sagemaker.core.jumpstart

This module contains JumpStart utilities for the SageMaker Python SDK.

class sagemaker.core.jumpstart.AdditionalModelDataSource(spec: Dict[str, Any])

Bases: JumpStartDataHolderType

Data class of additional model data source mirrors CreateModel API.

SERIALIZATION_EXCLUSION_SET = {'provider'}

channel_name

from_json(json_obj: Dict[str, Any]) → None

Sets fields in object based on json.

Parameters:

json_obj (Dict[str, Any]) – Dictionary representation of data source.

hosting_eula_key

s3_data_source

to_json(exclude_keys=True) → Dict[str, Any]

Returns json representation of AdditionalModelDataSource object.

class sagemaker.core.jumpstart.BaseDeploymentConfigDataHolder

Bases: JumpStartDataHolderType

Base class for Deployment Config Data.

to_json() → Dict[str, Any]

Represents This object as JSON.

class sagemaker.core.jumpstart.DeploymentArgs(init_kwargs: JumpStartModelInitKwargs | None = None, deploy_kwargs: JumpStartModelDeployKwargs | None = None, resolved_config: Dict[str, Any] | None = None)

Bases: BaseDeploymentConfigDataHolder

Dataclass representing a Deployment Args.

additional_data_sources

compute_resource_requirements

container_startup_health_check_timeout

environment

image_uri

instance_type

model_data

model_data_download_timeout

model_package_arn

class sagemaker.core.jumpstart.DeploymentConfigMetadata(config_name: str | None = None, metadata_config: JumpStartMetadataConfig | None = None, init_kwargs: JumpStartModelInitKwargs | None = None, deploy_kwargs: JumpStartModelDeployKwargs | None = None)

Bases: BaseDeploymentConfigDataHolder

Dataclass representing a Deployment Config Metadata

acceleration_configs

benchmark_metrics

deployment_args

deployment_config_name

class sagemaker.core.jumpstart.DeserializerType(value)

Bases: str, Enum

Enum class for deserializers associated with JumpStart models.

JSON = 'json'

class sagemaker.core.jumpstart.HubAccessConfig(spec: Dict[str, Any])

Bases: JumpStartDataHolderType

Data class of model access config that mirrors CreateModel API.

from_json(json_obj: Dict[str, Any]) → None

Sets fields in object based on json.

Parameters:

json_obj (Dict[str, Any]) – Dictionary representation of data source.

hub_content_arn

to_json() → Dict[str, Any]

Returns json representation of ModelAccessConfig object.

class sagemaker.core.jumpstart.HubArnExtractedInfo(partition: str, region: str, account_id: str, hub_name: str, hub_content_type: str | None = None, hub_content_name: str | None = None, hub_content_version: str | None = None)

Bases: JumpStartDataHolderType

Data class for info extracted from Hub arn.

account_id

static extract_region_from_arn(arn: str) → str | None

Extracts hub_name, content_name, and content_version from a HubContentArn

hub_content_name

hub_content_type

hub_content_version

hub_name

partition

region

class sagemaker.core.jumpstart.HubContentCapability(value)

Bases: str, Enum

Enum class for HubContent capabilities.

BEDROCK_CONSOLE = 'BEDROCK_CONSOLE'

class sagemaker.core.jumpstart.HubContentType(value)

Bases: str, Enum

Enum for Hub content objects.

MODEL = 'Model'

MODEL_REFERENCE = 'ModelReference'

NOTEBOOK = 'Notebook'

class sagemaker.core.jumpstart.HubType(value)

Bases: str, Enum

Enum for Hub objects.

HUB = 'Hub'

class sagemaker.core.jumpstart.HyperparameterValidationMode(value)

Bases: str, Enum

Possible modes for validating hyperparameters.

VALIDATE_ALGORITHM = 'validate_algorithm'

VALIDATE_ALL = 'validate_all'

VALIDATE_PROVIDED = 'validate_provided'

class sagemaker.core.jumpstart.JumpStartAdditionalDataSources(spec: Dict[str, Any])

Bases: JumpStartDataHolderType

Data class of additional data sources.

from_json(json_obj: Dict[str, Any]) → None

Sets fields in object based on json.

Parameters:

json_obj (Dict[str, Any]) – Dictionary representation of data source.

scripts

speculative_decoding

to_json() → Dict[str, Any]

Returns json representation of AdditionalDataSources object.

class sagemaker.core.jumpstart.JumpStartBenchmarkStat(spec: Dict[str, Any])

Bases: JumpStartDataHolderType

Data class JumpStart benchmark stat.

concurrency

from_json(json_obj: Dict[str, Any]) → None

Sets fields in object based on json.

Parameters:

json_obj (Dict[str, Any]) – Dictionary representation of benchmark stats.

name

to_json() → Dict[str, Any]

Returns json representation of JumpStartBenchmarkStat object.

unit

value

class sagemaker.core.jumpstart.JumpStartCachedContentKey(data_type: JumpStartS3FileType | HubType | HubContentType, id_info: str)

Bases: JumpStartDataHolderType

Data class for the cached content keys.

data_type

id_info

class sagemaker.core.jumpstart.JumpStartCachedContentValue(formatted_content: Dict[JumpStartVersionedModelId, JumpStartModelHeader] | JumpStartModelSpecs, md5_hash: str | None = None)

Bases: JumpStartDataHolderType

Data class for the s3 cached content values.

formatted_content

md5_hash

class sagemaker.core.jumpstart.JumpStartConfig(*, model_id: str, model_version: str | None = None, hub_name: str | None = None, accept_eula: bool | None = False, training_config_name: str | None = None, inference_config_name: str | None = None)

Bases: BaseConfig

Configuration Class for JumpStart.

model_id

The model ID of the JumpStart model.

Type:

str

model_version

The version of the JumpStart model. Defaults to None.

Type:

Optional[str]

hub_name

The name of the JumpStart hub. Defaults to None.

Type:

Optional[str]

accept_eula

Whether to accept the EULA. Defaults to None.

Type:

Optional[bool]

training_config_name

The name of the training configuration. Defaults to None.

Type:

Optional[str]

inference_config_name

The name of the inference configuration. Defaults to None.

Type:

Optional[str]

accept_eula: bool | None

hub_name: str | None

inference_config_name: str | None

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'frozen': True, 'validate_assignment': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_id: str

model_version: str | None

training_config_name: str | None

class sagemaker.core.jumpstart.JumpStartConfigComponent(component_name: str, component: Dict[str, Any] | None, is_hub_content=False)

Bases: JumpStartMetadataBaseFields

Data class of JumpStart config component.

CUSTOM_FIELD_MAP = {'sage_maker_sdk_predictor_specifications': 'predictor_specs'}

OVERRIDING_DENY_LIST = ['model_id', 'url', 'version', 'min_sdk_version', 'deprecated', 'deprecated_message', 'deprecate_warn_message', 'resource_name_base', 'gated_bucket', 'training_supported', 'incremental_training_supported']

capabilities

component_name

default_inference_instance_type

default_payloads

default_training_dataset_key

default_training_dataset_uri

default_training_instance_type

deploy_kwargs

deprecate_warn_message

deprecated

deprecated_message

dynamic_container_deployment_supported

estimator_kwargs

fit_kwargs

from_json(json_obj: Dict[str, Any]) → None

Initializes a JumpStartConfigComponent object from its json representation.

Parameters:

json_obj (Dict[str, Any]) – Dictionary representation of the config component.

gated_bucket

hosting_additional_data_sources

hosting_artifact_key

hosting_artifact_uri

hosting_ecr_specs

hosting_ecr_uri

hosting_eula_key

hosting_instance_type_variants

hosting_model_package_arns

hosting_neuron_model_id

hosting_neuron_model_version

hosting_prepacked_artifact_key

hosting_prepacked_artifact_version

hosting_resource_requirements

hosting_script_key

hosting_use_script_uri

hub_content_type

hyperparameters

incremental_training_supported

inference_dependencies

inference_enable_network_isolation

inference_environment_variables

inference_volume_size

inference_vulnerabilities

inference_vulnerable

metrics

min_sdk_version

model_id

model_kwargs

model_subscription_link

model_types

predictor_specs

resource_name_base

slots = ['component_name']

supported_inference_instance_types

supported_training_instance_types

training_artifact_key

training_dependencies

training_ecr_specs

training_ecr_uri

training_enable_network_isolation

training_instance_type_variants

training_model_package_artifact_uris

training_prepacked_script_key

training_prepacked_script_version

training_script_key

training_supported

training_volume_size

training_vulnerabilities

training_vulnerable

url

usage_info_message

version

class sagemaker.core.jumpstart.JumpStartConfigRanking(spec: Dict[str, Any] | None, is_hub_content=False)

Bases: JumpStartDataHolderType

Data class JumpStart config ranking.

description

from_json(json_obj: Dict[str, Any]) → None

Sets fields in object based on json.

Parameters:

json_obj (Dict[str, Any]) – Dictionary representation of config ranking.

rankings

to_json() → Dict[str, Any]

Returns json representation of JumpStartConfigRanking object.

class sagemaker.core.jumpstart.JumpStartConfigRankingName(value)

Bases: str, Enum

Enum class for ranking of JumpStart config.

DEFAULT = 'overall'

class sagemaker.core.jumpstart.JumpStartDataHolderType

Bases: object

Base class for many JumpStart types.

Allows objects to be added to dicts and sets, and improves string representation. This class overrides the __eq__ and __hash__ methods so that different objects with the same attributes/types can be compared.

class sagemaker.core.jumpstart.JumpStartECRSpecs(spec: Dict[str, Any], is_hub_content: bool | None = False)

Bases: JumpStartDataHolderType

Data class for JumpStart ECR specs.

framework

framework_version

from_json(json_obj: Dict[str, Any]) → None

Sets fields in object based on json.

Parameters:

json_obj (Dict[str, Any]) – Dictionary representation of spec.

huggingface_transformers_version

py_version

to_json() → Dict[str, Any]

Returns json representation of JumpStartECRSpecs object.

class sagemaker.core.jumpstart.JumpStartEnvironmentVariable(spec: Dict[str, Any], is_hub_content: bool | None = False)

Bases: JumpStartDataHolderType

Data class for JumpStart environment variable definitions in the hosting container.

default

from_json(json_obj: Dict[str, Any]) → None

Sets fields in object based on json.

Parameters:

json_obj (Dict[str, Any]) – Dictionary representation of environment variable.

name

required_for_model_class

scope

to_json() → Dict[str, Any]

Returns json representation of JumpStartEnvironmentVariable object.

type

class sagemaker.core.jumpstart.JumpStartEstimatorDeployKwargs(model_id: str, model_version: str | None = None, hub_arn: str | None = None, region: str | None = None, initial_instance_count: int | None = None, instance_type: str | None = None, serializer: Any | None = None, deserializer: Any | None = None, accelerator_type: str | None = None, endpoint_name: str | None = None, tags: List[Dict[str, str | PipelineVariable]] | Dict[str, str | PipelineVariable] | None = None, kms_key: str | None = None, wait: bool | None = None, data_capture_config: Any | None = None, async_inference_config: Any | None = None, serverless_inference_config: Any | None = None, volume_size: int | None = None, model_data_download_timeout: int | None = None, container_startup_health_check_timeout: int | None = None, inference_recommendation_id: str | None = None, explainer_config: Any | None = None, image_uri: str | Any | None = None, role: str | None = None, predictor_cls: Callable | None = None, env: Dict[str, str | Any] | None = None, model_name: str | None = None, vpc_config: Dict[str, List[str | Any]] | None = None, sagemaker_session: Any | None = None, enable_network_isolation: bool | Any | None = None, model_kms_key: str | None = None, image_config: Dict[str, str | Any] | None = None, source_dir: str | None = None, code_location: str | None = None, entry_point: str | None = None, container_log_level: int | Any | None = None, dependencies: List[str] | None = None, git_config: Dict[str, str] | None = None, tolerate_deprecated_model: bool | None = None, tolerate_vulnerable_model: bool | None = None, use_compiled_model: bool = False, config_name: str | None = None)

Bases: JumpStartKwargs

Data class for the inputs to JumpStartEstimator.deploy method.

SERIALIZATION_EXCLUSION_SET: Set[str] = {'config_name', 'hub_arn', 'model_id', 'model_version', 'region', 'sagemaker_session', 'tolerate_deprecated_model', 'tolerate_vulnerable_model'}

accelerator_type

async_inference_config

code_location

config_name

container_log_level

container_startup_health_check_timeout

data_capture_config

dependencies

deserializer

enable_network_isolation

endpoint_name

entry_point

env

explainer_config

git_config

hub_arn

image_config

image_uri

inference_recommendation_id

initial_instance_count

instance_type

kms_key

model_data_download_timeout

model_id

model_kms_key

model_name

model_version

predictor_cls

region

role

sagemaker_session

serializer

serverless_inference_config

source_dir

specs

tags

tolerate_deprecated_model

tolerate_vulnerable_model

use_compiled_model

volume_size

vpc_config

wait

class sagemaker.core.jumpstart.JumpStartEstimatorFitKwargs(model_id: str, model_version: str | None = None, hub_arn: str | None = None, model_type: JumpStartModelType | None = JumpStartModelType.OPEN_WEIGHTS, region: str | None = None, inputs: str | Dict | Any | None = None, wait: bool | None = None, logs: str | None = None, job_name: str | None = None, experiment_config: Dict[str, str] | None = None, tolerate_deprecated_model: bool | None = None, tolerate_vulnerable_model: bool | None = None, sagemaker_session: Session | None = None, config_name: str | None = None)

Bases: JumpStartKwargs

Data class for the inputs to JumpStartEstimator.fit method.

SERIALIZATION_EXCLUSION_SET: Set[str] = {'config_name', 'hub_arn', 'model_id', 'model_type', 'model_version', 'region', 'sagemaker_session', 'tolerate_deprecated_model', 'tolerate_vulnerable_model'}

config_name

experiment_config

hub_arn

inputs

job_name

logs

model_id

model_type

model_version

region

sagemaker_session

specs

tolerate_deprecated_model

tolerate_vulnerable_model

wait

class sagemaker.core.jumpstart.JumpStartEstimatorInitKwargs(model_id: str, model_version: str | None = None, hub_arn: str | None = None, model_type: JumpStartModelType | None = JumpStartModelType.OPEN_WEIGHTS, region: str | None = None, image_uri: str | Any | None = None, role: str | None = None, instance_count: int | Any | None = None, instance_type: str | Any | None = None, keep_alive_period_in_seconds: int | Any | None = None, volume_size: int | Any | None = None, volume_kms_key: str | Any | None = None, max_run: int | Any | None = None, input_mode: str | Any | None = None, output_path: str | Any | None = None, output_kms_key: str | Any | None = None, base_job_name: str | None = None, sagemaker_session: Any | None = None, hyperparameters: Dict[str, str | Any] | None = None, tags: List[Dict[str, str | PipelineVariable]] | Dict[str, str | PipelineVariable] | None = None, subnets: List[str | Any] | None = None, security_group_ids: List[str | Any] | None = None, model_uri: str | None = None, model_channel_name: str | Any | None = None, metric_definitions: List[Dict[str, str | Any]] | None = None, encrypt_inter_container_traffic: bool | Any | None = None, use_spot_instances: bool | Any | None = None, max_wait: int | Any | None = None, checkpoint_s3_uri: str | Any | None = None, checkpoint_local_path: str | Any | None = None, enable_network_isolation: bool | Any | None = None, rules: List[Any] | None = None, debugger_hook_config: bool | Any | None = None, tensorboard_output_config: Any | None = None, enable_sagemaker_metrics: bool | Any | None = None, profiler_config: Any | None = None, disable_profiler: bool | None = None, environment: Dict[str, str | Any] | None = None, max_retry_attempts: int | Any | None = None, source_dir: str | Any | None = None, git_config: Dict[str, str] | None = None, container_log_level: int | Any | None = None, code_location: str | None = None, entry_point: str | Any | None = None, dependencies: List[str] | None = None, instance_groups: List[Any] | None = None, training_repository_access_mode: str | Any | None = None, training_repository_credentials_provider_arn: str | Any | None = None, tolerate_vulnerable_model: bool | None = None, tolerate_deprecated_model: bool | None = None, container_entry_point: List[str] | None = None, container_arguments: List[str] | None = None, disable_output_compression: bool | None = None, enable_infra_check: bool | PipelineVariable | None = None, enable_remote_debug: bool | PipelineVariable | None = None, config_name: str | None = None, enable_session_tag_chaining: bool | PipelineVariable | None = None, training_plan: str | PipelineVariable | None = None)

Bases: JumpStartKwargs

Data class for the inputs to JumpStartEstimator.__init__ method.

SERIALIZATION_EXCLUSION_SET: Set[str] = {'config_name', 'hub_arn', 'hub_content_type', 'model_id', 'model_type', 'model_version', 'region', 'tolerate_deprecated_model', 'tolerate_vulnerable_model'}

base_job_name

checkpoint_local_path

checkpoint_s3_uri

code_location

config_name

container_arguments

container_entry_point

container_log_level

debugger_hook_config

dependencies

disable_output_compression

disable_profiler

enable_infra_check

enable_network_isolation

enable_remote_debug

enable_sagemaker_metrics

enable_session_tag_chaining

encrypt_inter_container_traffic

entry_point

environment

git_config

hub_arn

hub_content_type

hyperparameters

image_uri

input_mode

instance_count

instance_groups

instance_type

keep_alive_period_in_seconds

max_retry_attempts

max_run

max_wait

metric_definitions

model_channel_name

model_id

model_reference_arn

model_type

model_uri

model_version

output_kms_key

output_path

profiler_config

region

role

rules

sagemaker_session

security_group_ids

source_dir

specs

subnets

tags

tensorboard_output_config

tolerate_deprecated_model

tolerate_vulnerable_model

training_plan

training_repository_access_mode

training_repository_credentials_provider_arn

use_spot_instances

volume_kms_key

volume_size

class sagemaker.core.jumpstart.JumpStartHyperparameter(spec: Dict[str, Any], is_hub_content: bool | None = False)

Bases: JumpStartDataHolderType

Data class for JumpStart hyperparameter definition in the training container.

default

exclusive_max

exclusive_min

from_json(json_obj: Dict[str, Any]) → None

Sets fields in object based on json.

Parameters:

json_obj (Dict[str, Any]) – Dictionary representation of hyperparameter.

max

min

name

options

scope

to_json() → Dict[str, Any]

Returns json representation of JumpStartHyperparameter object.

type

class sagemaker.core.jumpstart.JumpStartInstanceTypeVariants(spec: Dict[str, Any] | None, is_hub_content: bool | None = False)

Bases: JumpStartDataHolderType

Data class for JumpStart instance type variants.

aliases

from_describe_hub_content_response(response: Dict[str, Any] | None) → None

Sets fields in object based on DescribeHubContent response.

Parameters:

response (Dict[str, Any]) – Dictionary representation of instance type variants.

from_json(json_obj: Dict[str, Any] | None) → None

Sets fields in object based on json.

Parameters:

json_obj (Dict[str, Any]) – Dictionary representation of instance type variants.

get_image_uri(instance_type: str, region: str | None = None) → str | None

Returns image uri from instance type and region.

Returns None if no instance type is available or found. None is also returned if the metadata is improperly formatted.

get_instance_specific_artifact_key(instance_type: str) → str | None

Returns instance specific model artifact key.

Returns None if a model, instance type tuple does not have specific artifact key.

get_instance_specific_default_inference_instance_type(instance_type: str) → str | None

Returns instance specific default inference instance type.

Returns None if a model, instance type tuple does not have instance specific inference instance types.

get_instance_specific_environment_variables(instance_type: str) → Dict[str, str]

Returns instance specific environment variables.

Returns empty dict if a model, instance type tuple does not have specific environment variables.

get_instance_specific_gated_model_key_env_var_value(instance_type: str) → str | None

Returns instance specific gated model env var s3 key.

Returns None if a model, instance type tuple does not have instance specific property.

get_instance_specific_hyperparameters(instance_type: str) → List[JumpStartHyperparameter]

Returns instance specific hyperparameters.

Returns empty list if a model, instance type tuple does not have specific hyperparameters.

get_instance_specific_metric_definitions(instance_type: str) → List[JumpStartHyperparameter]

Returns instance specific metric definitions.

Returns empty list if a model, instance type tuple does not have specific metric definitions.

get_instance_specific_prepacked_artifact_key(instance_type: str) → str | None

Returns instance specific model artifact key.

Returns None if a model, instance type tuple does not have specific artifact key.

get_instance_specific_resource_requirements(instance_type: str) → str | None

Returns instance specific resource requirements.

If a value exists for both the instance family and instance type, the instance type value is chosen.

get_instance_specific_supported_inference_instance_types(instance_type: str) → List[str]

Returns instance specific supported inference instance types.

Returns empty list if a model, instance type tuple does not have instance specific inference instance types.

get_instance_specific_training_artifact_key(instance_type: str) → str | None

Returns instance specific training artifact key.

Returns None if a model, instance type tuple does not have specific training artifact key.

get_model_package_arn(instance_type: str, region: str) → str | None

Returns model package arn from instance type and region.

Returns None if no instance type is available or found. None is also returned if the metadata is improperly formatted.

regional_aliases

regionalize(region: str) → Dict[str, Any] | None

Returns regionalized instance type variants.

to_json() → Dict[str, Any]

Returns json representation of JumpStartInstance object.

variants

class sagemaker.core.jumpstart.JumpStartKwargs

Bases: JumpStartDataHolderType

Data class for JumpStart object kwargs.

BASE_SERIALIZATION_EXCLUSION_SET: Set[str] = ['specs']

SERIALIZATION_EXCLUSION_SET: Set[str] = {}

to_kwargs_dict(exclude_keys: bool = True)

Serializes object to dictionary to be used for kwargs for method arguments.

class sagemaker.core.jumpstart.JumpStartLaunchedRegionInfo(content_bucket: str, region_name: str, gated_content_bucket: str | None = None, neo_content_bucket: str | None = None)

Bases: JumpStartDataHolderType

Data class for launched region info.

content_bucket

gated_content_bucket

neo_content_bucket

region_name

class sagemaker.core.jumpstart.JumpStartMetadataBaseFields(fields: Dict[str, Any], is_hub_content: bool | None = False)

Bases: JumpStartDataHolderType

Data class JumpStart metadata base fields that can be overridden.

capabilities

default_inference_instance_type

default_payloads

default_training_dataset_key

default_training_dataset_uri

default_training_instance_type

deploy_kwargs

deprecate_warn_message

deprecated

deprecated_message

dynamic_container_deployment_supported

estimator_kwargs

fit_kwargs

from_json(json_obj: Dict[str, Any]) → None

Sets fields in object based on json of header.

Parameters:

json_obj (Dict[str, Any]) – Dictionary representation of spec.

gated_bucket

hosting_additional_data_sources

hosting_artifact_key

hosting_artifact_uri

hosting_ecr_specs

hosting_ecr_uri

hosting_eula_key

hosting_instance_type_variants

hosting_model_package_arns

hosting_neuron_model_id

hosting_neuron_model_version

hosting_prepacked_artifact_key

hosting_prepacked_artifact_version

hosting_resource_requirements

hosting_script_key

hosting_use_script_uri

hub_content_type

hyperparameters

incremental_training_supported

inference_dependencies

inference_enable_network_isolation

inference_environment_variables

inference_volume_size

inference_vulnerabilities

inference_vulnerable

metrics

min_sdk_version

model_id

model_kwargs

model_subscription_link

model_types

predictor_specs

resource_name_base

set_hub_content_type(hub_content_type: HubContentType) → None

Sets the hub content type.

supported_inference_instance_types

supported_training_instance_types

to_json() → Dict[str, Any]

Returns json representation of JumpStartMetadataBaseFields object.

training_artifact_key

training_dependencies

training_ecr_specs

training_ecr_uri

training_enable_network_isolation

training_instance_type_variants

training_model_package_artifact_uris

training_prepacked_script_key

training_prepacked_script_version

training_script_key

training_supported

training_volume_size

training_vulnerabilities

training_vulnerable

url

usage_info_message

version

class sagemaker.core.jumpstart.JumpStartMetadataConfig(config_name: str, config: Dict[str, Any], base_fields: Dict[str, Any], config_components: Dict[str, JumpStartConfigComponent], is_hub_content=False)

Bases: JumpStartDataHolderType

Data class of JumpStart metadata config.

acceleration_configs

base_fields

benchmark_metrics: Dict[str, List[JumpStartBenchmarkStat]]

config_components: Dict[str, JumpStartConfigComponent]

config_name: str | None

default_incremental_training_config: str | None

default_inference_config: str | None

property resolved_config: Dict[str, Any]

Returns the final config that is resolved from the components map.

Construct the final config by applying the list of configs from list index, and apply to the base default fields in the current model specs.

resolved_metadata_config: Dict[str, Any] | None

supported_incremental_training_configs: List[str] | None

supported_inference_configs: List[str] | None

to_json() → Dict[str, Any]

Returns json representation of JumpStartMetadataConfig object.

class sagemaker.core.jumpstart.JumpStartMetadataConfigs(configs: Dict[str, JumpStartMetadataConfig] | None, config_rankings: Dict[str, JumpStartConfigRanking] | None, scope: JumpStartScriptScope = JumpStartScriptScope.INFERENCE)

Bases: JumpStartDataHolderType

Data class to hold the set of JumpStart Metadata configs.

config_rankings

configs

get_top_config_from_ranking(ranking_name: str = JumpStartConfigRankingName.DEFAULT, instance_type: str | None = None) → JumpStartMetadataConfig | None

Gets the best the config based on config ranking.

Fallback to use the ordering in config names if ranking is not available. :param ranking_name: The ranking name that config priority is based on. :type ranking_name: str :param instance_type: The instance type which the config selection is based on. :type instance_type: Optional[str]

Raises:

NotImplementedError – If the scope is unrecognized.

scope

to_json() → Dict[str, Any]

Returns json representation of JumpStartMetadataConfigs object.

class sagemaker.core.jumpstart.JumpStartModelDataSource(spec: Dict[str, Any])

Bases: AdditionalModelDataSource

Data class JumpStart additional model data source.

SERIALIZATION_EXCLUSION_SET = {'artifact_version', 'provider'}

artifact_version

channel_name

from_json(json_obj: Dict[str, Any]) → None

Sets fields in object based on json.

Parameters:

json_obj (Dict[str, Any]) – Dictionary representation of data source.

hosting_eula_key

provider

s3_data_source

class sagemaker.core.jumpstart.JumpStartModelDeployKwargs(model_id: str, model_version: str | None = None, hub_arn: str | None = None, model_type: JumpStartModelType | None = JumpStartModelType.OPEN_WEIGHTS, region: str | None = None, initial_instance_count: int | None = None, instance_type: str | None = None, serializer: Any | None = None, deserializer: Any | None = None, accelerator_type: str | None = None, endpoint_name: str | None = None, inference_component_name: str | None = None, tags: Tags | None = None, kms_key: str | None = None, wait: bool | None = None, data_capture_config: Any | None = None, async_inference_config: Any | None = None, serverless_inference_config: Any | None = None, volume_size: int | None = None, model_data_download_timeout: int | None = None, container_startup_health_check_timeout: int | None = None, inference_recommendation_id: str | None = None, explainer_config: Any | None = None, tolerate_deprecated_model: bool | None = None, tolerate_vulnerable_model: bool | None = None, sagemaker_session: Session | None = None, training_instance_type: str | None = None, accept_eula: bool | None = None, model_reference_arn: str | None = None, endpoint_logging: bool | None = None, resources: ResourceRequirements | None = None, endpoint_type: EndpointType | None = None, config_name: str | None = None, routing_config: Dict[str, Any] | None = None, model_access_configs: Dict[str, CoreModelAccessConfig] | None = None, inference_ami_version: str | None = None)

Bases: JumpStartKwargs

Data class for the inputs to JumpStartModel.deploy method.

SERIALIZATION_EXCLUSION_SET: Set[str] = {'config_name', 'hub_arn', 'model_access_configs', 'model_id', 'model_type', 'model_version', 'region', 'sagemaker_session', 'tolerate_deprecated_model', 'tolerate_vulnerable_model', 'training_instance_type'}

accelerator_type

accept_eula

async_inference_config

config_name

container_startup_health_check_timeout

data_capture_config

deserializer

endpoint_logging

endpoint_name

endpoint_type

explainer_config

hub_arn

inference_ami_version

inference_component_name

inference_recommendation_id

initial_instance_count

instance_type

kms_key

model_access_configs

model_data_download_timeout

model_id

model_reference_arn

model_type

model_version

region

resources

routing_config

sagemaker_session

serializer

serverless_inference_config

specs

tags

tolerate_deprecated_model

tolerate_vulnerable_model

training_instance_type

volume_size

wait

class sagemaker.core.jumpstart.JumpStartModelHeader(header: Dict[str, str])

Bases: JumpStartDataHolderType

Data class JumpStart model header.

from_json(json_obj: Dict[str, str]) → None

Sets fields in object based on json of header.

Parameters:

json_obj (Dict[str, str]) – Dictionary representation of header.

min_version

model_id

search_keywords

spec_key

to_json() → Dict[str, str]

Returns json representation of JumpStartModelHeader object.

version

class sagemaker.core.jumpstart.JumpStartModelInitKwargs(model_id: str, model_version: str | None = None, hub_arn: str | None = None, model_type: JumpStartModelType | None = JumpStartModelType.OPEN_WEIGHTS, region: str | None = None, instance_type: str | None = None, image_uri: str | Any | None = None, model_data: str | Any | dict | None = None, role: str | None = None, predictor_cls: Callable | None = None, env: Dict[str, str | Any] | None = None, name: str | None = None, vpc_config: Dict[str, List[str | Any]] | None = None, sagemaker_session: Any | None = None, enable_network_isolation: bool | Any = None, model_kms_key: str | None = None, image_config: Dict[str, str | Any] | None = None, source_dir: str | None = None, code_location: str | None = None, entry_point: str | None = None, container_log_level: int | Any | None = None, dependencies: List[str] | None = None, git_config: Dict[str, str] | None = None, tolerate_vulnerable_model: bool | None = None, tolerate_deprecated_model: bool | None = None, model_package_arn: str | None = None, training_instance_type: str | None = None, resources: ResourceRequirements | None = None, config_name: str | None = None, additional_model_data_sources: Dict[str, Any] | None = None)

Bases: JumpStartKwargs

Data class for the inputs to JumpStartModel.__init__ method.

SERIALIZATION_EXCLUSION_SET: Set[str] = {'config_name', 'hub_arn', 'hub_content_type', 'instance_type', 'model_id', 'model_package_arn', 'model_type', 'model_version', 'region', 'tolerate_deprecated_model', 'tolerate_vulnerable_model', 'training_instance_type'}

additional_model_data_sources

code_location

config_name

container_log_level

dependencies

enable_network_isolation

entry_point

env

git_config

hub_arn

hub_content_type

image_config

image_uri

instance_type

model_data

model_id

model_kms_key

model_package_arn

model_reference_arn

model_type

model_version

name

predictor_cls

region

resources

role

sagemaker_session

source_dir

specs

tolerate_deprecated_model

tolerate_vulnerable_model

training_instance_type

vpc_config

class sagemaker.core.jumpstart.JumpStartModelRegisterKwargs(model_id: str, model_version: str | None = None, hub_arn: str | None = None, region: str | None = None, model_type: JumpStartModelType | None = JumpStartModelType.OPEN_WEIGHTS, tolerate_deprecated_model: bool | None = None, tolerate_vulnerable_model: bool | None = None, sagemaker_session: Any | None = None, content_types: List[str] | None = None, response_types: List[str] | None = None, inference_instances: List[str] | None = None, transform_instances: List[str] | None = None, model_package_group_name: str | None = None, image_uri: str | None = None, model_metrics: ModelMetrics | None = None, metadata_properties: MetadataProperties | None = None, approval_status: str | None = None, description: str | None = None, drift_check_baselines: DriftCheckBaselines | None = None, customer_metadata_properties: Dict[str, str] | None = None, validation_specification: str | None = None, domain: str | None = None, task: str | None = None, sample_payload_url: str | None = None, framework: str | None = None, framework_version: str | None = None, nearest_model_name: str | None = None, data_input_configuration: str | None = None, skip_model_validation: str | None = None, source_uri: str | None = None, model_life_cycle: ModelLifeCycle | None = None, config_name: str | None = None, model_card: Dict[ModelCardContent, ModelCardContent] | None = None, accept_eula: bool | None = None)

Bases: JumpStartKwargs

Data class for the inputs to JumpStartEstimator.deploy method.

SERIALIZATION_EXCLUSION_SET: Set[str] = {'config_name', 'hub_arn', 'model_id', 'model_version', 'region', 'sagemaker_session', 'tolerate_deprecated_model', 'tolerate_vulnerable_model'}

accept_eula

approval_status

config_name

content_types

customer_metadata_properties

data_input_configuration

description

domain

drift_check_baselines

framework

framework_version

hub_arn

image_uri

inference_instances

metadata_properties

model_card

model_id

model_life_cycle

model_metrics

model_package_group_name

model_type

model_version

nearest_model_name

region

response_types

sagemaker_session

sample_payload_url

skip_model_validation

source_uri

specs

task

tolerate_deprecated_model

tolerate_vulnerable_model

transform_instances

validation_specification

class sagemaker.core.jumpstart.JumpStartModelSpecs(spec: Dict[str, Any], is_hub_content: bool | None = False)

Bases: JumpStartMetadataBaseFields

Data class JumpStart model specs.

capabilities

default_inference_instance_type

default_payloads

default_training_dataset_key

default_training_dataset_uri

default_training_instance_type

deploy_kwargs

deprecate_warn_message

deprecated

deprecated_message

dynamic_container_deployment_supported

estimator_kwargs

fit_kwargs

from_json(json_obj: Dict[str, Any]) → None

Sets fields in object based on json of header.

Parameters:

json_obj (Dict[str, Any]) – Dictionary representation of spec.

gated_bucket

get_additional_s3_data_sources() → List[JumpStartAdditionalDataSources]

Returns a list of the additional S3 data sources for use by the model.

get_speculative_decoding_s3_data_sources() → List[JumpStartModelDataSource]

Returns data sources for speculative decoding.

hosting_additional_data_sources

hosting_artifact_key

hosting_artifact_uri

hosting_ecr_specs

hosting_ecr_uri

hosting_eula_key

hosting_instance_type_variants

hosting_model_package_arns

hosting_neuron_model_id

hosting_neuron_model_version

hosting_prepacked_artifact_key

hosting_prepacked_artifact_version

hosting_resource_requirements

hosting_script_key

hosting_use_script_uri

hub_content_type

hyperparameters

incremental_training_supported

inference_config_components

inference_config_rankings

inference_configs

inference_dependencies

inference_enable_network_isolation

inference_environment_variables

inference_volume_size

inference_vulnerabilities

inference_vulnerable

is_gated_model() → bool

Returns True if the model has a EULA key or the model bucket is gated.

metrics

min_sdk_version

model_id

model_kwargs

model_subscription_link

model_types

predictor_specs

resource_name_base

set_config(config_name: str, scope: JumpStartScriptScope = JumpStartScriptScope.INFERENCE) → None

Apply the seleted config and resolve to the current model spec.

Parameters:

• config_name (str) – Name of the config.

• scope (JumpStartScriptScope, optional) – Scope of the config. Defaults to JumpStartScriptScope.INFERENCE.

Raises:

ValueError – If the scope is not supported, or cannot find config name.

slots = ['inference_configs', 'inference_config_components', 'inference_config_rankings', 'training_configs', 'training_config_components', 'training_config_rankings']

supported_inference_instance_types

supported_training_instance_types

supports_incremental_training() → bool

Returns True if the model supports incremental training.

supports_prepacked_inference() → bool

Returns True if the model has a prepacked inference artifact.

training_artifact_key

training_config_components

training_config_rankings

training_configs

training_dependencies

training_ecr_specs

training_ecr_uri

training_enable_network_isolation

training_instance_type_variants

training_model_package_artifact_uris

training_prepacked_script_key

training_prepacked_script_version

training_script_key

training_supported

training_volume_size

training_vulnerabilities

training_vulnerable

url

usage_info_message

use_inference_script_uri() → bool

Returns True if the model should use a script uri when deploying inference model.

use_training_model_artifact() → bool

Returns True if the model should use a model uri when kicking off training job.

version

class sagemaker.core.jumpstart.JumpStartModelType(value)

Bases: str, Enum

Enum class for JumpStart model type.

OPEN_WEIGHTS: Publicly available models have open weights and are onboarded and maintained by JumpStart. PROPRIETARY: Proprietary models from third-party providers do not have open weights. You must subscribe to proprietary models in AWS Marketplace before use.

OPEN_WEIGHTS = 'open_weights'

PROPRIETARY = 'proprietary'

class sagemaker.core.jumpstart.JumpStartModelsAccessor

Bases: object

Static class for storing the JumpStart models cache.

static get_jumpstart_content_bucket() → str | None

Returns JumpStart content bucket.

static get_jumpstart_gated_content_bucket() → str | None

Returns JumpStart gated content bucket.

static get_manifest(*args, **kwargs)

static get_model_header(region: str, model_id: str, version: str, model_type: JumpStartModelType = JumpStartModelType.OPEN_WEIGHTS) → JumpStartModelHeader

Returns model header from JumpStart models cache.

Parameters:

• region (str) – region for which to retrieve header.

• model_id (str) – model ID to retrieve.

• version (str) – semantic version to retrieve for the model ID.

static get_model_specs(region: str, model_id: str, version: str, hub_arn: str | None = None, s3_client: ~boto3.client | None = None, model_type=JumpStartModelType.OPEN_WEIGHTS, sagemaker_session: ~sagemaker.core.helper.session_helper.Session = <sagemaker.core.helper.session_helper.Session object>) → JumpStartModelSpecs

Returns model specs from JumpStart models cache.

Parameters:

• region (str) – region for which to retrieve header.

• model_id (str) – model ID to retrieve.

• version (str) – semantic version to retrieve for the model ID.

• s3_client (boto3.client) – boto3 client to use for accessing JumpStart models s3 cache. If not set, a default client will be made.

static reset_cache(cache_kwargs: Dict[str, Any] | None = None, region: str | None = None) → None

Resets cache, optionally allowing cache kwargs to be passed to the new cache.

Raises:

ValueError – If region in cache_kwargs is inconsistent with region argument.

Parameters:

• cache_kwargs (str) – cache kwargs to validate.

• region (str) – The region to validate along with the kwargs.

static set_cache_kwargs(cache_kwargs: Dict[str, Any], region: str | None = None) → None

Sets cache kwargs, clears the cache.

Raises:

ValueError – If region in cache_kwargs is inconsistent with region argument.

Parameters:

• cache_kwargs (str) – cache kwargs to validate.

• region (str) – Optional. The region to validate along with the kwargs.

static set_jumpstart_content_bucket(content_bucket: str) → None

Sets JumpStart content bucket.

static set_jumpstart_gated_content_bucket(gated_content_bucket: str) → None

Sets JumpStart gated content bucket.

class sagemaker.core.jumpstart.JumpStartPredictorSpecs(spec: Dict[str, Any] | None, is_hub_content: bool | None = False)

Bases: JumpStartDataHolderType

Data class for JumpStart Predictor specs.

default_accept_type

default_content_type

from_json(json_obj: Dict[str, Any] | None) → None

Sets fields in object based on json.

Parameters:

json_obj (Dict[str, Any]) – Dictionary representation of predictor specs.

supported_accept_types

supported_content_types

to_json() → Dict[str, Any]

Returns json representation of JumpStartPredictorSpecs object.

class sagemaker.core.jumpstart.JumpStartS3FileType(value)

Bases: str, Enum

Type of files published in JumpStart S3 distribution buckets.

OPEN_WEIGHT_MANIFEST = 'manifest'

OPEN_WEIGHT_SPECS = 'specs'

PROPRIETARY_MANIFEST = 'proprietary_manifest'

PROPRIETARY_SPECS = 'proprietary_specs'

class sagemaker.core.jumpstart.JumpStartS3PayloadAccessor

Bases: object

Static class for storing and retrieving S3 payload artifacts.

CACHE_SIZE = 16

MAX_CACHE_SIZE_BYTES = 100000000

MAX_PAYLOAD_SIZE_BYTES = 6000000

static clear_cache() → None

Clears LRU caches associated with S3 client and retrieved objects.

static get_object(bucket: str, key: str, region: str = 'us-west-2', s3_client: client | None = None) → bytes

Returns S3 object located at the bucket and key.

Raises:

ValueError – The object size is too large.

static get_object_cached(bucket: str, key: str, region: str = 'us-west-2', s3_client: client | None = None) → bytes

Returns S3 object located at the bucket and key.

Requests are cached so that the same S3 request is never made more than once, unless a different region or client is used.

class sagemaker.core.jumpstart.JumpStartScriptScope(value)

Bases: str, Enum

Enum class for JumpStart script scopes.

INFERENCE = 'inference'

TRAINING = 'training'

class sagemaker.core.jumpstart.JumpStartSerializablePayload(spec: Dict[str, Any] | None, is_hub_content: bool | None = False)

Bases: JumpStartDataHolderType

Data class for JumpStart serialized payload specs.

accept

body

content_type

from_json(json_obj: Dict[str, Any] | None) → None

Sets fields in object based on json.

Parameters:

json_obj (Dict[str, Any]) – Dictionary representation of serializable payload specs.

Raises:

KeyError – If the dictionary is missing keys.

prompt_key

raw_payload

to_json() → Dict[str, Any]

Returns json representation of JumpStartSerializablePayload object.

class sagemaker.core.jumpstart.JumpStartTag(value)

Bases: str, Enum

Enum class for tag keys to apply to JumpStart models.

BEDROCK = 'sagemaker-sdk:bedrock'

HUB_CONTENT_ARN = 'sagemaker-sdk:hub-content-arn'

INFERENCE_CONFIG_NAME = 'sagemaker-sdk:jumpstart-inference-config-name'

INFERENCE_MODEL_URI = 'aws-jumpstart-inference-model-uri'

INFERENCE_SCRIPT_URI = 'aws-jumpstart-inference-script-uri'

MODEL_ID = 'sagemaker-sdk:jumpstart-model-id'

MODEL_TYPE = 'sagemaker-sdk:jumpstart-model-type'

MODEL_VERSION = 'sagemaker-sdk:jumpstart-model-version'

TRAINING_CONFIG_NAME = 'sagemaker-sdk:jumpstart-training-config-name'

TRAINING_MODEL_URI = 'aws-jumpstart-training-model-uri'

TRAINING_SCRIPT_URI = 'aws-jumpstart-training-script-uri'

class sagemaker.core.jumpstart.JumpStartVersionedModelId(model_id: str, version: str)

Bases: JumpStartDataHolderType

Data class for versioned model IDs.

model_id

version

class sagemaker.core.jumpstart.MIMEType(value)

Bases: str, Enum

Enum class for MIME types associated with JumpStart models.

CSV = 'text/csv'

JSON = 'application/json'

LIST_TEXT = 'application/list-text'

WAV = 'audio/wav'

X_IMAGE = 'application/x-image'

X_TEXT = 'application/x-text'

static from_suffixed_type(mime_type_with_suffix: str) → MIMEType

Removes suffix from type and instantiates enum.

class sagemaker.core.jumpstart.ModelAccessConfig(spec: Dict[str, Any])

Bases: JumpStartDataHolderType

Data class of model access config that mirrors CreateModel API.

accept_eula

from_json(json_obj: Dict[str, Any]) → None

Sets fields in object based on json.

Parameters:

json_obj (Dict[str, Any]) – Dictionary representation of data source.

to_json() → Dict[str, Any]

Returns json representation of ModelAccessConfig object.

class sagemaker.core.jumpstart.ModelFramework(value)

Bases: str, Enum

Enum class for JumpStart model framework.

The ML framework as referenced in the prefix of the model ID. This value does not necessarily correspond to the container name.

CATBOOST = 'catboost'

HUGGINGFACE = 'huggingface'

LIGHTGBM = 'lightgbm'

MXNET = 'mxnet'

PYTORCH = 'pytorch'

SKLEARN = 'sklearn'

TENSORFLOW = 'tensorflow'

XGBOOST = 'xgboost'

class sagemaker.core.jumpstart.ModelSpecKwargType(value)

Bases: str, Enum

Enum class for types of kwargs for model hub content document and model specs.

DEPLOY = 'deploy_kwargs'

ESTIMATOR = 'estimator_kwargs'

FIT = 'fit_kwargs'

MODEL = 'model_kwargs'

classmethod arg_keys() → List[str]

Returns a list of kwargs keys that each type can have

class sagemaker.core.jumpstart.NamingConventionType(value)

Bases: str, Enum

Enum class for naming conventions.

DEFAULT = 'upper_camel_case'

SNAKE_CASE = 'snake_case'

UPPER_CAMEL_CASE = 'upper_camel_case'

class sagemaker.core.jumpstart.S3DataSource(spec: Dict[str, Any])

Bases: JumpStartDataHolderType

Data class of S3 data source that mirrors CreateModel API.

compression_type

from_json(json_obj: Dict[str, Any]) → None

Sets fields in object based on json.

Parameters:

json_obj (Dict[str, Any]) – Dictionary representation of data source.

hub_access_config

model_access_config

s3_data_type

s3_uri

set_bucket(bucket: str) → None

Sets bucket name from S3 URI.

to_json() → Dict[str, Any]

Returns json representation of S3DataSource object.

class sagemaker.core.jumpstart.SageMakerSettings

Bases: object

Static class for storing the SageMaker settings.

static get_sagemaker_version() → str

Return SageMaker version.

static set_sagemaker_version(version: str) → None

Set SageMaker version.

class sagemaker.core.jumpstart.SerializerType(value)

Bases: str, Enum

Enum class for serializers associated with JumpStart models.

CSV = 'csv'

JSON = 'json'

RAW_BYTES = 'raw_bytes'

TEXT = 'text'

class sagemaker.core.jumpstart.VariableScope(value)

Bases: str, Enum

Possible value of the scope attribute for a hyperparameter or environment variable.

Used for hosting environment variables and training hyperparameters.

ALGORITHM = 'algorithm'

CONTAINER = 'container'

class sagemaker.core.jumpstart.VariableTypes(value)

Bases: str, Enum

Possible types for hyperparameters and environment variables.

BOOL = 'bool'

FLOAT = 'float'

INT = 'int'

TEXT = 'text'

Modules

accessors	This module contains accessors related to SageMaker JumpStart.
artifacts	This module imports all JumpStart artifact functions from the respective sub-module.
cache	This module defines the JumpStartModelsCache class.
configs	This module contains utilites for JumpStart model metadata.
constants	This module contains constants for JumpStart.
deserializers	JumpStart deserializers module - provides retrieve_default function for backward compatibility.
document	This module contains utilites for JumpStart model metadata.
enums	This module stores enums related to SageMaker JumpStart.
exceptions	This module stores exceptions related to SageMaker JumpStart.
factory
filters	This module stores filters related to SageMaker JumpStart.
hub
models	This module contains the model for JumpStart HubContentDocument.
notebook_utils	This module stores notebook utils related to SageMaker JumpStart.
parameters	This module stores parameters related to SageMaker JumpStart.
payload_utils	This module stores inference payload utilities for JumpStart models.
search
serializers	JumpStart serializers module - provides retrieve_default function for backward compatibility.
session_utils	This module stores SageMaker Session utilities for JumpStart models.
types	This module stores types related to SageMaker JumpStart.
utils	This module contains utils for JumpStart.
validators	This module contains validators related to SageMaker JumpStart.