Class: Aws::SageMaker::Client

Inherits:
Seahorse::Client::Base show all
Includes:
ClientStubs
Defined in:
gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb

Overview

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

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

For details on configuring region and credentials see the developer guide.

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

Instance Attribute Summary

Attributes inherited from Seahorse::Client::Base

#config, #handlers

API Operations collapse

Instance Method Summary collapse

Methods included from ClientStubs

#api_requests, #stub_data, #stub_responses

Methods inherited from Seahorse::Client::Base

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

Methods included from Seahorse::Client::HandlerBuilder

#handle, #handle_request, #handle_response

Constructor Details

#initialize(options) ⇒ Client

Returns a new instance of Client.

Parameters:

  • options (Hash)

Options Hash (options):

  • :credentials (required, Aws::CredentialProvider) —

    Your AWS credentials. This can be an instance of any one of the following classes:

    • Aws::Credentials - Used for configuring static, non-refreshing credentials.

    • Aws::InstanceProfileCredentials - Used for loading credentials from an EC2 IMDS on an EC2 instance.

    • Aws::SharedCredentials - Used for loading credentials from a shared file, such as ~/.aws/config.

    • Aws::AssumeRoleCredentials - Used when you need to assume a role.

    When :credentials are not configured directly, the following locations will be searched for credentials:

    • Aws.config[:credentials]
    • The :access_key_id, :secret_access_key, and :session_token options.
    • ENV['AWS_ACCESS_KEY_ID'], ENV['AWS_SECRET_ACCESS_KEY']
    • ~/.aws/credentials
    • ~/.aws/config
    • EC2 IMDS instance profile - When used by default, the timeouts are very aggressive. Construct and pass an instance of Aws::InstanceProfileCredentails to enable retries and extended timeouts.
  • :region (required, String) —

    The AWS region to connect to. The configured :region is used to determine the service :endpoint. When not passed, a default :region is searched for in the following locations:

    • Aws.config[:region]
    • ENV['AWS_REGION']
    • ENV['AMAZON_REGION']
    • ENV['AWS_DEFAULT_REGION']
    • ~/.aws/credentials
    • ~/.aws/config
  • :access_key_id (String)
  • :active_endpoint_cache (Boolean) — default: false —

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

  • :adaptive_retry_wait_to_fill (Boolean) — default: true —

    Used only in adaptive retry mode. When true, the request will sleep until there is sufficent client side capacity to retry the request. When false, the request will raise a RetryCapacityNotAvailableError and will not retry instead of sleeping.

  • :client_side_monitoring (Boolean) — default: false —

    When true, client-side metrics will be collected for all API requests from this client.

  • :client_side_monitoring_client_id (String) — default: "" —

    Allows you to provide an identifier for this client which will be attached to all generated client side metrics. Defaults to an empty string.

  • :client_side_monitoring_host (String) — default: "127.0.0.1" —

    Allows you to specify the DNS hostname or IPv4 or IPv6 address that the client side monitoring agent is running on, where client metrics will be published via UDP.

  • :client_side_monitoring_port (Integer) — default: 31000 —

    Required for publishing client metrics. The port that the client side monitoring agent is running on, where client metrics will be published via UDP.

  • :client_side_monitoring_publisher (Aws::ClientSideMonitoring::Publisher) — default: Aws::ClientSideMonitoring::Publisher —

    Allows you to provide a custom client-side monitoring publisher class. By default, will use the Client Side Monitoring Agent Publisher.

  • :convert_params (Boolean) — default: true —

    When true, an attempt is made to coerce request parameters into the required types.

  • :correct_clock_skew (Boolean) — default: true —

    Used only in standard and adaptive retry modes. Specifies whether to apply a clock skew correction and retry requests with skewed client clocks.

  • :disable_host_prefix_injection (Boolean) — default: false —

    Set to true to disable SDK automatically adding host prefix to default service endpoint when available.

  • :endpoint (String) —

    The client endpoint is normally constructed from the :region option. You should only configure an :endpoint when connecting to test endpoints. This should be a valid HTTP(S) URI.

  • :endpoint_cache_max_entries (Integer) — default: 1000 —

    Used for the maximum size limit of the LRU cache storing endpoints data for endpoint discovery enabled operations. Defaults to 1000.

  • :endpoint_cache_max_threads (Integer) — default: 10 —

    Used for the maximum threads in use for polling endpoints to be cached, defaults to 10.

  • :endpoint_cache_poll_interval (Integer) — default: 60 —

    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.

  • :endpoint_discovery (Boolean) — default: false —

    When set to true, endpoint discovery will be enabled for operations when available.

  • :log_formatter (Aws::Log::Formatter) — default: Aws::Log::Formatter.default —

    The log formatter.

  • :log_level (Symbol) — default: :info —

    The log level to send messages to the :logger at.

  • :logger (Logger) —

    The Logger instance to send log messages to. If this option is not set, logging will be disabled.

  • :max_attempts (Integer) — default: 3 —

    An integer representing the maximum number attempts that will be made for a single request, including the initial attempt. For example, setting this value to 5 will result in a request being retried up to 4 times. Used in standard and adaptive retry modes.

  • :profile (String) — default: "default" —

    Used when loading credentials from the shared credentials file at HOME/.aws/credentials. When not specified, 'default' is used.

  • :retry_backoff (Proc) —

    A proc or lambda used for backoff. Defaults to 2**retries * retry_base_delay. This option is only used in the legacy retry mode.

  • :retry_base_delay (Float) — default: 0.3 —

    The base delay in seconds used by the default backoff function. This option is only used in the legacy retry mode.

  • :retry_jitter (Symbol) — default: :none —

    A delay randomiser function used by the default backoff function. Some predefined functions can be referenced by name - :none, :equal, :full, otherwise a Proc that takes and returns a number. This option is only used in the legacy retry mode.

    @see https://www.awsarchitectureblog.com/2015/03/backoff.html

  • :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, auth errors, endpoint discovery, and errors from expired credentials. This option is only used in the legacy retry mode.

  • :retry_max_delay (Integer) — default: 0 —

    The maximum number of seconds to delay between retries (0 for no limit) used by the default backoff function. This option is only used in the legacy retry mode.

  • :retry_mode (String) — default: "legacy" —

    Specifies which retry algorithm to use. Values are:

    • legacy - The pre-existing retry behavior. This is default value if no retry mode is provided.

    • standard - A standardized set of retry rules across the AWS SDKs. This includes support for retry quotas, which limit the number of unsuccessful retries a client can make.

    • adaptive - An experimental retry mode that includes all the functionality of standard mode along with automatic client side throttling. This is a provisional mode that may change behavior in the future.

  • :secret_access_key (String)
  • :session_token (String)
  • :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.

  • :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.

  • :validate_params (Boolean) — default: true —

    When true, request parameters are validated before sending the request.

  • :http_proxy (URI::HTTP, String) —

    A proxy to send requests through. Formatted like 'http://proxy.com:123'.

  • :http_open_timeout (Float) — default: 15 —

    The number of seconds to wait when opening a HTTP session before raising a Timeout::Error.

  • :http_read_timeout (Integer) — default: 60 —

    The default number of seconds to wait for response data. This value can safely be set per-request on the session.

  • :http_idle_timeout (Float) — default: 5 —

    The number of seconds a connection is allowed to sit idle before it is considered stale. Stale connections are closed and removed from the pool before making a request.

  • :http_continue_timeout (Float) — default: 1 —

    The number of seconds to wait for a 100-continue response before sending the request body. This option has no effect unless the request has "Expect" header set to "100-continue". Defaults to nil which disables this behaviour. This value can safely be set per request on the session.

  • :http_wire_trace (Boolean) — default: false —

    When true, HTTP debug output will be sent to the :logger.

  • :ssl_verify_peer (Boolean) — default: true —

    When true, SSL peer certificates are verified when establishing a connection.

  • :ssl_ca_bundle (String) —

    Full path to the SSL certificate authority bundle file that should be used when verifying peer certificates. If you do not pass :ssl_ca_bundle or :ssl_ca_directory the the system default will be used if available.

  • :ssl_ca_directory (String) —

    Full path of the directory that contains the unbundled SSL certificate authority files for verifying peer certificates. If you do not pass :ssl_ca_bundle or :ssl_ca_directory the the system default will be used if available.



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 315

def initialize(*args)
  super
end

Instance Method Details

#add_tags(params = {}) ⇒ 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

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :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:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 380

def add_tags(params = {}, options = {})
  req = build_request(:add_tags, params)
  req.send_request(options)
end

#associate_trial_component(params = {}) ⇒ Types::AssociateTrialComponentResponse

Associates a trial component with a trial. A trial component can be associated with multiple trials. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.

Examples:

Request syntax with placeholder values


resp = client.associate_trial_component({
  trial_component_name: "ExperimentEntityName", # required
  trial_name: "ExperimentEntityName", # required
})

Response structure


resp.trial_component_arn #=> String
resp.trial_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :trial_component_name (required, String) —

    The name of the component to associated with the trial.

  • :trial_name (required, String) —

    The name of the trial to associate with.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 416

def associate_trial_component(params = {}, options = {})
  req = build_request(:associate_trial_component, params)
  req.send_request(options)
end

#create_algorithm(params = {}) ⇒ 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.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.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, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.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, ml.inf1.xlarge, ml.inf1.2xlarge, ml.inf1.6xlarge, ml.inf1.24xlarge
    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.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.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, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.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

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :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:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 645

def create_algorithm(params = {}, options = {})
  req = build_request(:create_algorithm, params)
  req.send_request(options)
end

#create_app(params = {}) ⇒ Types::CreateAppResponse

Creates a running App for the specified UserProfile. Supported Apps are JupyterServer, KernelGateway, and TensorBoard. This operation is automatically invoked by Amazon SageMaker Studio upon access to the associated Studio Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously. Apps will automatically terminate and be deleted when stopped from within Studio, or when the DeleteApp API is manually called. UserProfiles are limited to 5 concurrently running Apps at a time.

Examples:

Request syntax with placeholder values


resp = client.create_app({
  domain_id: "DomainId", # required
  user_profile_name: "UserProfileName", # required
  app_type: "JupyterServer", # required, accepts JupyterServer, KernelGateway, TensorBoard
  app_name: "AppName", # required
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  resource_spec: {
    sage_maker_image_arn: "SageMakerImageArn",
    instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge
  },
})

Response structure


resp.app_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_id (required, String) —

    The domain ID.

  • :user_profile_name (required, String) —

    The user profile name.

  • :app_type (required, String) —

    The type of app.

  • :app_name (required, String) —

    The name of the app.

  • :tags (Array<Types::Tag>) —

    Each tag consists of a key and an optional value. Tag keys must be unique per resource.

  • :resource_spec (Types::ResourceSpec) —

    The instance type and the Amazon Resource Name (ARN) of the SageMaker image created on the instance.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 711

def create_app(params = {}, options = {})
  req = build_request(:create_app, params)
  req.send_request(options)
end

#create_auto_ml_job(params = {}) ⇒ Types::CreateAutoMLJobResponse

Creates an AutoPilot job.

After you run an AutoPilot job, you can find the best performing model by calling , and then deploy that model by following the steps described in Step 6.1: Deploy the Model to Amazon SageMaker Hosting Services.

For information about how to use AutoPilot, see Use AutoPilot to Automate Model Development.

Examples:

Request syntax with placeholder values


resp = client.create_auto_ml_job({
  auto_ml_job_name: "AutoMLJobName", # required
  input_data_config: [ # required
    {
      data_source: { # required
        s3_data_source: { # required
          s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix
          s3_uri: "S3Uri", # required
        },
      },
      compression_type: "None", # accepts None, Gzip
      target_attribute_name: "TargetAttributeName", # required
    },
  ],
  output_data_config: { # required
    kms_key_id: "KmsKeyId",
    s3_output_path: "S3Uri", # required
  },
  problem_type: "BinaryClassification", # accepts BinaryClassification, MulticlassClassification, Regression
  auto_ml_job_objective: {
    metric_name: "Accuracy", # required, accepts Accuracy, MSE, F1, F1macro
  },
  auto_ml_job_config: {
    completion_criteria: {
      max_candidates: 1,
      max_runtime_per_training_job_in_seconds: 1,
      max_auto_ml_job_runtime_in_seconds: 1,
    },
    security_config: {
      volume_kms_key_id: "KmsKeyId",
      enable_inter_container_traffic_encryption: false,
      vpc_config: {
        security_group_ids: ["SecurityGroupId"], # required
        subnets: ["SubnetId"], # required
      },
    },
  },
  role_arn: "RoleArn", # required
  generate_candidate_definitions_only: false,
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.auto_ml_job_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :auto_ml_job_name (required, String) —

    Identifies an AutoPilot job. Must be unique to your account and is case-insensitive.

  • :input_data_config (required, Array<Types::AutoMLChannel>) —

    Similar to InputDataConfig supported by Tuning. Format(s) supported: CSV. Minimum of 1000 rows.

  • :output_data_config (required, Types::AutoMLOutputDataConfig) —

    Similar to OutputDataConfig supported by Tuning. Format(s) supported: CSV.

  • :problem_type (String) —

    Defines the kind of preprocessing and algorithms intended for the candidates. Options include: BinaryClassification, MulticlassClassification, and Regression.

  • :auto_ml_job_objective (Types::AutoMLJobObjective) —

    Defines the job's objective. You provide a MetricName and AutoML will infer minimize or maximize. If this is not provided, the most commonly used ObjectiveMetric for problem type will be selected.

  • :auto_ml_job_config (Types::AutoMLJobConfig) —

    Contains CompletionCriteria and SecurityConfig.

  • :role_arn (required, String) —

    The ARN of the role that will be used to access the data.

  • :generate_candidate_definitions_only (Boolean) —

    This will generate possible candidates without training a model. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

  • :tags (Array<Types::Tag>) —

    Each tag consists of a key and an optional value. Tag keys must be unique per resource.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 829

def create_auto_ml_job(params = {}, options = {})
  req = build_request(:create_auto_ml_job, params)
  req.send_request(options)
end

#create_code_repository(params = {}) ⇒ 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

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :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:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 881

def create_code_repository(params = {}, options = {})
  req = build_request(:create_code_repository, params)
  req.send_request(options)
end

#create_compilation_job(params = {}) ⇒ 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, KERAS, MXNET, ONNX, PYTORCH, XGBOOST, TFLITE
  },
  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, ml_inf1, jetson_tx1, jetson_tx2, jetson_nano, jetson_xavier, rasp3b, imx8qm, deeplens, rk3399, rk3288, aisage, sbe_c, qcs605, qcs603, sitara_am57x, amba_cv22
  },
  stopping_condition: { # required
    max_runtime_in_seconds: 1,
    max_wait_time_in_seconds: 1,
  },
})

Response structure


resp.compilation_job_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :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:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 989

def create_compilation_job(params = {}, options = {})
  req = build_request(:create_compilation_job, params)
  req.send_request(options)
end

#create_domain(params = {}) ⇒ Types::CreateDomainResponse

Creates a Domain for Amazon SageMaker Studio, which can be accessed by end-users in a web browser. A Domain has an associated directory, list of authorized users, and a variety of security, application, policies, and Amazon Virtual Private Cloud configurations. An AWS account is limited to one Domain, per region. Users within a domain can share notebook files and other artifacts with each other. When a Domain is created, an Amazon Elastic File System (EFS) is also created for use by all of the users within the Domain. Each user receives a private home directory within the EFS for notebooks, Git repositories, and data files.

Examples:

Request syntax with placeholder values


resp = client.create_domain({
  domain_name: "DomainName", # required
  auth_mode: "SSO", # required, accepts SSO, IAM
  default_user_settings: { # required
    execution_role: "RoleArn",
    security_groups: ["SecurityGroupId"],
    sharing_settings: {
      notebook_output_option: "Allowed", # accepts Allowed, Disabled
      s3_output_path: "S3Uri",
      s3_kms_key_id: "KmsKeyId",
    },
    jupyter_server_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "SageMakerImageArn",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge
      },
    },
    kernel_gateway_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "SageMakerImageArn",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge
      },
    },
    tensor_board_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "SageMakerImageArn",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge
      },
    },
  },
  subnet_ids: ["SubnetId"], # required
  vpc_id: "VpcId", # required
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  home_efs_file_system_kms_key_id: "KmsKeyId",
})

Response structure


resp.domain_arn #=> String
resp.url #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_name (required, String) —

    A name for the domain.

  • :auth_mode (required, String) —

    The mode of authentication that member use to access the domain.

  • :default_user_settings (required, Types::UserSettings) —

    The default user settings.

  • :subnet_ids (required, Array<String>) —

    Security setting to limit to a set of subnets.

  • :vpc_id (required, String) —

    Security setting to limit the domain's communication to a Amazon Virtual Private Cloud.

  • :tags (Array<Types::Tag>) —

    Each tag consists of a key and an optional value. Tag keys must be unique per resource.

  • :home_efs_file_system_kms_key_id (String) —

    The AWS Key Management Service (KMS) encryption key ID. Encryption with a customer master key (CMK) is not supported.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1086

def create_domain(params = {}, options = {})
  req = build_request(:create_domain, params)
  req.send_request(options)
end

#create_endpoint(params = {}) ⇒ 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 to deploy models using Amazon SageMaker hosting services.

For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).

You must not delete an EndpointConfig that is 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.

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 in 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

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :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:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1178

def create_endpoint(params = {}, options = {})
  req = build_request(:create_endpoint, params)
  req.send_request(options)
end

#create_endpoint_config(params = {}) ⇒ 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 if you want to use Amazon SageMaker hosting services to deploy models into production.

In the request, you define a ProductionVariant, for each model that you want to deploy. 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.

For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).

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, ml.inf1.xlarge, ml.inf1.2xlarge, ml.inf1.6xlarge, ml.inf1.24xlarge
      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
    },
  ],
  data_capture_config: {
    enable_capture: false,
    initial_sampling_percentage: 1, # required
    destination_s3_uri: "DestinationS3Uri", # required
    kms_key_id: "KmsKeyId",
    capture_options: [ # required
      {
        capture_mode: "Input", # required, accepts Input, Output
      },
    ],
    capture_content_type_header: {
      csv_content_types: ["CsvContentType"],
      json_content_types: ["JsonContentType"],
    },
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  kms_key_id: "KmsKeyId",
})

Response structure


resp.endpoint_config_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :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.

  • :data_capture_config (Types::DataCaptureConfig)
  • :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.

    The KmsKeyId can be any of the following formats:

    • Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab

    • Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab

    • Alias name: alias/ExampleAlias

    • Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias

    The KMS key policy must grant permission to the IAM role that you specify in your CreateEndpoint, UpdateEndpoint requests. For more information, refer to the AWS Key Management Service section Using Key Policies in AWS KMS

    Certain Nitro-based instances include local storage, dependent on the instance type. Local storage volumes are encrypted using a hardware module on the instance. You can't request a KmsKeyId when using an instance type with local storage. If any of the models that you specify in the ProductionVariants parameter use nitro-based instances with local storage, do not specify a value for the KmsKeyId parameter. If you specify a value for KmsKeyId when using any nitro-based instances with local storage, the call to CreateEndpointConfig fails.

    For a list of instance types that support local instance storage, see Instance Store Volumes.

    For more information about local instance storage encryption, see SSD Instance Store Volumes.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1329

def create_endpoint_config(params = {}, options = {})
  req = build_request(:create_endpoint_config, params)
  req.send_request(options)
end

#create_experiment(params = {}) ⇒ Types::CreateExperimentResponse

Creates an Amazon SageMaker experiment. An experiment is a collection of trials that are observed, compared and evaluated as a group. A trial is a set of steps, called trial components, that produce a machine learning model.

The goal of an experiment is to determine the components that produce the best model. Multiple trials are performed, each one isolating and measuring the impact of a change to one or more inputs, while keeping the remaining inputs constant.

When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.

You can add tags to experiments, trials, trial components and then use the Search API to search for the tags.

To add a description to an experiment, specify the optional Description parameter. To add a description later, or to change the description, call the UpdateExperiment API.

To get a list of all your experiments, call the ListExperiments API. To view an experiment's properties, call the DescribeExperiment API. To get a list of all the trials associated with an experiment, call the ListTrials API. To create a trial call the CreateTrial API.

Examples:

Request syntax with placeholder values


resp = client.create_experiment({
  experiment_name: "ExperimentEntityName", # required
  display_name: "ExperimentEntityName",
  description: "ExperimentDescription",
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.experiment_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :experiment_name (required, String) —

    The name of the experiment. The name must be unique in your AWS account and is not case-sensitive.

  • :display_name (String) —

    The name of the experiment as displayed. The name doesn't need to be unique. If you don't specify DisplayName, the value in ExperimentName is displayed.

  • :description (String) —

    The description of the experiment.

  • :tags (Array<Types::Tag>) —

    A list of tags to associate with the experiment. You can use Search API to search on the tags.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1403

def create_experiment(params = {}, options = {})
  req = build_request(:create_experiment, params)
  req.send_request(options)
end

#create_flow_definition(params = {}) ⇒ Types::CreateFlowDefinitionResponse

Creates a flow definition.

Examples:

Request syntax with placeholder values


resp = client.create_flow_definition({
  flow_definition_name: "FlowDefinitionName", # required
  human_loop_request_source: {
    aws_managed_human_loop_request_source: "AWS/Rekognition/DetectModerationLabels/Image/V3", # required, accepts AWS/Rekognition/DetectModerationLabels/Image/V3, AWS/Textract/AnalyzeDocument/Forms/V1
  },
  human_loop_activation_config: {
    human_loop_activation_conditions_config: { # required
      human_loop_activation_conditions: "HumanLoopActivationConditions", # required
    },
  },
  human_loop_config: { # required
    workteam_arn: "WorkteamArn", # required
    human_task_ui_arn: "HumanTaskUiArn", # required
    task_title: "FlowDefinitionTaskTitle", # required
    task_description: "FlowDefinitionTaskDescription", # required
    task_count: 1, # required
    task_availability_lifetime_in_seconds: 1,
    task_time_limit_in_seconds: 1,
    task_keywords: ["FlowDefinitionTaskKeyword"],
    public_workforce_task_price: {
      amount_in_usd: {
        dollars: 1,
        cents: 1,
        tenth_fractions_of_a_cent: 1,
      },
    },
  },
  output_config: { # required
    s3_output_path: "S3Uri", # required
    kms_key_id: "KmsKeyId",
  },
  role_arn: "RoleArn", # required
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.flow_definition_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :flow_definition_name (required, String) —

    The name of your flow definition.

  • :human_loop_request_source (Types::HumanLoopRequestSource) —

    Container for configuring the source of human task requests. Use to specify if Amazon Rekognition or Amazon Textract is used as an integration source.

  • :human_loop_activation_config (Types::HumanLoopActivationConfig) —

    An object containing information about the events that trigger a human workflow.

  • :human_loop_config (required, Types::HumanLoopConfig) —

    An object containing information about the tasks the human reviewers will perform.

  • :output_config (required, Types::FlowDefinitionOutputConfig) —

    An object containing information about where the human review results will be uploaded.

  • :role_arn (required, String) —

    The Amazon Resource Name (ARN) of the role needed to call other services on your behalf. For example, arn:aws:iam::1234567890:role/service-role/AmazonSageMaker-ExecutionRole-20180111T151298.

  • :tags (Array<Types::Tag>) —

    An array of key-value pairs that contain metadata to help you categorize and organize a flow definition. Each tag consists of a key and a value, both of which you define.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1494

def create_flow_definition(params = {}, options = {})
  req = build_request(:create_flow_definition, params)
  req.send_request(options)
end

#create_human_task_ui(params = {}) ⇒ Types::CreateHumanTaskUiResponse

Defines the settings you will use for the human review workflow user interface. Reviewers will see a three-panel interface with an instruction area, the item to review, and an input area.

Examples:

Request syntax with placeholder values


resp = client.create_human_task_ui({
  human_task_ui_name: "HumanTaskUiName", # required
  ui_template: { # required
    content: "TemplateContent", # required
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.human_task_ui_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :human_task_ui_name (required, String) —

    The name of the user interface you are creating.

  • :ui_template (required, Types::UiTemplate) —

    The Liquid template for the worker user interface.

  • :tags (Array<Types::Tag>) —

    An array of key-value pairs that contain metadata to help you categorize and organize a human review workflow user interface. Each tag consists of a key and a value, both of which you define.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1541

def create_human_task_ui(params = {}, options = {})
  req = build_request(:create_human_task_ui, params)
  req.send_request(options)
end

#create_hyper_parameter_tuning_job(params = {}) ⇒ 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
    tuning_job_completion_criteria: {
      target_objective_metric_value: 1.0, # required
    },
  },
  training_job_definition: {
    definition_name: "HyperParameterTrainingJobDefinitionName",
    tuning_objective: {
      type: "Maximize", # required, accepts Maximize, Minimize
      metric_name: "MetricName", # required
    },
    hyper_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
        },
      ],
    },
    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.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.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, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.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",
    },
  },
  training_job_definitions: [
    {
      definition_name: "HyperParameterTrainingJobDefinitionName",
      tuning_objective: {
        type: "Maximize", # required, accepts Maximize, Minimize
        metric_name: "MetricName", # required
      },
      hyper_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
          },
        ],
      },
      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.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.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, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.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

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :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 How Hyperparameter Tuning Works.

  • :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.

  • :training_job_definitions (Array<Types::HyperParameterTrainingJobDefinition>)
  • :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:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1879

def create_hyper_parameter_tuning_job(params = {}, options = {})
  req = build_request(:create_hyper_parameter_tuning_job, params)
  req.send_request(options)
end

#create_labeling_job(params = {}) ⇒ 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",
    },
    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

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :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 labeling task and how it is presented to workers; including, but not limited to price, keywords, and batch size (task count).

  • :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:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2082

def create_labeling_job(params = {}, options = {})
  req = build_request(:create_labeling_job, params)
  req.send_request(options)
end

#create_model(params = {}) ⇒ 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 that contains inference code, artifacts (from prior training), and a 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.

For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (AWS SDK for Python (Boto 3)).

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",
    mode: "SingleModel", # accepts SingleModel, MultiModel
    model_data_url: "Url",
    environment: {
      "EnvironmentKey" => "EnvironmentValue",
    },
    model_package_name: "ArnOrName",
  },
  containers: [
    {
      container_hostname: "ContainerHostname",
      image: "Image",
      mode: "SingleModel", # accepts SingleModel, MultiModel
      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

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :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.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2229

def create_model(params = {}, options = {})
  req = build_request(:create_model, params)
  req.send_request(options)
end

#create_model_package(params = {}) ⇒ 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, ml.inf1.xlarge, ml.inf1.2xlarge, ml.inf1.6xlarge, ml.inf1.24xlarge
    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

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :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:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2356

def create_model_package(params = {}, options = {})
  req = build_request(:create_model_package, params)
  req.send_request(options)
end

#create_monitoring_schedule(params = {}) ⇒ Types::CreateMonitoringScheduleResponse

Creates a schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endoint.

Examples:

Request syntax with placeholder values


resp = client.create_monitoring_schedule({
  monitoring_schedule_name: "MonitoringScheduleName", # required
  monitoring_schedule_config: { # required
    schedule_config: {
      schedule_expression: "ScheduleExpression", # required
    },
    monitoring_job_definition: { # required
      baseline_config: {
        constraints_resource: {
          s3_uri: "S3Uri",
        },
        statistics_resource: {
          s3_uri: "S3Uri",
        },
      },
      monitoring_inputs: [ # required
        {
          endpoint_input: { # required
            endpoint_name: "EndpointName", # required
            local_path: "ProcessingLocalPath", # required
            s3_input_mode: "Pipe", # accepts Pipe, File
            s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
          },
        },
      ],
      monitoring_output_config: { # required
        monitoring_outputs: [ # required
          {
            s3_output: { # required
              s3_uri: "MonitoringS3Uri", # required
              local_path: "ProcessingLocalPath", # required
              s3_upload_mode: "Continuous", # accepts Continuous, EndOfJob
            },
          },
        ],
        kms_key_id: "KmsKeyId",
      },
      monitoring_resources: { # required
        cluster_config: { # required
          instance_count: 1, # required
          instance_type: "ml.t3.medium", # required, accepts 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.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, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge
          volume_size_in_gb: 1, # required
          volume_kms_key_id: "KmsKeyId",
        },
      },
      monitoring_app_specification: { # required
        image_uri: "ImageUri", # required
        container_entrypoint: ["ContainerEntrypointString"],
        container_arguments: ["ContainerArgument"],
        record_preprocessor_source_uri: "S3Uri",
        post_analytics_processor_source_uri: "S3Uri",
      },
      stopping_condition: {
        max_runtime_in_seconds: 1, # required
      },
      environment: {
        "ProcessingEnvironmentKey" => "ProcessingEnvironmentValue",
      },
      network_config: {
        enable_inter_container_traffic_encryption: false,
        enable_network_isolation: false,
        vpc_config: {
          security_group_ids: ["SecurityGroupId"], # required
          subnets: ["SubnetId"], # required
        },
      },
      role_arn: "RoleArn", # required
    },
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.monitoring_schedule_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :monitoring_schedule_name (required, String) —

    The name of the monitoring schedule. The name must be unique within an AWS Region within an AWS account.

  • :monitoring_schedule_config (required, Types::MonitoringScheduleConfig) —

    The configuration object that specifies the monitoring schedule and defines the monitoring job.

  • :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:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2469

def create_monitoring_schedule(params = {}, options = {})
  req = build_request(:create_monitoring_schedule, params)
  req.send_request(options)
end

#create_notebook_instance(params = {}) ⇒ 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

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :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) permissions to assume this role. For more information, see Amazon SageMaker Roles.

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

  • :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:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2681

def create_notebook_instance(params = {}, options = {})
  req = build_request(:create_notebook_instance, params)
  req.send_request(options)
end

#create_notebook_instance_lifecycle_config(params = {}) ⇒ 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

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :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:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2750

def create_notebook_instance_lifecycle_config(params = {}, options = {})
  req = build_request(:create_notebook_instance_lifecycle_config, params)
  req.send_request(options)
end

#create_presigned_domain_url(params = {}) ⇒ Types::CreatePresignedDomainUrlResponse

Creates a URL for a specified UserProfile in a Domain. When accessed in a web browser, the user will be automatically signed in to Amazon SageMaker Studio, and granted access to all of the Apps and files associated with that Amazon Elastic File System (EFS). This operation can only be called when AuthMode equals IAM.

Examples:

Request syntax with placeholder values


resp = client.create_presigned_domain_url({
  domain_id: "DomainId", # required
  user_profile_name: "UserProfileName", # required
  session_expiration_duration_in_seconds: 1,
})

Response structure


resp.authorized_url #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_id (required, String) —

    The domain ID.

  • :user_profile_name (required, String) —

    The name of the UserProfile to sign-in as.

  • :session_expiration_duration_in_seconds (Integer) —

    The session expiration duration in seconds.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2790

def create_presigned_domain_url(params = {}, options = {})
  req = build_request(:create_presigned_domain_url, params)
  req.send_request(options)
end

#create_presigned_notebook_instance_url(params = {}) ⇒ 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 CreatePresignedNotebookInstanceUrl 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

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :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:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2846

def create_presigned_notebook_instance_url(params = {}, options = {})
  req = build_request(:create_presigned_notebook_instance_url, params)
  req.send_request(options)
end

#create_processing_job(params = {}) ⇒ Types::CreateProcessingJobResponse

Creates a processing job.

Examples:

Request syntax with placeholder values


resp = client.create_processing_job({
  processing_inputs: [
    {
      input_name: "String", # required
      s3_input: { # required
        s3_uri: "S3Uri", # required
        local_path: "ProcessingLocalPath", # required
        s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix
        s3_input_mode: "Pipe", # required, accepts Pipe, File
        s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
        s3_compression_type: "None", # accepts None, Gzip
      },
    },
  ],
  processing_output_config: {
    outputs: [ # required
      {
        output_name: "String", # required
        s3_output: { # required
          s3_uri: "S3Uri", # required
          local_path: "ProcessingLocalPath", # required
          s3_upload_mode: "Continuous", # required, accepts Continuous, EndOfJob
        },
      },
    ],
    kms_key_id: "KmsKeyId",
  },
  processing_job_name: "ProcessingJobName", # required
  processing_resources: { # required
    cluster_config: { # required
      instance_count: 1, # required
      instance_type: "ml.t3.medium", # required, accepts 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.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, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge
      volume_size_in_gb: 1, # required
      volume_kms_key_id: "KmsKeyId",
    },
  },
  stopping_condition: {
    max_runtime_in_seconds: 1, # required
  },
  app_specification: { # required
    image_uri: "ImageUri", # required
    container_entrypoint: ["ContainerEntrypointString"],
    container_arguments: ["ContainerArgument"],
  },
  environment: {
    "ProcessingEnvironmentKey" => "ProcessingEnvironmentValue",
  },
  network_config: {
    enable_inter_container_traffic_encryption: false,
    enable_network_isolation: false,
    vpc_config: {
      security_group_ids: ["SecurityGroupId"], # required
      subnets: ["SubnetId"], # required
    },
  },
  role_arn: "RoleArn", # required
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  experiment_config: {
    experiment_name: "ExperimentEntityName",
    trial_name: "ExperimentEntityName",
    trial_component_display_name: "ExperimentEntityName",
  },
})

Response structure


resp.processing_job_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :processing_inputs (Array<Types::ProcessingInput>) —

    For each input, data is downloaded from S3 into the processing container before the processing job begins running if "S3InputMode" is set to File.

  • :processing_output_config (Types::ProcessingOutputConfig) —

    Output configuration for the processing job.

  • :processing_job_name (required, String) —

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

  • :processing_resources (required, Types::ProcessingResources) —

    Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.

  • :stopping_condition (Types::ProcessingStoppingCondition) —

    The time limit for how long the processing job is allowed to run.

  • :app_specification (required, Types::AppSpecification) —

    Configures the processing job to run a specified Docker container image.

  • :environment (Hash<String,String>) —

    Sets the environment variables in the Docker container.

  • :network_config (Types::NetworkConfig) —

    Networking options for a processing job.

  • :role_arn (required, String) —

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

  • :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.

  • :experiment_config (Types::ExperimentConfig) —

    Configuration for the experiment.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2982

def create_processing_job(params = {}, options = {})
  req = build_request(:create_processing_job, params)
  req.send_request(options)
end

#create_training_job(params = {}) ⇒ 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 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
      },
    ],
    enable_sage_maker_metrics_time_series: false,
  },
  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.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.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, ml.c5n.xlarge, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.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",
  },
  debug_hook_config: {
    local_path: "DirectoryPath",
    s3_output_path: "S3Uri", # required
    hook_parameters: {
      "ConfigKey" => "ConfigValue",
    },
    collection_configurations: [
      {
        collection_name: "CollectionName",
        collection_parameters: {
          "ConfigKey" => "ConfigValue",
        },
      },
    ],
  },
  debug_rule_configurations: [
    {
      rule_configuration_name: "RuleConfigurationName", # required
      local_path: "DirectoryPath",
      s3_output_path: "S3Uri",
      rule_evaluator_image: "AlgorithmImage", # required
      instance_type: "ml.t3.medium", # accepts 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.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, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge
      volume_size_in_gb: 1,
      rule_parameters: {
        "ConfigKey" => "ConfigValue",
      },
    },
  ],
  tensor_board_output_config: {
    local_path: "DirectoryPath",
    s3_output_path: "S3Uri", # required
  },
  experiment_config: {
    experiment_name: "ExperimentEntityName",
    trial_name: "ExperimentEntityName",
    trial_component_display_name: "ExperimentEntityName",
  },
})

Response structure


resp.training_job_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :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.

  • :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.

  • :debug_hook_config (Types::DebugHookConfig) —

    Configuration information for the debug hook parameters, collection configuration, and storage paths.

  • :debug_rule_configurations (Array<Types::DebugRuleConfiguration>) —

    Configuration information for debugging rules.

  • :tensor_board_output_config (Types::TensorBoardOutputConfig) —

    Configuration of storage locations for TensorBoard output.

  • :experiment_config (Types::ExperimentConfig) —

    Configuration for the experiment.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3334

def create_training_job(params = {}, options = {})
  req = build_request(:create_training_job, params)
  req.send_request(options)
end

#create_transform_job(params = {}) ⇒ 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, see Batch Transform.

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
    },
  ],
  experiment_config: {
    experiment_name: "ExperimentEntityName",
    trial_name: "ExperimentEntityName",
    trial_component_display_name: "ExperimentEntityName",
  },
})

Response structure


resp.transform_job_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :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 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 the SplitType property 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.

  • :experiment_config (Types::ExperimentConfig) —

    Configuration for the experiment.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3529

def create_transform_job(params = {}, options = {})
  req = build_request(:create_transform_job, params)
  req.send_request(options)
end

#create_trial(params = {}) ⇒ Types::CreateTrialResponse

Creates an Amazon SageMaker trial. A trial is a set of steps called trial components that produce a machine learning model. A trial is part of a single Amazon SageMaker experiment.

When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.

You can add tags to a trial and then use the Search API to search for the tags.

To get a list of all your trials, call the ListTrials API. To view a trial's properties, call the DescribeTrial API. To create a trial component, call the CreateTrialComponent API.

Examples:

Request syntax with placeholder values


resp = client.create_trial({
  trial_name: "ExperimentEntityName", # required
  display_name: "ExperimentEntityName",
  experiment_name: "ExperimentEntityName", # required
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.trial_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :trial_name (required, String) —

    The name of the trial. The name must be unique in your AWS account and is not case-sensitive.

  • :display_name (String) —

    The name of the trial as displayed. The name doesn't need to be unique. If DisplayName isn't specified, TrialName is displayed.

  • :experiment_name (required, String) —

    The name of the experiment to associate the trial with.

  • :tags (Array<Types::Tag>) —

    A list of tags to associate with the trial. You can use Search API to search on the tags.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3591

def create_trial(params = {}, options = {})
  req = build_request(:create_trial, params)
  req.send_request(options)
end

#create_trial_component(params = {}) ⇒ Types::CreateTrialComponentResponse

Creates a trial component, which is a stage of a machine learning trial. A trial is composed of one or more trial components. A trial component can be used in multiple trials.

Trial components include pre-processing jobs, training jobs, and batch transform jobs.

When you use Amazon SageMaker Studio or the Amazon SageMaker Python SDK, all experiments, trials, and trial components are automatically tracked, logged, and indexed. When you use the AWS SDK for Python (Boto), you must use the logging APIs provided by the SDK.

You can add tags to a trial component and then use the Search API to search for the tags.

CreateTrialComponent can only be invoked from within an Amazon SageMaker managed environment. This includes Amazon SageMaker training jobs, processing jobs, transform jobs, and Amazon SageMaker notebooks. A call to CreateTrialComponent from outside one of these environments results in an error.

Examples:

Request syntax with placeholder values


resp = client.create_trial_component({
  trial_component_name: "ExperimentEntityName", # required
  display_name: "ExperimentEntityName",
  status: {
    primary_status: "InProgress", # accepts InProgress, Completed, Failed, Stopping, Stopped
    message: "TrialComponentStatusMessage",
  },
  start_time: Time.now,
  end_time: Time.now,
  parameters: {
    "TrialComponentKey256" => {
      string_value: "StringParameterValue",
      number_value: 1.0,
    },
  },
  input_artifacts: {
    "TrialComponentKey64" => {
      media_type: "MediaType",
      value: "TrialComponentArtifactValue", # required
    },
  },
  output_artifacts: {
    "TrialComponentKey64" => {
      media_type: "MediaType",
      value: "TrialComponentArtifactValue", # required
    },
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.trial_component_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :trial_component_name (required, String) —

    The name of the component. The name must be unique in your AWS account and is not case-sensitive.

  • :display_name (String) —

    The name of the component as displayed. The name doesn't need to be unique. If DisplayName isn't specified, TrialComponentName is displayed.

  • :status (Types::TrialComponentStatus) —

    The status of the component. States include:

    • InProgress

    • Completed

    • Failed

  • :start_time (Time, DateTime, Date, Integer, String) —

    When the component started.

  • :end_time (Time, DateTime, Date, Integer, String) —

    When the component ended.

  • :parameters (Hash<String,Types::TrialComponentParameterValue>) —

    The hyperparameters for the component.

  • :input_artifacts (Hash<String,Types::TrialComponentArtifact>) —

    The input artifacts for the component. Examples of input artifacts are datasets, algorithms, hyperparameters, source code, and instance types.

  • :output_artifacts (Hash<String,Types::TrialComponentArtifact>) —

    The output artifacts for the component. Examples of output artifacts are metrics, snapshots, logs, and images.

  • :tags (Array<Types::Tag>) —

    A list of tags to associate with the component. You can use Search API to search on the tags.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3708

def create_trial_component(params = {}, options = {})
  req = build_request(:create_trial_component, params)
  req.send_request(options)
end

#create_user_profile(params = {}) ⇒ Types::CreateUserProfileResponse

Creates a user profile. A user profile represents a single user within a Domain, and is the main way to reference a "person" for the purposes of sharing, reporting and other user-oriented features. This entity is created during on-boarding to Amazon SageMaker Studio. If an administrator invites a person by email or imports them from SSO, a UserProfile is automatically created.

This entity is the primary holder of settings for an individual user and, through the domain, has a reference to the user's private Amazon Elastic File System (EFS) home directory.

Examples:

Request syntax with placeholder values


resp = client.({
  domain_id: "DomainId", # required
  user_profile_name: "UserProfileName", # required
  single_sign_on_user_identifier: "SingleSignOnUserIdentifier",
  single_sign_on_user_value: "String256",
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  user_settings: {
    execution_role: "RoleArn",
    security_groups: ["SecurityGroupId"],
    sharing_settings: {
      notebook_output_option: "Allowed", # accepts Allowed, Disabled
      s3_output_path: "S3Uri",
      s3_kms_key_id: "KmsKeyId",
    },
    jupyter_server_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "SageMakerImageArn",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge
      },
    },
    kernel_gateway_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "SageMakerImageArn",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge
      },
    },
    tensor_board_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "SageMakerImageArn",
        instance_type: "system", # accepts system, ml.t3.micro, ml.t3.small, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.m5.large, ml.m5.xlarge, ml.m5.2xlarge, ml.m5.4xlarge, ml.m5.8xlarge, ml.m5.12xlarge, ml.m5.16xlarge, ml.m5.24xlarge, ml.c5.large, ml.c5.xlarge, ml.c5.2xlarge, ml.c5.4xlarge, ml.c5.9xlarge, ml.c5.12xlarge, ml.c5.18xlarge, ml.c5.24xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge
      },
    },
  },
})

Response structure


resp. #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_id (required, String) —

    The ID of the associated Domain.

  • :user_profile_name (required, String) —

    A name for the UserProfile.

  • :single_sign_on_user_identifier (String) —

    A specifier for the type of value specified in SingleSignOnUserValue. Currently, the only supported value is "UserName". If the Domain's AuthMode is SSO, this field is required. If the Domain's AuthMode is not SSO, this field cannot be specified.

  • :single_sign_on_user_value (String) —

    The username of the associated AWS Single Sign-On User for this UserProfile. If the Domain's AuthMode is SSO, this field is required, and must match a valid username of a user in your directory. If the Domain's AuthMode is not SSO, this field cannot be specified.

  • :tags (Array<Types::Tag>) —

    Each tag consists of a key and an optional value. Tag keys must be unique per resource.

  • :user_settings (Types::UserSettings) —

    A collection of settings.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3803

def (params = {}, options = {})
  req = build_request(:create_user_profile, params)
  req.send_request(options)
end

#create_workteam(params = {}) ⇒ 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

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :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:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3884

def create_workteam(params = {}, options = {})
  req = build_request(:create_workteam, params)
  req.send_request(options)
end

#delete_algorithm(params = {}) ⇒ Struct

Removes the specified algorithm from your account.

Examples:

Request syntax with placeholder values


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

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :algorithm_name (required, String) —

    The name of the algorithm to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3906

def delete_algorithm(params = {}, options = {})
  req = build_request(:delete_algorithm, params)
  req.send_request(options)
end

#delete_app(params = {}) ⇒ Struct

Used to stop and delete an app.

Examples:

Request syntax with placeholder values


resp = client.delete_app({
  domain_id: "DomainId", # required
  user_profile_name: "UserProfileName", # required
  app_type: "JupyterServer", # required, accepts JupyterServer, KernelGateway, TensorBoard
  app_name: "AppName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_id (required, String) —

    The domain ID.

  • :user_profile_name (required, String) —

    The user profile name.

  • :app_type (required, String) —

    The type of app.

  • :app_name (required, String) —

    The name of the app.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3940

def delete_app(params = {}, options = {})
  req = build_request(:delete_app, params)
  req.send_request(options)
end

#delete_code_repository(params = {}) ⇒ 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
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :code_repository_name (required, String) —

    The name of the Git repository to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3962

def delete_code_repository(params = {}, options = {})
  req = build_request(:delete_code_repository, params)
  req.send_request(options)
end

#delete_domain(params = {}) ⇒ Struct

Used to delete a domain. Use with caution. If RetentionPolicy is set to Delete, all of the members of the domain will lose access to their EFS volume, including data, notebooks, and other artifacts.

Examples:

Request syntax with placeholder values


resp = client.delete_domain({
  domain_id: "DomainId", # required
  retention_policy: {
    home_efs_file_system: "Retain", # accepts Retain, Delete
  },
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_id (required, String) —

    The domain ID.

  • :retention_policy (Types::RetentionPolicy) —

    The retention policy for this domain, which specifies whether resources will be retained after the Domain is deleted. By default, all resources are retained (not automatically deleted).

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3994

def delete_domain(params = {}, options = {})
  req = build_request(:delete_domain, params)
  req.send_request(options)
end

#delete_endpoint(params = {}) ⇒ 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
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :endpoint_name (required, String) —

    The name of the endpoint that you want to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4025

def delete_endpoint(params = {}, options = {})
  req = build_request(:delete_endpoint, params)
  req.send_request(options)
end

#delete_endpoint_config(params = {}) ⇒ 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
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :endpoint_config_name (required, String) —

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

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4049

def delete_endpoint_config(params = {}, options = {})
  req = build_request(:delete_endpoint_config, params)
  req.send_request(options)
end

#delete_experiment(params = {}) ⇒ Types::DeleteExperimentResponse

Deletes an Amazon SageMaker experiment. All trials associated with the experiment must be deleted first. Use the ListTrials API to get a list of the trials associated with the experiment.

Examples:

Request syntax with placeholder values


resp = client.delete_experiment({
  experiment_name: "ExperimentEntityName", # required
})

Response structure


resp.experiment_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :experiment_name (required, String) —

    The name of the experiment to delete.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4079

def delete_experiment(params = {}, options = {})
  req = build_request(:delete_experiment, params)
  req.send_request(options)
end

#delete_flow_definition(params = {}) ⇒ Struct

Deletes the specified flow definition.

Examples:

Request syntax with placeholder values


resp = client.delete_flow_definition({
  flow_definition_name: "FlowDefinitionName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :flow_definition_name (required, String) —

    The name of the flow definition you are deleting.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4101

def delete_flow_definition(params = {}, options = {})
  req = build_request(:delete_flow_definition, params)
  req.send_request(options)
end

#delete_model(params = {}) ⇒ 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
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :model_name (required, String) —

    The name of the model to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4126

def delete_model(params = {}, options = {})
  req = build_request(:delete_model, params)
  req.send_request(options)
end

#delete_model_package(params = {}) ⇒ 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
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :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:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4153

def delete_model_package(params = {}, options = {})
  req = build_request(:delete_model_package, params)
  req.send_request(options)
end

#delete_monitoring_schedule(params = {}) ⇒ Struct

Deletes a monitoring schedule. Also stops the schedule had not already been stopped. This does not delete the job execution history of the monitoring schedule.

Examples:

Request syntax with placeholder values


resp = client.delete_monitoring_schedule({
  monitoring_schedule_name: "MonitoringScheduleName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :monitoring_schedule_name (required, String) —

    The name of the monitoring schedule to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4177

def delete_monitoring_schedule(params = {}, options = {})
  req = build_request(:delete_monitoring_schedule, params)
  req.send_request(options)
end

#delete_notebook_instance(params = {}) ⇒ 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
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :notebook_instance_name (required, String) —

    The name of the Amazon SageMaker notebook instance to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4205

def delete_notebook_instance(params = {}, options = {})
  req = build_request(:delete_notebook_instance, params)
  req.send_request(options)
end

#delete_notebook_instance_lifecycle_config(params = {}) ⇒ 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
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :notebook_instance_lifecycle_config_name (required, String) —

    The name of the lifecycle configuration to delete.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4227

def delete_notebook_instance_lifecycle_config(params = {}, options = {})
  req = build_request(:delete_notebook_instance_lifecycle_config, params)
  req.send_request(options)
end

#delete_tags(params = {}) ⇒ 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
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :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:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4262

def delete_tags(params = {}, options = {})
  req = build_request(:delete_tags, params)
  req.send_request(options)
end

#delete_trial(params = {}) ⇒ Types::DeleteTrialResponse

Deletes the specified trial. All trial components that make up the trial must be deleted first. Use the DescribeTrialComponent API to get the list of trial components.

Examples:

Request syntax with placeholder values


resp = client.delete_trial({
  trial_name: "ExperimentEntityName", # required
})

Response structure


resp.trial_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :trial_name (required, String) —

    The name of the trial to delete.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4292

def delete_trial(params = {}, options = {})
  req = build_request(:delete_trial, params)
  req.send_request(options)
end

#delete_trial_component(params = {}) ⇒ Types::DeleteTrialComponentResponse

Deletes the specified trial component. A trial component must be disassociated from all trials before the trial component can be deleted. To disassociate a trial component from a trial, call the DisassociateTrialComponent API.

Examples:

Request syntax with placeholder values


resp = client.delete_trial_component({
  trial_component_name: "ExperimentEntityName", # required
})

Response structure


resp.trial_component_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :trial_component_name (required, String) —

    The name of the component to delete.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4323

def delete_trial_component(params = {}, options = {})
  req = build_request(:delete_trial_component, params)
  req.send_request(options)
end

#delete_user_profile(params = {}) ⇒ Struct

Deletes a user profile.

Examples:

Request syntax with placeholder values


resp = client.({
  domain_id: "DomainId", # required
  user_profile_name: "UserProfileName", # required
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_id (required, String) —

    The domain ID.

  • :user_profile_name (required, String) —

    The user profile name.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4349

def (params = {}, options = {})
  req = build_request(:delete_user_profile, params)
  req.send_request(options)
end

#delete_workteam(params = {}) ⇒ 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 #=> Boolean

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :workteam_name (required, String) —

    The name of the work team to delete.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4377

def delete_workteam(params = {}, options = {})
  req = build_request(:delete_workteam, params)
  req.send_request(options)
end

#describe_algorithm(params = {}) ⇒ 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 #=> Boolean
resp.training_specification.supported_hyper_parameters[0].is_required #=> Boolean
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.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.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", "ml.c5n.xlarge", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge"
resp.training_specification.supports_distributed_training #=> Boolean
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 #=> Boolean
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", "ml.inf1.xlarge", "ml.inf1.2xlarge", "ml.inf1.6xlarge", "ml.inf1.24xlarge"
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.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.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", "ml.c5n.xlarge", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.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 #=> Boolean

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :algorithm_name (required, String) —

    The name of the algorithm to describe.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4525

def describe_algorithm(params = {}, options = {})
  req = build_request(:describe_algorithm, params)
  req.send_request(options)
end

#describe_app(params = {}) ⇒ Types::DescribeAppResponse

Describes the app.

Examples:

Request syntax with placeholder values


resp = client.describe_app({
  domain_id: "DomainId", # required
  user_profile_name: "UserProfileName", # required
  app_type: "JupyterServer", # required, accepts JupyterServer, KernelGateway, TensorBoard
  app_name: "AppName", # required
})

Response structure


resp.app_arn #=> String
resp.app_type #=> String, one of "JupyterServer", "KernelGateway", "TensorBoard"
resp.app_name #=> String
resp.domain_id #=> String
resp. #=> String
resp.status #=> String, one of "Deleted", "Deleting", "Failed", "InService", "Pending"
resp.last_health_check_timestamp #=> Time
resp.last_user_activity_timestamp #=> Time
resp.creation_time #=> Time
resp.failure_reason #=> String
resp.resource_spec.sage_maker_image_arn #=> String
resp.resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge"

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_id (required, String) —

    The domain ID.

  • :user_profile_name (required, String) —

    The user profile name.

  • :app_type (required, String) —

    The type of app.

  • :app_name (required, String) —

    The name of the app.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4586

def describe_app(params = {}, options = {})
  req = build_request(:describe_app, params)
  req.send_request(options)
end

#describe_auto_ml_job(params = {}) ⇒ Types::DescribeAutoMLJobResponse

Returns information about an Amazon SageMaker job.

Examples:

Request syntax with placeholder values


resp = client.describe_auto_ml_job({
  auto_ml_job_name: "AutoMLJobName", # required
})

Response structure


resp.auto_ml_job_name #=> String
resp.auto_ml_job_arn #=> String
resp.input_data_config #=> Array
resp.input_data_config[0].data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix"
resp.input_data_config[0].data_source.s3_data_source.s3_uri #=> String
resp.input_data_config[0].compression_type #=> String, one of "None", "Gzip"
resp.input_data_config[0].target_attribute_name #=> String
resp.output_data_config.kms_key_id #=> String
resp.output_data_config.s3_output_path #=> String
resp.role_arn #=> String
resp.auto_ml_job_objective.metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro"
resp.problem_type #=> String, one of "BinaryClassification", "MulticlassClassification", "Regression"
resp.auto_ml_job_config.completion_criteria.max_candidates #=> Integer
resp.auto_ml_job_config.completion_criteria.max_runtime_per_training_job_in_seconds #=> Integer
resp.auto_ml_job_config.completion_criteria.max_auto_ml_job_runtime_in_seconds #=> Integer
resp.auto_ml_job_config.security_config.volume_kms_key_id #=> String
resp.auto_ml_job_config.security_config.enable_inter_container_traffic_encryption #=> Boolean
resp.auto_ml_job_config.security_config.vpc_config.security_group_ids #=> Array
resp.auto_ml_job_config.security_config.vpc_config.security_group_ids[0] #=> String
resp.auto_ml_job_config.security_config.vpc_config.subnets #=> Array
resp.auto_ml_job_config.security_config.vpc_config.subnets[0] #=> String
resp.creation_time #=> Time
resp.end_time #=> Time
resp.last_modified_time #=> Time
resp.failure_reason #=> String
resp.best_candidate.candidate_name #=> String
resp.best_candidate.final_auto_ml_job_objective_metric.type #=> String, one of "Maximize", "Minimize"
resp.best_candidate.final_auto_ml_job_objective_metric.metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro"
resp.best_candidate.final_auto_ml_job_objective_metric.value #=> Float
resp.best_candidate.objective_status #=> String, one of "Succeeded", "Pending", "Failed"
resp.best_candidate.candidate_steps #=> Array
resp.best_candidate.candidate_steps[0].candidate_step_type #=> String, one of "AWS::SageMaker::TrainingJob", "AWS::SageMaker::TransformJob", "AWS::SageMaker::ProcessingJob"
resp.best_candidate.candidate_steps[0].candidate_step_arn #=> String
resp.best_candidate.candidate_steps[0].candidate_step_name #=> String
resp.best_candidate.candidate_status #=> String, one of "Completed", "InProgress", "Failed", "Stopped", "Stopping"
resp.best_candidate.inference_containers #=> Array
resp.best_candidate.inference_containers[0].image #=> String
resp.best_candidate.inference_containers[0].model_data_url #=> String
resp.best_candidate.inference_containers[0].environment #=> Hash
resp.best_candidate.inference_containers[0].environment["EnvironmentKey"] #=> String
resp.best_candidate.creation_time #=> Time
resp.best_candidate.end_time #=> Time
resp.best_candidate.last_modified_time #=> Time
resp.best_candidate.failure_reason #=> String
resp.auto_ml_job_status #=> String, one of "Completed", "InProgress", "Failed", "Stopped", "Stopping"
resp.auto_ml_job_secondary_status #=> String, one of "Starting", "AnalyzingData", "FeatureEngineering", "ModelTuning", "MaxCandidatesReached", "Failed", "Stopped", "MaxAutoMLJobRuntimeReached", "Stopping", "CandidateDefinitionsGenerated"
resp.generate_candidate_definitions_only #=> Boolean
resp.auto_ml_job_artifacts.candidate_definition_notebook_location #=> String
resp.auto_ml_job_artifacts.data_exploration_notebook_location #=> String
resp.resolved_attributes.auto_ml_job_objective.metric_name #=> String, one of "Accuracy", "MSE", "F1", "F1macro"
resp.resolved_attributes.problem_type #=> String, one of "BinaryClassification", "MulticlassClassification", "Regression"
resp.resolved_attributes.completion_criteria.max_candidates #=> Integer
resp.resolved_attributes.completion_criteria.max_runtime_per_training_job_in_seconds #=> Integer
resp.resolved_attributes.completion_criteria.max_auto_ml_job_runtime_in_seconds #=> Integer

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :auto_ml_job_name (required, String) —

    Request information about a job using that job's unique name.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4684

def describe_auto_ml_job(params = {}, options = {})
  req = build_request(:describe_auto_ml_job, params)
  req.send_request(options)
end

#describe_code_repository(params = {}) ⇒ 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

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :code_repository_name (required, String) —

    The name of the Git repository to describe.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4722

def describe_code_repository(params = {}, options = {})
  req = build_request(:describe_code_repository, params)
  req.send_request(options)
end

#describe_compilation_job(params = {}) ⇒ 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", "KERAS", "MXNET", "ONNX", "PYTORCH", "XGBOOST", "TFLITE"
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", "ml_inf1", "jetson_tx1", "jetson_tx2", "jetson_nano", "jetson_xavier", "rasp3b", "imx8qm", "deeplens", "rk3399", "rk3288", "aisage", "sbe_c", "qcs605", "qcs603", "sitara_am57x", "amba_cv22"

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :compilation_job_name (required, String) —

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

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4782

def describe_compilation_job(params = {}, options = {})
  req = build_request(:describe_compilation_job, params)
  req.send_request(options)
end

#describe_domain(params = {}) ⇒ Types::DescribeDomainResponse

The desciption of the domain.

Examples:

Request syntax with placeholder values


resp = client.describe_domain({
  domain_id: "DomainId", # required
})

Response structure


resp.domain_arn #=> String
resp.domain_id #=> String
resp.domain_name #=> String
resp.home_efs_file_system_id #=> String
resp.single_sign_on_managed_application_instance_id #=> String
resp.status #=> String, one of "Deleting", "Failed", "InService", "Pending"
resp.creation_time #=> Time
resp.last_modified_time #=> Time
resp.failure_reason #=> String
resp.auth_mode #=> String, one of "SSO", "IAM"
resp..execution_role #=> String
resp..security_groups #=> Array
resp..security_groups[0] #=> String
resp..sharing_settings.notebook_output_option #=> String, one of "Allowed", "Disabled"
resp..sharing_settings.s3_output_path #=> String
resp..sharing_settings.s3_kms_key_id #=> String
resp..jupyter_server_app_settings.default_resource_spec.sage_maker_image_arn #=> String
resp..jupyter_server_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge"
resp..kernel_gateway_app_settings.default_resource_spec.sage_maker_image_arn #=> String
resp..kernel_gateway_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge"
resp..tensor_board_app_settings.default_resource_spec.sage_maker_image_arn #=> String
resp..tensor_board_app_settings.default_resource_spec.instance_type #=> String, one of "system", "ml.t3.micro", "ml.t3.small", "ml.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.m5.large", "ml.m5.xlarge", "ml.m5.2xlarge", "ml.m5.4xlarge", "ml.m5.8xlarge", "ml.m5.12xlarge", "ml.m5.16xlarge", "ml.m5.24xlarge", "ml.c5.large", "ml.c5.xlarge", "ml.c5.2xlarge", "ml.c5.4xlarge", "ml.c5.9xlarge", "ml.c5.12xlarge", "ml.c5.18xlarge", "ml.c5.24xlarge", "ml.p3.2xlarge", "ml.p3.8xlarge", "ml.p3.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge"
resp.home_efs_file_system_kms_key_id #=> String
resp.subnet_ids #=> Array
resp.subnet_ids[0] #=> String
resp.url #=> String
resp.vpc_id #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_id (required, String) —

    The domain ID.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4850

def describe_domain(params = {}, options = {})
  req = build_request(:describe_domain, params)
  req.send_request(options)
end

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

Returns the description of an endpoint.

The following waiters are defined for this operation (see #wait_until for detailed usage):

  • endpoint_deleted
  • endpoint_in_service

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.data_capture_config.enable_capture #=> Boolean
resp.data_capture_config.capture_status #=> String, one of "Started", "Stopped"
resp.data_capture_config.current_sampling_percentage #=> Integer
resp.data_capture_config.destination_s3_uri #=> String
resp.data_capture_config.kms_key_id #=> String
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

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :endpoint_name (required, String) —

    The name of the endpoint.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4913

def describe_endpoint(params = {}, options = {})
  req = build_request(:describe_endpoint, params)
  req.send_request(options)
end

#describe_endpoint_config(params = {}) ⇒ 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", "ml.inf1.xlarge", "ml.inf1.2xlarge", "ml.inf1.6xlarge", "ml.inf1.24xlarge"
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.data_capture_config.enable_capture #=> Boolean
resp.data_capture_config.initial_sampling_percentage #=> Integer
resp.data_capture_config.destination_s3_uri #=> String
resp.data_capture_config.kms_key_id #=> String
resp.data_capture_config.capture_options #=> Array
resp.data_capture_config.capture_options[0].capture_mode #=> String, one of "Input", "Output"
resp.data_capture_config.capture_content_type_header.csv_content_types #=> Array
resp.data_capture_config.capture_content_type_header.csv_content_types[0] #=> String
resp.data_capture_config.capture_content_type_header.json_content_types #=> Array
resp.data_capture_config.capture_content_type_header.json_content_types[0] #=> String
resp.kms_key_id #=> String
resp.creation_time #=> Time

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :endpoint_config_name (required, String) —

    The name of the endpoint configuration.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4967

def describe_endpoint_config(params = {}, options = {})
  req = build_request(:describe_endpoint_config, params)
  req.send_request(options)
end

#describe_experiment(params = {}) ⇒ Types::DescribeExperimentResponse

Provides a list of an experiment's properties.

Examples:

Request syntax with placeholder values


resp = client.describe_experiment({
  experiment_name: "ExperimentEntityName", # required
})

Response structure


resp.experiment_name #=> String
resp.experiment_arn #=> String
resp.display_name #=> String
resp.source.source_arn #=> String
resp.source.source_type #=> String
resp.description #=> String
resp.creation_time #=> Time
resp.created_by. #=> String
resp.created_by. #=> String
resp.created_by.domain_id #=> String
resp.last_modified_time #=> Time
resp.last_modified_by. #=> String
resp.last_modified_by. #=> String
resp.last_modified_by.domain_id #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :experiment_name (required, String) —

    The name of the experiment to describe.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5016

def describe_experiment(params = {}, options = {})
  req = build_request(:describe_experiment, params)
  req.send_request(options)
end

#describe_flow_definition(params = {}) ⇒ Types::DescribeFlowDefinitionResponse

Returns information about the specified flow definition.

Examples:

Request syntax with placeholder values


resp = client.describe_flow_definition({
  flow_definition_name: "FlowDefinitionName", # required
})

Response structure


resp.flow_definition_arn #=> String
resp.flow_definition_name #=> String
resp.flow_definition_status #=> String, one of "Initializing", "Active", "Failed", "Deleting"
resp.creation_time #=> Time
resp.human_loop_request_source.aws_managed_human_loop_request_source #=> String, one of "AWS/Rekognition/DetectModerationLabels/Image/V3", "AWS/Textract/AnalyzeDocument/Forms/V1"
resp.human_loop_activation_config.human_loop_activation_conditions_config.human_loop_activation_conditions #=> String
resp.human_loop_config.workteam_arn #=> String
resp.human_loop_config.human_task_ui_arn #=> String
resp.human_loop_config.task_title #=> String
resp.human_loop_config.task_description #=> String
resp.human_loop_config.task_count #=> Integer
resp.human_loop_config.task_availability_lifetime_in_seconds #=> Integer
resp.human_loop_config.task_time_limit_in_seconds #=> Integer
resp.human_loop_config.task_keywords #=> Array
resp.human_loop_config.task_keywords[0] #=> String
resp.human_loop_config.public_workforce_task_price.amount_in_usd.dollars #=> Integer
resp.human_loop_config.public_workforce_task_price.amount_in_usd.cents #=> Integer
resp.human_loop_config.public_workforce_task_price.amount_in_usd.tenth_fractions_of_a_cent #=> Integer
resp.output_config.s3_output_path #=> String
resp.output_config.kms_key_id #=> String
resp.role_arn #=> String
resp.failure_reason #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :flow_definition_name (required, String) —

    The name of the flow definition.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5074

def describe_flow_definition(params = {}, options = {})
  req = build_request(:describe_flow_definition, params)
  req.send_request(options)
end

#describe_human_task_ui(params = {}) ⇒ Types::DescribeHumanTaskUiResponse

Returns information about the requested human task user interface.

Examples:

Request syntax with placeholder values


resp = client.describe_human_task_ui({
  human_task_ui_name: "HumanTaskUiName", # required
})

Response structure


resp.human_task_ui_arn #=> String
resp.human_task_ui_name #=> String
resp.creation_time #=> Time
resp.ui_template.url #=> String
resp.ui_template.content_sha_256 #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :human_task_ui_name (required, String) —

    The name of the human task user interface you want information about.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5109

def describe_human_task_ui(params = {}, options = {})
  req = build_request(:describe_human_task_ui, params)
  req.send_request(options)
end

#describe_hyper_parameter_tuning_job(params = {}) ⇒ 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.hyper_parameter_tuning_job_config.tuning_job_completion_criteria.target_objective_metric_value #=> Float
resp.training_job_definition.definition_name #=> String
resp.training_job_definition.tuning_objective.type #=> String, one of "Maximize", "Minimize"
resp.training_job_definition.tuning_objective.metric_name #=> String
resp.training_job_definition.hyper_parameter_ranges.integer_parameter_ranges #=> Array
resp.training_job_definition.hyper_parameter_ranges.integer_parameter_ranges[0].name #=> String
resp.training_job_definition.hyper_parameter_ranges.integer_parameter_ranges[0].min_value #=> String
resp.training_job_definition.hyper_parameter_ranges.integer_parameter_ranges[0].max_value #=> String
resp.training_job_definition.hyper_parameter_ranges.integer_parameter_ranges[0].scaling_type #=> String, one of "Auto", "Linear", "Logarithmic", "ReverseLogarithmic"
resp.training_job_definition.hyper_parameter_ranges.continuous_parameter_ranges #=> Array
resp.training_job_definition.hyper_parameter_ranges.continuous_parameter_ranges[0].name #=> String
resp.training_job_definition.hyper_parameter_ranges.continuous_parameter_ranges[0].min_value #=> String
resp.training_job_definition.hyper_parameter_ranges.continuous_parameter_ranges[0].max_value #=> String
resp.training_job_definition.hyper_parameter_ranges.continuous_parameter_ranges[0].scaling_type #=> String, one of "Auto", "Linear", "Logarithmic", "ReverseLogarithmic"
resp.training_job_definition.hyper_parameter_ranges.categorical_parameter_ranges #=> Array
resp.training_job_definition.hyper_parameter_ranges.categorical_parameter_ranges[0].name #=> String
resp.training_job_definition.hyper_parameter_ranges.categorical_parameter_ranges[0].values #=> Array
resp.training_job_definition.hyper_parameter_ranges.categorical_parameter_ranges[0].values[0] #=> String
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.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.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", "ml.c5n.xlarge", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.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 #=> Boolean
resp.training_job_definition.enable_inter_container_traffic_encryption #=> Boolean
resp.training_job_definition.enable_managed_spot_training #=> Boolean
resp.training_job_definition.checkpoint_config.s3_uri #=> String
resp.training_job_definition.checkpoint_config.local_path #=> String
resp.training_job_definitions #=> Array
resp.training_job_definitions[0].definition_name #=> String
resp.training_job_definitions[0].tuning_objective.type #=> String, one of "Maximize", "Minimize"
resp.training_job_definitions[0].tuning_objective.metric_name #=> String
resp.training_job_definitions[0].hyper_parameter_ranges.integer_parameter_ranges #=> Array
resp.training_job_definitions[0].hyper_parameter_ranges.integer_parameter_ranges[0].name #=> String
resp.training_job_definitions[0].hyper_parameter_ranges.integer_parameter_ranges[0].min_value #=> String
resp.training_job_definitions[0].hyper_parameter_ranges.integer_parameter_ranges[0].max_value #=> String
resp.training_job_definitions[0].hyper_parameter_ranges.integer_parameter_ranges[0].scaling_type #=> String, one of "Auto", "Linear", "Logarithmic", "ReverseLogarithmic"
resp.training_job_definitions[0].hyper_parameter_ranges.continuous_parameter_ranges #=> Array
resp.training_job_definitions[0].hyper_parameter_ranges.continuous_parameter_ranges[0].name #=> String
resp.training_job_definitions[0].hyper_parameter_ranges.continuous_parameter_ranges[0].min_value #=> String
resp.training_job_definitions[0].hyper_parameter_ranges.continuous_parameter_ranges[0].max_value #=> String
resp.training_job_definitions[0].hyper_parameter_ranges.continuous_parameter_ranges[0].scaling_type #=> String, one of "Auto", "Linear", "Logarithmic", "ReverseLogarithmic"
resp.training_job_definitions[0].hyper_parameter_ranges.categorical_parameter_ranges #=> Array
resp.training_job_definitions[0].hyper_parameter_ranges.categorical_parameter_ranges[0].name #=> String
resp.training_job_definitions[0].hyper_parameter_ranges.categorical_parameter_ranges[0].values #=> Array
resp.training_job_definitions[0].hyper_parameter_ranges.categorical_parameter_ranges[0].values[0] #=> String
resp.training_job_definitions[0].static_hyper_parameters #=> Hash
resp.training_job_definitions[0].static_hyper_parameters["ParameterKey"] #=> String
resp.training_job_definitions[0].algorithm_specification.training_image #=> String
resp.training_job_definitions[0].algorithm_specification.training_input_mode #=> String, one of "Pipe", "File"
resp.training_job_definitions[0].algorithm_specification.algorithm_name #=> String
resp.training_job_definitions[0].algorithm_specification.metric_definitions #=> Array
resp.training_job_definitions[0].algorithm_specification.metric_definitions[0].name #=> String
resp.training_job_definitions[0].algorithm_specification.metric_definitions[0].regex #=> String
resp.training_job_definitions[0].role_arn #=> String
resp.training_job_definitions[0].input_data_config #=> Array
resp.training_job_definitions[0].input_data_config[0].channel_name #=> String
resp.training_job_definitions[0].input_data_config[0].data_source.s3_data_source.s3_data_type #=> String, one of "ManifestFile", "S3Prefix", "AugmentedManifestFile"
resp.training_job_definitions[0].input_data_config[0].data_source.s3_data_source.s3_uri #=> String
resp.training_job_definitions[0].input_data_config[0].data_source.s3_data_source.s3_data_distribution_type #=> String, one of "FullyReplicated", "ShardedByS3Key"
resp.training_job_definitions[0].input_data_config[0].data_source.s3_data_source.attribute_names #=> Array
resp.training_job_definitions[0].input_data_config[0].data_source.s3_data_source.attribute_names[0] #=> String
resp.training_job_definitions[0].input_data_config[0].data_source.file_system_data_source.file_system_id #=> String
resp.training_job_definitions[0].input_data_config[0].data_source.file_system_data_source.file_system_access_mode #=> String, one of "rw", "ro"
resp.training_job_definitions[0].input_data_config[0].data_source.file_system_data_source.file_system_type #=> String, one of "EFS", "FSxLustre"
resp.training_job_definitions[0].input_data_config[0].data_source.file_system_data_source.directory_path #=> String
resp.training_job_definitions[0].input_data_config[0].content_type #=> String
resp.training_job_definitions[0].input_data_config[0].compression_type #=> String, one of "None", "Gzip"
resp.training_job_definitions[0].input_data_config[0].record_wrapper_type #=> String, one of "None", "RecordIO"
resp.training_job_definitions[0].input_data_config[0].input_mode #=> String, one of "Pipe", "File"
resp.training_job_definitions[0].input_data_config[0].shuffle_config.seed #=> Integer
resp.training_job_definitions[0].vpc_config.security_group_ids #=> Array
resp.training_job_definitions[0].vpc_config.security_group_ids[0] #=> String
resp.training_job_definitions[0].vpc_config.subnets #=> Array
resp.training_job_definitions[0].vpc_config.subnets[0] #=> String
resp.training_job_definitions[0].output_data_config.kms_key_id #=> String
resp.training_job_definitions[0].output_data_config.s3_output_path #=> String
resp.training_job_definitions[0].resource_config.instance_type #=> String, one of "ml.m4.xlarge", "ml.m4.2xlarge", "ml.m4.4xlarge", "ml.m4.10xlarge", "ml.m4.16xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.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", "ml.c5n.xlarge", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge"
resp.training_job_definitions[0].resource_config.instance_count #=> Integer
resp.training_job_definitions[0].resource_config.volume_size_in_gb #=> Integer
resp.training_job_definitions[0].resource_config.volume_kms_key_id #=> String
resp.training_job_definitions[0].stopping_condition.max_runtime_in_seconds #=> Integer
resp.training_job_definitions[0].stopping_condition.max_wait_time_in_seconds #=> Integer
resp.training_job_definitions[0].enable_network_isolation #=> Boolean
resp.training_job_definitions[0].enable_inter_container_traffic_encryption #=> Boolean
resp.training_job_definitions[0].enable_managed_spot_training #=> Boolean
resp.training_job_definitions[0].checkpoint_config.s3_uri #=> String
resp.training_job_definitions[0].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_definition_name #=> String
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_definition_name #=> String
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

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :hyper_parameter_tuning_job_name (required, String) —

    The name of the tuning job to describe.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5338

def describe_hyper_parameter_tuning_job(params = {}, options = {})
  req = build_request(:describe_hyper_parameter_tuning_job, params)
  req.send_request(options)
end

#describe_labeling_job(params = {}) ⇒ 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

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :labeling_job_name (required, String) —

    The name of the labeling job to return information for.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5427

def describe_labeling_job(params = {}, options = {})
  req = build_request(:describe_labeling_job, params)
  req.send_request(options)
end

#describe_model(params = {}) ⇒ 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.mode #=> String, one of "SingleModel", "MultiModel"
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].mode #=> String, one of "SingleModel", "MultiModel"
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 #=> Boolean

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :model_name (required, String) —

    The name of the model.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 5485

def describe_model(params = {}, options = {})
  req = build_request(:describe_model, params)
  req.send_request(options)
end

#describe_model_package(params = {}) ⇒ 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", "ml.inf1.xlarge", "ml.inf1.2xlarge", "ml.inf1.6xlarge", "ml.inf1.24xlarge"
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_specifi