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):

  • :plugins (Array<Seahorse::Client::Plugin>) — default: []] —

    A list of plugins to apply to the client. Each plugin is either a class name or an instance of a plugin class.

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

    Your AWS credentials used for authentication. This can be any class that includes and implements Aws::CredentialProvider, or instance of any one of the following classes:

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

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

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

    • Aws::AssumeRoleWebIdentityCredentials - Used when you need to assume a role after providing credentials via the web.

    • Aws::SSOCredentials - Used for loading credentials from AWS SSO using an access token generated from aws login.

    • Aws::ProcessCredentials - Used for loading credentials from a process that outputs to stdout.

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

    • Aws::ECSCredentials - Used for loading credentials from instances running in ECS.

    • Aws::CognitoIdentityCredentials - Used for loading credentials from the Cognito Identity service.

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

    • Aws.config[:credentials]

    • The :access_key_id, :secret_access_key, :session_token, and :account_id options.

    • ENV['AWS_ACCESS_KEY_ID'], ENV['AWS_SECRET_ACCESS_KEY'], ENV['AWS_SESSION_TOKEN'], and ENV['AWS_ACCOUNT_ID'].

    • ~/.aws/credentials

    • ~/.aws/config

    • EC2/ECS IMDS instance profile - When used by default, the timeouts are very aggressive. Construct and pass an instance of Aws::InstanceProfileCredentials or Aws::ECSCredentials to enable retries and extended timeouts. Instance profile credential fetching can be disabled by setting ENV['AWS_EC2_METADATA_DISABLED'] to true.

  • :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)
  • :account_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.

  • :auth_scheme_preference (Array<String>) —

    A list of preferred authentication schemes to use when making a request. Supported values are: sigv4, sigv4a, httpBearerAuth, and noAuth. When set using ENV['AWS_AUTH_SCHEME_PREFERENCE'] or in shared config as auth_scheme_preference, the value should be a comma-separated list.

  • :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.

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

    See DefaultsModeConfiguration for a list of the accepted modes and the configuration defaults that are included.

  • :disable_host_prefix_injection (Boolean) — default: false —

    When true, the SDK will not prepend the modeled host prefix to the endpoint.

  • :disable_request_compression (Boolean) — default: false —

    When set to 'true' the request body will not be compressed for supported operations.

  • :endpoint (String, URI::HTTPS, URI::HTTP) —

    Normally you should not configure the :endpoint option directly. This is normally constructed from the :region option. Configuring :endpoint is normally reserved for connecting to test or custom endpoints. The endpoint should be a URI formatted like:

    'http://example.com'
'https://example.com'
'http://example.com:123'
  • :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.

  • :ignore_configured_endpoint_urls (Boolean) —

    Setting to true disables use of endpoint URLs provided via environment variables and the shared configuration file.

  • :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.

  • :request_checksum_calculation (String) — default: "when_supported" —

    Determines when a checksum will be calculated for request payloads. Values are:

    • when_supported - (default) When set, a checksum will be calculated for all request payloads of operations modeled with the httpChecksum trait where requestChecksumRequired is true and/or a requestAlgorithmMember is modeled.
    • when_required - When set, a checksum will only be calculated for request payloads of operations modeled with the httpChecksum trait where requestChecksumRequired is true or where a requestAlgorithmMember is modeled and supplied.
  • :request_min_compression_size_bytes (Integer) — default: 10240 —

    The minimum size in bytes that triggers compression for request bodies. The value must be non-negative integer value between 0 and 10485780 bytes inclusive.

  • :response_checksum_validation (String) — default: "when_supported" —

    Determines when checksum validation will be performed on response payloads. Values are:

    • when_supported - (default) When set, checksum validation is performed on all response payloads of operations modeled with the httpChecksum trait where responseAlgorithms is modeled, except when no modeled checksum algorithms are supported.
    • when_required - When set, checksum validation is not performed on response payloads of operations unless the checksum algorithm is supported and the requestValidationModeMember member is set to ENABLED.
  • :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.

  • :sdk_ua_app_id (String) —

    A unique and opaque application ID that is appended to the User-Agent header as app/sdk_ua_app_id. It should have a maximum length of 50. This variable is sourced from environment variable AWS_SDK_UA_APP_ID or the shared config profile attribute sdk_ua_app_id.

  • :secret_access_key (String)
  • :session_token (String)
  • :sigv4a_signing_region_set (Array) —

    A list of regions that should be signed with SigV4a signing. When not passed, a default :sigv4a_signing_region_set is searched for in the following locations:

    • Aws.config[:sigv4a_signing_region_set]
    • ENV['AWS_SIGV4A_SIGNING_REGION_SET']
    • ~/.aws/config
  • :simple_json (Boolean) — default: false —

    Disables request parameter conversion, validation, and formatting. Also disables response data type conversions. The request parameters hash must be formatted exactly as the API expects.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.

  • :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.

  • :telemetry_provider (Aws::Telemetry::TelemetryProviderBase) — default: Aws::Telemetry::NoOpTelemetryProvider —

    Allows you to provide a telemetry provider, which is used to emit telemetry data. By default, uses NoOpTelemetryProvider which will not record or emit any telemetry data. The SDK supports the following telemetry providers:

    • OpenTelemetry (OTel) - To use the OTel provider, install and require the opentelemetry-sdk gem and then, pass in an instance of a Aws::Telemetry::OTelProvider for telemetry provider.
  • :token_provider (Aws::TokenProvider) —

    Your Bearer token used for authentication. This can be any class that includes and implements Aws::TokenProvider, or instance of any one of the following classes:

    • Aws::StaticTokenProvider - Used for configuring static, non-refreshing tokens.

    • Aws::SSOTokenProvider - Used for loading tokens from AWS SSO using an access token generated from aws login.

    When :token_provider is not configured directly, the Aws::TokenProviderChain will be used to search for tokens configured for your profile in shared configuration files.

  • :use_dualstack_endpoint (Boolean) —

    When set to true, dualstack enabled endpoints (with .aws TLD) will be used if available.

  • :use_fips_endpoint (Boolean) —

    When set to true, fips compatible endpoints will be used if available. When a fips region is used, the region is normalized and this config is set to true.

  • :validate_params (Boolean) — default: true —

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

  • :endpoint_provider (Aws::SageMaker::EndpointProvider) —

    The endpoint provider used to resolve endpoints. Any object that responds to #resolve_endpoint(parameters) where parameters is a Struct similar to Aws::SageMaker::EndpointParameters.

  • :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_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_open_timeout (Float) — default: 15 —

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

  • :http_proxy (URI::HTTP, String) —

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

  • :http_read_timeout (Float) — default: 60 —

    The default number of seconds to wait for response data. 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.

  • :on_chunk_received (Proc) —

    When a Proc object is provided, it will be used as callback when each chunk of the response body is received. It provides three arguments: the chunk, the number of bytes received, and the total number of bytes in the response (or nil if the server did not send a content-length).

  • :on_chunk_sent (Proc) —

    When a Proc object is provided, it will be used as callback when each chunk of the request body is sent. It provides three arguments: the chunk, the number of bytes read from the body, and the total number of bytes in the body.

  • :raise_response_errors (Boolean) — default: true —

    When true, response errors are raised.

  • :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.

  • :ssl_ca_store (String) —

    Sets the X509::Store to verify peer certificate.

  • :ssl_cert (OpenSSL::X509::Certificate) —

    Sets a client certificate when creating http connections.

  • :ssl_key (OpenSSL::PKey) —

    Sets a client key when creating http connections.

  • :ssl_timeout (Float) —

    Sets the SSL timeout in seconds

  • :ssl_verify_peer (Boolean) — default: true —

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



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 480

def initialize(*args)
  super
end

Instance Method Details

#add_association(params = {}) ⇒ Types::AddAssociationResponse

Creates an association between the source and the destination. A source can be associated with multiple destinations, and a destination can be associated with multiple sources. An association is a lineage tracking entity. For more information, see Amazon SageMaker ML Lineage Tracking.

Examples:

Request syntax with placeholder values


resp = client.add_association({
  source_arn: "AssociationEntityArn", # required
  destination_arn: "AssociationEntityArn", # required
  association_type: "ContributedTo", # accepts ContributedTo, AssociatedWith, DerivedFrom, Produced, SameAs
})

Response structure


resp.source_arn #=> String
resp.destination_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :source_arn (required, String) —

    The ARN of the source.

  • :destination_arn (required, String) —

    The Amazon Resource Name (ARN) of the destination.

  • :association_type (String) —

    The type of association. The following are suggested uses for each type. Amazon SageMaker places no restrictions on their use.

    • ContributedTo - The source contributed to the destination or had a part in enabling the destination. For example, the training data contributed to the training job.

    • AssociatedWith - The source is connected to the destination. For example, an approval workflow is associated with a model deployment.

    • DerivedFrom - The destination is a modification of the source. For example, a digest output of a channel input for a processing job is derived from the original inputs.

    • Produced - The source generated the destination. For example, a training job produced a model artifact.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 542

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

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

Adds or overwrites one or more tags for the specified 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 Amazon Web Services 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

Tags that you add to a SageMaker Domain or User Profile by calling this API are also added to any Apps that the Domain or User Profile launches after you call this API, but not to Apps that the Domain or User Profile launched before you called this API. To make sure that the tags associated with a Domain or User Profile are also added to all Apps that the Domain or User Profile launches, add the tags when you first create the Domain or User Profile by specifying them in the Tags parameter of CreateDomain or CreateUserProfile.

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 key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 625

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 665

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

#attach_cluster_node_volume(params = {}) ⇒ Types::AttachClusterNodeVolumeResponse

Attaches your Amazon Elastic Block Store (Amazon EBS) volume to a node in your EKS orchestrated HyperPod cluster.

This API works with the Amazon Elastic Block Store (Amazon EBS) Container Storage Interface (CSI) driver to manage the lifecycle of persistent storage in your HyperPod EKS clusters.

Examples:

Request syntax with placeholder values


resp = client.attach_cluster_node_volume({
  cluster_arn: "ClusterArn", # required
  node_id: "ClusterNodeId", # required
  volume_id: "VolumeId", # required
})

Response structure


resp.cluster_arn #=> String
resp.node_id #=> String
resp.volume_id #=> String
resp.attach_time #=> Time
resp.status #=> String, one of "attaching", "attached", "detaching", "detached", "busy"
resp.device_name #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :cluster_arn (required, String) —

    The Amazon Resource Name (ARN) of your SageMaker HyperPod cluster containing the target node. Your cluster must use EKS as the orchestration and be in the InService state.

  • :node_id (required, String) —

    The unique identifier of the cluster node to which you want to attach the volume. The node must belong to your specified HyperPod cluster and cannot be part of a Restricted Instance Group (RIG).

  • :volume_id (required, String) —

    The unique identifier of your EBS volume to attach. The volume must be in the available state.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 721

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

#batch_add_cluster_nodes(params = {}) ⇒ Types::BatchAddClusterNodesResponse

Adds nodes to a HyperPod cluster by incrementing the target count for one or more instance groups. This operation returns a unique NodeLogicalId for each node being added, which can be used to track the provisioning status of the node. This API provides a safer alternative to UpdateCluster for scaling operations by avoiding unintended configuration changes.

This API is only supported for clusters using Continuous as the NodeProvisioningMode.

Examples:

Request syntax with placeholder values


resp = client.batch_add_cluster_nodes({
  cluster_name: "ClusterNameOrArn", # required
  client_token: "BatchAddClusterNodesRequestClientTokenString",
  nodes_to_add: [ # required
    {
      instance_group_name: "ClusterInstanceGroupName", # required
      increment_target_count_by: 1, # required
      availability_zones: ["ClusterAvailabilityZone"],
      instance_types: ["ml.p4d.24xlarge"], # accepts ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.p5.4xlarge, ml.p6e-gb200.36xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge, 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.c5n.large, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge, 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.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.16xlarge, ml.g6.12xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.gr6.4xlarge, ml.gr6.8xlarge, ml.g6e.xlarge, ml.g6e.2xlarge, ml.g6e.4xlarge, ml.g6e.8xlarge, ml.g6e.16xlarge, ml.g6e.12xlarge, ml.g6e.24xlarge, ml.g6e.48xlarge, ml.p5e.48xlarge, ml.p5en.48xlarge, ml.p6-b200.48xlarge, ml.trn2.3xlarge, ml.trn2.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.i3en.large, ml.i3en.xlarge, ml.i3en.2xlarge, ml.i3en.3xlarge, ml.i3en.6xlarge, ml.i3en.12xlarge, ml.i3en.24xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.r5d.16xlarge, ml.g7e.2xlarge, ml.g7e.4xlarge, ml.g7e.8xlarge, ml.g7e.12xlarge, ml.g7e.24xlarge, ml.g7e.48xlarge, ml.p6-b300.48xlarge
    },
  ],
})

Response structure


resp.successful #=> Array
resp.successful[0].node_logical_id #=> String
resp.successful[0].instance_group_name #=> String
resp.successful[0].status #=> String, one of "Running", "Failure", "Pending", "ShuttingDown", "SystemUpdating", "DeepHealthCheckInProgress", "NotFound"
resp.successful[0].availability_zones #=> Array
resp.successful[0].availability_zones[0] #=> String
resp.successful[0].instance_types #=> Array
resp.successful[0].instance_types[0] #=> String, one of "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.p5.48xlarge", "ml.p5.4xlarge", "ml.p6e-gb200.36xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "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.c5n.large", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge", "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.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.16xlarge", "ml.g6.12xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.gr6.4xlarge", "ml.gr6.8xlarge", "ml.g6e.xlarge", "ml.g6e.2xlarge", "ml.g6e.4xlarge", "ml.g6e.8xlarge", "ml.g6e.16xlarge", "ml.g6e.12xlarge", "ml.g6e.24xlarge", "ml.g6e.48xlarge", "ml.p5e.48xlarge", "ml.p5en.48xlarge", "ml.p6-b200.48xlarge", "ml.trn2.3xlarge", "ml.trn2.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.i3en.large", "ml.i3en.xlarge", "ml.i3en.2xlarge", "ml.i3en.3xlarge", "ml.i3en.6xlarge", "ml.i3en.12xlarge", "ml.i3en.24xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.r5d.16xlarge", "ml.g7e.2xlarge", "ml.g7e.4xlarge", "ml.g7e.8xlarge", "ml.g7e.12xlarge", "ml.g7e.24xlarge", "ml.g7e.48xlarge", "ml.p6-b300.48xlarge"
resp.failed #=> Array
resp.failed[0].instance_group_name #=> String
resp.failed[0].error_code #=> String, one of "InstanceGroupNotFound", "InvalidInstanceGroupStatus", "IncompatibleAvailabilityZones", "IncompatibleInstanceTypes"
resp.failed[0].failed_count #=> Integer
resp.failed[0].availability_zones #=> Array
resp.failed[0].availability_zones[0] #=> String
resp.failed[0].instance_types #=> Array
resp.failed[0].instance_types[0] #=> String, one of "ml.p4d.24xlarge", "ml.p4de.24xlarge", "ml.p5.48xlarge", "ml.p5.4xlarge", "ml.p6e-gb200.36xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "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.c5n.large", "ml.c5n.2xlarge", "ml.c5n.4xlarge", "ml.c5n.9xlarge", "ml.c5n.18xlarge", "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.t3.medium", "ml.t3.large", "ml.t3.xlarge", "ml.t3.2xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.16xlarge", "ml.g6.12xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.gr6.4xlarge", "ml.gr6.8xlarge", "ml.g6e.xlarge", "ml.g6e.2xlarge", "ml.g6e.4xlarge", "ml.g6e.8xlarge", "ml.g6e.16xlarge", "ml.g6e.12xlarge", "ml.g6e.24xlarge", "ml.g6e.48xlarge", "ml.p5e.48xlarge", "ml.p5en.48xlarge", "ml.p6-b200.48xlarge", "ml.trn2.3xlarge", "ml.trn2.48xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.i3en.large", "ml.i3en.xlarge", "ml.i3en.2xlarge", "ml.i3en.3xlarge", "ml.i3en.6xlarge", "ml.i3en.12xlarge", "ml.i3en.24xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.r5d.16xlarge", "ml.g7e.2xlarge", "ml.g7e.4xlarge", "ml.g7e.8xlarge", "ml.g7e.12xlarge", "ml.g7e.24xlarge", "ml.g7e.48xlarge", "ml.p6-b300.48xlarge"
resp.failed[0].message #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :cluster_name (required, String) —

    The name of the HyperPod cluster to which you want to add nodes.

  • :client_token (String) —

    A unique, case-sensitive identifier that you provide to ensure the idempotency of the request. This token is valid for 8 hours. If you retry the request with the same client token within this timeframe and the same parameters, the API returns the same set of NodeLogicalIds with their latest status.

    A suitable default value is auto-generated. You should normally not need to pass this option.**

  • :nodes_to_add (required, Array<Types::AddClusterNodeSpecification>) —

    A list of instance groups and the number of nodes to add to each. You can specify up to 5 instance groups in a single request, with a maximum of 50 nodes total across all instance groups.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 800

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

#batch_delete_cluster_nodes(params = {}) ⇒ Types::BatchDeleteClusterNodesResponse

Deletes specific nodes within a SageMaker HyperPod cluster. BatchDeleteClusterNodes accepts a cluster name and a list of node IDs.

Examples:

Request syntax with placeholder values


resp = client.batch_delete_cluster_nodes({
  cluster_name: "ClusterNameOrArn", # required
  node_ids: ["ClusterNodeId"],
  node_logical_ids: ["ClusterNodeLogicalId"],
})

Response structure


resp.failed #=> Array
resp.failed[0].code #=> String, one of "NodeIdNotFound", "InvalidNodeStatus", "NodeIdInUse"
resp.failed[0].message #=> String
resp.failed[0].node_id #=> String
resp.successful #=> Array
resp.successful[0] #=> String
resp.failed_node_logical_ids #=> Array
resp.failed_node_logical_ids[0].code #=> String, one of "NodeIdNotFound", "InvalidNodeStatus", "NodeIdInUse"
resp.failed_node_logical_ids[0].message #=> String
resp.failed_node_logical_ids[0].node_logical_id #=> String
resp.successful_node_logical_ids #=> Array
resp.successful_node_logical_ids[0] #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :cluster_name (required, String) —

    The name of the SageMaker HyperPod cluster from which to delete the specified nodes.

  • :node_ids (Array<String>) —

    A list of node IDs to be deleted from the specified cluster.

    * For SageMaker HyperPod clusters using the Slurm workload manager, you cannot remove instances that are configured as Slurm controller nodes.

    • If you need to delete more than 99 instances, contact Support for assistance.

  • :node_logical_ids (Array<String>) —

    A list of NodeLogicalIds identifying the nodes to be deleted. You can specify up to 50 NodeLogicalIds. You must specify either NodeLogicalIds, InstanceIds, or both, with a combined maximum of 50 identifiers.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 886

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

#batch_describe_model_package(params = {}) ⇒ Types::BatchDescribeModelPackageOutput

This action batch describes a list of versioned model packages

Examples:

Request syntax with placeholder values


resp = client.batch_describe_model_package({
  model_package_arn_list: ["ModelPackageArn"], # required
})

Response structure


resp.model_package_summaries #=> Hash
resp.model_package_summaries["ModelPackageArn"].model_package_group_name #=> String
resp.model_package_summaries["ModelPackageArn"].model_package_version #=> Integer
resp.model_package_summaries["ModelPackageArn"].model_package_arn #=> String
resp.model_package_summaries["ModelPackageArn"].model_package_description #=> String
resp.model_package_summaries["ModelPackageArn"].creation_time #=> Time
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers #=> Array
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].container_hostname #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].image #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].image_digest #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].model_data_url #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].model_data_source.s3_data_source.s3_uri #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].model_data_source.s3_data_source.s3_data_type #=> String, one of "S3Prefix", "S3Object"
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].model_data_source.s3_data_source.compression_type #=> String, one of "None", "Gzip"
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].model_data_source.s3_data_source.model_access_config.accept_eula #=> Boolean
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].model_data_source.s3_data_source.hub_access_config.hub_content_arn #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].model_data_source.s3_data_source.manifest_s3_uri #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].model_data_source.s3_data_source.etag #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].model_data_source.s3_data_source.manifest_etag #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].product_id #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].environment #=> Hash
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].environment["EnvironmentKey"] #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].model_input.data_input_config #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].framework #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].framework_version #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].nearest_model_name #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].additional_model_data_sources #=> Array
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].additional_model_data_sources[0].channel_name #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].additional_model_data_sources[0].s3_data_source.s3_uri #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].additional_model_data_sources[0].s3_data_source.s3_data_type #=> String, one of "S3Prefix", "S3Object"
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].additional_model_data_sources[0].s3_data_source.compression_type #=> String, one of "None", "Gzip"
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].additional_model_data_sources[0].s3_data_source.model_access_config.accept_eula #=> Boolean
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].additional_model_data_sources[0].s3_data_source.hub_access_config.hub_content_arn #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].additional_model_data_sources[0].s3_data_source.manifest_s3_uri #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].additional_model_data_sources[0].s3_data_source.etag #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].additional_model_data_sources[0].s3_data_source.manifest_etag #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].additional_s3_data_source.s3_data_type #=> String, one of "S3Object", "S3Prefix"
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].additional_s3_data_source.s3_uri #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].additional_s3_data_source.compression_type #=> String, one of "None", "Gzip"
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].additional_s3_data_source.etag #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].model_data_etag #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].is_checkpoint #=> Boolean
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].base_model.hub_content_name #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].base_model.hub_content_version #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.containers[0].base_model.recipe_name #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.supported_transform_instance_types #=> Array
resp.model_package_summaries["ModelPackageArn"].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", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.g4dn.xlarge", "ml.g4dn.2xlarge", "ml.g4dn.4xlarge", "ml.g4dn.8xlarge", "ml.g4dn.12xlarge", "ml.g4dn.16xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.inf2.xlarge", "ml.inf2.8xlarge", "ml.inf2.24xlarge", "ml.inf2.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge"
resp.model_package_summaries["ModelPackageArn"].inference_specification.supported_realtime_inference_instance_types #=> Array
resp.model_package_summaries["ModelPackageArn"].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", "ml.dl1.24xlarge", "ml.c6i.large", "ml.c6i.xlarge", "ml.c6i.2xlarge", "ml.c6i.4xlarge", "ml.c6i.8xlarge", "ml.c6i.12xlarge", "ml.c6i.16xlarge", "ml.c6i.24xlarge", "ml.c6i.32xlarge", "ml.m6i.large", "ml.m6i.xlarge", "ml.m6i.2xlarge", "ml.m6i.4xlarge", "ml.m6i.8xlarge", "ml.m6i.12xlarge", "ml.m6i.16xlarge", "ml.m6i.24xlarge", "ml.m6i.32xlarge", "ml.r6i.large", "ml.r6i.xlarge", "ml.r6i.2xlarge", "ml.r6i.4xlarge", "ml.r6i.8xlarge", "ml.r6i.12xlarge", "ml.r6i.16xlarge", "ml.r6i.24xlarge", "ml.r6i.32xlarge", "ml.g5.xlarge", "ml.g5.2xlarge", "ml.g5.4xlarge", "ml.g5.8xlarge", "ml.g5.12xlarge", "ml.g5.16xlarge", "ml.g5.24xlarge", "ml.g5.48xlarge", "ml.g6.xlarge", "ml.g6.2xlarge", "ml.g6.4xlarge", "ml.g6.8xlarge", "ml.g6.12xlarge", "ml.g6.16xlarge", "ml.g6.24xlarge", "ml.g6.48xlarge", "ml.r8g.medium", "ml.r8g.large", "ml.r8g.xlarge", "ml.r8g.2xlarge", "ml.r8g.4xlarge", "ml.r8g.8xlarge", "ml.r8g.12xlarge", "ml.r8g.16xlarge", "ml.r8g.24xlarge", "ml.r8g.48xlarge", "ml.g6e.xlarge", "ml.g6e.2xlarge", "ml.g6e.4xlarge", "ml.g6e.8xlarge", "ml.g6e.12xlarge", "ml.g6e.16xlarge", "ml.g6e.24xlarge", "ml.g6e.48xlarge", "ml.g7e.2xlarge", "ml.g7e.4xlarge", "ml.g7e.8xlarge", "ml.g7e.12xlarge", "ml.g7e.24xlarge", "ml.g7e.48xlarge", "ml.p4d.24xlarge", "ml.c7g.large", "ml.c7g.xlarge", "ml.c7g.2xlarge", "ml.c7g.4xlarge", "ml.c7g.8xlarge", "ml.c7g.12xlarge", "ml.c7g.16xlarge", "ml.m6g.large", "ml.m6g.xlarge", "ml.m6g.2xlarge", "ml.m6g.4xlarge", "ml.m6g.8xlarge", "ml.m6g.12xlarge", "ml.m6g.16xlarge", "ml.m6gd.large", "ml.m6gd.xlarge", "ml.m6gd.2xlarge", "ml.m6gd.4xlarge", "ml.m6gd.8xlarge", "ml.m6gd.12xlarge", "ml.m6gd.16xlarge", "ml.c6g.large", "ml.c6g.xlarge", "ml.c6g.2xlarge", "ml.c6g.4xlarge", "ml.c6g.8xlarge", "ml.c6g.12xlarge", "ml.c6g.16xlarge", "ml.c6gd.large", "ml.c6gd.xlarge", "ml.c6gd.2xlarge", "ml.c6gd.4xlarge", "ml.c6gd.8xlarge", "ml.c6gd.12xlarge", "ml.c6gd.16xlarge", "ml.c6gn.large", "ml.c6gn.xlarge", "ml.c6gn.2xlarge", "ml.c6gn.4xlarge", "ml.c6gn.8xlarge", "ml.c6gn.12xlarge", "ml.c6gn.16xlarge", "ml.r6g.large", "ml.r6g.xlarge", "ml.r6g.2xlarge", "ml.r6g.4xlarge", "ml.r6g.8xlarge", "ml.r6g.12xlarge", "ml.r6g.16xlarge", "ml.r6gd.large", "ml.r6gd.xlarge", "ml.r6gd.2xlarge", "ml.r6gd.4xlarge", "ml.r6gd.8xlarge", "ml.r6gd.12xlarge", "ml.r6gd.16xlarge", "ml.p4de.24xlarge", "ml.trn1.2xlarge", "ml.trn1.32xlarge", "ml.trn1n.32xlarge", "ml.trn2.48xlarge", "ml.inf2.xlarge", "ml.inf2.8xlarge", "ml.inf2.24xlarge", "ml.inf2.48xlarge", "ml.p5.48xlarge", "ml.p5e.48xlarge", "ml.p5en.48xlarge", "ml.m7i.large", "ml.m7i.xlarge", "ml.m7i.2xlarge", "ml.m7i.4xlarge", "ml.m7i.8xlarge", "ml.m7i.12xlarge", "ml.m7i.16xlarge", "ml.m7i.24xlarge", "ml.m7i.48xlarge", "ml.c7i.large", "ml.c7i.xlarge", "ml.c7i.2xlarge", "ml.c7i.4xlarge", "ml.c7i.8xlarge", "ml.c7i.12xlarge", "ml.c7i.16xlarge", "ml.c7i.24xlarge", "ml.c7i.48xlarge", "ml.r7i.large", "ml.r7i.xlarge", "ml.r7i.2xlarge", "ml.r7i.4xlarge", "ml.r7i.8xlarge", "ml.r7i.12xlarge", "ml.r7i.16xlarge", "ml.r7i.24xlarge", "ml.r7i.48xlarge", "ml.c8g.medium", "ml.c8g.large", "ml.c8g.xlarge", "ml.c8g.2xlarge", "ml.c8g.4xlarge", "ml.c8g.8xlarge", "ml.c8g.12xlarge", "ml.c8g.16xlarge", "ml.c8g.24xlarge", "ml.c8g.48xlarge", "ml.r7gd.medium", "ml.r7gd.large", "ml.r7gd.xlarge", "ml.r7gd.2xlarge", "ml.r7gd.4xlarge", "ml.r7gd.8xlarge", "ml.r7gd.12xlarge", "ml.r7gd.16xlarge", "ml.m8g.medium", "ml.m8g.large", "ml.m8g.xlarge", "ml.m8g.2xlarge", "ml.m8g.4xlarge", "ml.m8g.8xlarge", "ml.m8g.12xlarge", "ml.m8g.16xlarge", "ml.m8g.24xlarge", "ml.m8g.48xlarge", "ml.c6in.large", "ml.c6in.xlarge", "ml.c6in.2xlarge", "ml.c6in.4xlarge", "ml.c6in.8xlarge", "ml.c6in.12xlarge", "ml.c6in.16xlarge", "ml.c6in.24xlarge", "ml.c6in.32xlarge", "ml.p6-b200.48xlarge", "ml.p6-b300.48xlarge", "ml.p6e-gb200.36xlarge", "ml.p5.4xlarge"
resp.model_package_summaries["ModelPackageArn"].inference_specification.supported_content_types #=> Array
resp.model_package_summaries["ModelPackageArn"].inference_specification.supported_content_types[0] #=> String
resp.model_package_summaries["ModelPackageArn"].inference_specification.supported_response_mime_types #=> Array
resp.model_package_summaries["ModelPackageArn"].inference_specification.supported_response_mime_types[0] #=> String
resp.model_package_summaries["ModelPackageArn"].model_package_status #=> String, one of "Pending", "InProgress", "Completed", "Failed", "Deleting"
resp.model_package_summaries["ModelPackageArn"].model_approval_status #=> String, one of "Approved", "Rejected", "PendingManualApproval"
resp.model_package_summaries["ModelPackageArn"].model_package_registration_type #=> String, one of "Logged", "Registered"
resp.batch_describe_model_package_error_map #=> Hash
resp.batch_describe_model_package_error_map["ModelPackageArn"].error_code #=> String
resp.batch_describe_model_package_error_map["ModelPackageArn"].error_response #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

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

    The list of Amazon Resource Name (ARN) of the model package groups.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 973

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

#batch_reboot_cluster_nodes(params = {}) ⇒ Types::BatchRebootClusterNodesResponse

Reboots specific nodes within a SageMaker HyperPod cluster using a soft recovery mechanism. BatchRebootClusterNodes performs a graceful reboot of the specified nodes by calling the Amazon Elastic Compute Cloud RebootInstances API, which attempts to cleanly shut down the operating system before restarting the instance.

This operation is useful for recovering from transient issues or applying certain configuration changes that require a restart.

* Rebooting a node may cause temporary service interruption for workloads running on that node. Ensure your workloads can handle node restarts or use appropriate scheduling to minimize impact.

  • You can reboot up to 25 nodes in a single request.

  • For SageMaker HyperPod clusters using the Slurm workload manager, ensure rebooting nodes will not disrupt critical cluster operations.

Examples:

Request syntax with placeholder values


resp = client.batch_reboot_cluster_nodes({
  cluster_name: "ClusterNameOrArn", # required
  node_ids: ["ClusterNodeId"],
  node_logical_ids: ["ClusterNodeLogicalId"],
})

Response structure


resp.successful #=> Array
resp.successful[0] #=> String
resp.failed #=> Array
resp.failed[0].node_id #=> String
resp.failed[0].error_code #=> String, one of "InstanceIdNotFound", "InvalidInstanceStatus", "InstanceIdInUse", "InternalServerError"
resp.failed[0].message #=> String
resp.failed_node_logical_ids #=> Array
resp.failed_node_logical_ids[0].node_logical_id #=> String
resp.failed_node_logical_ids[0].error_code #=> String, one of "InstanceIdNotFound", "InvalidInstanceStatus", "InstanceIdInUse", "InternalServerError"
resp.failed_node_logical_ids[0].message #=> String
resp.successful_node_logical_ids #=> Array
resp.successful_node_logical_ids[0] #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :cluster_name (required, String) —

    The name or Amazon Resource Name (ARN) of the SageMaker HyperPod cluster containing the nodes to reboot.

  • :node_ids (Array<String>) —

    A list of EC2 instance IDs to reboot using soft recovery. You can specify between 1 and 25 instance IDs.

    * Either NodeIds or NodeLogicalIds must be provided (or both), but at least one is required.

    • Each instance ID must follow the pattern i- followed by 17 hexadecimal characters (for example, i-0123456789abcdef0).

  • :node_logical_ids (Array<String>) —

    A list of logical node IDs to reboot using soft recovery. You can specify between 1 and 25 logical node IDs.

    The NodeLogicalId is a unique identifier that persists throughout the node's lifecycle and can be used to track nodes that are still being provisioned and don't yet have an EC2 instance ID assigned.

    • This parameter is only supported for clusters using Continuous as the NodeProvisioningMode. For clusters using the default provisioning mode, use NodeIds instead.

    • Either NodeIds or NodeLogicalIds must be provided (or both), but at least one is required.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1063

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

#batch_replace_cluster_nodes(params = {}) ⇒ Types::BatchReplaceClusterNodesResponse

Replaces specific nodes within a SageMaker HyperPod cluster with new hardware. BatchReplaceClusterNodes terminates the specified instances and provisions new replacement instances with the same configuration but fresh hardware. The Amazon Machine Image (AMI) and instance configuration remain the same.

This operation is useful for recovering from hardware failures or persistent issues that cannot be resolved through a reboot.

  • Data Loss Warning: Replacing nodes destroys all instance volumes, including both root and secondary volumes. All data stored on these volumes will be permanently lost and cannot be recovered.

  • To safeguard your work, back up your data to Amazon S3 or an FSx for Lustre file system before invoking the API on a worker node group. This will help prevent any potential data loss from the instance root volume. For more information about backup, see Use the backup script provided by SageMaker HyperPod.

  • If you want to invoke this API on an existing cluster, you'll first need to patch the cluster by running the UpdateClusterSoftware API. For more information about patching a cluster, see Update the SageMaker HyperPod platform software of a cluster.

  • You can replace up to 25 nodes in a single request.

Examples:

Request syntax with placeholder values


resp = client.batch_replace_cluster_nodes({
  cluster_name: "ClusterNameOrArn", # required
  node_ids: ["ClusterNodeId"],
  node_logical_ids: ["ClusterNodeLogicalId"],
})

Response structure


resp.successful #=> Array
resp.successful[0] #=> String
resp.failed #=> Array
resp.failed[0].node_id #=> String
resp.failed[0].error_code #=> String, one of "InstanceIdNotFound", "InvalidInstanceStatus", "InstanceIdInUse", "InternalServerError"
resp.failed[0].message #=> String
resp.failed_node_logical_ids #=> Array
resp.failed_node_logical_ids[0].node_logical_id #=> String
resp.failed_node_logical_ids[0].error_code #=> String, one of "InstanceIdNotFound", "InvalidInstanceStatus", "InstanceIdInUse", "InternalServerError"
resp.failed_node_logical_ids[0].message #=> String
resp.successful_node_logical_ids #=> Array
resp.successful_node_logical_ids[0] #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :cluster_name (required, String) —

    The name or Amazon Resource Name (ARN) of the SageMaker HyperPod cluster containing the nodes to replace.

  • :node_ids (Array<String>) —

    A list of EC2 instance IDs to replace with new hardware. You can specify between 1 and 25 instance IDs.

    Replace operations destroy all instance volumes (root and secondary). Ensure you have backed up any important data before proceeding.

    * Either NodeIds or NodeLogicalIds must be provided (or both), but at least one is required.

    • Each instance ID must follow the pattern i- followed by 17 hexadecimal characters (for example, i-0123456789abcdef0).

    • For SageMaker HyperPod clusters using the Slurm workload manager, you cannot replace instances that are configured as Slurm controller nodes.

  • :node_logical_ids (Array<String>) —

    A list of logical node IDs to replace with new hardware. You can specify between 1 and 25 logical node IDs.

    The NodeLogicalId is a unique identifier that persists throughout the node's lifecycle and can be used to track nodes that are still being provisioned and don't yet have an EC2 instance ID assigned.

    • Replace operations destroy all instance volumes (root and secondary). Ensure you have backed up any important data before proceeding.

    • This parameter is only supported for clusters using Continuous as the NodeProvisioningMode. For clusters using the default provisioning mode, use NodeIds instead.

    • Either NodeIds or NodeLogicalIds must be provided (or both), but at least one is required.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1176

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

#create_action(params = {}) ⇒ Types::CreateActionResponse

Creates an action. An action is a lineage tracking entity that represents an action or activity. For example, a model deployment or an HPO job. Generally, an action involves at least one input or output artifact. For more information, see Amazon SageMaker ML Lineage Tracking.

Examples:

Request syntax with placeholder values


resp = client.create_action({
  action_name: "ExperimentEntityName", # required
  source: { # required
    source_uri: "SourceUri", # required
    source_type: "String256",
    source_id: "String256",
  },
  action_type: "String256", # required
  description: "ExperimentDescription",
  status: "Unknown", # accepts Unknown, InProgress, Completed, Failed, Stopping, Stopped
  properties: {
    "StringParameterValue" => "StringParameterValue",
  },
  metadata_properties: {
    commit_id: "MetadataPropertyValue",
    repository: "MetadataPropertyValue",
    generated_by: "MetadataPropertyValue",
    project_id: "MetadataPropertyValue",
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.action_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :action_name (required, String) —

    The name of the action. Must be unique to your account in an Amazon Web Services Region.

  • :source (required, Types::ActionSource) —

    The source type, ID, and URI.

  • :action_type (required, String) —

    The action type.

  • :description (String) —

    The description of the action.

  • :status (String) —

    The status of the action.

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

    A list of properties to add to the action.

  • :metadata_properties (Types::MetadataProperties) —

    Metadata properties of the tracking entity, trial, or trial component.

  • :tags (Array<Types::Tag>) —

    A list of tags to apply to the action.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1525

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

#create_ai_benchmark_job(params = {}) ⇒ Types::CreateAIBenchmarkJobResponse

Creates a benchmark job that runs performance benchmarks against inference infrastructure using a predefined AI workload configuration. The benchmark job measures metrics such as latency, throughput, and cost for your generative AI inference endpoints.

Examples:

Request syntax with placeholder values


resp = client.create_ai_benchmark_job({
  ai_benchmark_job_name: "AIEntityName", # required
  benchmark_target: { # required
    endpoint: {
      identifier: "AIResourceIdentifier", # required
      target_container_hostname: "String",
      inference_components: [
        {
          identifier: "AIResourceIdentifier", # required
        },
      ],
    },
  },
  output_config: { # required
    s3_output_location: "S3Uri", # required
  },
  ai_workload_config_identifier: "AIResourceIdentifier", # required
  role_arn: "RoleArn", # required
  network_config: {
    vpc_config: {
      security_group_ids: ["SecurityGroupId"], # required
      subnets: ["SubnetId"], # required
    },
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.ai_benchmark_job_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :ai_benchmark_job_name (required, String) —

    The name of the AI benchmark job. The name must be unique within your Amazon Web Services account in the current Amazon Web Services Region.

  • :benchmark_target (required, Types::AIBenchmarkTarget) —

    The target endpoint to benchmark. Specify a SageMaker endpoint by providing its name or Amazon Resource Name (ARN).

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

    The output configuration for the benchmark job, including the Amazon S3 location where benchmark results are stored.

  • :ai_workload_config_identifier (required, String) —

    The name or Amazon Resource Name (ARN) of the AI workload configuration to use for this benchmark job.

  • :role_arn (required, String) —

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

  • :network_config (Types::AIBenchmarkNetworkConfig) —

    The network configuration for the benchmark job, including VPC settings.

  • :tags (Array<Types::Tag>) —

    The metadata that you apply to Amazon Web Services resources to help you categorize and organize them. 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 1261

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

#create_ai_recommendation_job(params = {}) ⇒ Types::CreateAIRecommendationJobResponse

Creates a recommendation job that generates intelligent optimization recommendations for generative AI inference deployments. The job analyzes your model, workload configuration, and performance targets to recommend optimal instance types, model optimization techniques (such as quantization and speculative decoding), and deployment configurations.

Examples:

Request syntax with placeholder values


resp = client.create_ai_recommendation_job({
  ai_recommendation_job_name: "AIEntityName", # required
  model_source: { # required
    s3: {
      s3_uri: "S3Uri",
    },
  },
  output_config: { # required
    s3_output_location: "S3Uri",
    model_package_group_identifier: "AIResourceIdentifier",
  },
  ai_workload_config_identifier: "AIResourceIdentifier", # required
  performance_target: { # required
    constraints: [ # required
      {
        metric: "ttft-ms", # required, accepts ttft-ms, throughput, cost
      },
    ],
  },
  role_arn: "RoleArn", # required
  inference_specification: {
    framework: "LMI", # accepts LMI, VLLM
  },
  optimize_model: false,
  compute_spec: {
    instance_types: ["ml.g5.xlarge"], # accepts ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.g6e.xlarge, ml.g6e.2xlarge, ml.g6e.4xlarge, ml.g6e.8xlarge, ml.g6e.12xlarge, ml.g6e.16xlarge, ml.g6e.24xlarge, ml.g6e.48xlarge, ml.g7e.2xlarge, ml.g7e.4xlarge, ml.g7e.8xlarge, ml.g7e.12xlarge, ml.g7e.24xlarge, ml.g7e.48xlarge, ml.p3.2xlarge, ml.p3.8xlarge, ml.p3.16xlarge, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.4xlarge, ml.p5.48xlarge, ml.p5e.48xlarge, ml.p5en.48xlarge
    capacity_reservation_config: {
      capacity_reservation_preference: "capacity-reservations-only", # accepts capacity-reservations-only
      ml_reservation_arns: ["AIMlReservationArn"],
    },
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.ai_recommendation_job_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :ai_recommendation_job_name (required, String) —

    The name of the AI recommendation job. The name must be unique within your Amazon Web Services account in the current Amazon Web Services Region.

  • :model_source (required, Types::AIModelSource) —

    The source of the model to optimize. Specify the Amazon S3 location of the model artifacts.

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

    The output configuration for the recommendation job, including the Amazon S3 location for results and an optional model package group where the optimized model is registered.

  • :ai_workload_config_identifier (required, String) —

    The name or Amazon Resource Name (ARN) of the AI workload configuration to use for this recommendation job.

  • :performance_target (required, Types::AIRecommendationPerformanceTarget) —

    The performance targets for the recommendation job. Specify constraints on metrics such as time to first token (ttft-ms), throughput, or cost.

  • :role_arn (required, String) —

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

  • :inference_specification (Types::AIRecommendationInferenceSpecification) —

    The inference framework configuration. Specify the framework (such as LMI or vLLM) for the recommendation job.

  • :optimize_model (Boolean) —

    Whether to allow model optimization techniques such as quantization, speculative decoding, and kernel tuning. The default is true.

  • :compute_spec (Types::AIRecommendationComputeSpec) —

    The compute resource specification for the recommendation job. You can specify up to 3 instance types to consider, and optionally provide capacity reservation configuration.

  • :tags (Array<Types::Tag>) —

    The metadata that you apply to Amazon Web Services resources to help you categorize and organize them.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1370

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

#create_ai_workload_config(params = {}) ⇒ Types::CreateAIWorkloadConfigResponse

Creates a reusable AI workload configuration that defines datasets, data sources, and benchmark tool settings for consistent performance testing of generative AI inference deployments on Amazon SageMaker AI.

Examples:

Request syntax with placeholder values


resp = client.create_ai_workload_config({
  ai_workload_config_name: "AIEntityName", # required
  dataset_config: {
    input_data_config: [
      {
        channel_name: "AIChannelName", # required
        data_source: { # required
          s3_data_source: {
            s3_uri: "S3Uri", # required
          },
        },
      },
    ],
  },
  ai_workload_configs: {
    workload_spec: { # required
      inline: "String",
    },
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.ai_workload_config_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :ai_workload_config_name (required, String) —

    The name of the AI workload configuration. The name must be unique within your Amazon Web Services account in the current Amazon Web Services Region.

  • :dataset_config (Types::AIDatasetConfig) —

    The dataset configuration for the workload. Specify input data channels with their data sources for benchmark workloads.

  • :ai_workload_configs (Types::AIWorkloadConfigs) —

    The benchmark tool configuration and workload specification. Provide the specification as an inline YAML or JSON string.

  • :tags (Array<Types::Tag>) —

    The metadata that you apply to Amazon Web Services resources to help you categorize and organize them. Each tag consists of a key and a value, both of which you define. For more information, see Tagging Amazon Web Services Resources in the Amazon Web Services General Reference.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1444

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

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

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

Examples:

Request syntax with placeholder values


resp = client.create_algorithm({
  algorithm_name: "EntityName", # required
  algorithm_description: "EntityDescription",
  training_specification: { # required
    training_image: "ContainerImage", # 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: "HyperParameterValue",
      },
    ],
    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.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.p5e.48xlarge, ml.p5en.48xlarge, 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, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.16xlarge, ml.g6.12xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.g6e.xlarge, ml.g6e.2xlarge, ml.g6e.4xlarge, ml.g6e.8xlarge, ml.g6e.16xlarge, ml.g6e.12xlarge, ml.g6e.24xlarge, ml.g6e.48xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.trn2.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.8xlarge, ml.c6i.4xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.p6-b200.48xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.p6e-gb200.36xlarge, ml.p5.4xlarge, ml.p6-b300.48xlarge, ml.g7e.2xlarge, ml.g7e.4xlarge, ml.g7e.8xlarge, ml.g7e.12xlarge, ml.g7e.24xlarge, ml.g7e.48xlarge
    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, FastFile
      },
    ],
    supported_tuning_job_objective_metrics: [
      {
        type: "Maximize", # required, accepts Maximize, Minimize
        metric_name: "MetricName", # required
      },
    ],
    additional_s3_data_source: {
      s3_data_type: "S3Object", # required, accepts S3Object, S3Prefix
      s3_uri: "S3Uri", # required
      compression_type: "None", # accepts None, Gzip
      etag: "String",
    },
  },
  inference_specification: {
    containers: [ # required
      {
        container_hostname: "ContainerHostname",
        image: "ContainerImage",
        image_digest: "ImageDigest",
        model_data_url: "Url",
        model_data_source: {
          s3_data_source: {
            s3_uri: "S3ModelUri", # required
            s3_data_type: "S3Prefix", # required, accepts S3Prefix, S3Object
            compression_type: "None", # required, accepts None, Gzip
            model_access_config: {
              accept_eula: false, # required
            },
            hub_access_config: {
              hub_content_arn: "HubContentArn", # required
            },
            manifest_s3_uri: "S3ModelUri",
            etag: "String",
            manifest_etag: "String",
          },
        },
        product_id: "ProductId",
        environment: {
          "EnvironmentKey" => "EnvironmentValue",
        },
        model_input: {
          data_input_config: "DataInputConfig", # required
        },
        framework: "String",
        framework_version: "ModelPackageFrameworkVersion",
        nearest_model_name: "String",
        additional_model_data_sources: [
          {
            channel_name: "AdditionalModelChannelName", # required
            s3_data_source: { # required
              s3_uri: "S3ModelUri", # required
              s3_data_type: "S3Prefix", # required, accepts S3Prefix, S3Object
              compression_type: "None", # required, accepts None, Gzip
              model_access_config: {
                accept_eula: false, # required
              },
              hub_access_config: {
                hub_content_arn: "HubContentArn", # required
              },
              manifest_s3_uri: "S3ModelUri",
              etag: "String",
              manifest_etag: "String",
            },
          },
        ],
        additional_s3_data_source: {
          s3_data_type: "S3Object", # required, accepts S3Object, S3Prefix
          s3_uri: "S3Uri", # required
          compression_type: "None", # accepts None, Gzip
          etag: "String",
        },
        model_data_etag: "String",
        is_checkpoint: false,
        base_model: {
          hub_content_name: "HubContentName",
          hub_content_version: "HubContentVersion",
          recipe_name: "RecipeName",
        },
      },
    ],
    supported_transform_instance_types: ["ml.m4.xlarge"], # 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, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.inf2.xlarge, ml.inf2.8xlarge, ml.inf2.24xlarge, ml.inf2.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge
    supported_realtime_inference_instance_types: ["ml.t2.medium"], # 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, ml.dl1.24xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.r8g.medium, ml.r8g.large, ml.r8g.xlarge, ml.r8g.2xlarge, ml.r8g.4xlarge, ml.r8g.8xlarge, ml.r8g.12xlarge, ml.r8g.16xlarge, ml.r8g.24xlarge, ml.r8g.48xlarge, ml.g6e.xlarge, ml.g6e.2xlarge, ml.g6e.4xlarge, ml.g6e.8xlarge, ml.g6e.12xlarge, ml.g6e.16xlarge, ml.g6e.24xlarge, ml.g6e.48xlarge, ml.g7e.2xlarge, ml.g7e.4xlarge, ml.g7e.8xlarge, ml.g7e.12xlarge, ml.g7e.24xlarge, ml.g7e.48xlarge, ml.p4d.24xlarge, ml.c7g.large, ml.c7g.xlarge, ml.c7g.2xlarge, ml.c7g.4xlarge, ml.c7g.8xlarge, ml.c7g.12xlarge, ml.c7g.16xlarge, ml.m6g.large, ml.m6g.xlarge, ml.m6g.2xlarge, ml.m6g.4xlarge, ml.m6g.8xlarge, ml.m6g.12xlarge, ml.m6g.16xlarge, ml.m6gd.large, ml.m6gd.xlarge, ml.m6gd.2xlarge, ml.m6gd.4xlarge, ml.m6gd.8xlarge, ml.m6gd.12xlarge, ml.m6gd.16xlarge, ml.c6g.large, ml.c6g.xlarge, ml.c6g.2xlarge, ml.c6g.4xlarge, ml.c6g.8xlarge, ml.c6g.12xlarge, ml.c6g.16xlarge, ml.c6gd.large, ml.c6gd.xlarge, ml.c6gd.2xlarge, ml.c6gd.4xlarge, ml.c6gd.8xlarge, ml.c6gd.12xlarge, ml.c6gd.16xlarge, ml.c6gn.large, ml.c6gn.xlarge, ml.c6gn.2xlarge, ml.c6gn.4xlarge, ml.c6gn.8xlarge, ml.c6gn.12xlarge, ml.c6gn.16xlarge, ml.r6g.large, ml.r6g.xlarge, ml.r6g.2xlarge, ml.r6g.4xlarge, ml.r6g.8xlarge, ml.r6g.12xlarge, ml.r6g.16xlarge, ml.r6gd.large, ml.r6gd.xlarge, ml.r6gd.2xlarge, ml.r6gd.4xlarge, ml.r6gd.8xlarge, ml.r6gd.12xlarge, ml.r6gd.16xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.trn2.48xlarge, ml.inf2.xlarge, ml.inf2.8xlarge, ml.inf2.24xlarge, ml.inf2.48xlarge, ml.p5.48xlarge, ml.p5e.48xlarge, ml.p5en.48xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.c8g.medium, ml.c8g.large, ml.c8g.xlarge, ml.c8g.2xlarge, ml.c8g.4xlarge, ml.c8g.8xlarge, ml.c8g.12xlarge, ml.c8g.16xlarge, ml.c8g.24xlarge, ml.c8g.48xlarge, ml.r7gd.medium, ml.r7gd.large, ml.r7gd.xlarge, ml.r7gd.2xlarge, ml.r7gd.4xlarge, ml.r7gd.8xlarge, ml.r7gd.12xlarge, ml.r7gd.16xlarge, ml.m8g.medium, ml.m8g.large, ml.m8g.xlarge, ml.m8g.2xlarge, ml.m8g.4xlarge, ml.m8g.8xlarge, ml.m8g.12xlarge, ml.m8g.16xlarge, ml.m8g.24xlarge, ml.m8g.48xlarge, ml.c6in.large, ml.c6in.xlarge, ml.c6in.2xlarge, ml.c6in.4xlarge, ml.c6in.8xlarge, ml.c6in.12xlarge, ml.c6in.16xlarge, ml.c6in.24xlarge, ml.c6in.32xlarge, ml.p6-b200.48xlarge, ml.p6-b300.48xlarge, ml.p6e-gb200.36xlarge, ml.p5.4xlarge
    supported_content_types: ["ContentType"],
    supported_response_mime_types: ["ResponseMIMEType"],
  },
  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, FastFile
          hyper_parameters: {
            "HyperParameterKey" => "HyperParameterValue",
          },
          input_data_config: [ # required
            {
              channel_name: "ChannelName", # required
              data_source: { # required
                s3_data_source: {
                  s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile, Converse
                  s3_uri: "S3Uri", # required
                  s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
                  attribute_names: ["AttributeName"],
                  instance_group_names: ["InstanceGroupName"],
                  model_access_config: {
                    accept_eula: false, # required
                  },
                  hub_access_config: {
                    hub_content_arn: "HubContentArn", # required
                  },
                },
                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
                },
                dataset_source: {
                  dataset_arn: "HubDataSetArn", # required
                },
              },
              content_type: "ContentType",
              compression_type: "None", # accepts None, Gzip
              record_wrapper_type: "None", # accepts None, RecordIO
              input_mode: "Pipe", # accepts Pipe, File, FastFile
              shuffle_config: {
                seed: 1, # required
              },
            },
          ],
          output_data_config: { # required
            kms_key_id: "KmsKeyId",
            s3_output_path: "S3Uri", # required
            compression_type: "GZIP", # accepts GZIP, NONE
          },
          resource_config: { # required
            instance_type: "ml.m4.xlarge", # 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.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.p5e.48xlarge, ml.p5en.48xlarge, 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, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.16xlarge, ml.g6.12xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.g6e.xlarge, ml.g6e.2xlarge, ml.g6e.4xlarge, ml.g6e.8xlarge, ml.g6e.16xlarge, ml.g6e.12xlarge, ml.g6e.24xlarge, ml.g6e.48xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.trn2.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.8xlarge, ml.c6i.4xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.p6-b200.48xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.p6e-gb200.36xlarge, ml.p5.4xlarge, ml.p6-b300.48xlarge, ml.g7e.2xlarge, ml.g7e.4xlarge, ml.g7e.8xlarge, ml.g7e.12xlarge, ml.g7e.24xlarge, ml.g7e.48xlarge
            instance_count: 1,
            volume_size_in_gb: 1,
            volume_kms_key_id: "KmsKeyId",
            keep_alive_period_in_seconds: 1,
            instance_groups: [
              {
                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.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.p5e.48xlarge, ml.p5en.48xlarge, 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, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.16xlarge, ml.g6.12xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.g6e.xlarge, ml.g6e.2xlarge, ml.g6e.4xlarge, ml.g6e.8xlarge, ml.g6e.16xlarge, ml.g6e.12xlarge, ml.g6e.24xlarge, ml.g6e.48xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.trn2.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.8xlarge, ml.c6i.4xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge, ml.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.r5.large, ml.r5.xlarge, ml.r5.2xlarge, ml.r5.4xlarge, ml.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.p6-b200.48xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.p6e-gb200.36xlarge, ml.p5.4xlarge, ml.p6-b300.48xlarge, ml.g7e.2xlarge, ml.g7e.4xlarge, ml.g7e.8xlarge, ml.g7e.12xlarge, ml.g7e.24xlarge, ml.g7e.48xlarge
                instance_count: 1, # required
                instance_group_name: "InstanceGroupName", # required
              },
            ],
            training_plan_arn: "TrainingPlanArn",
            instance_placement_config: {
              enable_multiple_jobs: false,
              placement_specifications: [
                {
                  ultra_server_id: "String256",
                  instance_count: 1, # required
                },
              ],
            },
          },
          stopping_condition: { # required
            max_runtime_in_seconds: 1,
            max_wait_time_in_seconds: 1,
            max_pending_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, Converse
                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, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.inf2.xlarge, ml.inf2.8xlarge, ml.inf2.24xlarge, ml.inf2.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge
            instance_count: 1, # required
            volume_kms_key_id: "KmsKeyId",
            transform_ami_version: "TransformAmiVersion",
          },
        },
      },
    ],
  },
  certify_for_marketplace: false,
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

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 SageMaker runs to test the algorithm's training code and, optionally, one or more batch transform jobs that 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 Amazon Web Services Marketplace.

  • :tags (Array<Types::Tag>) —

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1864

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. This operation is automatically invoked by Amazon SageMaker AI upon access to the associated Domain, and when new kernel configurations are selected by the user. A user may have multiple Apps active simultaneously.

Examples:

Request syntax with placeholder values


resp = client.create_app({
  domain_id: "DomainId", # required
  user_profile_name: "UserProfileName",
  space_name: "SpaceName",
  app_type: "JupyterServer", # required, accepts JupyterServer, KernelGateway, DetailedProfiler, TensorBoard, CodeEditor, JupyterLab, RStudioServerPro, RSessionGateway, Canvas
  app_name: "AppName", # required
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  resource_spec: {
    sage_maker_image_arn: "ImageArn",
    sage_maker_image_version_arn: "ImageVersionArn",
    sage_maker_image_version_alias: "ImageVersionAlias",
    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.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.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.p3dn.24xlarge, 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.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.g6e.xlarge, ml.g6e.2xlarge, ml.g6e.4xlarge, ml.g6e.8xlarge, ml.g6e.12xlarge, ml.g6e.16xlarge, ml.g6e.24xlarge, ml.g6e.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.p5en.48xlarge, ml.p6-b200.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
    lifecycle_config_arn: "StudioLifecycleConfigArn",
  },
  recovery_mode: false,
})

Response structure


resp.app_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :domain_id (required, String) —

    The domain ID.

  • :user_profile_name (String) —

    The user profile name. If this value is not set, then SpaceName must be set.

  • :space_name (String) —

    The name of the space. If this value is not set, then UserProfileName must be set.

  • :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 AI image created on the instance.

    The value of InstanceType passed as part of the ResourceSpec in the CreateApp call overrides the value passed as part of the ResourceSpec configured for the user profile or the domain. If InstanceType is not specified in any of those three ResourceSpec values for a KernelGateway app, the CreateApp call fails with a request validation error.

  • :recovery_mode (Boolean) —

    Indicates whether the application is launched in recovery mode.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 1947

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

#create_app_image_config(params = {}) ⇒ Types::CreateAppImageConfigResponse

Creates a configuration for running a SageMaker AI image as a KernelGateway app. The configuration specifies the Amazon Elastic File System storage volume on the image, and a list of the kernels in the image.

Examples:

Request syntax with placeholder values


resp = client.create_app_image_config({
  app_image_config_name: "AppImageConfigName", # required
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  kernel_gateway_image_config: {
    kernel_specs: [ # required
      {
        name: "KernelName", # required
        display_name: "KernelDisplayName",
      },
    ],
    file_system_config: {
      mount_path: "MountPath",
      default_uid: 1,
      default_gid: 1,
    },
  },
  jupyter_lab_app_image_config: {
    file_system_config: {
      mount_path: "MountPath",
      default_uid: 1,
      default_gid: 1,
    },
    container_config: {
      container_arguments: ["NonEmptyString64"],
      container_entrypoint: ["NonEmptyString256"],
      container_environment_variables: {
        "NonEmptyString256" => "String256",
      },
    },
  },
  code_editor_app_image_config: {
    file_system_config: {
      mount_path: "MountPath",
      default_uid: 1,
      default_gid: 1,
    },
    container_config: {
      container_arguments: ["NonEmptyString64"],
      container_entrypoint: ["NonEmptyString256"],
      container_environment_variables: {
        "NonEmptyString256" => "String256",
      },
    },
  },
})

Response structure


resp.app_image_config_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :app_image_config_name (required, String) —

    The name of the AppImageConfig. Must be unique to your account.

  • :tags (Array<Types::Tag>) —

    A list of tags to apply to the AppImageConfig.

  • :kernel_gateway_image_config (Types::KernelGatewayImageConfig) —

    The KernelGatewayImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel will be shown to users before the image starts. Once the image runs, all kernels are visible in JupyterLab.

  • :jupyter_lab_app_image_config (Types::JupyterLabAppImageConfig) —

    The JupyterLabAppImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel is shown to users before the image starts. After the image runs, all kernels are visible in JupyterLab.

  • :code_editor_app_image_config (Types::CodeEditorAppImageConfig) —

    The CodeEditorAppImageConfig. You can only specify one image kernel in the AppImageConfig API. This kernel is shown to users before the image starts. After the image runs, all kernels are visible in Code Editor.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2046

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

#create_artifact(params = {}) ⇒ Types::CreateArtifactResponse

Creates an artifact. An artifact is a lineage tracking entity that represents a URI addressable object or data. Some examples are the S3 URI of a dataset and the ECR registry path of an image. For more information, see Amazon SageMaker ML Lineage Tracking.

Examples:

Request syntax with placeholder values


resp = client.create_artifact({
  artifact_name: "ExperimentEntityName",
  source: { # required
    source_uri: "SourceUri", # required
    source_types: [
      {
        source_id_type: "MD5Hash", # required, accepts MD5Hash, S3ETag, S3Version, Custom
        value: "String256", # required
      },
    ],
  },
  artifact_type: "String256", # required
  properties: {
    "StringParameterValue" => "ArtifactPropertyValue",
  },
  metadata_properties: {
    commit_id: "MetadataPropertyValue",
    repository: "MetadataPropertyValue",
    generated_by: "MetadataPropertyValue",
    project_id: "MetadataPropertyValue",
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.artifact_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :artifact_name (String) —

    The name of the artifact. Must be unique to your account in an Amazon Web Services Region.

  • :source (required, Types::ArtifactSource) —

    The ID, ID type, and URI of the source.

  • :artifact_type (required, String) —

    The artifact type.

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

    A list of properties to add to the artifact.

  • :metadata_properties (Types::MetadataProperties) —

    Metadata properties of the tracking entity, trial, or trial component.

  • :tags (Array<Types::Tag>) —

    A list of tags to apply to the artifact.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2122

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

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

Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.

An AutoML job in SageMaker AI is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker AI then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AI AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment.

For more information about AutoML jobs, see https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html in the SageMaker AI developer guide.

We recommend using the new versions CreateAutoMLJobV2 and DescribeAutoMLJobV2, which offer backward compatibility.

CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning).

Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2.

You can find the best-performing model after you run an AutoML job by calling DescribeAutoMLJobV2 (recommended) or DescribeAutoMLJob.

Examples:

Request syntax with placeholder values


resp = client.create_auto_ml_job({
  auto_ml_job_name: "AutoMLJobName", # required
  input_data_config: [ # required
    {
      data_source: {
        s3_data_source: { # required
          s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile
          s3_uri: "S3Uri", # required
        },
      },
      compression_type: "None", # accepts None, Gzip
      target_attribute_name: "TargetAttributeName", # required
      content_type: "ContentType",
      channel_type: "training", # accepts training, validation
      sample_weight_attribute_name: "SampleWeightAttributeName",
    },
  ],
  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, AUC, RMSE, BalancedAccuracy, R2, Recall, RecallMacro, Precision, PrecisionMacro, MAE, MAPE, MASE, WAPE, AverageWeightedQuantileLoss
  },
  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
      },
    },
    candidate_generation_config: {
      feature_specification_s3_uri: "S3Uri",
      algorithms_config: [
        {
          auto_ml_algorithms: ["xgboost"], # required, accepts xgboost, linear-learner, mlp, lightgbm, catboost, randomforest, extra-trees, nn-torch, fastai, cnn-qr, deepar, prophet, npts, arima, ets
        },
      ],
    },
    data_split_config: {
      validation_fraction: 1.0,
    },
    mode: "AUTO", # accepts AUTO, ENSEMBLING, HYPERPARAMETER_TUNING
  },
  role_arn: "RoleArn", # required
  generate_candidate_definitions_only: false,
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  model_deploy_config: {
    auto_generate_endpoint_name: false,
    endpoint_name: "EndpointName",
  },
})

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. The name must be unique to your account and is case insensitive.

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

    An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

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

    Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.

  • :problem_type (String) —

    Defines the type of supervised learning problem available for the candidates. For more information, see SageMaker Autopilot problem types.

  • :auto_ml_job_objective (Types::AutoMLJobObjective) —

    Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See AutoMLJobObjective for the default values.

  • :auto_ml_job_config (Types::AutoMLJobConfig) —

    A collection of settings used to configure an AutoML job.

  • :role_arn (required, String) —

    The ARN of the role that is used to access the data.

  • :generate_candidate_definitions_only (Boolean) —

    Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

  • :tags (Array<Types::Tag>) —

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.

  • :model_deploy_config (Types::ModelDeployConfig) —

    Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2321

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

#create_auto_ml_job_v2(params = {}) ⇒ Types::CreateAutoMLJobV2Response

Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.

An AutoML job in SageMaker AI is a fully automated process that allows you to build machine learning models with minimal effort and machine learning expertise. When initiating an AutoML job, you provide your data and optionally specify parameters tailored to your use case. SageMaker AI then automates the entire model development lifecycle, including data preprocessing, model training, tuning, and evaluation. AutoML jobs are designed to simplify and accelerate the model building process by automating various tasks and exploring different combinations of machine learning algorithms, data preprocessing techniques, and hyperparameter values. The output of an AutoML job comprises one or more trained models ready for deployment and inference. Additionally, SageMaker AI AutoML jobs generate a candidate model leaderboard, allowing you to select the best-performing model for deployment.

For more information about AutoML jobs, see https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html in the SageMaker AI developer guide.

AutoML jobs V2 support various problem types such as regression, binary, and multiclass classification with tabular data, text and image classification, time-series forecasting, and fine-tuning of large language models (LLMs) for text generation.

CreateAutoMLJobV2 and DescribeAutoMLJobV2 are new versions of CreateAutoMLJob and DescribeAutoMLJob which offer backward compatibility.

CreateAutoMLJobV2 can manage tabular problem types identical to those of its previous version CreateAutoMLJob, as well as time-series forecasting, non-tabular problem types such as image or text classification, and text generation (LLMs fine-tuning).

Find guidelines about how to migrate a CreateAutoMLJob to CreateAutoMLJobV2 in Migrate a CreateAutoMLJob to CreateAutoMLJobV2.

For the list of available problem types supported by CreateAutoMLJobV2, see AutoMLProblemTypeConfig.

You can find the best-performing model after you run an AutoML job V2 by calling DescribeAutoMLJobV2.

Examples:

Request syntax with placeholder values


resp = client.create_auto_ml_job_v2({
  auto_ml_job_name: "AutoMLJobName", # required
  auto_ml_job_input_data_config: [ # required
    {
      channel_type: "training", # accepts training, validation
      content_type: "ContentType",
      compression_type: "None", # accepts None, Gzip
      data_source: {
        s3_data_source: { # required
          s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix, AugmentedManifestFile
          s3_uri: "S3Uri", # required
        },
      },
    },
  ],
  output_data_config: { # required
    kms_key_id: "KmsKeyId",
    s3_output_path: "S3Uri", # required
  },
  auto_ml_problem_type_config: { # required
    image_classification_job_config: {
      completion_criteria: {
        max_candidates: 1,
        max_runtime_per_training_job_in_seconds: 1,
        max_auto_ml_job_runtime_in_seconds: 1,
      },
    },
    text_classification_job_config: {
      completion_criteria: {
        max_candidates: 1,
        max_runtime_per_training_job_in_seconds: 1,
        max_auto_ml_job_runtime_in_seconds: 1,
      },
      content_column: "ContentColumn", # required
      target_label_column: "TargetLabelColumn", # required
    },
    time_series_forecasting_job_config: {
      feature_specification_s3_uri: "S3Uri",
      completion_criteria: {
        max_candidates: 1,
        max_runtime_per_training_job_in_seconds: 1,
        max_auto_ml_job_runtime_in_seconds: 1,
      },
      forecast_frequency: "ForecastFrequency", # required
      forecast_horizon: 1, # required
      forecast_quantiles: ["ForecastQuantile"],
      transformations: {
        filling: {
          "TransformationAttributeName" => {
            "frontfill" => "FillingTransformationValue",
          },
        },
        aggregation: {
          "TransformationAttributeName" => "sum", # accepts sum, avg, first, min, max
        },
      },
      time_series_config: { # required
        target_attribute_name: "TargetAttributeName", # required
        timestamp_attribute_name: "TimestampAttributeName", # required
        item_identifier_attribute_name: "ItemIdentifierAttributeName", # required
        grouping_attribute_names: ["GroupingAttributeName"],
      },
      holiday_config: [
        {
          country_code: "CountryCode",
        },
      ],
      candidate_generation_config: {
        algorithms_config: [
          {
            auto_ml_algorithms: ["xgboost"], # required, accepts xgboost, linear-learner, mlp, lightgbm, catboost, randomforest, extra-trees, nn-torch, fastai, cnn-qr, deepar, prophet, npts, arima, ets
          },
        ],
      },
    },
    tabular_job_config: {
      candidate_generation_config: {
        algorithms_config: [
          {
            auto_ml_algorithms: ["xgboost"], # required, accepts xgboost, linear-learner, mlp, lightgbm, catboost, randomforest, extra-trees, nn-torch, fastai, cnn-qr, deepar, prophet, npts, arima, ets
          },
        ],
      },
      completion_criteria: {
        max_candidates: 1,
        max_runtime_per_training_job_in_seconds: 1,
        max_auto_ml_job_runtime_in_seconds: 1,
      },
      feature_specification_s3_uri: "S3Uri",
      mode: "AUTO", # accepts AUTO, ENSEMBLING, HYPERPARAMETER_TUNING
      generate_candidate_definitions_only: false,
      problem_type: "BinaryClassification", # accepts BinaryClassification, MulticlassClassification, Regression
      target_attribute_name: "TargetAttributeName", # required
      sample_weight_attribute_name: "SampleWeightAttributeName",
    },
    text_generation_job_config: {
      completion_criteria: {
        max_candidates: 1,
        max_runtime_per_training_job_in_seconds: 1,
        max_auto_ml_job_runtime_in_seconds: 1,
      },
      base_model_name: "BaseModelName",
      text_generation_hyper_parameters: {
        "TextGenerationHyperParameterKey" => "TextGenerationHyperParameterValue",
      },
      model_access_config: {
        accept_eula: false, # required
      },
    },
  },
  role_arn: "RoleArn", # required
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  security_config: {
    volume_kms_key_id: "KmsKeyId",
    enable_inter_container_traffic_encryption: false,
    vpc_config: {
      security_group_ids: ["SecurityGroupId"], # required
      subnets: ["SubnetId"], # required
    },
  },
  auto_ml_job_objective: {
    metric_name: "Accuracy", # required, accepts Accuracy, MSE, F1, F1macro, AUC, RMSE, BalancedAccuracy, R2, Recall, RecallMacro, Precision, PrecisionMacro, MAE, MAPE, MASE, WAPE, AverageWeightedQuantileLoss
  },
  model_deploy_config: {
    auto_generate_endpoint_name: false,
    endpoint_name: "EndpointName",
  },
  data_split_config: {
    validation_fraction: 1.0,
  },
  auto_ml_compute_config: {
    emr_serverless_compute_config: {
      execution_role_arn: "RoleArn", # 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. The name must be unique to your account and is case insensitive.

  • :auto_ml_job_input_data_config (required, Array<Types::AutoMLJobChannel>) —

    An array of channel objects describing the input data and their location. Each channel is a named input source. Similar to the InputDataConfig attribute in the CreateAutoMLJob input parameters. The supported formats depend on the problem type:

    • For tabular problem types: S3Prefix, ManifestFile.

    • For image classification: S3Prefix, ManifestFile, AugmentedManifestFile.

    • For text classification: S3Prefix.

    • For time-series forecasting: S3Prefix.

    • For text generation (LLMs fine-tuning): S3Prefix.

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

    Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.

  • :auto_ml_problem_type_config (required, Types::AutoMLProblemTypeConfig) —

    Defines the configuration settings of one of the supported problem types.

  • :role_arn (required, String) —

    The ARN of the role that is used to access the data.

  • :tags (Array<Types::Tag>) —

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, such as by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.

  • :security_config (Types::AutoMLSecurityConfig) —

    The security configuration for traffic encryption or Amazon VPC settings.

  • :auto_ml_job_objective (Types::AutoMLJobObjective) —

    Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. For the list of default values per problem type, see AutoMLJobObjective.

    * For tabular problem types: You must either provide both the AutoMLJobObjective and indicate the type of supervised learning problem in AutoMLProblemTypeConfig (TabularJobConfig.ProblemType), or none at all.

    • For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the AutoMLJobObjective field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated. Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics. For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.

  • :model_deploy_config (Types::ModelDeployConfig) —

    Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

  • :data_split_config (Types::AutoMLDataSplitConfig) —

    This structure specifies how to split the data into train and validation datasets.

    The validation and training datasets must contain the same headers. For jobs created by calling CreateAutoMLJob, the validation dataset must be less than 2 GB in size.

    This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits the input dataset into training and validation sets.

  • :auto_ml_compute_config (Types::AutoMLComputeConfig) —

    Specifies the compute configuration for the AutoML job V2.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2639

def create_auto_ml_job_v2(params = {}, options = {})
  req = build_request(:create_auto_ml_job_v2, params)
  req.send_request(options)
end

#create_cluster(params = {}) ⇒ Types::CreateClusterResponse

Creates an Amazon SageMaker HyperPod cluster. SageMaker HyperPod is a capability of SageMaker for creating and managing persistent clusters for developing large machine learning models, such as large language models (LLMs) and diffusion models. To learn more, see Amazon SageMaker HyperPod in the Amazon SageMaker Developer Guide.

Examples:

Request syntax with placeholder values


resp = client.create_cluster({
  cluster_name: "ClusterName", # required
  instance_groups: [
    {
      instance_count: 1, # required
      min_instance_count: 1,
      instance_group_name: "ClusterInstanceGroupName", # required
      instance_type: "ml.p4d.24xlarge", # accepts ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.p5.4xlarge, ml.p6e-gb200.36xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge, 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.c5n.large, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge, 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.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.16xlarge, ml.g6.12xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.gr6.4xlarge, ml.gr6.8xlarge, ml.g6e.xlarge, ml.g6e.2xlarge, ml.g6e.4xlarge, ml.g6e.8xlarge, ml.g6e.16xlarge, ml.g6e.12xlarge, ml.g6e.24xlarge, ml.g6e.48xlarge, ml.p5e.48xlarge, ml.p5en.48xlarge, ml.p6-b200.48xlarge, ml.trn2.3xlarge, ml.trn2.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.i3en.large, ml.i3en.xlarge, ml.i3en.2xlarge, ml.i3en.3xlarge, ml.i3en.6xlarge, ml.i3en.12xlarge, ml.i3en.24xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.r5d.16xlarge, ml.g7e.2xlarge, ml.g7e.4xlarge, ml.g7e.8xlarge, ml.g7e.12xlarge, ml.g7e.24xlarge, ml.g7e.48xlarge, ml.p6-b300.48xlarge
      instance_requirements: {
        instance_types: ["ml.p4d.24xlarge"], # required, accepts ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.p5.4xlarge, ml.p6e-gb200.36xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge, 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.c5n.large, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge, 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.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.16xlarge, ml.g6.12xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.gr6.4xlarge, ml.gr6.8xlarge, ml.g6e.xlarge, ml.g6e.2xlarge, ml.g6e.4xlarge, ml.g6e.8xlarge, ml.g6e.16xlarge, ml.g6e.12xlarge, ml.g6e.24xlarge, ml.g6e.48xlarge, ml.p5e.48xlarge, ml.p5en.48xlarge, ml.p6-b200.48xlarge, ml.trn2.3xlarge, ml.trn2.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.i3en.large, ml.i3en.xlarge, ml.i3en.2xlarge, ml.i3en.3xlarge, ml.i3en.6xlarge, ml.i3en.12xlarge, ml.i3en.24xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.r5d.16xlarge, ml.g7e.2xlarge, ml.g7e.4xlarge, ml.g7e.8xlarge, ml.g7e.12xlarge, ml.g7e.24xlarge, ml.g7e.48xlarge, ml.p6-b300.48xlarge
      },
      life_cycle_config: {
        source_s3_uri: "S3Uri",
        on_create: "ClusterLifeCycleConfigFileName",
        on_init_complete: "ClusterLifeCycleConfigFileName",
      },
      execution_role: "RoleArn", # required
      threads_per_core: 1,
      instance_storage_configs: [
        {
          ebs_volume_config: {
            volume_size_in_gb: 1,
            volume_kms_key_id: "KmsKeyId",
            root_volume: false,
          },
          fsx_lustre_config: {
            dns_name: "ClusterDnsName", # required
            mount_name: "ClusterMountName", # required
            mount_path: "ClusterFsxMountPath",
          },
          fsx_open_zfs_config: {
            dns_name: "ClusterDnsName", # required
            mount_path: "ClusterFsxMountPath",
          },
        },
      ],
      on_start_deep_health_checks: ["InstanceStress"], # accepts InstanceStress, InstanceConnectivity
      training_plan_arn: "TrainingPlanArn",
      override_vpc_config: {
        security_group_ids: ["SecurityGroupId"], # required
        subnets: ["SubnetId"], # required
      },
      scheduled_update_config: {
        schedule_expression: "CronScheduleExpression", # required
        deployment_config: {
          rolling_update_policy: {
            maximum_batch_size: { # required
              type: "INSTANCE_COUNT", # required, accepts INSTANCE_COUNT, CAPACITY_PERCENTAGE
              value: 1, # required
            },
            rollback_maximum_batch_size: {
              type: "INSTANCE_COUNT", # required, accepts INSTANCE_COUNT, CAPACITY_PERCENTAGE
              value: 1, # required
            },
          },
          wait_interval_in_seconds: 1,
          auto_rollback_configuration: [
            {
              alarm_name: "AlarmName", # required
            },
          ],
        },
      },
      image_id: "ImageId",
      kubernetes_config: {
        labels: {
          "ClusterKubernetesLabelKey" => "ClusterKubernetesLabelValue",
        },
        taints: [
          {
            key: "ClusterKubernetesTaintKey", # required
            value: "ClusterKubernetesTaintValue",
            effect: "NoSchedule", # required, accepts NoSchedule, PreferNoSchedule, NoExecute
          },
        ],
      },
      slurm_config: {
        node_type: "Controller", # required, accepts Controller, Login, Compute
        partition_names: ["ClusterPartitionName"],
      },
      capacity_requirements: {
        spot: {
        },
        on_demand: {
        },
      },
      network_interface: {
        interface_type: "efa", # accepts efa, efa-only
      },
    },
  ],
  restricted_instance_groups: [
    {
      instance_count: 1, # required
      instance_group_name: "ClusterInstanceGroupName", # required
      instance_type: "ml.p4d.24xlarge", # required, accepts ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.p5.4xlarge, ml.p6e-gb200.36xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge, 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.c5n.large, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge, 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.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.16xlarge, ml.g6.12xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.gr6.4xlarge, ml.gr6.8xlarge, ml.g6e.xlarge, ml.g6e.2xlarge, ml.g6e.4xlarge, ml.g6e.8xlarge, ml.g6e.16xlarge, ml.g6e.12xlarge, ml.g6e.24xlarge, ml.g6e.48xlarge, ml.p5e.48xlarge, ml.p5en.48xlarge, ml.p6-b200.48xlarge, ml.trn2.3xlarge, ml.trn2.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.i3en.large, ml.i3en.xlarge, ml.i3en.2xlarge, ml.i3en.3xlarge, ml.i3en.6xlarge, ml.i3en.12xlarge, ml.i3en.24xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.r5d.16xlarge, ml.g7e.2xlarge, ml.g7e.4xlarge, ml.g7e.8xlarge, ml.g7e.12xlarge, ml.g7e.24xlarge, ml.g7e.48xlarge, ml.p6-b300.48xlarge
      execution_role: "RoleArn", # required
      threads_per_core: 1,
      instance_storage_configs: [
        {
          ebs_volume_config: {
            volume_size_in_gb: 1,
            volume_kms_key_id: "KmsKeyId",
            root_volume: false,
          },
          fsx_lustre_config: {
            dns_name: "ClusterDnsName", # required
            mount_name: "ClusterMountName", # required
            mount_path: "ClusterFsxMountPath",
          },
          fsx_open_zfs_config: {
            dns_name: "ClusterDnsName", # required
            mount_path: "ClusterFsxMountPath",
          },
        },
      ],
      on_start_deep_health_checks: ["InstanceStress"], # accepts InstanceStress, InstanceConnectivity
      training_plan_arn: "TrainingPlanArn",
      override_vpc_config: {
        security_group_ids: ["SecurityGroupId"], # required
        subnets: ["SubnetId"], # required
      },
      scheduled_update_config: {
        schedule_expression: "CronScheduleExpression", # required
        deployment_config: {
          rolling_update_policy: {
            maximum_batch_size: { # required
              type: "INSTANCE_COUNT", # required, accepts INSTANCE_COUNT, CAPACITY_PERCENTAGE
              value: 1, # required
            },
            rollback_maximum_batch_size: {
              type: "INSTANCE_COUNT", # required, accepts INSTANCE_COUNT, CAPACITY_PERCENTAGE
              value: 1, # required
            },
          },
          wait_interval_in_seconds: 1,
          auto_rollback_configuration: [
            {
              alarm_name: "AlarmName", # required
            },
          ],
        },
      },
      environment_config: {
        f_sx_lustre_config: {
          size_in_gi_b: 1, # required
          per_unit_storage_throughput: 1, # required
        },
      },
    },
  ],
  vpc_config: {
    security_group_ids: ["SecurityGroupId"], # required
    subnets: ["SubnetId"], # required
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  orchestrator: {
    eks: {
      cluster_arn: "EksClusterArn", # required
    },
    slurm: {
      slurm_config_strategy: "Overwrite", # accepts Overwrite, Managed, Merge
    },
  },
  node_recovery: "Automatic", # accepts Automatic, None
  tiered_storage_config: {
    mode: "Enable", # required, accepts Enable, Disable
    instance_memory_allocation_percentage: 1,
  },
  node_provisioning_mode: "Continuous", # accepts Continuous
  cluster_role: "RoleArn",
  auto_scaling: {
    mode: "Enable", # required, accepts Enable, Disable
    auto_scaler_type: "Karpenter", # accepts Karpenter
  },
})

Response structure


resp.cluster_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :cluster_name (required, String) —

    The name for the new SageMaker HyperPod cluster.

  • :instance_groups (Array<Types::ClusterInstanceGroupSpecification>) —

    The instance groups to be created in the SageMaker HyperPod cluster.

  • :restricted_instance_groups (Array<Types::ClusterRestrictedInstanceGroupSpecification>) —

    The specialized instance groups for training models like Amazon Nova to be created in the SageMaker HyperPod cluster.

  • :vpc_config (Types::VpcConfig) —

    Specifies the Amazon Virtual Private Cloud (VPC) that is associated with the Amazon SageMaker HyperPod cluster. You can control access to and from your resources by configuring your VPC. For more information, see Give SageMaker access to resources in your Amazon VPC.

    When your Amazon VPC and subnets support IPv6, network communications differ based on the cluster orchestration platform:

    • Slurm-orchestrated clusters automatically configure nodes with dual IPv6 and IPv4 addresses, allowing immediate IPv6 network communications.

    • In Amazon EKS-orchestrated clusters, nodes receive dual-stack addressing, but pods can only use IPv6 when the Amazon EKS cluster is explicitly IPv6-enabled. For information about deploying an IPv6 Amazon EKS cluster, see Amazon EKS IPv6 Cluster Deployment.

    Additional resources for IPv6 configuration:

  • :tags (Array<Types::Tag>) —

    Custom tags for managing the SageMaker HyperPod cluster as an Amazon Web Services resource. You can add tags to your cluster in the same way you add them in other Amazon Web Services services that support tagging. To learn more about tagging Amazon Web Services resources in general, see Tagging Amazon Web Services Resources User Guide.

  • :orchestrator (Types::ClusterOrchestrator) —

    The type of orchestrator to use for the SageMaker HyperPod cluster. Currently, supported values are "Eks" and "Slurm", which is to use an Amazon Elastic Kubernetes Service or Slurm cluster as the orchestrator.

    If you specify the Orchestrator field, you must provide exactly one orchestrator configuration: either Eks or Slurm. Specifying both or providing an empty configuration returns a validation error.

  • :node_recovery (String) —

    The node recovery mode for the SageMaker HyperPod cluster. When set to Automatic, SageMaker HyperPod will automatically reboot or replace faulty nodes when issues are detected. When set to None, cluster administrators will need to manually manage any faulty cluster instances.

  • :tiered_storage_config (Types::ClusterTieredStorageConfig) —

    The configuration for managed tier checkpointing on the HyperPod cluster. When enabled, this feature uses a multi-tier storage approach for storing model checkpoints, providing faster checkpoint operations and improved fault tolerance across cluster nodes.

  • :node_provisioning_mode (String) —

    The mode for provisioning nodes in the cluster. You can specify the following modes:

    • Continuous: Scaling behavior that enables 1) concurrent operation execution within instance groups, 2) continuous retry mechanisms for failed operations, 3) enhanced customer visibility into cluster events through detailed event streams, 4) partial provisioning capabilities. Your clusters and instance groups remain InService while scaling. This mode is only supported for EKS orchestrated clusters.

    ^

  • :cluster_role (String) —

    The Amazon Resource Name (ARN) of the IAM role that HyperPod assumes to perform cluster autoscaling operations. This role must have permissions for sagemaker:BatchAddClusterNodes and sagemaker:BatchDeleteClusterNodes. This is only required when autoscaling is enabled and when HyperPod is performing autoscaling operations.

  • :auto_scaling (Types::ClusterAutoScalingConfig) —

    The autoscaling configuration for the cluster. Enables automatic scaling of cluster nodes based on workload demand using a Karpenter-based system.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 2962

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

#create_cluster_scheduler_config(params = {}) ⇒ Types::CreateClusterSchedulerConfigResponse

Create cluster policy configuration. This policy is used for task prioritization and fair-share allocation of idle compute. This helps prioritize critical workloads and distributes idle compute across entities.

Examples:

Request syntax with placeholder values


resp = client.create_cluster_scheduler_config({
  name: "EntityName", # required
  cluster_arn: "ClusterArn", # required
  scheduler_config: { # required
    priority_classes: [
      {
        name: "ClusterSchedulerPriorityClassName", # required
        weight: 1, # required
      },
    ],
    fair_share: "Enabled", # accepts Enabled, Disabled
    idle_resource_sharing: "Enabled", # accepts Enabled, Disabled
  },
  description: "EntityDescription",
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.cluster_scheduler_config_arn #=> String
resp.cluster_scheduler_config_id #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :name (required, String) —

    Name for the cluster policy.

  • :cluster_arn (required, String) —

    ARN of the cluster.

  • :scheduler_config (required, Types::SchedulerConfig) —

    Configuration about the monitoring schedule.

  • :description (String) —

    Description of the cluster policy.

  • :tags (Array<Types::Tag>) —

    Tags of the cluster policy.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3025

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

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

Creates a Git repository as a resource in your SageMaker AI 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 SageMaker AI 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 Amazon Web Services 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",
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

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.

  • :tags (Array<Types::Tag>) —

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3093

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 AI 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 AI hosting services, you can use the resulting model artifacts as part of the model. You can also use the artifacts with Amazon Web Services 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 AI 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
  model_package_version_arn: "ModelPackageArn",
  input_config: {
    s3_uri: "S3Uri", # required
    data_input_config: "DataInputConfig",
    framework: "TENSORFLOW", # required, accepts TENSORFLOW, KERAS, MXNET, ONNX, PYTORCH, XGBOOST, TFLITE, DARKNET, SKLEARN
    framework_version: "FrameworkVersion",
  },
  output_config: { # required
    s3_output_location: "S3Uri", # required
    target_device: "lambda", # accepts lambda, ml_m4, ml_m5, ml_m6g, ml_c4, ml_c5, ml_c6g, ml_p2, ml_p3, ml_g4dn, ml_inf1, ml_inf2, ml_trn1, ml_eia2, jetson_tx1, jetson_tx2, jetson_nano, jetson_xavier, rasp3b, rasp4b, imx8qm, deeplens, rk3399, rk3288, aisage, sbe_c, qcs605, qcs603, sitara_am57x, amba_cv2, amba_cv22, amba_cv25, x86_win32, x86_win64, coreml, jacinto_tda4vm, imx8mplus
    target_platform: {
      os: "ANDROID", # required, accepts ANDROID, LINUX
      arch: "X86_64", # required, accepts X86_64, X86, ARM64, ARM_EABI, ARM_EABIHF
      accelerator: "INTEL_GRAPHICS", # accepts INTEL_GRAPHICS, MALI, NVIDIA, NNA
    },
    compiler_options: "CompilerOptions",
    kms_key_id: "KmsKeyId",
  },
  vpc_config: {
    security_group_ids: ["NeoVpcSecurityGroupId"], # required
    subnets: ["NeoVpcSubnetId"], # required
  },
  stopping_condition: { # required
    max_runtime_in_seconds: 1,
    max_wait_time_in_seconds: 1,
    max_pending_time_in_seconds: 1,
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

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 Amazon Web Services Region and within your Amazon Web Services account.

  • :role_arn (required, String) —

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

    During model compilation, Amazon SageMaker AI 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 AI, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker AI Roles.

  • :model_package_version_arn (String) —

    The Amazon Resource Name (ARN) of a versioned model package. Provide either a ModelPackageVersionArn or an InputConfig object in the request syntax. The presence of both objects in the CreateCompilationJob request will return an exception.

  • :input_config (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.

  • :vpc_config (Types::NeoVpcConfig) —

    A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud.

  • :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 AI ends the compilation job. Use this API to cap model training costs.

  • :tags (Array<Types::Tag>) —

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3256

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

#create_compute_quota(params = {}) ⇒ Types::CreateComputeQuotaResponse

Create compute allocation definition. This defines how compute is allocated, shared, and borrowed for specified entities. Specifically, how to lend and borrow idle compute and assign a fair-share weight to the specified entities.

Examples:

Request syntax with placeholder values


resp = client.create_compute_quota({
  name: "EntityName", # required
  description: "EntityDescription",
  cluster_arn: "ClusterArn", # required
  compute_quota_config: { # required
    compute_quota_resources: [
      {
        instance_type: "ml.p4d.24xlarge", # required, accepts ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.p5.4xlarge, ml.p6e-gb200.36xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge, 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.c5n.large, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge, 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.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.16xlarge, ml.g6.12xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.gr6.4xlarge, ml.gr6.8xlarge, ml.g6e.xlarge, ml.g6e.2xlarge, ml.g6e.4xlarge, ml.g6e.8xlarge, ml.g6e.16xlarge, ml.g6e.12xlarge, ml.g6e.24xlarge, ml.g6e.48xlarge, ml.p5e.48xlarge, ml.p5en.48xlarge, ml.p6-b200.48xlarge, ml.trn2.3xlarge, ml.trn2.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.i3en.large, ml.i3en.xlarge, ml.i3en.2xlarge, ml.i3en.3xlarge, ml.i3en.6xlarge, ml.i3en.12xlarge, ml.i3en.24xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.r5d.16xlarge, ml.g7e.2xlarge, ml.g7e.4xlarge, ml.g7e.8xlarge, ml.g7e.12xlarge, ml.g7e.24xlarge, ml.g7e.48xlarge, ml.p6-b300.48xlarge
        count: 1,
        accelerators: 1,
        v_cpu: 1.0,
        memory_in_gi_b: 1.0,
        accelerator_partition: {
          type: "mig-1g.5gb", # required, accepts mig-1g.5gb, mig-1g.10gb, mig-1g.18gb, mig-1g.20gb, mig-1g.23gb, mig-1g.35gb, mig-1g.45gb, mig-1g.47gb, mig-2g.10gb, mig-2g.20gb, mig-2g.35gb, mig-2g.45gb, mig-2g.47gb, mig-3g.20gb, mig-3g.40gb, mig-3g.71gb, mig-3g.90gb, mig-3g.93gb, mig-4g.20gb, mig-4g.40gb, mig-4g.71gb, mig-4g.90gb, mig-4g.93gb, mig-7g.40gb, mig-7g.80gb, mig-7g.141gb, mig-7g.180gb, mig-7g.186gb
          count: 1, # required
        },
      },
    ],
    resource_sharing_config: {
      strategy: "Lend", # required, accepts Lend, DontLend, LendAndBorrow
      borrow_limit: 1,
      absolute_borrow_limits: [
        {
          instance_type: "ml.p4d.24xlarge", # required, accepts ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, ml.p5.4xlarge, ml.p6e-gb200.36xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.12xlarge, ml.g5.16xlarge, ml.g5.24xlarge, ml.g5.48xlarge, 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.c5n.large, ml.c5n.2xlarge, ml.c5n.4xlarge, ml.c5n.9xlarge, ml.c5n.18xlarge, 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.t3.medium, ml.t3.large, ml.t3.xlarge, ml.t3.2xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.16xlarge, ml.g6.12xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.gr6.4xlarge, ml.gr6.8xlarge, ml.g6e.xlarge, ml.g6e.2xlarge, ml.g6e.4xlarge, ml.g6e.8xlarge, ml.g6e.16xlarge, ml.g6e.12xlarge, ml.g6e.24xlarge, ml.g6e.48xlarge, ml.p5e.48xlarge, ml.p5en.48xlarge, ml.p6-b200.48xlarge, ml.trn2.3xlarge, ml.trn2.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.i3en.large, ml.i3en.xlarge, ml.i3en.2xlarge, ml.i3en.3xlarge, ml.i3en.6xlarge, ml.i3en.12xlarge, ml.i3en.24xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.r5d.16xlarge, ml.g7e.2xlarge, ml.g7e.4xlarge, ml.g7e.8xlarge, ml.g7e.12xlarge, ml.g7e.24xlarge, ml.g7e.48xlarge, ml.p6-b300.48xlarge
          count: 1,
          accelerators: 1,
          v_cpu: 1.0,
          memory_in_gi_b: 1.0,
          accelerator_partition: {
            type: "mig-1g.5gb", # required, accepts mig-1g.5gb, mig-1g.10gb, mig-1g.18gb, mig-1g.20gb, mig-1g.23gb, mig-1g.35gb, mig-1g.45gb, mig-1g.47gb, mig-2g.10gb, mig-2g.20gb, mig-2g.35gb, mig-2g.45gb, mig-2g.47gb, mig-3g.20gb, mig-3g.40gb, mig-3g.71gb, mig-3g.90gb, mig-3g.93gb, mig-4g.20gb, mig-4g.40gb, mig-4g.71gb, mig-4g.90gb, mig-4g.93gb, mig-7g.40gb, mig-7g.80gb, mig-7g.141gb, mig-7g.180gb, mig-7g.186gb
            count: 1, # required
          },
        },
      ],
    },
    preempt_team_tasks: "Never", # accepts Never, LowerPriority
  },
  compute_quota_target: { # required
    team_name: "ComputeQuotaTargetTeamName", # required
    fair_share_weight: 1,
  },
  activation_state: "Enabled", # accepts Enabled, Disabled
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.compute_quota_arn #=> String
resp.compute_quota_id #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :name (required, String) —

    Name to the compute allocation definition.

  • :description (String) —

    Description of the compute allocation definition.

  • :cluster_arn (required, String) —

    ARN of the cluster.

  • :compute_quota_config (required, Types::ComputeQuotaConfig) —

    Configuration of the compute allocation definition. This includes the resource sharing option, and the setting to preempt low priority tasks.

  • :compute_quota_target (required, Types::ComputeQuotaTarget) —

    The target entity to allocate compute resources to.

  • :activation_state (String) —

    The state of the compute allocation being described. Use to enable or disable compute allocation.

    Default is Enabled.

  • :tags (Array<Types::Tag>) —

    Tags of the compute allocation definition.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3358

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

#create_context(params = {}) ⇒ Types::CreateContextResponse

Creates a context. A context is a lineage tracking entity that represents a logical grouping of other tracking or experiment entities. Some examples are an endpoint and a model package. For more information, see Amazon SageMaker ML Lineage Tracking.

Examples:

Request syntax with placeholder values


resp = client.create_context({
  context_name: "ContextName", # required
  source: { # required
    source_uri: "SourceUri", # required
    source_type: "String256",
    source_id: "String256",
  },
  context_type: "String256", # required
  description: "ExperimentDescription",
  properties: {
    "StringParameterValue" => "StringParameterValue",
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.context_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :context_name (required, String) —

    The name of the context. Must be unique to your account in an Amazon Web Services Region.

  • :source (required, Types::ContextSource) —

    The source type, ID, and URI.

  • :context_type (required, String) —

    The context type.

  • :description (String) —

    The description of the context.

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

    A list of properties to add to the context.

  • :tags (Array<Types::Tag>) —

    A list of tags to apply to the context.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3425

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

#create_data_quality_job_definition(params = {}) ⇒ Types::CreateDataQualityJobDefinitionResponse

Creates a definition for a job that monitors data quality and drift. For information about model monitor, see Amazon SageMaker AI Model Monitor.

Examples:

Request syntax with placeholder values


resp = client.create_data_quality_job_definition({
  job_definition_name: "MonitoringJobDefinitionName", # required
  data_quality_baseline_config: {
    baselining_job_name: "ProcessingJobName",
    constraints_resource: {
      s3_uri: "S3Uri",
    },
    statistics_resource: {
      s3_uri: "S3Uri",
    },
  },
  data_quality_app_specification: { # required
    image_uri: "ImageUri", # required
    container_entrypoint: ["ContainerEntrypointString"],
    container_arguments: ["ContainerArgument"],
    record_preprocessor_source_uri: "S3Uri",
    post_analytics_processor_source_uri: "S3Uri",
    environment: {
      "ProcessingEnvironmentKey" => "ProcessingEnvironmentValue",
    },
  },
  data_quality_job_input: { # required
    endpoint_input: {
      endpoint_name: "EndpointName", # required
      local_path: "ProcessingLocalPath", # required
      s3_input_mode: "Pipe", # accepts Pipe, File
      s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
      features_attribute: "String",
      inference_attribute: "String",
      probability_attribute: "String",
      probability_threshold_attribute: 1.0,
      start_time_offset: "MonitoringTimeOffsetString",
      end_time_offset: "MonitoringTimeOffsetString",
      exclude_features_attribute: "ExcludeFeaturesAttribute",
    },
    batch_transform_input: {
      data_captured_destination_s3_uri: "DestinationS3Uri", # required
      dataset_format: { # required
        csv: {
          header: false,
        },
        json: {
          line: false,
        },
        parquet: {
        },
      },
      local_path: "ProcessingLocalPath", # required
      s3_input_mode: "Pipe", # accepts Pipe, File
      s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
      features_attribute: "String",
      inference_attribute: "String",
      probability_attribute: "String",
      probability_threshold_attribute: 1.0,
      start_time_offset: "MonitoringTimeOffsetString",
      end_time_offset: "MonitoringTimeOffsetString",
      exclude_features_attribute: "ExcludeFeaturesAttribute",
    },
  },
  data_quality_job_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",
  },
  job_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, ml.g4dn.xlarge, ml.g4dn.2xlarge, ml.g4dn.4xlarge, ml.g4dn.8xlarge, ml.g4dn.12xlarge, ml.g4dn.16xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.r5d.large, ml.r5d.xlarge, ml.r5d.2xlarge, ml.r5d.4xlarge, ml.r5d.8xlarge, ml.r5d.12xlarge, ml.r5d.16xlarge, ml.r5d.24xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.g6e.xlarge, ml.g6e.2xlarge, ml.g6e.4xlarge, ml.g6e.8xlarge, ml.g6e.12xlarge, ml.g6e.16xlarge, ml.g6e.24xlarge, ml.g6e.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.p5.4xlarge, ml.g7e.2xlarge, ml.g7e.4xlarge, ml.g7e.8xlarge, ml.g7e.12xlarge, ml.g7e.24xlarge, ml.g7e.48xlarge
      volume_size_in_gb: 1, # required
      volume_kms_key_id: "KmsKeyId",
    },
  },
  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
  stopping_condition: {
    max_runtime_in_seconds: 1, # required
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.job_definition_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :job_definition_name (required, String) —

    The name for the monitoring job definition.

  • :data_quality_baseline_config (Types::DataQualityBaselineConfig) —

    Configures the constraints and baselines for the monitoring job.

  • :data_quality_app_specification (required, Types::DataQualityAppSpecification) —

    Specifies the container that runs the monitoring job.

  • :data_quality_job_input (required, Types::DataQualityJobInput) —

    A list of inputs for the monitoring job. Currently endpoints are supported as monitoring inputs.

  • :data_quality_job_output_config (required, Types::MonitoringOutputConfig) —

    The output configuration for monitoring jobs.

  • :job_resources (required, Types::MonitoringResources) —

    Identifies the resources to deploy for a monitoring job.

  • :network_config (Types::MonitoringNetworkConfig) —

    Specifies networking configuration for the monitoring job.

  • :role_arn (required, String) —

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

  • :stopping_condition (Types::MonitoringStoppingCondition) —

    A time limit for how long the monitoring job is allowed to run before stopping.

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

    An array of key-value pairs. For more information, see Using Cost Allocation Tags in the Amazon Web Services 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 3590

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

#create_device_fleet(params = {}) ⇒ Struct

Creates a device fleet.

Examples:

Request syntax with placeholder values


resp = client.create_device_fleet({
  device_fleet_name: "EntityName", # required
  role_arn: "RoleArn",
  description: "DeviceFleetDescription",
  output_config: { # required
    s3_output_location: "S3Uri", # required
    kms_key_id: "KmsKeyId",
    preset_deployment_type: "GreengrassV2Component", # accepts GreengrassV2Component
    preset_deployment_config: "String",
  },
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  enable_iot_role_alias: false,
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :device_fleet_name (required, String) —

    The name of the fleet that the device belongs to.

  • :role_arn (String) —

    The Amazon Resource Name (ARN) that has access to Amazon Web Services Internet of Things (IoT).

  • :description (String) —

    A description of the fleet.

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

    The output configuration for storing sample data collected by the fleet.

  • :tags (Array<Types::Tag>) —

    Creates tags for the specified fleet.

  • :enable_iot_role_alias (Boolean) —

    Whether to create an Amazon Web Services IoT Role Alias during device fleet creation. The name of the role alias generated will match this pattern: "SageMakerEdge-DeviceFleetName".

    For example, if your device fleet is called "demo-fleet", the name of the role alias will be "SageMakerEdge-demo-fleet".

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 3649

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

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

Creates a Domain. A domain consists of an associated Amazon Elastic File System volume, a list of authorized users, and a variety of security, application, policy, and Amazon Virtual Private Cloud (VPC) configurations. Users within a domain can share notebook files and other artifacts with each other.

EFS storage

When a domain is created, an EFS volume is created for use by all of the users within the domain. Each user receives a private home directory within the EFS volume for notebooks, Git repositories, and data files.

SageMaker AI uses the Amazon Web Services Key Management Service (Amazon Web Services KMS) to encrypt the EFS volume attached to the domain with an Amazon Web Services managed key by default. For more control, you can specify a customer managed key. For more information, see Protect Data at Rest Using Encryption.

VPC configuration

All traffic between the domain and the Amazon EFS volume is through the specified VPC and subnets. For other traffic, you can specify the AppNetworkAccessType parameter. AppNetworkAccessType corresponds to the network access type that you choose when you onboard to the domain. The following options are available:

  • PublicInternetOnly - Non-EFS traffic goes through a VPC managed by Amazon SageMaker AI, which allows internet access. This is the default value.

  • VpcOnly - All traffic is through the specified VPC and subnets. Internet access is disabled by default. To allow internet access, you must specify a NAT gateway.

    When internet access is disabled, you won't be able to run a Amazon SageMaker AI Studio notebook or to train or host models unless your VPC has an interface endpoint to the SageMaker AI API and runtime or a NAT gateway and your security groups allow outbound connections.

NFS traffic over TCP on port 2049 needs to be allowed in both inbound and outbound rules in order to launch a Amazon SageMaker AI Studio app successfully.

For more information, see Connect Amazon SageMaker AI Studio Notebooks to Resources in a VPC.

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: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        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.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.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.p3dn.24xlarge, 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.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.g6e.xlarge, ml.g6e.2xlarge, ml.g6e.4xlarge, ml.g6e.8xlarge, ml.g6e.12xlarge, ml.g6e.16xlarge, ml.g6e.24xlarge, ml.g6e.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.p5en.48xlarge, ml.p6-b200.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
      code_repositories: [
        {
          repository_url: "RepositoryUrl", # required
        },
      ],
    },
    kernel_gateway_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        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.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.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.p3dn.24xlarge, 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.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.g6e.xlarge, ml.g6e.2xlarge, ml.g6e.4xlarge, ml.g6e.8xlarge, ml.g6e.12xlarge, ml.g6e.16xlarge, ml.g6e.24xlarge, ml.g6e.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.p5en.48xlarge, ml.p6-b200.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
    },
    tensor_board_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        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.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.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.p3dn.24xlarge, 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.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.g6e.xlarge, ml.g6e.2xlarge, ml.g6e.4xlarge, ml.g6e.8xlarge, ml.g6e.12xlarge, ml.g6e.16xlarge, ml.g6e.24xlarge, ml.g6e.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.p5en.48xlarge, ml.p6-b200.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
    },
    r_studio_server_pro_app_settings: {
      access_status: "ENABLED", # accepts ENABLED, DISABLED
      user_group: "R_STUDIO_ADMIN", # accepts R_STUDIO_ADMIN, R_STUDIO_USER
    },
    r_session_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        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.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.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.p3dn.24xlarge, 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.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.g6e.xlarge, ml.g6e.2xlarge, ml.g6e.4xlarge, ml.g6e.8xlarge, ml.g6e.12xlarge, ml.g6e.16xlarge, ml.g6e.24xlarge, ml.g6e.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.p5en.48xlarge, ml.p6-b200.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
    },
    canvas_app_settings: {
      time_series_forecasting_settings: {
        status: "ENABLED", # accepts ENABLED, DISABLED
        amazon_forecast_role_arn: "RoleArn",
      },
      model_register_settings: {
        status: "ENABLED", # accepts ENABLED, DISABLED
        cross_account_model_register_role_arn: "RoleArn",
      },
      workspace_settings: {
        s3_artifact_path: "S3Uri",
        s3_kms_key_id: "KmsKeyId",
      },
      identity_provider_o_auth_settings: [
        {
          data_source_name: "SalesforceGenie", # accepts SalesforceGenie, Snowflake
          status: "ENABLED", # accepts ENABLED, DISABLED
          secret_arn: "SecretArn",
        },
      ],
      direct_deploy_settings: {
        status: "ENABLED", # accepts ENABLED, DISABLED
      },
      kendra_settings: {
        status: "ENABLED", # accepts ENABLED, DISABLED
      },
      generative_ai_settings: {
        amazon_bedrock_role_arn: "RoleArn",
      },
      emr_serverless_settings: {
        execution_role_arn: "RoleArn",
        status: "ENABLED", # accepts ENABLED, DISABLED
      },
    },
    code_editor_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        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.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.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.p3dn.24xlarge, 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.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.g6e.xlarge, ml.g6e.2xlarge, ml.g6e.4xlarge, ml.g6e.8xlarge, ml.g6e.12xlarge, ml.g6e.16xlarge, ml.g6e.24xlarge, ml.g6e.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.p5en.48xlarge, ml.p6-b200.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
      app_lifecycle_management: {
        idle_settings: {
          lifecycle_management: "ENABLED", # accepts ENABLED, DISABLED
          idle_timeout_in_minutes: 1,
          min_idle_timeout_in_minutes: 1,
          max_idle_timeout_in_minutes: 1,
        },
      },
      built_in_lifecycle_config_arn: "StudioLifecycleConfigArn",
    },
    jupyter_lab_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        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.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.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.p3dn.24xlarge, 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.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.g6e.xlarge, ml.g6e.2xlarge, ml.g6e.4xlarge, ml.g6e.8xlarge, ml.g6e.12xlarge, ml.g6e.16xlarge, ml.g6e.24xlarge, ml.g6e.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.p5en.48xlarge, ml.p6-b200.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
      code_repositories: [
        {
          repository_url: "RepositoryUrl", # required
        },
      ],
      app_lifecycle_management: {
        idle_settings: {
          lifecycle_management: "ENABLED", # accepts ENABLED, DISABLED
          idle_timeout_in_minutes: 1,
          min_idle_timeout_in_minutes: 1,
          max_idle_timeout_in_minutes: 1,
        },
      },
      emr_settings: {
        assumable_role_arns: ["RoleArn"],
        execution_role_arns: ["RoleArn"],
      },
      built_in_lifecycle_config_arn: "StudioLifecycleConfigArn",
    },
    space_storage_settings: {
      default_ebs_storage_settings: {
        default_ebs_volume_size_in_gb: 1, # required
        maximum_ebs_volume_size_in_gb: 1, # required
      },
    },
    default_landing_uri: "LandingUri",
    studio_web_portal: "ENABLED", # accepts ENABLED, DISABLED
    custom_posix_user_config: {
      uid: 1, # required
      gid: 1, # required
    },
    custom_file_system_configs: [
      {
        efs_file_system_config: {
          file_system_id: "FileSystemId", # required
          file_system_path: "FileSystemPath",
        },
        f_sx_lustre_file_system_config: {
          file_system_id: "FileSystemId", # required
          file_system_path: "FileSystemPath",
        },
        s3_file_system_config: {
          mount_path: "String1024",
          s3_uri: "S3SchemaUri", # required
        },
      },
    ],
    studio_web_portal_settings: {
      hidden_ml_tools: ["DataWrangler"], # accepts DataWrangler, FeatureStore, EmrClusters, AutoMl, Experiments, Training, ModelEvaluation, Pipelines, Models, JumpStart, InferenceRecommender, Endpoints, Projects, InferenceOptimization, PerformanceEvaluation, LakeraGuard, Comet, DeepchecksLLMEvaluation, Fiddler, HyperPodClusters, RunningInstances, Datasets, Evaluators
      hidden_app_types: ["JupyterServer"], # accepts JupyterServer, KernelGateway, DetailedProfiler, TensorBoard, CodeEditor, JupyterLab, RStudioServerPro, RSessionGateway, Canvas
      hidden_instance_types: ["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.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.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.p3dn.24xlarge, 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.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.g6e.xlarge, ml.g6e.2xlarge, ml.g6e.4xlarge, ml.g6e.8xlarge, ml.g6e.12xlarge, ml.g6e.16xlarge, ml.g6e.24xlarge, ml.g6e.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.p5en.48xlarge, ml.p6-b200.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
      hidden_sage_maker_image_version_aliases: [
        {
          sage_maker_image_name: "sagemaker_distribution", # accepts sagemaker_distribution
          version_aliases: ["ImageVersionAliasPattern"],
        },
      ],
    },
    auto_mount_home_efs: "Enabled", # accepts Enabled, Disabled, DefaultAsDomain
  },
  domain_settings: {
    security_group_ids: ["SecurityGroupId"],
    r_studio_server_pro_domain_settings: {
      domain_execution_role_arn: "RoleArn", # required
      r_studio_connect_url: "String",
      r_studio_package_manager_url: "String",
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        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.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.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.p3dn.24xlarge, 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.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.g6e.xlarge, ml.g6e.2xlarge, ml.g6e.4xlarge, ml.g6e.8xlarge, ml.g6e.12xlarge, ml.g6e.16xlarge, ml.g6e.24xlarge, ml.g6e.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.p5en.48xlarge, ml.p6-b200.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
    },
    execution_role_identity_config: "USER_PROFILE_NAME", # accepts USER_PROFILE_NAME, DISABLED
    trusted_identity_propagation_settings: {
      status: "ENABLED", # required, accepts ENABLED, DISABLED
    },
    docker_settings: {
      enable_docker_access: "ENABLED", # accepts ENABLED, DISABLED
      vpc_only_trusted_accounts: ["AccountId"],
      rootless_docker: "ENABLED", # accepts ENABLED, DISABLED
    },
    amazon_q_settings: {
      status: "ENABLED", # accepts ENABLED, DISABLED
      q_profile_arn: "QProfileArn",
    },
    unified_studio_settings: {
      studio_web_portal_access: "ENABLED", # accepts ENABLED, DISABLED
      domain_account_id: "AccountId",
      domain_region: "RegionName",
      domain_id: "UnifiedStudioDomainId",
      project_id: "UnifiedStudioProjectId",
      environment_id: "UnifiedStudioEnvironmentId",
      project_s3_path: "S3Uri",
      single_sign_on_application_arn: "SingleSignOnApplicationArn",
    },
    ip_address_type: "ipv4", # accepts ipv4, dualstack
  },
  subnet_ids: ["SubnetId"],
  vpc_id: "VpcId",
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
  app_network_access_type: "PublicInternetOnly", # accepts PublicInternetOnly, VpcOnly
  home_efs_file_system_kms_key_id: "KmsKeyId",
  kms_key_id: "KmsKeyId",
  app_security_group_management: "Service", # accepts Service, Customer
  tag_propagation: "ENABLED", # accepts ENABLED, DISABLED
  default_space_settings: {
    execution_role: "RoleArn",
    security_groups: ["SecurityGroupId"],
    jupyter_server_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        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.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.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.p3dn.24xlarge, 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.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.g6e.xlarge, ml.g6e.2xlarge, ml.g6e.4xlarge, ml.g6e.8xlarge, ml.g6e.12xlarge, ml.g6e.16xlarge, ml.g6e.24xlarge, ml.g6e.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.p5en.48xlarge, ml.p6-b200.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
      code_repositories: [
        {
          repository_url: "RepositoryUrl", # required
        },
      ],
    },
    kernel_gateway_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        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.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.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.p3dn.24xlarge, 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.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.g6e.xlarge, ml.g6e.2xlarge, ml.g6e.4xlarge, ml.g6e.8xlarge, ml.g6e.12xlarge, ml.g6e.16xlarge, ml.g6e.24xlarge, ml.g6e.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.p5en.48xlarge, ml.p6-b200.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
    },
    jupyter_lab_app_settings: {
      default_resource_spec: {
        sage_maker_image_arn: "ImageArn",
        sage_maker_image_version_arn: "ImageVersionArn",
        sage_maker_image_version_alias: "ImageVersionAlias",
        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.m5d.large, ml.m5d.xlarge, ml.m5d.2xlarge, ml.m5d.4xlarge, ml.m5d.8xlarge, ml.m5d.12xlarge, ml.m5d.16xlarge, ml.m5d.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.p3dn.24xlarge, 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.r5.8xlarge, ml.r5.12xlarge, ml.r5.16xlarge, ml.r5.24xlarge, ml.g5.xlarge, ml.g5.2xlarge, ml.g5.4xlarge, ml.g5.8xlarge, ml.g5.16xlarge, ml.g5.12xlarge, ml.g5.24xlarge, ml.g5.48xlarge, ml.g6.xlarge, ml.g6.2xlarge, ml.g6.4xlarge, ml.g6.8xlarge, ml.g6.12xlarge, ml.g6.16xlarge, ml.g6.24xlarge, ml.g6.48xlarge, ml.g6e.xlarge, ml.g6e.2xlarge, ml.g6e.4xlarge, ml.g6e.8xlarge, ml.g6e.12xlarge, ml.g6e.16xlarge, ml.g6e.24xlarge, ml.g6e.48xlarge, ml.geospatial.interactive, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.trn1.2xlarge, ml.trn1.32xlarge, ml.trn1n.32xlarge, ml.p5.48xlarge, ml.p5en.48xlarge, ml.p6-b200.48xlarge, ml.m6i.large, ml.m6i.xlarge, ml.m6i.2xlarge, ml.m6i.4xlarge, ml.m6i.8xlarge, ml.m6i.12xlarge, ml.m6i.16xlarge, ml.m6i.24xlarge, ml.m6i.32xlarge, ml.m7i.large, ml.m7i.xlarge, ml.m7i.2xlarge, ml.m7i.4xlarge, ml.m7i.8xlarge, ml.m7i.12xlarge, ml.m7i.16xlarge, ml.m7i.24xlarge, ml.m7i.48xlarge, ml.c6i.large, ml.c6i.xlarge, ml.c6i.2xlarge, ml.c6i.4xlarge, ml.c6i.8xlarge, ml.c6i.12xlarge, ml.c6i.16xlarge, ml.c6i.24xlarge, ml.c6i.32xlarge, ml.c7i.large, ml.c7i.xlarge, ml.c7i.2xlarge, ml.c7i.4xlarge, ml.c7i.8xlarge, ml.c7i.12xlarge, ml.c7i.16xlarge, ml.c7i.24xlarge, ml.c7i.48xlarge, ml.r6i.large, ml.r6i.xlarge, ml.r6i.2xlarge, ml.r6i.4xlarge, ml.r6i.8xlarge, ml.r6i.12xlarge, ml.r6i.16xlarge, ml.r6i.24xlarge, ml.r6i.32xlarge, ml.r7i.large, ml.r7i.xlarge, ml.r7i.2xlarge, ml.r7i.4xlarge, ml.r7i.8xlarge, ml.r7i.12xlarge, ml.r7i.16xlarge, ml.r7i.24xlarge, ml.r7i.48xlarge, ml.m6id.large, ml.m6id.xlarge, ml.m6id.2xlarge, ml.m6id.4xlarge, ml.m6id.8xlarge, ml.m6id.12xlarge, ml.m6id.16xlarge, ml.m6id.24xlarge, ml.m6id.32xlarge, ml.c6id.large, ml.c6id.xlarge, ml.c6id.2xlarge, ml.c6id.4xlarge, ml.c6id.8xlarge, ml.c6id.12xlarge, ml.c6id.16xlarge, ml.c6id.24xlarge, ml.c6id.32xlarge, ml.r6id.large, ml.r6id.xlarge, ml.r6id.2xlarge, ml.r6id.4xlarge, ml.r6id.8xlarge, ml.r6id.12xlarge, ml.r6id.16xlarge, ml.r6id.24xlarge, ml.r6id.32xlarge
        lifecycle_config_arn: "StudioLifecycleConfigArn",
      },
      custom_images: [
        {
          image_name: "ImageName", # required
          image_version_number: 1,
          app_image_config_name: "AppImageConfigName", # required
        },
      ],
      lifecycle_config_arns: ["StudioLifecycleConfigArn"],
      code_repositories: [
        {
          repository_url: "RepositoryUrl", # required
        },
      ],
      app_lifecycle_management: {
        idle_settings: {
          lifecycle_management: "ENABLED", # accepts ENABLED, DISABLED
          idle_timeout_in_minutes: 1,
          min_idle_timeout_in_minutes: 1,
          max_idle_timeout_in_minutes: 1,
        },
      },
      emr_settings: {
        assumable_role_arns: ["RoleArn"],
        execution_role_arns: ["RoleArn"],
      },
      built_in_lifecycle_config_arn: "StudioLifecycleConfigArn",
    },
    space_storage_settings: {
      default_ebs_storage_settings: {
        default_ebs_volume_size_in_gb: 1, # required
        maximum_ebs_volume_size_in_gb: 1, # required
      },
    },
    custom_posix_user_config: {
      uid: 1, # required
      gid: 1, # required
    },
    custom_file_system_configs: [
      {
        efs_file_system_config: {
          file_system_id: "FileSystemId", # required
          file_system_path: "FileSystemPath",
        },
        f_sx_lustre_file_system_config: {
          file_system_id: "FileSystemId", # required
          file_system_path: "FileSystemPath",
        },
        s3_file_system_config: {
          mount_path: "String1024",
          s3_uri: "S3SchemaUri", # required
        },
      },
    ],
  },
})

Response structure


resp.domain_arn #=> String
resp.domain_id #=> 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 members use to access the domain.

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

    The default settings to use to create a user profile when UserSettings isn't specified in the call to the CreateUserProfile API.

    SecurityGroups is aggregated when specified in both calls. For all other settings in UserSettings, the values specified in CreateUserProfile take precedence over those specified in CreateDomain.

  • :domain_settings (Types::DomainSettings) —

    A collection of Domain settings.

  • :subnet_ids (Array<String>) —

    The VPC subnets that the domain uses for communication.

    The field is optional when the AppNetworkAccessType parameter is set to PublicInternetOnly for domains created from Amazon SageMaker Unified Studio.

  • :vpc_id (String) —

    The ID of the Amazon Virtual Private Cloud (VPC) that the domain uses for communication.

    The field is optional when the AppNetworkAccessType parameter is set to PublicInternetOnly for domains created from Amazon SageMaker Unified Studio.

  • :tags (Array<Types::Tag>) —

    Tags to associated with the Domain. Each tag consists of a key and an optional value. Tag keys must be unique per resource. Tags are searchable using the Search API.

    Tags that you specify for the Domain are also added to all Apps that the Domain launches.

  • :app_network_access_type (String) —

    Specifies the VPC used for non-EFS traffic. The default value is PublicInternetOnly.

    • PublicInternetOnly - Non-EFS traffic is through a VPC managed by Amazon SageMaker AI, which allows direct internet access

    • VpcOnly - All traffic is through the specified VPC and subnets

  • :home_efs_file_system_kms_key_id (String) —

    Use KmsKeyId.

  • :kms_key_id (String) —

    SageMaker AI uses Amazon Web Services KMS to encrypt EFS and EBS volumes attached to the domain with an Amazon Web Services managed key by default. For more control, specify a customer managed key.

  • :app_security_group_management (String) —

    The entity that creates and manages the required security groups for inter-app communication in VPCOnly mode. Required when CreateDomain.AppNetworkAccessType is VPCOnly and DomainSettings.RStudioServerProDomainSettings.DomainExecutionRoleArn is provided. If setting up the domain for use with RStudio, this value must be set to Service.

  • :tag_propagation (String) —

    Indicates whether custom tag propagation is supported for the domain. Defaults to DISABLED.

  • :default_space_settings (Types::DefaultSpaceSettings) —

    The default settings for shared spaces that users create in the domain.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4158

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

#create_edge_deployment_plan(params = {}) ⇒ Types::CreateEdgeDeploymentPlanResponse

Creates an edge deployment plan, consisting of multiple stages. Each stage may have a different deployment configuration and devices.

Examples:

Request syntax with placeholder values


resp = client.create_edge_deployment_plan({
  edge_deployment_plan_name: "EntityName", # required
  model_configs: [ # required
    {
      model_handle: "EntityName", # required
      edge_packaging_job_name: "EntityName", # required
    },
  ],
  device_fleet_name: "EntityName", # required
  stages: [
    {
      stage_name: "EntityName", # required
      device_selection_config: { # required
        device_subset_type: "PERCENTAGE", # required, accepts PERCENTAGE, SELECTION, NAMECONTAINS
        percentage: 1,
        device_names: ["DeviceName"],
        device_name_contains: "DeviceName",
      },
      deployment_config: {
        failure_handling_policy: "ROLLBACK_ON_FAILURE", # required, accepts ROLLBACK_ON_FAILURE, DO_NOTHING
      },
    },
  ],
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Response structure


resp.edge_deployment_plan_arn #=> String

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :edge_deployment_plan_name (required, String) —

    The name of the edge deployment plan.

  • :model_configs (required, Array<Types::EdgeDeploymentModelConfig>) —

    List of models associated with the edge deployment plan.

  • :device_fleet_name (required, String) —

    The device fleet used for this edge deployment plan.

  • :stages (Array<Types::DeploymentStage>) —

    List of stages of the edge deployment plan. The number of stages is limited to 10 per deployment.

  • :tags (Array<Types::Tag>) —

    List of tags with which to tag the edge deployment plan.

Returns:

See Also:



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

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

#create_edge_deployment_stage(params = {}) ⇒ Struct

Creates a new stage in an existing edge deployment plan.

Examples:

Request syntax with placeholder values


resp = client.create_edge_deployment_stage({
  edge_deployment_plan_name: "EntityName", # required
  stages: [ # required
    {
      stage_name: "EntityName", # required
      device_selection_config: { # required
        device_subset_type: "PERCENTAGE", # required, accepts PERCENTAGE, SELECTION, NAMECONTAINS
        percentage: 1,
        device_names: ["DeviceName"],
        device_name_contains: "DeviceName",
      },
      deployment_config: {
        failure_handling_policy: "ROLLBACK_ON_FAILURE", # required, accepts ROLLBACK_ON_FAILURE, DO_NOTHING
      },
    },
  ],
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :edge_deployment_plan_name (required, String) —

    The name of the edge deployment plan.

  • :stages (required, Array<Types::DeploymentStage>) —

    List of stages to be added to the edge deployment plan.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4266

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

#create_edge_packaging_job(params = {}) ⇒ Struct

Starts a SageMaker Edge Manager model packaging job. Edge Manager will use the model artifacts from the Amazon Simple Storage Service bucket that you specify. After the model has been packaged, Amazon SageMaker saves the resulting artifacts to an S3 bucket that you specify.

Examples:

Request syntax with placeholder values


resp = client.create_edge_packaging_job({
  edge_packaging_job_name: "EntityName", # required
  compilation_job_name: "EntityName", # required
  model_name: "EntityName", # required
  model_version: "EdgeVersion", # required
  role_arn: "RoleArn", # required
  output_config: { # required
    s3_output_location: "S3Uri", # required
    kms_key_id: "KmsKeyId",
    preset_deployment_type: "GreengrassV2Component", # accepts GreengrassV2Component
    preset_deployment_config: "String",
  },
  resource_key: "KmsKeyId",
  tags: [
    {
      key: "TagKey", # required
      value: "TagValue", # required
    },
  ],
})

Parameters:

  • params (Hash) (defaults to: {}) —

    ({})

Options Hash (params):

  • :edge_packaging_job_name (required, String) —

    The name of the edge packaging job.

  • :compilation_job_name (required, String) —

    The name of the SageMaker Neo compilation job that will be used to locate model artifacts for packaging.

  • :model_name (required, String) —

    The name of the model.

  • :model_version (required, String) —

    The version of the model.

  • :role_arn (required, String) —

    The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker to download and upload the model, and to contact SageMaker Neo.

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

    Provides information about the output location for the packaged model.

  • :resource_key (String) —

    The Amazon Web Services KMS key to use when encrypting the EBS volume the edge packaging job runs on.

  • :tags (Array<Types::Tag>) —

    Creates tags for the packaging job.

Returns:

  • (Struct) —

    Returns an empty response.

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4333

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

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

Creates an endpoint using the endpoint configuration specified in the request. 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 SageMaker hosting services.

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 Amazon Web Services Region in your Amazon Web Services account.

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

When you call CreateEndpoint, a load call is made to DynamoDB to verify that your endpoint configuration exists. When you read data from a DynamoDB table supporting Eventually Consistent Reads , the response might not reflect the results of a recently completed write operation. The response might include some stale data. If the dependent entities are not yet in DynamoDB, this causes a validation error. If you repeat your read request after a short time, the response should return the latest data. So retry logic is recommended to handle these possible issues. We also recommend that customers call DescribeEndpointConfig before calling CreateEndpoint to minimize the potential impact of a DynamoDB eventually consistent read.

When SageMaker receives the request, it sets the endpoint status to Creating. After it creates the endpoint, it sets the status to InService. 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, SageMaker uses Amazon Web Services Security Token Service to download model artifacts from the S3 path you provided. Amazon Web Services STS is activated in your Amazon Web Services account by default. If you previously deactivated Amazon Web Services STS for a region, you need to reactivate Amazon Web Services STS for that region. For more information, see Activating and Deactivating Amazon Web Services STS in an Amazon Web Services Region in the Amazon Web Services Identity and Access Management User Guide.

To add the IAM role policies for using this API operation, go to the IAM console, and choose Roles in the left navigation pane. Search the IAM role that you want to grant access to use the CreateEndpoint and CreateEndpointConfig API operations, add the following policies to the role.

  • Option 1: For a full SageMaker access, search and attach the AmazonSageMakerFullAccess policy.

  • Option 2: For granting a limited access to an IAM role, paste the following Action elements manually into the JSON file of the IAM role:

    "Action": ["sagemaker:CreateEndpoint", "sagemaker:CreateEndpointConfig"]

    "Resource": [

    "arn:aws:sagemaker:region:account-id:endpoint/endpointName"

    "arn:aws:sagemaker:region:account-id:endpoint-config/endpointConfigName"

    ]

    For more information, see SageMaker API Permissions: Actions, Permissions, and Resources Reference.

Examples:

Request syntax with placeholder values


resp = client.create_endpoint({
  endpoint_name: "EndpointName", # required
  endpoint_config_name: "EndpointConfigName", # required
  deployment_config: {
    blue_green_update_policy: {
      traffic_routing_configuration: { # required
        type: "ALL_AT_ONCE", # required, accepts ALL_AT_ONCE, CANARY, LINEAR
        wait_interval_in_seconds: 1, # required
        canary_size: {
          type: "INSTANCE_COUNT", # required, accepts INSTANCE_COUNT, CAPACITY_PERCENT
          value: 1, # required
        },
        linear_step_size: {
          type: "INSTANCE_COUNT", # required, accepts INSTANCE_COUNT, CAPACITY_PERCENT
          value: 1, # required
        },
      },
      termination_wait_in_seconds: 1,
      maximum_execution_timeout_in_seconds: 1,
    },
    rolling_update_policy: {
      maximum_batch_size: { # required
        type: "INSTANCE_COUNT", # required, accepts INSTANCE_COUNT, CAPACITY_PERCENT
        value: 1, # required
      },
      wait_interval_in_seconds: 1, # required
      maximum_execution_timeout_in_seconds: 1,
      rollback_maximum_batch_size: {
        type: "INSTANCE_COUNT", # required, accepts INSTANCE_COUNT, CAPACITY_PERCENT
        value: 1, # required
      },
    },
    auto_rollback_configuration: {
      alarms: [
        {
          alarm_name: "AlarmName",
        },
      ],
    },
  },
  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 Amazon Web Services Region in your Amazon Web Services account. The name is case-insensitive in CreateEndpoint, but the case is preserved and must be matched in InvokeEndpoint.

  • :endpoint_config_name (required, String) —

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

  • :deployment_config (Types::DeploymentConfig) —

    The deployment configuration for an endpoint, which contains the desired deployment strategy and rollback configurations.

  • :tags (Array<Types::Tag>) —

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

Returns:

See Also:



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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/client.rb', line 4524

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 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 SageMaker to provision. Then you call the