoding, set the value to 0. This feature works only in supported algorithms. Currently, Amazon SageMaker built-in algorithms do not support HTTP chunked encoding.

  • :batch_strategy (String)

    Specifies the number of records to include in a mini-batch for an HTTP inference request. A record ** is a single unit of input data that inference can be made on. For example, a single line in a CSV file is a record.

    To enable the batch strategy, you must set SplitType to Line, RecordIO, or TFRecord.

    To use only one record when making an HTTP invocation request to a container, set BatchStrategy to SingleRecord and SplitType to Line.

    To fit as many records in a mini-batch as can fit within the MaxPayloadInMB limit, set BatchStrategy to MultiRecord and SplitType to Line.

  • :environment (Hash<String,String>)

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

  • :transform_input (required, Types::TransformInput)

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

  • :transform_output (required, Types::TransformOutput)

    Describes the results of the transform job.

  • :transform_resources (required, Types::TransformResources)

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

  • :data_processing (Types::DataProcessing)

    The data structure used to specify the data to be used for inference in a batch transform job and to associate the data that is relevant to the prediction results in the output. The input filter provided allows you to exclude input data that is not needed for inference in a batch transform job. The output filter provided allows you to include input data relevant to interpreting the predictions in the output from the job. For more information, see Associate Prediction Results with their Corresponding Input Records.

  • :tags (Array<Types::Tag>) — default: Optional

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

Returns:

See Also:

#create_workteam(options = {}) ⇒ Types::CreateWorkteamResponse

Creates a new work team for labeling your data. A work team is defined by one or more Amazon Cognito user pools. You must first create the user pools before you can create a work team.

You cannot create more than 25 work teams in an account and region.

Examples:

Request syntax with placeholder values


resp = client.create_workteam({
  workteam_name: "WorkteamName", # required
  member_definitions: [ # required
    {
      cognito_member_definition: {
        user_pool: "CognitoUserPool", # required
        user_group: "CognitoUserGroup", # required
        client_id: "CognitoClientId", # required
      },
    },
  ],
  description: "String200", # required
  notification_configuration: {
    notification_topic_arn: "NotificationTopicArn",
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.workteam_arn #=> String

Options Hash (options):

  • :workteam_name (required, String)

    The name of the work team. Use this name to identify the work team.

  • :member_definitions (required, Array<Types::MemberDefinition>)

    A list of MemberDefinition objects that contains objects that identify the Amazon Cognito user pool that makes up the work team. For more information, see Amazon Cognito User Pools.

    All of the CognitoMemberDefinition objects that make up the member definition must have the same ClientId and UserPool values.

  • :description (required, String)

    A description of the work team.

  • :notification_configuration (Types::NotificationConfiguration)

    Configures notification of workers regarding available or expiring work items.

  • :tags (Array<Types::Tag>)

    An array of key-value pairs.

    For more information, see Resource Tag and Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide.

Returns:

See Also:

#delete_algorithm(options = {}) ⇒ Struct

Removes the specified algorithm from your account.

Examples:

Request syntax with placeholder values


resp = client.delete_algorithm({
  algorithm_name: "EntityName", # required
})

Options Hash (options):

  • :algorithm_name (required, String)

    The name of the algorithm to delete.

Returns:

  • (Struct)

    Returns an empty response.

See Also:

#delete_code_repository(options = {}) ⇒ Struct

Deletes the specified Git repository from your account.

Examples:

Request syntax with placeholder values


resp = client.delete_code_repository({
  code_repository_name: "EntityName", # required
})

Options Hash (options):

  • :code_repository_name (required, String)

    The name of the Git repository to delete.

Returns:

  • (Struct)

    Returns an empty response.

See Also:

#delete_endpoint(options = {}) ⇒ Struct

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.

Examples:

Request syntax with placeholder values


resp = client.delete_endpoint({
  endpoint_name: "EndpointName", # required
})

Options Hash (options):

  • :endpoint_name (required, String)

    The name of the endpoint that you want to delete.

Returns:

  • (Struct)

    Returns an empty response.

See Also:

#delete_endpoint_config(options = {}) ⇒ Struct

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

Examples:

Request syntax with placeholder values


resp = client.delete_endpoint_config({
  endpoint_config_name: "EndpointConfigName", # required
})

Options Hash (options):

  • :endpoint_config_name (required, String)

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

Returns:

  • (Struct)

    Returns an empty response.

See Also:

#delete_model(options = {}) ⇒ Struct

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.

Examples:

Request syntax with placeholder values


resp = client.delete_model({
  model_name: "ModelName", # required
})

Options Hash (options):

  • :model_name (required, String)

    The name of the model to delete.

Returns:

  • (Struct)

    Returns an empty response.

See Also:

#delete_model_package(options = {}) ⇒ Struct

Deletes a model package.

A model package is used to create Amazon SageMaker models or list on AWS Marketplace. Buyers can subscribe to model packages listed on AWS Marketplace to create models in Amazon SageMaker.

Examples:

Request syntax with placeholder values


resp = client.delete_model_package({
  model_package_name: "EntityName", # required
})

Options Hash (options):

  • :model_package_name (required, String)

    The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

Returns:

  • (Struct)

    Returns an empty response.

See Also:

#delete_notebook_instance(options = {}) ⇒ Struct

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.

Examples:

Request syntax with placeholder values


resp = client.delete_notebook_instance({
  notebook_instance_name: "NotebookInstanceName", # required
})

Options Hash (options):

  • :notebook_instance_name (required, String)

    The name of the Amazon SageMaker notebook instance to delete.

Returns:

  • (Struct)

    Returns an empty response.

See Also:

#delete_notebook_instance_lifecycle_config(options = {}) ⇒ Struct

Deletes a notebook instance lifecycle configuration.

Examples:

Request syntax with placeholder values


resp = client.delete_notebook_instance_lifecycle_config({
  notebook_instance_lifecycle_config_name: "NotebookInstanceLifecycleConfigName", # required
})

Options Hash (options):

  • :notebook_instance_lifecycle_config_name (required, String)

    The name of the lifecycle configuration to delete.

Returns:

  • (Struct)

    Returns an empty response.

See Also:

#delete_tags(options = {}) ⇒ Struct

Deletes the specified tags from an Amazon SageMaker resource.

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

When you call this API to delete tags from a hyperparameter tuning job, the deleted tags are not removed from training jobs that the hyperparameter tuning job launched before you called this API.

Examples:

Request syntax with placeholder values


resp = client.delete_tags({
  resource_arn: "ResourceArn", # required
  tag_keys: ["TagKey"], # required
})

Options Hash (options):

  • :resource_arn (required, String)

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

  • :tag_keys (required, Array<String>)

    An array or one or more tag keys to delete.

Returns:

  • (Struct)

    Returns an empty response.

See Also:

#delete_workteam(options = {}) ⇒ Types::DeleteWorkteamResponse

Deletes an existing work team. This operation can't be undone.

Examples:

Request syntax with placeholder values


resp = client.delete_workteam({
  workteam_name: "WorkteamName", # required
})

Response structure


resp.success #=> true/false

Options Hash (options):

  • :workteam_name (required, String)

    The name of the work team to delete.

Returns:

See Also:

#describe_algorithm(options = {}) ⇒ Types::DescribeAlgorithmOutput

Returns a description of the specified algorithm that is in your account.

Examples:

Request syntax with placeholder values


resp = client.describe_algorithm({
  algorithm_name: "ArnOrName", # required
})

Response structure


resp.algorithm_name #=> String
resp.algorithm_arn #=> String
resp.algorithm_description #=> String
resp.creation_time #=> Time
resp.training_specification.training_image #=> String
resp.training_specification.training_image_digest #=> String
resp.training_specification.supported_hyper_parameters #=> Array
resp.training_specification.supported_hyper_parameters[0].name #=> String
resp.training_specification.supported_hyper_parameters[0].description #=> String
resp.training_specification.supported_hyper_parameters[0].type #=> String, one of "Integer", "Continuous", "Categorical", "FreeText"
resp.training_specification.supported_hyper_parameters[0].range.integer_parameter_range_specification.min_value #=> String
resp.training_specification.supported_hyper_parameters[0].range.integer_parameter_range_specification.max_value #=> String
resp.training_specification.supported_hyper_parameters[0].range.continuous_parameter_range_specification.min_value #=> String
resp.training_specification.supported_hyper_parameters[0].range.continuous_parameter_range_specification.max_value #=> String
resp.training_specification.supported_hyper_parameters[0].range.categorical_parameter_range_specification.values #=> Array
resp.training_specification.supported_hyper_parameters[0].range.categorical_parameter_range_specification.values[0] #=> String
resp.training_specification.supported_hyper_parameters[0].is_tunable #=> true/false
resp.training_specification.supported_hyper_parameters[0].is_required #=> true/false
resp.training_specification.supported_hyper_parameters[0].default_value #=> String
resp.training_specification.supported_training_instance_types #=> Array
resp.training_specification.supported_training_instance_types[0] #=> String, one of "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.p3dn.24xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge"
resp.training_specification.supports_distributed_training #=> true/false
resp.training_specification.metric_definitions #=> Array
resp.training_specification.metric_definitions[0].name #=> String
resp.training_specification.metric_definitions[0].regex #=> String
resp.training_specification.training_channels #=> Array
resp.training_specification.training_channels[0].name #=> String
resp.training_specification.training_channels[0].description #=> String
resp.training_specification.training_channels[0].is_required #=> true/false
resp.training_specification.training_channels[0].supported_content_types #=> Array
resp.training_specification.training_channels[0].supported_content_types[0] #=> String
resp.training_specification.training_channels[0].supported_compression_types #=> Array
resp.training_specification.training_channels[0].supported_compression_types[0] #=> String, one of "None", "Gzip"
resp.training_specification.training_channels[0].supported_input_modes #=> Array
resp.training_specification.training_channels[0].supported_input_modes[0] #=> String, one of "Pipe", "File"
resp.training_specification.supported_tuning_job_objective_metrics #=> Array
resp.training_specification.supported_tuning_job_objective_metrics[0].type #=> String, one of "Maximize", "Minimize"
resp.training_specification.supported_tuning_job_objective_metrics[0].metric_name #=> String
resp.inference_specification.containers #=> Array
resp.inference_specification.containers[0].container_hostname #=> String
resp.inference_specification.containers[0].image #=> String
resp.inference_specification.containers[0].image_digest #=> String
resp.inference_specification.containers[0].model_data_url #=> String
resp.inference_specification.containers[0].product_id #=> String
resp.inference_specification.supported_transform_instance_types #=> Array
resp.inference_specification.supported_transform_instance_types[0] #=> String, one of "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"
resp.inference_specification.supported_realtime_inference_instance_types #=> Array
resp.inference_specification.supported_realtime_inference_instance_types[0] #=> String, one of "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.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.12xlarge", "ml.m5d.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", "ml.c5d.large", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.12xlarge", "ml.r5.24xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.12xlarge", "ml.r5d.24xlarge"
resp.inference_specification.supported_content_types #=> Array
resp.inference_specification.supported_content_types[0] #=> String
resp.inference_specification.supported_response_mime_types #=> Array
resp.inference_specification.supported_response_mime_types[0] #=> String
resp.validation_specification.validation_role #=> String
resp.validation_specification.validation_profiles #=> Array
resp.validation_specification.validation_profiles[0].profile_name #=> String
resp.validation_specification.validation_profiles[0].training_job_definition.training_input_mode #=> String, one of "Pipe", "File"
resp.validation_specification.validation_profiles[0].training_job_definition.hyper_parameters #=> Hash
resp.validation_specification.validation_profiles[0].training_job_definition.hyper_parameters["ParameterKey"] #=> String
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config #=> Array
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].channel_name #=> String
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile"
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.s3_data_source.s3_uri #=> String
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.s3_data_source.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key"
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.s3_data_source.attribute_names #=> Array
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.s3_data_source.attribute_names[0] #=> String
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.file_system_data_source.file_system_id #=> String
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.file_system_data_source.file_system_access_mode #=> String, one of "rw", "ro"
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.file_system_data_source.file_system_type #=> String, one of "EFS", "FSxLustre"
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].data_source.file_system_data_source.directory_path #=> String
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].content_type #=> String
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].compression_type #=> String, one of "None", "Gzip"
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].record_wrapper_type #=> String, one of "None", "RecordIO"
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].input_mode #=> String, one of "Pipe", "File"
resp.validation_specification.validation_profiles[0].training_job_definition.input_data_config[0].shuffle_config.seed #=> Integer
resp.validation_specification.validation_profiles[0].training_job_definition.output_data_config.kms_key_id #=> String
resp.validation_specification.validation_profiles[0].training_job_definition.output_data_config.s3_output_path #=> String
resp.validation_specification.validation_profiles[0].training_job_definition.resource_config.instance_type #=> String, one of "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.p3dn.24xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge"
resp.validation_specification.validation_profiles[0].training_job_definition.resource_config.instance_count #=> Integer
resp.validation_specification.validation_profiles[0].training_job_definition.resource_config.volume_size_in_gb #=> Integer
resp.validation_specification.validation_profiles[0].training_job_definition.resource_config.volume_kms_key_id #=> String
resp.validation_specification.validation_profiles[0].training_job_definition.stopping_condition.max_runtime_in_seconds #=> Integer
resp.validation_specification.validation_profiles[0].training_job_definition.stopping_condition.max_wait_time_in_seconds #=> Integer
resp.validation_specification.validation_profiles[0].transform_job_definition.max_concurrent_transforms #=> Integer
resp.validation_specification.validation_profiles[0].transform_job_definition.max_payload_in_mb #=> Integer
resp.validation_specification.validation_profiles[0].transform_job_definition.batch_strategy #=> String, one of "MultiRecord", "SingleRecord"
resp.validation_specification.validation_profiles[0].transform_job_definition.environment #=> Hash
resp.validation_specification.validation_profiles[0].transform_job_definition.environment["TransformEnvironmentKey"] #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile"
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.data_source.s3_data_source.s3_uri #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.content_type #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.compression_type #=> String, one of "None", "Gzip"
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.split_type #=> String, one of "None", "Line", "RecordIO", "TFRecord"
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_output.s3_output_path #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_output.accept #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_output.assemble_with #=> String, one of "None", "Line"
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_output.kms_key_id #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_resources.instance_type #=> String, one of "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"
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_resources.instance_count #=> Integer
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_resources.volume_kms_key_id #=> String
resp.algorithm_status #=> String, one of "Pending", "InProgress", "Completed", "Failed", "Deleting"
resp.algorithm_status_details.validation_statuses #=> Array
resp.algorithm_status_details.validation_statuses[0].name #=> String
resp.algorithm_status_details.validation_statuses[0].status #=> String, one of "NotStarted", "InProgress", "Completed", "Failed"
resp.algorithm_status_details.validation_statuses[0].failure_reason #=> String
resp.algorithm_status_details.image_scan_statuses #=> Array
resp.algorithm_status_details.image_scan_statuses[0].name #=> String
resp.algorithm_status_details.image_scan_statuses[0].status #=> String, one of "NotStarted", "InProgress", "Completed", "Failed"
resp.algorithm_status_details.image_scan_statuses[0].failure_reason #=> String
resp.product_id #=> String
resp.certify_for_marketplace #=> true/false

Options Hash (options):

  • :algorithm_name (required, String)

    The name of the algorithm to describe.

Returns:

See Also:

#describe_code_repository(options = {}) ⇒ Types::DescribeCodeRepositoryOutput

Gets details about the specified Git repository.

Examples:

Request syntax with placeholder values


resp = client.describe_code_repository({
  code_repository_name: "EntityName", # required
})

Response structure


resp.code_repository_name #=> String
resp.code_repository_arn #=> String
resp.creation_time #=> Time
resp.last_modified_time #=> Time
resp.git_config.repository_url #=> String
resp.git_config.branch #=> String
resp.git_config.secret_arn #=> String

Options Hash (options):

  • :code_repository_name (required, String)

    The name of the Git repository to describe.

Returns:

See Also:

#describe_compilation_job(options = {}) ⇒ Types::DescribeCompilationJobResponse

Returns information about a model compilation job.

To create a model compilation job, use CreateCompilationJob. To get information about multiple model compilation jobs, use ListCompilationJobs.

Examples:

Request syntax with placeholder values


resp = client.describe_compilation_job({
  compilation_job_name: "EntityName", # required
})

Response structure


resp.compilation_job_name #=> String
resp.compilation_job_arn #=> String
resp.compilation_job_status #=> String, one of "INPROGRESS", "COMPLETED", "FAILED", "STARTING", "STOPPING", "STOPPED"
resp.compilation_start_time #=> Time
resp.compilation_end_time #=> Time
resp.stopping_condition.max_runtime_in_seconds #=> Integer
resp.stopping_condition.max_wait_time_in_seconds #=> Integer
resp.creation_time #=> Time
resp.last_modified_time #=> Time
resp.failure_reason #=> String
resp.model_artifacts.s3_model_artifacts #=> String
resp.role_arn #=> String
resp.input_config.s3_uri #=> String
resp.input_config.data_input_config #=> String
resp.input_config.framework #=> String, one of "TENSORFLOW", "MXNET", "ONNX", "PYTORCH", "XGBOOST"
resp.output_config.s3_output_location #=> String
resp.output_config.target_device #=> String, one of "lambda", "ml_m4", "ml_m5", "ml_c4", "ml_c5", "ml_p2", "ml_p3", "jetson_tx1", "jetson_tx2", "jetson_nano", "rasp3b", "deeplens", "rk3399", "rk3288", "aisage", "sbe_c", "qcs605", "qcs603"

Options Hash (options):

  • :compilation_job_name (required, String)

    The name of the model compilation job that you want information about.

Returns:

See Also:

#describe_endpoint(options = {}) ⇒ Types::DescribeEndpointOutput

Returns the description of an endpoint.

Examples:

Request syntax with placeholder values


resp = client.describe_endpoint({
  endpoint_name: "EndpointName", # required
})

Response structure


resp.endpoint_name #=> String
resp.endpoint_arn #=> String
resp.endpoint_config_name #=> String
resp.production_variants #=> Array
resp.production_variants[0].variant_name #=> String
resp.production_variants[0].deployed_images #=> Array
resp.production_variants[0].deployed_images[0].specified_image #=> String
resp.production_variants[0].deployed_images[0].resolved_image #=> String
resp.production_variants[0].deployed_images[0].resolution_time #=> Time
resp.production_variants[0].current_weight #=> Float
resp.production_variants[0].desired_weight #=> Float
resp.production_variants[0].current_instance_count #=> Integer
resp.production_variants[0].desired_instance_count #=> Integer
resp.endpoint_status #=> String, one of "OutOfService", "Creating", "Updating", "SystemUpdating", "RollingBack", "InService", "Deleting", "Failed"
resp.failure_reason #=> String
resp.creation_time #=> Time
resp.last_modified_time #=> Time

Options Hash (options):

  • :endpoint_name (required, String)

    The name of the endpoint.

Returns:

See Also:

#describe_endpoint_config(options = {}) ⇒ Types::DescribeEndpointConfigOutput

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

Examples:

Request syntax with placeholder values


resp = client.describe_endpoint_config({
  endpoint_config_name: "EndpointConfigName", # required
})

Response structure


resp.endpoint_config_name #=> String
resp.endpoint_config_arn #=> String
resp.production_variants #=> Array
resp.production_variants[0].variant_name #=> String
resp.production_variants[0].model_name #=> String
resp.production_variants[0].initial_instance_count #=> Integer
resp.production_variants[0].instance_type #=> String, one of "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.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.12xlarge", "ml.m5d.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", "ml.c5d.large", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.12xlarge", "ml.r5.24xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.12xlarge", "ml.r5d.24xlarge"
resp.production_variants[0].initial_variant_weight #=> Float
resp.production_variants[0].accelerator_type #=> String, one of "ml.eia1.medium", "ml.eia1.large", "ml.eia1.xlarge", "ml.eia2.medium", "ml.eia2.large", "ml.eia2.xlarge"
resp.kms_key_id #=> String
resp.creation_time #=> Time

Options Hash (options):

  • :endpoint_config_name (required, String)

    The name of the endpoint configuration.

Returns:

See Also:

#describe_hyper_parameter_tuning_job(options = {}) ⇒ Types::DescribeHyperParameterTuningJobResponse

Gets a description of a hyperparameter tuning job.

Examples:

Request syntax with placeholder values


resp = client.describe_hyper_parameter_tuning_job({
  hyper_parameter_tuning_job_name: "HyperParameterTuningJobName", # required
})

Response structure


resp.hyper_parameter_tuning_job_name #=> String
resp.hyper_parameter_tuning_job_arn #=> String
resp.hyper_parameter_tuning_job_config.strategy #=> String, one of "Bayesian", "Random"
resp.hyper_parameter_tuning_job_config.hyper_parameter_tuning_job_objective.type #=> String, one of "Maximize", "Minimize"
resp.hyper_parameter_tuning_job_config.hyper_parameter_tuning_job_objective.metric_name #=> String
resp.hyper_parameter_tuning_job_config.resource_limits.max_number_of_training_jobs #=> Integer
resp.hyper_parameter_tuning_job_config.resource_limits.max_parallel_training_jobs #=> Integer
resp.hyper_parameter_tuning_job_config.parameter_ranges.integer_parameter_ranges #=> Array
resp.hyper_parameter_tuning_job_config.parameter_ranges.integer_parameter_ranges[0].name #=> String
resp.hyper_parameter_tuning_job_config.parameter_ranges.integer_parameter_ranges[0].min_value #=> String
resp.hyper_parameter_tuning_job_config.parameter_ranges.integer_parameter_ranges[0].max_value #=> String
resp.hyper_parameter_tuning_job_config.parameter_ranges.integer_parameter_ranges[0].scaling_type #=> String, one of "Auto", "Linear", "Logarithmic", "ReverseLogarithmic"
resp.hyper_parameter_tuning_job_config.parameter_ranges.continuous_parameter_ranges #=> Array
resp.hyper_parameter_tuning_job_config.parameter_ranges.continuous_parameter_ranges[0].name #=> String
resp.hyper_parameter_tuning_job_config.parameter_ranges.continuous_parameter_ranges[0].min_value #=> String
resp.hyper_parameter_tuning_job_config.parameter_ranges.continuous_parameter_ranges[0].max_value #=> String
resp.hyper_parameter_tuning_job_config.parameter_ranges.continuous_parameter_ranges[0].scaling_type #=> String, one of "Auto", "Linear", "Logarithmic", "ReverseLogarithmic"
resp.hyper_parameter_tuning_job_config.parameter_ranges.categorical_parameter_ranges #=> Array
resp.hyper_parameter_tuning_job_config.parameter_ranges.categorical_parameter_ranges[0].name #=> String
resp.hyper_parameter_tuning_job_config.parameter_ranges.categorical_parameter_ranges[0].values #=> Array
resp.hyper_parameter_tuning_job_config.parameter_ranges.categorical_parameter_ranges[0].values[0] #=> String
resp.hyper_parameter_tuning_job_config.training_job_early_stopping_type #=> String, one of "Off", "Auto"
resp.training_job_definition.static_hyper_parameters #=> Hash
resp.training_job_definition.static_hyper_parameters["ParameterKey"] #=> String
resp.training_job_definition.algorithm_specification.training_image #=> String
resp.training_job_definition.algorithm_specification.training_input_mode #=> String, one of "Pipe", "File"
resp.training_job_definition.algorithm_specification.algorithm_name #=> String
resp.training_job_definition.algorithm_specification.metric_definitions #=> Array
resp.training_job_definition.algorithm_specification.metric_definitions[0].name #=> String
resp.training_job_definition.algorithm_specification.metric_definitions[0].regex #=> String
resp.training_job_definition.role_arn #=> String
resp.training_job_definition.input_data_config #=> Array
resp.training_job_definition.input_data_config[0].channel_name #=> String
resp.training_job_definition.input_data_config[0].data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile"
resp.training_job_definition.input_data_config[0].data_source.s3_data_source.s3_uri #=> String
resp.training_job_definition.input_data_config[0].data_source.s3_data_source.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key"
resp.training_job_definition.input_data_config[0].data_source.s3_data_source.attribute_names #=> Array
resp.training_job_definition.input_data_config[0].data_source.s3_data_source.attribute_names[0] #=> String
resp.training_job_definition.input_data_config[0].data_source.file_system_data_source.file_system_id #=> String
resp.training_job_definition.input_data_config[0].data_source.file_system_data_source.file_system_access_mode #=> String, one of "rw", "ro"
resp.training_job_definition.input_data_config[0].data_source.file_system_data_source.file_system_type #=> String, one of "EFS", "FSxLustre"
resp.training_job_definition.input_data_config[0].data_source.file_system_data_source.directory_path #=> String
resp.training_job_definition.input_data_config[0].content_type #=> String
resp.training_job_definition.input_data_config[0].compression_type #=> String, one of "None", "Gzip"
resp.training_job_definition.input_data_config[0].record_wrapper_type #=> String, one of "None", "RecordIO"
resp.training_job_definition.input_data_config[0].input_mode #=> String, one of "Pipe", "File"
resp.training_job_definition.input_data_config[0].shuffle_config.seed #=> Integer
resp.training_job_definition.vpc_config.security_group_ids #=> Array
resp.training_job_definition.vpc_config.security_group_ids[0] #=> String
resp.training_job_definition.vpc_config.subnets #=> Array
resp.training_job_definition.vpc_config.subnets[0] #=> String
resp.training_job_definition.output_data_config.kms_key_id #=> String
resp.training_job_definition.output_data_config.s3_output_path #=> String
resp.training_job_definition.resource_config.instance_type #=> String, one of "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.p3dn.24xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge"
resp.training_job_definition.resource_config.instance_count #=> Integer
resp.training_job_definition.resource_config.volume_size_in_gb #=> Integer
resp.training_job_definition.resource_config.volume_kms_key_id #=> String
resp.training_job_definition.stopping_condition.max_runtime_in_seconds #=> Integer
resp.training_job_definition.stopping_condition.max_wait_time_in_seconds #=> Integer
resp.training_job_definition.enable_network_isolation #=> true/false
resp.training_job_definition.enable_inter_container_traffic_encryption #=> true/false
resp.training_job_definition.enable_managed_spot_training #=> true/false
resp.training_job_definition.checkpoint_config.s3_uri #=> String
resp.training_job_definition.checkpoint_config.local_path #=> String
resp.hyper_parameter_tuning_job_status #=> String, one of "Completed", "InProgress", "Failed", "Stopped", "Stopping"
resp.creation_time #=> Time
resp.hyper_parameter_tuning_end_time #=> Time
resp.last_modified_time #=> Time
resp.training_job_status_counters.completed #=> Integer
resp.training_job_status_counters.in_progress #=> Integer
resp.training_job_status_counters.retryable_error #=> Integer
resp.training_job_status_counters.non_retryable_error #=> Integer
resp.training_job_status_counters.stopped #=> Integer
resp.objective_status_counters.succeeded #=> Integer
resp.objective_status_counters.pending #=> Integer
resp.objective_status_counters.failed #=> Integer
resp.best_training_job.training_job_name #=> String
resp.best_training_job.training_job_arn #=> String
resp.best_training_job.tuning_job_name #=> String
resp.best_training_job.creation_time #=> Time
resp.best_training_job.training_start_time #=> Time
resp.best_training_job.training_end_time #=> Time
resp.best_training_job.training_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.best_training_job.tuned_hyper_parameters #=> Hash
resp.best_training_job.tuned_hyper_parameters["ParameterKey"] #=> String
resp.best_training_job.failure_reason #=> String
resp.best_training_job.final_hyper_parameter_tuning_job_objective_metric.type #=> String, one of "Maximize", "Minimize"
resp.best_training_job.final_hyper_parameter_tuning_job_objective_metric.metric_name #=> String
resp.best_training_job.final_hyper_parameter_tuning_job_objective_metric.value #=> Float
resp.best_training_job.objective_status #=> String, one of "Succeeded", "Pending", "Failed"
resp.overall_best_training_job.training_job_name #=> String
resp.overall_best_training_job.training_job_arn #=> String
resp.overall_best_training_job.tuning_job_name #=> String
resp.overall_best_training_job.creation_time #=> Time
resp.overall_best_training_job.training_start_time #=> Time
resp.overall_best_training_job.training_end_time #=> Time
resp.overall_best_training_job.training_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.overall_best_training_job.tuned_hyper_parameters #=> Hash
resp.overall_best_training_job.tuned_hyper_parameters["ParameterKey"] #=> String
resp.overall_best_training_job.failure_reason #=> String
resp.overall_best_training_job.final_hyper_parameter_tuning_job_objective_metric.type #=> String, one of "Maximize", "Minimize"
resp.overall_best_training_job.final_hyper_parameter_tuning_job_objective_metric.metric_name #=> String
resp.overall_best_training_job.final_hyper_parameter_tuning_job_objective_metric.value #=> Float
resp.overall_best_training_job.objective_status #=> String, one of "Succeeded", "Pending", "Failed"
resp.warm_start_config.parent_hyper_parameter_tuning_jobs #=> Array
resp.warm_start_config.parent_hyper_parameter_tuning_jobs[0].hyper_parameter_tuning_job_name #=> String
resp.warm_start_config.warm_start_type #=> String, one of "IdenticalDataAndAlgorithm", "TransferLearning"
resp.failure_reason #=> String

Options Hash (options):

  • :hyper_parameter_tuning_job_name (required, String)

    The name of the tuning job to describe.

Returns:

See Also:

#describe_labeling_job(options = {}) ⇒ Types::DescribeLabelingJobResponse

Gets information about a labeling job.

Examples:

Request syntax with placeholder values


resp = client.describe_labeling_job({
  labeling_job_name: "LabelingJobName", # required
})

Response structure


resp.labeling_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.label_counters.total_labeled #=> Integer
resp.label_counters.human_labeled #=> Integer
resp.label_counters.machine_labeled #=> Integer
resp.label_counters.failed_non_retryable_error #=> Integer
resp.label_counters.unlabeled #=> Integer
resp.failure_reason #=> String
resp.creation_time #=> Time
resp.last_modified_time #=> Time
resp.job_reference_code #=> String
resp.labeling_job_name #=> String
resp.labeling_job_arn #=> String
resp.label_attribute_name #=> String
resp.input_config.data_source.s3_data_source.manifest_s3_uri #=> String
resp.input_config.data_attributes.content_classifiers #=> Array
resp.input_config.data_attributes.content_classifiers[0] #=> String, one of "FreeOfPersonallyIdentifiableInformation", "FreeOfAdultContent"
resp.output_config.s3_output_path #=> String
resp.output_config.kms_key_id #=> String
resp.role_arn #=> String
resp.label_category_config_s3_uri #=> String
resp.stopping_conditions.max_human_labeled_object_count #=> Integer
resp.stopping_conditions.max_percentage_of_input_dataset_labeled #=> Integer
resp.labeling_job_algorithms_config.labeling_job_algorithm_specification_arn #=> String
resp.labeling_job_algorithms_config.initial_active_learning_model_arn #=> String
resp.labeling_job_algorithms_config.labeling_job_resource_config.volume_kms_key_id #=> String
resp.human_task_config.workteam_arn #=> String
resp.human_task_config.ui_config.ui_template_s3_uri #=> String
resp.human_task_config.pre_human_task_lambda_arn #=> String
resp.human_task_config.task_keywords #=> Array
resp.human_task_config.task_keywords[0] #=> String
resp.human_task_config.task_title #=> String
resp.human_task_config.task_description #=> String
resp.human_task_config.number_of_human_workers_per_data_object #=> Integer
resp.human_task_config.task_time_limit_in_seconds #=> Integer
resp.human_task_config.task_availability_lifetime_in_seconds #=> Integer
resp.human_task_config.max_concurrent_task_count #=> Integer
resp.human_task_config.annotation_consolidation_config.annotation_consolidation_lambda_arn #=> String
resp.human_task_config.public_workforce_task_price.amount_in_usd.dollars #=> Integer
resp.human_task_config.public_workforce_task_price.amount_in_usd.cents #=> Integer
resp.human_task_config.public_workforce_task_price.amount_in_usd.tenth_fractions_of_a_cent #=> Integer
resp.tags #=> Array
resp.tags[0].key #=> String
resp.tags[0].value #=> String
resp.labeling_job_output.output_dataset_s3_uri #=> String
resp.labeling_job_output.final_active_learning_model_arn #=> String

Options Hash (options):

  • :labeling_job_name (required, String)

    The name of the labeling job to return information for.

Returns:

See Also:

#describe_model(options = {}) ⇒ Types::DescribeModelOutput

Describes a model that you created using the CreateModel API.

Examples:

Request syntax with placeholder values


resp = client.describe_model({
  model_name: "ModelName", # required
})

Response structure


resp.model_name #=> String
resp.primary_container.container_hostname #=> String
resp.primary_container.image #=> String
resp.primary_container.model_data_url #=> String
resp.primary_container.environment #=> Hash
resp.primary_container.environment["EnvironmentKey"] #=> String
resp.primary_container.model_package_name #=> String
resp.containers #=> Array
resp.containers[0].container_hostname #=> String
resp.containers[0].image #=> String
resp.containers[0].model_data_url #=> String
resp.containers[0].environment #=> Hash
resp.containers[0].environment["EnvironmentKey"] #=> String
resp.containers[0].model_package_name #=> String
resp.execution_role_arn #=> String
resp.vpc_config.security_group_ids #=> Array
resp.vpc_config.security_group_ids[0] #=> String
resp.vpc_config.subnets #=> Array
resp.vpc_config.subnets[0] #=> String
resp.creation_time #=> Time
resp.model_arn #=> String
resp.enable_network_isolation #=> true/false

Options Hash (options):

  • :model_name (required, String)

    The name of the model.

Returns:

See Also:

#describe_model_package(options = {}) ⇒ Types::DescribeModelPackageOutput

Returns a description of the specified model package, which is used to create Amazon SageMaker models or list them on AWS Marketplace.

To create models in Amazon SageMaker, buyers can subscribe to model packages listed on AWS Marketplace.

Examples:

Request syntax with placeholder values


resp = client.describe_model_package({
  model_package_name: "ArnOrName", # required
})

Response structure


resp.model_package_name #=> String
resp.model_package_arn #=> String
resp.model_package_description #=> String
resp.creation_time #=> Time
resp.inference_specification.containers #=> Array
resp.inference_specification.containers[0].container_hostname #=> String
resp.inference_specification.containers[0].image #=> String
resp.inference_specification.containers[0].image_digest #=> String
resp.inference_specification.containers[0].model_data_url #=> String
resp.inference_specification.containers[0].product_id #=> String
resp.inference_specification.supported_transform_instance_types #=> Array
resp.inference_specification.supported_transform_instance_types[0] #=> String, one of "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"
resp.inference_specification.supported_realtime_inference_instance_types #=> Array
resp.inference_specification.supported_realtime_inference_instance_types[0] #=> String, one of "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.m5d.large", "ml.m5d.xlarge", "ml.m5d.2xlarge", "ml.m5d.4xlarge", "ml.m5d.12xlarge", "ml.m5d.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", "ml.c5d.large", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.r5.large", "ml.r5.xlarge", "ml.r5.2xlarge", "ml.r5.4xlarge", "ml.r5.12xlarge", "ml.r5.24xlarge", "ml.r5d.large", "ml.r5d.xlarge", "ml.r5d.2xlarge", "ml.r5d.4xlarge", "ml.r5d.12xlarge", "ml.r5d.24xlarge"
resp.inference_specification.supported_content_types #=> Array
resp.inference_specification.supported_content_types[0] #=> String
resp.inference_specification.supported_response_mime_types #=> Array
resp.inference_specification.supported_response_mime_types[0] #=> String
resp.source_algorithm_specification.source_algorithms #=> Array
resp.source_algorithm_specification.source_algorithms[0].model_data_url #=> String
resp.source_algorithm_specification.source_algorithms[0].algorithm_name #=> String
resp.validation_specification.validation_role #=> String
resp.validation_specification.validation_profiles #=> Array
resp.validation_specification.validation_profiles[0].profile_name #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.max_concurrent_transforms #=> Integer
resp.validation_specification.validation_profiles[0].transform_job_definition.max_payload_in_mb #=> Integer
resp.validation_specification.validation_profiles[0].transform_job_definition.batch_strategy #=> String, one of "MultiRecord", "SingleRecord"
resp.validation_specification.validation_profiles[0].transform_job_definition.environment #=> Hash
resp.validation_specification.validation_profiles[0].transform_job_definition.environment["TransformEnvironmentKey"] #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile"
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.data_source.s3_data_source.s3_uri #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.content_type #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.compression_type #=> String, one of "None", "Gzip"
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_input.split_type #=> String, one of "None", "Line", "RecordIO", "TFRecord"
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_output.s3_output_path #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_output.accept #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_output.assemble_with #=> String, one of "None", "Line"
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_output.kms_key_id #=> String
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_resources.instance_type #=> String, one of "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"
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_resources.instance_count #=> Integer
resp.validation_specification.validation_profiles[0].transform_job_definition.transform_resources.volume_kms_key_id #=> String
resp.model_package_status #=> String, one of "Pending", "InProgress", "Completed", "Failed", "Deleting"
resp.model_package_status_details.validation_statuses #=> Array
resp.model_package_status_details.validation_statuses[0].name #=> String
resp.model_package_status_details.validation_statuses[0].status #=> String, one of "NotStarted", "InProgress", "Completed", "Failed"
resp.model_package_status_details.validation_statuses[0].failure_reason #=> String
resp.model_package_status_details.image_scan_statuses #=> Array
resp.model_package_status_details.image_scan_statuses[0].name #=> String
resp.model_package_status_details.image_scan_statuses[0].status #=> String, one of "NotStarted", "InProgress", "Completed", "Failed"
resp.model_package_status_details.image_scan_statuses[0].failure_reason #=> String
resp.certify_for_marketplace #=> true/false

Options Hash (options):

  • :model_package_name (required, String)

    The name of the model package to describe.

Returns:

See Also:

#describe_notebook_instance(options = {}) ⇒ Types::DescribeNotebookInstanceOutput

Returns information about a notebook instance.

Examples:

Request syntax with placeholder values


resp = client.describe_notebook_instance({
  notebook_instance_name: "NotebookInstanceName", # required
})

Response structure


resp.notebook_instance_arn #=> String
resp.notebook_instance_name #=> String
resp.notebook_instance_status #=> String, one of "Pending", "InService", "Stopping", "Stopped", "Failed", "Deleting", "Updating"
resp.failure_reason #=> String
resp.url #=> String
resp.instance_type #=> String, one of "ml.t2.medium", "ml.t2.large", "ml.t2.xlarge", "ml.t2.2xlarge", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "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.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge"
resp.subnet_id #=> String
resp.security_groups #=> Array
resp.security_groups[0] #=> String
resp.role_arn #=> String
resp.kms_key_id #=> String
resp.network_interface_id #=> String
resp.last_modified_time #=> Time
resp.creation_time #=> Time
resp.notebook_instance_lifecycle_config_name #=> String
resp.direct_internet_access #=> String, one of "Enabled", "Disabled"
resp.volume_size_in_gb #=> Integer
resp.accelerator_types #=> Array
resp.accelerator_types[0] #=> String, one of "ml.eia1.medium", "ml.eia1.large", "ml.eia1.xlarge", "ml.eia2.medium", "ml.eia2.large", "ml.eia2.xlarge"
resp.default_code_repository #=> String
resp.additional_code_repositories #=> Array
resp.additional_code_repositories[0] #=> String
resp.root_access #=> String, one of "Enabled", "Disabled"

Options Hash (options):

  • :notebook_instance_name (required, String)

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

Returns:

See Also:

#describe_notebook_instance_lifecycle_config(options = {}) ⇒ Types::DescribeNotebookInstanceLifecycleConfigOutput

Returns a description of a notebook instance lifecycle configuration.

For information about notebook instance lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

Examples:

Request syntax with placeholder values


resp = client.describe_notebook_instance_lifecycle_config({
  notebook_instance_lifecycle_config_name: "NotebookInstanceLifecycleConfigName", # required
})

Response structure


resp.notebook_instance_lifecycle_config_arn #=> String
resp.notebook_instance_lifecycle_config_name #=> String
resp.on_create #=> Array
resp.on_create[0].content #=> String
resp.on_start #=> Array
resp.on_start[0].content #=> String
resp.last_modified_time #=> Time
resp.creation_time #=> Time

Options Hash (options):

  • :notebook_instance_lifecycle_config_name (required, String)

    The name of the lifecycle configuration to describe.

Returns:

See Also:

#describe_subscribed_workteam(options = {}) ⇒ Types::DescribeSubscribedWorkteamResponse

Gets information about a work team provided by a vendor. It returns details about the subscription with a vendor in the AWS Marketplace.

Examples:

Request syntax with placeholder values


resp = client.describe_subscribed_workteam({
  workteam_arn: "WorkteamArn", # required
})

Response structure


resp.subscribed_workteam.workteam_arn #=> String
resp.subscribed_workteam.marketplace_title #=> String
resp.subscribed_workteam.seller_name #=> String
resp.subscribed_workteam.marketplace_description #=> String
resp.subscribed_workteam.listing_id #=> String

Options Hash (options):

  • :workteam_arn (required, String)

    The Amazon Resource Name (ARN) of the subscribed work team to describe.

Returns:

See Also:

#describe_training_job(options = {}) ⇒ Types::DescribeTrainingJobResponse

Returns information about a training job.

Examples:

Request syntax with placeholder values


resp = client.describe_training_job({
  training_job_name: "TrainingJobName", # required
})

Response structure


resp.training_job_name #=> String
resp.training_job_arn #=> String
resp.tuning_job_arn #=> String
resp.labeling_job_arn #=> String
resp.model_artifacts.s3_model_artifacts #=> String
resp.training_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.secondary_status #=> String, one of "Starting", "LaunchingMLInstances", "PreparingTrainingStack", "Downloading", "DownloadingTrainingImage", "Training", "Uploading", "Stopping", "Stopped", "MaxRuntimeExceeded", "Completed", "Failed", "Interrupted", "MaxWaitTimeExceeded"
resp.failure_reason #=> String
resp.hyper_parameters #=> Hash
resp.hyper_parameters["ParameterKey"] #=> String
resp.algorithm_specification.training_image #=> String
resp.algorithm_specification.algorithm_name #=> String
resp.algorithm_specification.training_input_mode #=> String, one of "Pipe", "File"
resp.algorithm_specification.metric_definitions #=> Array
resp.algorithm_specification.metric_definitions[0].name #=> String
resp.algorithm_specification.metric_definitions[0].regex #=> String
resp.role_arn #=> String
resp.input_data_config #=> Array
resp.input_data_config[0].channel_name #=> String
resp.input_data_config[0].data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile"
resp.input_data_config[0].data_source.s3_data_source.s3_uri #=> String
resp.input_data_config[0].data_source.s3_data_source.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key"
resp.input_data_config[0].data_source.s3_data_source.attribute_names #=> Array
resp.input_data_config[0].data_source.s3_data_source.attribute_names[0] #=> String
resp.input_data_config[0].data_source.file_system_data_source.file_system_id #=> String
resp.input_data_config[0].data_source.file_system_data_source.file_system_access_mode #=> String, one of "rw", "ro"
resp.input_data_config[0].data_source.file_system_data_source.file_system_type #=> String, one of "EFS", "FSxLustre"
resp.input_data_config[0].data_source.file_system_data_source.directory_path #=> String
resp.input_data_config[0].content_type #=> String
resp.input_data_config[0].compression_type #=> String, one of "None", "Gzip"
resp.input_data_config[0].record_wrapper_type #=> String, one of "None", "RecordIO"
resp.input_data_config[0].input_mode #=> String, one of "Pipe", "File"
resp.input_data_config[0].shuffle_config.seed #=> Integer
resp.output_data_config.kms_key_id #=> String
resp.output_data_config.s3_output_path #=> String
resp.resource_config.instance_type #=> String, one of "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.p3dn.24xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge"
resp.resource_config.instance_count #=> Integer
resp.resource_config.volume_size_in_gb #=> Integer
resp.resource_config.volume_kms_key_id #=> String
resp.vpc_config.security_group_ids #=> Array
resp.vpc_config.security_group_ids[0] #=> String
resp.vpc_config.subnets #=> Array
resp.vpc_config.subnets[0] #=> String
resp.stopping_condition.max_runtime_in_seconds #=> Integer
resp.stopping_condition.max_wait_time_in_seconds #=> Integer
resp.creation_time #=> Time
resp.training_start_time #=> Time
resp.training_end_time #=> Time
resp.last_modified_time #=> Time
resp.secondary_status_transitions #=> Array
resp.secondary_status_transitions[0].status #=> String, one of "Starting", "LaunchingMLInstances", "PreparingTrainingStack", "Downloading", "DownloadingTrainingImage", "Training", "Uploading", "Stopping", "Stopped", "MaxRuntimeExceeded", "Completed", "Failed", "Interrupted", "MaxWaitTimeExceeded"
resp.secondary_status_transitions[0].start_time #=> Time
resp.secondary_status_transitions[0].end_time #=> Time
resp.secondary_status_transitions[0].status_message #=> String
resp.final_metric_data_list #=> Array
resp.final_metric_data_list[0].metric_name #=> String
resp.final_metric_data_list[0].value #=> Float
resp.final_metric_data_list[0].timestamp #=> Time
resp.enable_network_isolation #=> true/false
resp.enable_inter_container_traffic_encryption #=> true/false
resp.enable_managed_spot_training #=> true/false
resp.checkpoint_config.s3_uri #=> String
resp.checkpoint_config.local_path #=> String
resp.training_time_in_seconds #=> Integer
resp.billable_time_in_seconds #=> Integer

Options Hash (options):

  • :training_job_name (required, String)

    The name of the training job.

Returns:

See Also:

#describe_transform_job(options = {}) ⇒ Types::DescribeTransformJobResponse

Returns information about a transform job.

Examples:

Request syntax with placeholder values


resp = client.describe_transform_job({
  transform_job_name: "TransformJobName", # required
})

Response structure


resp.transform_job_name #=> String
resp.transform_job_arn #=> String
resp.transform_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.failure_reason #=> String
resp.model_name #=> String
resp.max_concurrent_transforms #=> Integer
resp.max_payload_in_mb #=> Integer
resp.batch_strategy #=> String, one of "MultiRecord", "SingleRecord"
resp.environment #=> Hash
resp.environment["TransformEnvironmentKey"] #=> String
resp.transform_input.data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile"
resp.transform_input.data_source.s3_data_source.s3_uri #=> String
resp.transform_input.content_type #=> String
resp.transform_input.compression_type #=> String, one of "None", "Gzip"
resp.transform_input.split_type #=> String, one of "None", "Line", "RecordIO", "TFRecord"
resp.transform_output.s3_output_path #=> String
resp.transform_output.accept #=> String
resp.transform_output.assemble_with #=> String, one of "None", "Line"
resp.transform_output.kms_key_id #=> String
resp.transform_resources.instance_type #=> String, one of "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"
resp.transform_resources.instance_count #=> Integer
resp.transform_resources.volume_kms_key_id #=> String
resp.creation_time #=> Time
resp.transform_start_time #=> Time
resp.transform_end_time #=> Time
resp.labeling_job_arn #=> String
resp.data_processing.input_filter #=> String
resp.data_processing.output_filter #=> String
resp.data_processing.join_source #=> String, one of "Input", "None"

Options Hash (options):

  • :transform_job_name (required, String)

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

Returns:

See Also:

#describe_workteam(options = {}) ⇒ Types::DescribeWorkteamResponse

Gets information about a specific work team. You can see information such as the create date, the last updated date, membership information, and the work team's Amazon Resource Name (ARN).

Examples:

Request syntax with placeholder values


resp = client.describe_workteam({
  workteam_name: "WorkteamName", # required
})

Response structure


resp.workteam.workteam_name #=> String
resp.workteam.member_definitions #=> Array
resp.workteam.member_definitions[0].cognito_member_definition.user_pool #=> String
resp.workteam.member_definitions[0].cognito_member_definition.user_group #=> String
resp.workteam.member_definitions[0].cognito_member_definition.client_id #=> String
resp.workteam.workteam_arn #=> String
resp.workteam.product_listing_ids #=> Array
resp.workteam.product_listing_ids[0] #=> String
resp.workteam.description #=> String
resp.workteam.sub_domain #=> String
resp.workteam.create_date #=> Time
resp.workteam.last_updated_date #=> Time
resp.workteam.notification_configuration.notification_topic_arn #=> String

Options Hash (options):

  • :workteam_name (required, String)

    The name of the work team to return a description of.

Returns:

See Also:

#get_search_suggestions(options = {}) ⇒ Types::GetSearchSuggestionsResponse

An auto-complete API for the search functionality in the Amazon SageMaker console. It returns suggestions of possible matches for the property name to use in Search queries. Provides suggestions for HyperParameters, Tags, and Metrics.

Examples:

Request syntax with placeholder values


resp = client.get_search_suggestions({
  resource: "TrainingJob", # required, accepts TrainingJob
  suggestion_query: {
    property_name_query: {
      property_name_hint: "PropertyNameHint", # required
    },
  },
})

Response structure


resp.property_name_suggestions #=> Array
resp.property_name_suggestions[0].property_name #=> String

Options Hash (options):

  • :resource (required, String)

    The name of the Amazon SageMaker resource to Search for. The only valid Resource value is TrainingJob.

  • :suggestion_query (Types::SuggestionQuery)

    Limits the property names that are included in the response.

Returns:

See Also:

#list_algorithms(options = {}) ⇒ Types::ListAlgorithmsOutput

Lists the machine learning algorithms that have been created.

Examples:

Request syntax with placeholder values


resp = client.list_algorithms({
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  max_results: 1,
  name_contains: "NameContains",
  next_token: "NextToken",
  sort_by: "Name", # accepts Name, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.algorithm_summary_list #=> Array
resp.algorithm_summary_list[0].algorithm_name #=> String
resp.algorithm_summary_list[0].algorithm_arn #=> String
resp.algorithm_summary_list[0].algorithm_description #=> String
resp.algorithm_summary_list[0].creation_time #=> Time
resp.algorithm_summary_list[0].algorithm_status #=> String, one of "Pending", "InProgress", "Completed", "Failed", "Deleting"
resp.next_token #=> String

Options Hash (options):

  • :creation_time_after (Time)

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

  • :creation_time_before (Time)

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

  • :max_results (Integer)

    The maximum number of algorithms to return in the response.

  • :name_contains (String)

    A string in the algorithm name. This filter returns only algorithms whose name contains the specified string.

  • :next_token (String)

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

  • :sort_by (String)

    The parameter by which to sort the results. The default is CreationTime.

  • :sort_order (String)

    The sort order for the results. The default is Ascending.

Returns:

See Also:

#list_code_repositories(options = {}) ⇒ Types::ListCodeRepositoriesOutput

Gets a list of the Git repositories in your account.

Examples:

Request syntax with placeholder values


resp = client.list_code_repositories({
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  last_modified_time_after: Time.now,
  last_modified_time_before: Time.now,
  max_results: 1,
  name_contains: "CodeRepositoryNameContains",
  next_token: "NextToken",
  sort_by: "Name", # accepts Name, CreationTime, LastModifiedTime
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.code_repository_summary_list #=> Array
resp.code_repository_summary_list[0].code_repository_name #=> String
resp.code_repository_summary_list[0].code_repository_arn #=> String
resp.code_repository_summary_list[0].creation_time #=> Time
resp.code_repository_summary_list[0].last_modified_time #=> Time
resp.code_repository_summary_list[0].git_config.repository_url #=> String
resp.code_repository_summary_list[0].git_config.branch #=> String
resp.code_repository_summary_list[0].git_config.secret_arn #=> String
resp.next_token #=> String

Options Hash (options):

  • :creation_time_after (Time)

    A filter that returns only Git repositories that were created after the specified time.

  • :creation_time_before (Time)

    A filter that returns only Git repositories that were created before the specified time.

  • :last_modified_time_after (Time)

    A filter that returns only Git repositories that were last modified after the specified time.

  • :last_modified_time_before (Time)

    A filter that returns only Git repositories that were last modified before the specified time.

  • :max_results (Integer)

    The maximum number of Git repositories to return in the response.

  • :name_contains (String)

    A string in the Git repositories name. This filter returns only repositories whose name contains the specified string.

  • :next_token (String)

    If the result of a ListCodeRepositoriesOutput request was truncated, the response includes a NextToken. To get the next set of Git repositories, use the token in the next request.

  • :sort_by (String)

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

  • :sort_order (String)

    The sort order for results. The default is Ascending.

Returns:

See Also:

#list_compilation_jobs(options = {}) ⇒ Types::ListCompilationJobsResponse

Lists model compilation jobs that satisfy various filters.

To create a model compilation job, use CreateCompilationJob. To get information about a particular model compilation job you have created, use DescribeCompilationJob.

Examples:

Request syntax with placeholder values


resp = client.list_compilation_jobs({
  next_token: "NextToken",
  max_results: 1,
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  last_modified_time_after: Time.now,
  last_modified_time_before: Time.now,
  name_contains: "NameContains",
  status_equals: "INPROGRESS", # accepts INPROGRESS, COMPLETED, FAILED, STARTING, STOPPING, STOPPED
  sort_by: "Name", # accepts Name, CreationTime, Status
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.compilation_job_summaries #=> Array
resp.compilation_job_summaries[0].compilation_job_name #=> String
resp.compilation_job_summaries[0].compilation_job_arn #=> String
resp.compilation_job_summaries[0].creation_time #=> Time
resp.compilation_job_summaries[0].compilation_start_time #=> Time
resp.compilation_job_summaries[0].compilation_end_time #=> Time
resp.compilation_job_summaries[0].compilation_target_device #=> String, one of "lambda", "ml_m4", "ml_m5", "ml_c4", "ml_c5", "ml_p2", "ml_p3", "jetson_tx1", "jetson_tx2", "jetson_nano", "rasp3b", "deeplens", "rk3399", "rk3288", "aisage", "sbe_c", "qcs605", "qcs603"
resp.compilation_job_summaries[0].last_modified_time #=> Time
resp.compilation_job_summaries[0].compilation_job_status #=> String, one of "INPROGRESS", "COMPLETED", "FAILED", "STARTING", "STOPPING", "STOPPED"
resp.next_token #=> String

Options Hash (options):

  • :next_token (String)

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

  • :max_results (Integer)

    The maximum number of model compilation jobs to return in the response.

  • :creation_time_after (Time)

    A filter that returns the model compilation jobs that were created after a specified time.

  • :creation_time_before (Time)

    A filter that returns the model compilation jobs that were created before a specified time.

  • :last_modified_time_after (Time)

    A filter that returns the model compilation jobs that were modified after a specified time.

  • :last_modified_time_before (Time)

    A filter that returns the model compilation jobs that were modified before a specified time.

  • :name_contains (String)

    A filter that returns the model compilation jobs whose name contains a specified string.

  • :status_equals (String)

    A filter that retrieves model compilation jobs with a specific DescribeCompilationJobResponse$CompilationJobStatus status.

  • :sort_by (String)

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

  • :sort_order (String)

    The sort order for results. The default is Ascending.

Returns:

See Also:

#list_endpoint_configs(options = {}) ⇒ Types::ListEndpointConfigsOutput

Lists endpoint configurations.

Examples:

Request syntax with placeholder values


resp = client.list_endpoint_configs({
  sort_by: "Name", # accepts Name, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "PaginationToken",
  max_results: 1,
  name_contains: "EndpointConfigNameContains",
  creation_time_before: Time.now,
  creation_time_after: Time.now,
})

Response structure


resp.endpoint_configs #=> Array
resp.endpoint_configs[0].endpoint_config_name #=> String
resp.endpoint_configs[0].endpoint_config_arn #=> String
resp.endpoint_configs[0].creation_time #=> Time
resp.next_token #=> String

Options Hash (options):

  • :sort_by (String)

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

  • :sort_order (String)

    The sort order for results. The default is Descending.

  • :next_token (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.

  • :max_results (Integer)

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

  • :name_contains (String)

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

  • :creation_time_before (Time)

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

  • :creation_time_after (Time)

    A filter that returns only endpoint configurations with a creation time greater than or equal to the specified time (timestamp).

Returns:

See Also:

#list_endpoints(options = {}) ⇒ Types::ListEndpointsOutput

Lists endpoints.

Examples:

Request syntax with placeholder values


resp = client.list_endpoints({
  sort_by: "Name", # accepts Name, CreationTime, Status
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "PaginationToken",
  max_results: 1,
  name_contains: "EndpointNameContains",
  creation_time_before: Time.now,
  creation_time_after: Time.now,
  last_modified_time_before: Time.now,
  last_modified_time_after: Time.now,
  status_equals: "OutOfService", # accepts OutOfService, Creating, Updating, SystemUpdating, RollingBack, InService, Deleting, Failed
})

Response structure


resp.endpoints #=> Array
resp.endpoints[0].endpoint_name #=> String
resp.endpoints[0].endpoint_arn #=> String
resp.endpoints[0].creation_time #=> Time
resp.endpoints[0].last_modified_time #=> Time
resp.endpoints[0].endpoint_status #=> String, one of "OutOfService", "Creating", "Updating", "SystemUpdating", "RollingBack", "InService", "Deleting", "Failed"
resp.next_token #=> String

Options Hash (options):

  • :sort_by (String)

    Sorts the list of results. The default is CreationTime.

  • :sort_order (String)

    The sort order for results. The default is Descending.

  • :next_token (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.

  • :max_results (Integer)

    The maximum number of endpoints to return in the response.

  • :name_contains (String)

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

  • :creation_time_before (Time)

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

  • :creation_time_after (Time)

    A filter that returns only endpoints with a creation time greater than or equal to the specified time (timestamp).

  • :last_modified_time_before (Time)

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

  • :last_modified_time_after (Time)

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

  • :status_equals (String)

    A filter that returns only endpoints with the specified status.

Returns:

See Also:

#list_hyper_parameter_tuning_jobs(options = {}) ⇒ Types::ListHyperParameterTuningJobsResponse

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

Examples:

Request syntax with placeholder values


resp = client.list_hyper_parameter_tuning_jobs({
  next_token: "NextToken",
  max_results: 1,
  sort_by: "Name", # accepts Name, Status, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
  name_contains: "NameContains",
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  last_modified_time_after: Time.now,
  last_modified_time_before: Time.now,
  status_equals: "Completed", # accepts Completed, InProgress, Failed, Stopped, Stopping
})

Response structure


resp.hyper_parameter_tuning_job_summaries #=> Array
resp.hyper_parameter_tuning_job_summaries[0].hyper_parameter_tuning_job_name #=> String
resp.hyper_parameter_tuning_job_summaries[0].hyper_parameter_tuning_job_arn #=> String
resp.hyper_parameter_tuning_job_summaries[0].hyper_parameter_tuning_job_status #=> String, one of "Completed", "InProgress", "Failed", "Stopped", "Stopping"
resp.hyper_parameter_tuning_job_summaries[0].strategy #=> String, one of "Bayesian", "Random"
resp.hyper_parameter_tuning_job_summaries[0].creation_time #=> Time
resp.hyper_parameter_tuning_job_summaries[0].hyper_parameter_tuning_end_time #=> Time
resp.hyper_parameter_tuning_job_summaries[0].last_modified_time #=> Time
resp.hyper_parameter_tuning_job_summaries[0].training_job_status_counters.completed #=> Integer
resp.hyper_parameter_tuning_job_summaries[0].training_job_status_counters.in_progress #=> Integer
resp.hyper_parameter_tuning_job_summaries[0].training_job_status_counters.retryable_error #=> Integer
resp.hyper_parameter_tuning_job_summaries[0].training_job_status_counters.non_retryable_error #=> Integer
resp.hyper_parameter_tuning_job_summaries[0].training_job_status_counters.stopped #=> Integer
resp.hyper_parameter_tuning_job_summaries[0].objective_status_counters.succeeded #=> Integer
resp.hyper_parameter_tuning_job_summaries[0].objective_status_counters.pending #=> Integer
resp.hyper_parameter_tuning_job_summaries[0].objective_status_counters.failed #=> Integer
resp.hyper_parameter_tuning_job_summaries[0].resource_limits.max_number_of_training_jobs #=> Integer
resp.hyper_parameter_tuning_job_summaries[0].resource_limits.max_parallel_training_jobs #=> Integer
resp.next_token #=> String

Options Hash (options):

  • :next_token (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.

  • :max_results (Integer)

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

  • :sort_by (String)

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

  • :sort_order (String)

    The sort order for results. The default is Ascending.

  • :name_contains (String)

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

  • :creation_time_after (Time)

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

  • :creation_time_before (Time)

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

  • :last_modified_time_after (Time)

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

  • :last_modified_time_before (Time)

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

  • :status_equals (String)

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

Returns:

See Also:

#list_labeling_jobs(options = {}) ⇒ Types::ListLabelingJobsResponse

Gets a list of labeling jobs.

Examples:

Request syntax with placeholder values


resp = client.list_labeling_jobs({
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  last_modified_time_after: Time.now,
  last_modified_time_before: Time.now,
  max_results: 1,
  next_token: "NextToken",
  name_contains: "NameContains",
  sort_by: "Name", # accepts Name, CreationTime, Status
  sort_order: "Ascending", # accepts Ascending, Descending
  status_equals: "InProgress", # accepts InProgress, Completed, Failed, Stopping, Stopped
})

Response structure


resp.labeling_job_summary_list #=> Array
resp.labeling_job_summary_list[0].labeling_job_name #=> String
resp.labeling_job_summary_list[0].labeling_job_arn #=> String
resp.labeling_job_summary_list[0].creation_time #=> Time
resp.labeling_job_summary_list[0].last_modified_time #=> Time
resp.labeling_job_summary_list[0].labeling_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.labeling_job_summary_list[0].label_counters.total_labeled #=> Integer
resp.labeling_job_summary_list[0].label_counters.human_labeled #=> Integer
resp.labeling_job_summary_list[0].label_counters.machine_labeled #=> Integer
resp.labeling_job_summary_list[0].label_counters.failed_non_retryable_error #=> Integer
resp.labeling_job_summary_list[0].label_counters.unlabeled #=> Integer
resp.labeling_job_summary_list[0].workteam_arn #=> String
resp.labeling_job_summary_list[0].pre_human_task_lambda_arn #=> String
resp.labeling_job_summary_list[0].annotation_consolidation_lambda_arn #=> String
resp.labeling_job_summary_list[0].failure_reason #=> String
resp.labeling_job_summary_list[0].labeling_job_output.output_dataset_s3_uri #=> String
resp.labeling_job_summary_list[0].labeling_job_output.final_active_learning_model_arn #=> String
resp.labeling_job_summary_list[0].input_config.data_source.s3_data_source.manifest_s3_uri #=> String
resp.labeling_job_summary_list[0].input_config.data_attributes.content_classifiers #=> Array
resp.labeling_job_summary_list[0].input_config.data_attributes.content_classifiers[0] #=> String, one of "FreeOfPersonallyIdentifiableInformation", "FreeOfAdultContent"
resp.next_token #=> String

Options Hash (options):

  • :creation_time_after (Time)

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

  • :creation_time_before (Time)

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

  • :last_modified_time_after (Time)

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

  • :last_modified_time_before (Time)

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

  • :max_results (Integer)

    The maximum number of labeling jobs to return in each page of the response.

  • :next_token (String)

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

  • :name_contains (String)

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

  • :sort_by (String)

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

  • :sort_order (String)

    The sort order for results. The default is Ascending.

  • :status_equals (String)

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

Returns:

See Also:

#list_labeling_jobs_for_workteam(options = {}) ⇒ Types::ListLabelingJobsForWorkteamResponse

Gets a list of labeling jobs assigned to a specified work team.

Examples:

Request syntax with placeholder values


resp = client.list_labeling_jobs_for_workteam({
  workteam_arn: "WorkteamArn", # required
  max_results: 1,
  next_token: "NextToken",
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  job_reference_code_contains: "JobReferenceCodeContains",
  sort_by: "CreationTime", # accepts CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.labeling_job_summary_list #=> Array
resp.labeling_job_summary_list[0].labeling_job_name #=> String
resp.labeling_job_summary_list[0].job_reference_code #=> String
resp.labeling_job_summary_list[0]. #=> String
resp.labeling_job_summary_list[0].creation_time #=> Time
resp.labeling_job_summary_list[0].label_counters.human_labeled #=> Integer
resp.labeling_job_summary_list[0].label_counters.pending_human #=> Integer
resp.labeling_job_summary_list[0].label_counters.total #=> Integer
resp.labeling_job_summary_list[0].number_of_human_workers_per_data_object #=> Integer
resp.next_token #=> String

Options Hash (options):

  • :workteam_arn (required, String)

    The Amazon Resource Name (ARN) of the work team for which you want to see labeling jobs for.

  • :max_results (Integer)

    The maximum number of labeling jobs to return in each page of the response.

  • :next_token (String)

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

  • :creation_time_after (Time)

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

  • :creation_time_before (Time)

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

  • :job_reference_code_contains (String)

    A filter the limits jobs to only the ones whose job reference code contains the specified string.

  • :sort_by (String)

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

  • :sort_order (String)

    The sort order for results. The default is Ascending.

Returns:

See Also:

#list_model_packages(options = {}) ⇒ Types::ListModelPackagesOutput

Lists the model packages that have been created.

Examples:

Request syntax with placeholder values


resp = client.list_model_packages({
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  max_results: 1,
  name_contains: "NameContains",
  next_token: "NextToken",
  sort_by: "Name", # accepts Name, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.model_package_summary_list #=> Array
resp.model_package_summary_list[0].model_package_name #=> String
resp.model_package_summary_list[0].model_package_arn #=> String
resp.model_package_summary_list[0].model_package_description #=> String
resp.model_package_summary_list[0].creation_time #=> Time
resp.model_package_summary_list[0].model_package_status #=> String, one of "Pending", "InProgress", "Completed", "Failed", "Deleting"
resp.next_token #=> String

Options Hash (options):

  • :creation_time_after (Time)

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

  • :creation_time_before (Time)

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

  • :max_results (Integer)

    The maximum number of model packages to return in the response.

  • :name_contains (String)

    A string in the model package name. This filter returns only model packages whose name contains the specified string.

  • :next_token (String)

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

  • :sort_by (String)

    The parameter by which to sort the results. The default is CreationTime.

  • :sort_order (String)

    The sort order for the results. The default is Ascending.

Returns:

See Also:

#list_models(options = {}) ⇒ Types::ListModelsOutput

Lists models created with the CreateModel API.

Examples:

Request syntax with placeholder values


resp = client.list_models({
  sort_by: "Name", # accepts Name, CreationTime
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "PaginationToken",
  max_results: 1,
  name_contains: "ModelNameContains",
  creation_time_before: Time.now,
  creation_time_after: Time.now,
})

Response structure


resp.models #=> Array
resp.models[0].model_name #=> String
resp.models[0].model_arn #=> String
resp.models[0].creation_time #=> Time
resp.next_token #=> String

Options Hash (options):

  • :sort_by (String)

    Sorts the list of results. The default is CreationTime.

  • :sort_order (String)

    The sort order for results. The default is Descending.

  • :next_token (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.

  • :max_results (Integer)

    The maximum number of models to return in the response.

  • :name_contains (String)

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

  • :creation_time_before (Time)

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

  • :creation_time_after (Time)

    A filter that returns only models with a creation time greater than or equal to the specified time (timestamp).

Returns:

See Also:

#list_notebook_instance_lifecycle_configs(options = {}) ⇒ Types::ListNotebookInstanceLifecycleConfigsOutput

Lists notebook instance lifestyle configurations created with the CreateNotebookInstanceLifecycleConfig API.

Examples:

Request syntax with placeholder values


resp = client.list_notebook_instance_lifecycle_configs({
  next_token: "NextToken",
  max_results: 1,
  sort_by: "Name", # accepts Name, CreationTime, LastModifiedTime
  sort_order: "Ascending", # accepts Ascending, Descending
  name_contains: "NotebookInstanceLifecycleConfigNameContains",
  creation_time_before: Time.now,
  creation_time_after: Time.now,
  last_modified_time_before: Time.now,
  last_modified_time_after: Time.now,
})

Response structure


resp.next_token #=> String
resp.notebook_instance_lifecycle_configs #=> Array
resp.notebook_instance_lifecycle_configs[0].notebook_instance_lifecycle_config_name #=> String
resp.notebook_instance_lifecycle_configs[0].notebook_instance_lifecycle_config_arn #=> String
resp.notebook_instance_lifecycle_configs[0].creation_time #=> Time
resp.notebook_instance_lifecycle_configs[0].last_modified_time #=> Time

Options Hash (options):

  • :next_token (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.

  • :max_results (Integer)

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

  • :sort_by (String)

    Sorts the list of results. The default is CreationTime.

  • :sort_order (String)

    The sort order for results.

  • :name_contains (String)

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

  • :creation_time_before (Time)

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

  • :creation_time_after (Time)

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

  • :last_modified_time_before (Time)

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

  • :last_modified_time_after (Time)

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

Returns:

See Also:

#list_notebook_instances(options = {}) ⇒ Types::ListNotebookInstancesOutput

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

Examples:

Request syntax with placeholder values


resp = client.list_notebook_instances({
  next_token: "NextToken",
  max_results: 1,
  sort_by: "Name", # accepts Name, CreationTime, Status
  sort_order: "Ascending", # accepts Ascending, Descending
  name_contains: "NotebookInstanceNameContains",
  creation_time_before: Time.now,
  creation_time_after: Time.now,
  last_modified_time_before: Time.now,
  last_modified_time_after: Time.now,
  status_equals: "Pending", # accepts Pending, InService, Stopping, Stopped, Failed, Deleting, Updating
  notebook_instance_lifecycle_config_name_contains: "NotebookInstanceLifecycleConfigName",
  default_code_repository_contains: "CodeRepositoryContains",
  additional_code_repository_equals: "CodeRepositoryNameOrUrl",
})

Response structure


resp.next_token #=> String
resp.notebook_instances #=> Array
resp.notebook_instances[0].notebook_instance_name #=> String
resp.notebook_instances[0].notebook_instance_arn #=> String
resp.notebook_instances[0].notebook_instance_status #=> String, one of "Pending", "InService", "Stopping", "Stopped", "Failed", "Deleting", "Updating"
resp.notebook_instances[0].url #=> String
resp.notebook_instances[0].instance_type #=> String, one of "ml.t2.medium", "ml.t2.large", "ml.t2.xlarge", "ml.t2.2xlarge", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "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.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge", "ml.c5d.xlarge", "ml.c5d.2xlarge", "ml.c5d.4xlarge", "ml.c5d.9xlarge", "ml.c5d.18xlarge", "ml.p2.xlarge", "ml.p2.8xlarge", "ml.p2.16xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge"
resp.notebook_instances[0].creation_time #=> Time
resp.notebook_instances[0].last_modified_time #=> Time
resp.notebook_instances[0].notebook_instance_lifecycle_config_name #=> String
resp.notebook_instances[0].default_code_repository #=> String
resp.notebook_instances[0].additional_code_repositories #=> Array
resp.notebook_instances[0].additional_code_repositories[0] #=> String

Options Hash (options):

  • :next_token (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.

  • :max_results (Integer)

    The maximum number of notebook instances to return.

  • :sort_by (String)

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

  • :sort_order (String)

    The sort order for results.

  • :name_contains (String)

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

  • :creation_time_before (Time)

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

  • :creation_time_after (Time)

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

  • :last_modified_time_before (Time)

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

  • :last_modified_time_after (Time)

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

  • :status_equals (String)

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

  • :notebook_instance_lifecycle_config_name_contains (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.

  • :default_code_repository_contains (String)

    A string in the name or URL of a Git repository associated with this notebook instance. This filter returns only notebook instances associated with a git repository with a name that contains the specified string.

  • :additional_code_repository_equals (String)

    A filter that returns only notebook instances with associated with the specified git repository.

Returns:

See Also:

#list_subscribed_workteams(options = {}) ⇒ Types::ListSubscribedWorkteamsResponse

Gets a list of the work teams that you are subscribed to in the AWS Marketplace. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.

Examples:

Request syntax with placeholder values


resp = client.list_subscribed_workteams({
  name_contains: "WorkteamName",
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.subscribed_workteams #=> Array
resp.subscribed_workteams[0].workteam_arn #=> String
resp.subscribed_workteams[0].marketplace_title #=> String
resp.subscribed_workteams[0].seller_name #=> String
resp.subscribed_workteams[0].marketplace_description #=> String
resp.subscribed_workteams[0].listing_id #=> String
resp.next_token #=> String

Options Hash (options):

  • :name_contains (String)

    A string in the work team name. This filter returns only work teams whose name contains the specified string.

  • :next_token (String)

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

  • :max_results (Integer)

    The maximum number of work teams to return in each page of the response.

Returns:

See Also:

#list_tags(options = {}) ⇒ Types::ListTagsOutput

Returns the tags for the specified Amazon SageMaker resource.

Examples:

Request syntax with placeholder values


resp = client.list_tags({
  resource_arn: "ResourceArn", # required
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.tags #=> Array
resp.tags[0].key #=> String
resp.tags[0].value #=> String
resp.next_token #=> String

Options Hash (options):

  • :resource_arn (required, String)

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

  • :next_token (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.

  • :max_results (Integer)

    Maximum number of tags to return.

Returns:

See Also:

#list_training_jobs(options = {}) ⇒ Types::ListTrainingJobsResponse

Lists training jobs.

Examples:

Request syntax with placeholder values


resp = client.list_training_jobs({
  next_token: "NextToken",
  max_results: 1,
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  last_modified_time_after: Time.now,
  last_modified_time_before: Time.now,
  name_contains: "NameContains",
  status_equals: "InProgress", # accepts InProgress, Completed, Failed, Stopping, Stopped
  sort_by: "Name", # accepts Name, CreationTime, Status
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.training_job_summaries #=> Array
resp.training_job_summaries[0].training_job_name #=> String
resp.training_job_summaries[0].training_job_arn #=> String
resp.training_job_summaries[0].creation_time #=> Time
resp.training_job_summaries[0].training_end_time #=> Time
resp.training_job_summaries[0].last_modified_time #=> Time
resp.training_job_summaries[0].training_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.next_token #=> String

Options Hash (options):

  • :next_token (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.

  • :max_results (Integer)

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

  • :creation_time_after (Time)

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

  • :creation_time_before (Time)

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

  • :last_modified_time_after (Time)

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

  • :last_modified_time_before (Time)

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

  • :name_contains (String)

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

  • :status_equals (String)

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

  • :sort_by (String)

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

  • :sort_order (String)

    The sort order for results. The default is Ascending.

Returns:

See Also:

#list_training_jobs_for_hyper_parameter_tuning_job(options = {}) ⇒ Types::ListTrainingJobsForHyperParameterTuningJobResponse

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

Examples:

Request syntax with placeholder values


resp = client.list_training_jobs_for_hyper_parameter_tuning_job({
  hyper_parameter_tuning_job_name: "HyperParameterTuningJobName", # required
  next_token: "NextToken",
  max_results: 1,
  status_equals: "InProgress", # accepts InProgress, Completed, Failed, Stopping, Stopped
  sort_by: "Name", # accepts Name, CreationTime, Status, FinalObjectiveMetricValue
  sort_order: "Ascending", # accepts Ascending, Descending
})

Response structure


resp.training_job_summaries #=> Array
resp.training_job_summaries[0].training_job_name #=> String
resp.training_job_summaries[0].training_job_arn #=> String
resp.training_job_summaries[0].tuning_job_name #=> String
resp.training_job_summaries[0].creation_time #=> Time
resp.training_job_summaries[0].training_start_time #=> Time
resp.training_job_summaries[0].training_end_time #=> Time
resp.training_job_summaries[0].training_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.training_job_summaries[0].tuned_hyper_parameters #=> Hash
resp.training_job_summaries[0].tuned_hyper_parameters["ParameterKey"] #=> String
resp.training_job_summaries[0].failure_reason #=> String
resp.training_job_summaries[0].final_hyper_parameter_tuning_job_objective_metric.type #=> String, one of "Maximize", "Minimize"
resp.training_job_summaries[0].final_hyper_parameter_tuning_job_objective_metric.metric_name #=> String
resp.training_job_summaries[0].final_hyper_parameter_tuning_job_objective_metric.value #=> Float
resp.training_job_summaries[0].objective_status #=> String, one of "Succeeded", "Pending", "Failed"
resp.next_token #=> String

Options Hash (options):

  • :hyper_parameter_tuning_job_name (required, String)

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

  • :next_token (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.

  • :max_results (Integer)

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

  • :status_equals (String)

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

  • :sort_by (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.

  • :sort_order (String)

    The sort order for results. The default is Ascending.

Returns:

See Also:

#list_transform_jobs(options = {}) ⇒ Types::ListTransformJobsResponse

Lists transform jobs.

Examples:

Request syntax with placeholder values


resp = client.list_transform_jobs({
  creation_time_after: Time.now,
  creation_time_before: Time.now,
  last_modified_time_after: Time.now,
  last_modified_time_before: Time.now,
  name_contains: "NameContains",
  status_equals: "InProgress", # accepts InProgress, Completed, Failed, Stopping, Stopped
  sort_by: "Name", # accepts Name, CreationTime, Status
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.transform_job_summaries #=> Array
resp.transform_job_summaries[0].transform_job_name #=> String
resp.transform_job_summaries[0].transform_job_arn #=> String
resp.transform_job_summaries[0].creation_time #=> Time
resp.transform_job_summaries[0].transform_end_time #=> Time
resp.transform_job_summaries[0].last_modified_time #=> Time
resp.transform_job_summaries[0].transform_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.transform_job_summaries[0].failure_reason #=> String
resp.next_token #=> String

Options Hash (options):

  • :creation_time_after (Time)

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

  • :creation_time_before (Time)

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

  • :last_modified_time_after (Time)

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

  • :last_modified_time_before (Time)

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

  • :name_contains (String)

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

  • :status_equals (String)

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

  • :sort_by (String)

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

  • :sort_order (String)

    The sort order for results. The default is Descending.

  • :next_token (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.

  • :max_results (Integer)

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

Returns:

See Also:

#list_workteams(options = {}) ⇒ Types::ListWorkteamsResponse

Gets a list of work teams that you have defined in a region. The list may be empty if no work team satisfies the filter specified in the NameContains parameter.

Examples:

Request syntax with placeholder values


resp = client.list_workteams({
  sort_by: "Name", # accepts Name, CreateDate
  sort_order: "Ascending", # accepts Ascending, Descending
  name_contains: "WorkteamName",
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.workteams #=> Array
resp.workteams[0].workteam_name #=> String
resp.workteams[0].member_definitions #=> Array
resp.workteams[0].member_definitions[0].cognito_member_definition.user_pool #=> String
resp.workteams[0].member_definitions[0].cognito_member_definition.user_group #=> String
resp.workteams[0].member_definitions[0].cognito_member_definition.client_id #=> String
resp.workteams[0].workteam_arn #=> String
resp.workteams[0].product_listing_ids #=> Array
resp.workteams[0].product_listing_ids[0] #=> String
resp.workteams[0].description #=> String
resp.workteams[0].sub_domain #=> String
resp.workteams[0].create_date #=> Time
resp.workteams[0].last_updated_date #=> Time
resp.workteams[0].notification_configuration.notification_topic_arn #=> String
resp.next_token #=> String

Options Hash (options):

  • :sort_by (String)

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

  • :sort_order (String)

    The sort order for results. The default is Ascending.

  • :name_contains (String)

    A string in the work team\'s name. This filter returns only work teams whose name contains the specified string.

  • :next_token (String)

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

  • :max_results (Integer)

    The maximum number of work teams to return in each page of the response.

Returns:

See Also:

#render_ui_template(options = {}) ⇒ Types::RenderUiTemplateResponse

Renders the UI template so that you can preview the worker's experience.

Examples:

Request syntax with placeholder values


resp = client.render_ui_template({
  ui_template: { # required
    content: "TemplateContent", # required
  },
  task: { # required
    input: "TaskInput", # required
  },
  role_arn: "RoleArn", # required
})

Response structure


resp.rendered_content #=> String
resp.errors #=> Array
resp.errors[0].code #=> String
resp.errors[0].message #=> String

Options Hash (options):

  • :ui_template (required, Types::UiTemplate)

    A Template object containing the worker UI template to render.

  • :task (required, Types::RenderableTask)

    A RenderableTask object containing a representative task to render.

  • :role_arn (required, String)

    The Amazon Resource Name (ARN) that has access to the S3 objects that are used by the template.

Returns:

See Also:

#search(options = {}) ⇒ Types::SearchResponse

Finds Amazon SageMaker resources that match a search query. Matching resource objects are returned as a list of SearchResult objects in the response. You can sort the search results by any resource property in a ascending or descending order.

You can query against the following value types: numerical, text, Booleans, and timestamps.

Examples:

Request syntax with placeholder values


resp = client.search({
  resource: "TrainingJob", # required, accepts TrainingJob
  search_expression: {
    filters: [
      {
        name: "ResourcePropertyName", # required
        operator: "Equals", # accepts Equals, NotEquals, GreaterThan, GreaterThanOrEqualTo, LessThan, LessThanOrEqualTo, Contains
        value: "FilterValue",
      },
    ],
    nested_filters: [
      {
        nested_property_name: "ResourcePropertyName", # required
        filters: [ # required
          {
            name: "ResourcePropertyName", # required
            operator: "Equals", # accepts Equals, NotEquals, GreaterThan, GreaterThanOrEqualTo, LessThan, LessThanOrEqualTo, Contains
            value: "FilterValue",
          },
        ],
      },
    ],
    sub_expressions: [
      {
        # recursive SearchExpression
      },
    ],
    operator: "And", # accepts And, Or
  },
  sort_by: "ResourcePropertyName",
  sort_order: "Ascending", # accepts Ascending, Descending
  next_token: "NextToken",
  max_results: 1,
})

Response structure


resp.results #=> Array
resp.results[0].training_job.training_job_name #=> String
resp.results[0].training_job.training_job_arn #=> String
resp.results[0].training_job.tuning_job_arn #=> String
resp.results[0].training_job.labeling_job_arn #=> String
resp.results[0].training_job.model_artifacts.s3_model_artifacts #=> String
resp.results[0].training_job.training_job_status #=> String, one of "InProgress", "Completed", "Failed", "Stopping", "Stopped"
resp.results[0].training_job.secondary_status #=> String, one of "Starting", "LaunchingMLInstances", "PreparingTrainingStack", "Downloading", "DownloadingTrainingImage", "Training", "Uploading", "Stopping", "Stopped", "MaxRuntimeExceeded", "Completed", "Failed", "Interrupted", "MaxWaitTimeExceeded"
resp.results[0].training_job.failure_reason #=> String
resp.results[0].training_job.hyper_parameters #=> Hash
resp.results[0].training_job.hyper_parameters["ParameterKey"] #=> String
resp.results[0].training_job.algorithm_specification.training_image #=> String
resp.results[0].training_job.algorithm_specification.algorithm_name #=> String
resp.results[0].training_job.algorithm_specification.training_input_mode #=> String, one of "Pipe", "File"
resp.results[0].training_job.algorithm_specification.metric_definitions #=> Array
resp.results[0].training_job.algorithm_specification.metric_definitions[0].name #=> String
resp.results[0].training_job.algorithm_specification.metric_definitions[0].regex #=> String
resp.results[0].training_job.role_arn #=> String
resp.results[0].training_job.input_data_config #=> Array
resp.results[0].training_job.input_data_config[0].channel_name #=> String
resp.results[0].training_job.input_data_config[0].data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile"
resp.results[0].training_job.input_data_config[0].data_source.s3_data_source.s3_uri #=> String
resp.results[0].training_job.input_data_config[0].data_source.s3_data_source.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key"
resp.results[0].training_job.input_data_config[0].data_source.s3_data_source.attribute_names #=> Array
resp.results[0].training_job.input_data_config[0].data_source.s3_data_source.attribute_names[0] #=> String
resp.results[0].training_job.input_data_config[0].data_source.file_system_data_source.file_system_id #=> String
resp.results[0].training_job.input_data_config[0].data_source.file_system_data_source.file_system_access_mode #=> String, one of "rw", "ro"
resp.results[0].training_job.input_data_config[0].data_source.file_system_data_source.file_system_type #=> String, one of "EFS", "FSxLustre"
resp.results[0].training_job.input_data_config[0].data_source.file_system_data_source.directory_path #=> String
resp.results[0].training_job.input_data_config[0].content_type #=> String
resp.results[0].training_job.input_data_config[0].compression_type #=> String, one of "None", "Gzip"
resp.results[0].training_job.input_data_config[0].record_wrapper_type #=> String, one of "None", "RecordIO"
resp.results[0].training_job.input_data_config[0].input_mode #=> String, one of "Pipe", "File"
resp.results[0].training_job.input_data_config[0].shuffle_config.seed #=> Integer
resp.results[0].training_job.output_data_config.kms_key_id #=> String
resp.results[0].training_job.output_data_config.s3_output_path #=> String
resp.results[0].training_job.resource_config.instance_type #=> String, one of "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.p3dn.24xlarge", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.18xlarge"
resp.results[0].training_job.resource_config.instance_count #=> Integer
resp.results[0].training_job.resource_config.volume_size_in_gb #=> Integer
resp.results[0].training_job.resource_config.volume_kms_key_id #=> String
resp.results[0].training_job.vpc_config.security_group_ids #=> Array
resp.results[0].training_job.vpc_config.security_group_ids[0] #=> String
resp.results[0].training_job.vpc_config.subnets #=> Array
resp.results[0].training_job.vpc_config.subnets[0] #=> String
resp.results[0].training_job.stopping_condition.max_runtime_in_seconds #=> Integer
resp.results[0].training_job.stopping_condition.max_wait_time_in_seconds #=> Integer
resp.results[0].training_job.creation_time #=> Time
resp.results[0].training_job.training_start_time #=> Time
resp.results[0].training_job.training_end_time #=> Time
resp.results[0].training_job.last_modified_time #=> Time
resp.results[0].training_job.secondary_status_transitions #=> Array
resp.results[0].training_job.secondary_status_transitions[0].status #=> String, one of "Starting", "LaunchingMLInstances", "PreparingTrainingStack", "Downloading", "DownloadingTrainingImage", "Training", "Uploading", "Stopping", "Stopped", "MaxRuntimeExceeded", "Completed", "Failed", "Interrupted", "MaxWaitTimeExceeded"
resp.results[0].training_job.secondary_status_transitions[0].start_time #=> Time
resp.results[0].training_job.secondary_status_transitions[0].end_time #=> Time
resp.results[0].training_job.secondary_status_transitions[0].status_message #=> String
resp.results[0].training_job.final_metric_data_list #=> Array
resp.results[0].training_job.final_metric_data_list[0].metric_name #=> String
resp.results[0].training_job.final_metric_data_list[0].value #=> Float
resp.results[0].training_job.final_metric_data_list[0].timestamp #=> Time
resp.results[0].training_job.enable_network_isolation #=> true/false
resp.results[0].training_job.enable_inter_container_traffic_encryption #=> true/false
resp.results[0].training_job.tags #=> Array
resp.results[0].training_job.tags[0].key #=> String
resp.results[0].training_job.tags[0].value #=> String
resp.next_token #=> String

Options Hash (options):

  • :resource (required, String)

    The name of the Amazon SageMaker resource to search for. Currently, the only valid Resource value is TrainingJob.

  • :search_expression (Types::SearchExpression)

    A Boolean conditional statement. Resource objects must satisfy this condition to be included in search results. You must provide at least one subexpression, filter, or nested filter. The maximum number of recursive SubExpressions, NestedFilters, and Filters that can be included in a SearchExpression object is 50.

  • :sort_by (String)

    The name of the resource property used to sort the SearchResults. The default is LastModifiedTime.

  • :sort_order (String)

    How SearchResults are ordered. Valid values are Ascending or Descending. The default is Descending.

  • :next_token (String)

    If more than MaxResults resource objects match the specified SearchExpression, the SearchResponse includes a NextToken. The NextToken can be passed to the next SearchRequest to continue retrieving results for the specified SearchExpression and Sort parameters.

  • :max_results (Integer)

    The maximum number of results to return in a SearchResponse.

Returns:

See Also:

#start_notebook_instance(options = {}) ⇒ Struct

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.

Examples:

Request syntax with placeholder values


resp = client.start_notebook_instance({
  notebook_instance_name: "NotebookInstanceName", # required
})

Options Hash (options):

  • :notebook_instance_name (required, String)

    The name of the notebook instance to start.

Returns:

  • (Struct)

    Returns an empty response.

See Also:

#stop_compilation_job(options = {}) ⇒ Struct

Stops a model compilation job.

To stop a job, Amazon SageMaker sends the algorithm the SIGTERM signal. This gracefully shuts the job down. If the job hasn't stopped, it sends the SIGKILL signal.

When it receives a StopCompilationJob request, Amazon SageMaker changes the CompilationJobSummary$CompilationJobStatus of the job to Stopping. After Amazon SageMaker stops the job, it sets the CompilationJobSummary$CompilationJobStatus to Stopped.

Examples:

Request syntax with placeholder values


resp = client.stop_compilation_job({
  compilation_job_name: "EntityName", # required
})

Options Hash (options):

  • :compilation_job_name (required, String)

    The name of the model compilation job to stop.

Returns:

  • (Struct)

    Returns an empty response.

See Also:

#stop_hyper_parameter_tuning_job(options = {}) ⇒ Struct

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.

Examples:

Request syntax with placeholder values


resp = client.stop_hyper_parameter_tuning_job({
  hyper_parameter_tuning_job_name: "HyperParameterTuningJobName", # required
})

Options Hash (options):

  • :hyper_parameter_tuning_job_name (required, String)

    The name of the tuning job to stop.

Returns:

  • (Struct)

    Returns an empty response.

See Also:

#stop_labeling_job(options = {}) ⇒ Struct

Stops a running labeling job. A job that is stopped cannot be restarted. Any results obtained before the job is stopped are placed in the Amazon S3 output bucket.

Examples:

Request syntax with placeholder values


resp = client.stop_labeling_job({
  labeling_job_name: "LabelingJobName", # required
})

Options Hash (options):

  • :labeling_job_name (required, String)

    The name of the labeling job to stop.

Returns:

  • (Struct)

    Returns an empty response.

See Also:

#stop_notebook_instance(options = {}) ⇒ Struct

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. Amazon SageMaker stops charging you for the ML compute instance when you call StopNotebookInstance.

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.

Examples:

Request syntax with placeholder values


resp = client.stop_notebook_instance({
  notebook_instance_name: "NotebookInstanceName", # required
})

Options Hash (options):

  • :notebook_instance_name (required, String)

    The name of the notebook instance to terminate.

Returns:

  • (Struct)

    Returns an empty response.

See Also:

#stop_training_job(options = {}) ⇒ Struct

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.

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.

Examples:

Request syntax with placeholder values


resp = client.stop_training_job({
  training_job_name: "TrainingJobName", # required
})

Options Hash (options):

  • :training_job_name (required, String)

    The name of the training job to stop.

Returns:

  • (Struct)

    Returns an empty response.

See Also:

#stop_transform_job(options = {}) ⇒ Struct

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.

Examples:

Request syntax with placeholder values


resp = client.stop_transform_job({
  transform_job_name: "TransformJobName", # required
})

Options Hash (options):

  • :transform_job_name (required, String)

    The name of the transform job to stop.

Returns:

  • (Struct)

    Returns an empty response.

See Also:

#update_code_repository(options = {}) ⇒ Types::UpdateCodeRepositoryOutput

Updates the specified Git repository with the specified values.

Examples:

Request syntax with placeholder values


resp = client.update_code_repository({
  code_repository_name: "EntityName", # required
  git_config: {
    secret_arn: "SecretArn",
  },
})

Response structure


resp.code_repository_arn #=> String

Options Hash (options):

  • :code_repository_name (required, String)

    The name of the Git repository to update.

  • :git_config (Types::GitConfigForUpdate)

    The configuration of the git repository, including the URL and the Amazon Resource Name (ARN) of the AWS Secrets Manager secret that contains the credentials used to access the repository. The secret must have a staging label of AWSCURRENT and must be in the following format:

    {"username": UserName, "password": Password}

Returns:

See Also:

#update_endpoint(options = {}) ⇒ Types::UpdateEndpointOutput

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 must not delete an EndpointConfig in use by an endpoint that is live or while the UpdateEndpoint or CreateEndpoint operations are being performed on the endpoint. To update an endpoint, you must create a new EndpointConfig.

Examples:

Request syntax with placeholder values


resp = client.update_endpoint({
  endpoint_name: "EndpointName", # required
  endpoint_config_name: "EndpointConfigName", # required
})

Response structure


resp.endpoint_arn #=> String

Options Hash (options):

  • :endpoint_name (required, String)

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

  • :endpoint_config_name (required, String)

    The name of the new endpoint configuration.

Returns:

See Also:

#update_endpoint_weights_and_capacities(options = {}) ⇒ Types::UpdateEndpointWeightsAndCapacitiesOutput

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.

Examples:

Request syntax with placeholder values


resp = client.update_endpoint_weights_and_capacities({
  endpoint_name: "EndpointName", # required
  desired_weights_and_capacities: [ # required
    {
      variant_name: "VariantName", # required
      desired_weight: 1.0,
      desired_instance_count: 1,
    },
  ],
})

Response structure


resp.endpoint_arn #=> String

Options Hash (options):

  • :endpoint_name (required, String)

    The name of an existing Amazon SageMaker endpoint.

  • :desired_weights_and_capacities (required, Array<Types::DesiredWeightAndCapacity>)

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

Returns:

See Also:

#update_notebook_instance(options = {}) ⇒ Struct

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.

Examples:

Request syntax with placeholder values


resp = client.update_notebook_instance({
  notebook_instance_name: "NotebookInstanceName", # required
  instance_type: "ml.t2.medium", # accepts ml.t2.medium, ml.t2.large, ml.t2.xlarge, ml.t2.2xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m4.xlarge, ml.m4.2xlarge, ml.m4.4xlarge, ml.m4.10xlarge, ml.m4.16xlarge, 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.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.18xlarge, ml.c5d.xlarge, ml.c5d.2xlarge, ml.c5d.4xlarge, ml.c5d.9xlarge, ml.c5d.18xlarge, ml.p2.xlarge, ml.p2.8xlarge, ml.p2.16xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge
  role_arn: "RoleArn",
  lifecycle_config_name: "NotebookInstanceLifecycleConfigName",
  disassociate_lifecycle_config: false,
  volume_size_in_gb: 1,
  default_code_repository: "CodeRepositoryNameOrUrl",
  additional_code_repositories: ["CodeRepositoryNameOrUrl"],
  accelerator_types: ["ml.eia1.medium"], # accepts ml.eia1.medium, ml.eia1.large, ml.eia1.xlarge, ml.eia2.medium, ml.eia2.large, ml.eia2.xlarge
  disassociate_accelerator_types: false,
  disassociate_default_code_repository: false,
  disassociate_additional_code_repositories: false,
  root_access: "Enabled", # accepts Enabled, Disabled
})

Options Hash (options):

  • :notebook_instance_name (required, String)

    The name of the notebook instance to update.

  • :instance_type (String)

    The Amazon ML compute instance type.

  • :role_arn (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.

  • :lifecycle_config_name (String)

    The name of a lifecycle configuration to associate with the notebook instance. For information about lifestyle configurations, see Step 2.1: (Optional) Customize a Notebook Instance.

  • :disassociate_lifecycle_config (Boolean)

    Set to true to remove the notebook instance lifecycle configuration currently associated with the notebook instance. This operation is idempotent. If you specify a lifecycle configuration that is not associated with the notebook instance when you call this method, it does not throw an error.

  • :volume_size_in_gb (Integer)

    The size, in GB, of the ML storage volume to attach to the notebook instance. The default value is 5 GB. ML storage volumes are encrypted, so Amazon SageMaker can\'t determine the amount of available free space on the volume. Because of this, you can increase the volume size when you update a notebook instance, but you can\'t decrease the volume size. If you want to decrease the size of the ML storage volume in use, create a new notebook instance with the desired size.

  • :default_code_repository (String)

    The Git repository to associate with the notebook instance as its default code repository. This can be either the name of a Git repository stored as a resource in your account, or the URL of a Git repository in AWS CodeCommit or in any other Git repository. When you open a notebook instance, it opens in the directory that contains this repository. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

  • :additional_code_repositories (Array<String>)

    An array of up to three Git repositories to associate with the notebook instance. These can be either the names of Git repositories stored as resources in your account, or the URL of Git repositories in AWS CodeCommit or in any other Git repository. These repositories are cloned at the same level as the default repository of your notebook instance. For more information, see Associating Git Repositories with Amazon SageMaker Notebook Instances.

  • :accelerator_types (Array<String>)

    A list of the Elastic Inference (EI) instance types to associate with this notebook instance. Currently only one EI instance type can be associated with a notebook instance. For more information, see Using Elastic Inference in Amazon SageMaker.

  • :disassociate_accelerator_types (Boolean)

    A list of the Elastic Inference (EI) instance types to remove from this notebook instance. This operation is idempotent. If you specify an accelerator type that is not associated with the notebook instance when you call this method, it does not throw an error.

  • :disassociate_default_code_repository (Boolean)

    The name or URL of the default Git repository to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.

  • :disassociate_additional_code_repositories (Boolean)

    A list of names or URLs of the default Git repositories to remove from this notebook instance. This operation is idempotent. If you specify a Git repository that is not associated with the notebook instance when you call this method, it does not throw an error.

  • :root_access (String)

    Whether root access is enabled or disabled for users of the notebook instance. The default value is Enabled.

    If you set this to Disabled, users don\'t have root access on the notebook instance, but lifecycle configuration scripts still run with root permissions.

Returns:

  • (Struct)

    Returns an empty response.

See Also:

#update_notebook_instance_lifecycle_config(options = {}) ⇒ Struct

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

Examples:

Request syntax with placeholder values


resp = client.update_notebook_instance_lifecycle_config({
  notebook_instance_lifecycle_config_name: "NotebookInstanceLifecycleConfigName", # required
  on_create: [
    {
      content: "NotebookInstanceLifecycleConfigContent",
    },
  ],
  on_start: [
    {
      content: "NotebookInstanceLifecycleConfigContent",
    },
  ],
})

Options Hash (options):

  • :notebook_instance_lifecycle_config_name (required, String)

    The name of the lifecycle configuration.

  • :on_create (Array<Types::NotebookInstanceLifecycleHook>)

    The shell script that runs only once, when you create a notebook instance. The shell script must be a base64-encoded string.

  • :on_start (Array<Types::NotebookInstanceLifecycleHook>)

    The shell script that runs every time you start a notebook instance, including when you create the notebook instance. The shell script must be a base64-encoded string.

Returns:

  • (Struct)

    Returns an empty response.

See Also:

#update_workteam(options = {}) ⇒ Types::UpdateWorkteamResponse

Updates an existing work team with new member definitions or description.

Examples:

Request syntax with placeholder values


resp = client.update_workteam({
  workteam_name: "WorkteamName", # required
  member_definitions: [
    {
      cognito_member_definition: {
        user_pool: "CognitoUserPool", # required
        user_group: "CognitoUserGroup", # required
        client_id: "CognitoClientId", # required
      },
    },
  ],
  description: "String200",
  notification_configuration: {
    notification_topic_arn: "NotificationTopicArn",
  },
})

Response structure


resp.workteam.workteam_name #=> String
resp.workteam.member_definitions #=> Array
resp.workteam.member_definitions[0].cognito_member_definition.user_pool #=> String
resp.workteam.member_definitions[0].cognito_member_definition.user_group #=> String
resp.workteam.member_definitions[0].cognito_member_definition.client_id #=> String
resp.workteam.workteam_arn #=> String
resp.workteam.product_listing_ids #=> Array
resp.workteam.product_listing_ids[0] #=> String
resp.workteam.description #=> String
resp.workteam.sub_domain #=> String
resp.workteam.create_date #=> Time
resp.workteam.last_updated_date #=> Time
resp.workteam.notification_configuration.notification_topic_arn #=> String

Options Hash (options):

  • :workteam_name (required, String)

    The name of the work team to update.

  • :member_definitions (Array<Types::MemberDefinition>)

    A list of MemberDefinition objects that contain the updated work team members.

  • :description (String)

    An updated description for the work team.

  • :notification_configuration (Types::NotificationConfiguration)

    Configures SNS topic notifications for available or expiring work items

Returns:

See Also:

#wait_until(waiter_name, params = {}) {|waiter| ... } ⇒ Boolean

Waiters polls an API operation until a resource enters a desired state.

Basic Usage

Waiters will poll until they are succesful, they fail by entering a terminal state, or until a maximum number of attempts are made.

# polls in a loop, sleeping between attempts client.waiter_until(waiter_name, params)

Configuration

You can configure the maximum number of polling attempts, and the delay (in seconds) between each polling attempt. You configure waiters by passing a block to #wait_until:

# poll for ~25 seconds
client.wait_until(...) do |w|
  w.max_attempts = 5
  w.delay = 5
end

Callbacks

You can be notified before each polling attempt and before each delay. If you throw :success or :failure from these callbacks, it will terminate the waiter.

started_at = Time.now
client.wait_until(...) do |w|

  # disable max attempts
  w.max_attempts = nil

  # poll for 1 hour, instead of a number of attempts
  w.before_wait do |attempts, response|
    throw :failure if Time.now - started_at > 3600
  end

end

Handling Errors

When a waiter is successful, it returns true. When a waiter fails, it raises an error. All errors raised extend from Waiters::Errors::WaiterFailed.

begin
  client.wait_until(...)
rescue Aws::Waiters::Errors::WaiterFailed
  # resource did not enter the desired state in time
end

Parameters:

  • waiter_name (Symbol)

    The name of the waiter. See #waiter_names for a full list of supported waiters.

  • params (Hash) (defaults to: {})

    Additional request parameters. See the #waiter_names for a list of supported waiters and what request they call. The called request determines the list of accepted parameters.

Yield Parameters:

Returns:

  • (Boolean)

    Returns true if the waiter was successful.

Raises:

  • (Errors::FailureStateError)

    Raised when the waiter terminates because the waiter has entered a state that it will not transition out of, preventing success.

  • (Errors::TooManyAttemptsError)

    Raised when the configured maximum number of attempts have been made, and the waiter is not yet successful.

  • (Errors::UnexpectedError)

    Raised when an error is encounted while polling for a resource that is not expected.

  • (Errors::NoSuchWaiterError)

    Raised when you request to wait for an unknown state.

#waiter_namesArray<Symbol>

Returns the list of supported waiters. The following table lists the supported waiters and the client method they call:

Waiter NameClient MethodDefault Delay:Default Max Attempts:
:endpoint_deleted#describe_endpoint3060
:endpoint_in_service#describe_endpoint30120
:notebook_instance_deleted#describe_notebook_instance3060
:notebook_instance_in_service#describe_notebook_instance3060
:notebook_instance_stopped#describe_notebook_instance3060
:training_job_completed_or_stopped#describe_training_job120180
:transform_job_completed_or_stopped#describe_transform_job6060

Returns:

  • (Array<Symbol>)

    the list of supported waiters.