sagemaker.core.helper.session_helper

Functions

botocore_resolver()	Get the DNS suffix for the given region.
container_def(image_uri[, model_data_url, ...])	Create a definition for executing a container as part of a SageMaker model.
expand_role(self, role)	Expand an IAM role name into an ARN.
get_add_model_package_inference_args(...[, ...])	Get request dictionary for UpdateModelPackage API for additional inference.
get_execution_role([sagemaker_session, ...])	Return the role ARN whose credentials are used to call the API.
get_log_events_for_inference_recommender(...)	Retrieves log events from the specified CloudWatch log group and log stream.
get_update_model_package_inference_args(...)	Get request dictionary for UpdateModelPackage API for inference specification.
production_variant([model_name, ...])	Create a production variant description suitable for use in a ProductionVariant list.
s3_path_join(*args[, with_end_slash])	Returns the arguments joined by a slash ("/"), similar to os.path.join() (on Unix).
sts_regional_endpoint(region)	Get the AWS STS endpoint specific for the given region.
update_args(args, **kwargs)	Updates the request arguments dict with the value if populated.

Classes

LogState()	Placeholder docstring
Session([boto_session, sagemaker_client, ...])	Manage interactions with the Amazon SageMaker APIs and any other AWS services needed.

class sagemaker.core.helper.session_helper.LogState

Bases: object

Placeholder docstring

COMPLETE = 5

JOB_COMPLETE = 4

STARTING = 1

TAILING = 3

WAIT_IN_PROGRESS = 2

class sagemaker.core.helper.session_helper.Session(boto_session=None, sagemaker_client=None, sagemaker_runtime_client=None, sagemaker_featurestore_runtime_client=None, default_bucket=None, sagemaker_config: dict | None = None, settings=None, sagemaker_metrics_client=None, default_bucket_prefix: str | None = None)

Bases: object

Manage interactions with the Amazon SageMaker APIs and any other AWS services needed.

This class provides convenient methods for manipulating entities and resources that Amazon SageMaker uses, such as training jobs, endpoints, and input datasets in S3. AWS service calls are delegated to an underlying Boto3 session, which by default is initialized using the AWS configuration chain. When you make an Amazon SageMaker API call that accesses an S3 bucket location and one is not specified, the Session creates a default bucket based on a naming convention which includes the current AWS account ID.

account_id() → str

Get the AWS account id of the caller.

Returns:

AWS account ID.

property boto_region_name

Placeholder docstring

property config: Dict | None

The config for the local mode, unused in a normal session

create_bucket_for_not_exist_error(bucket_name, region, s3)

Creates the S3 bucket in the given region

Parameters:

• bucket_name (str) – Name of the S3 bucket

• s3 (str) – S3 object from boto session

• region (str) – The region in which to create the bucket.

create_endpoint(endpoint_name, config_name, tags=None, wait=True, live_logging=False)

Create an Amazon SageMaker Endpoint according to the configuration in the request.

Once the Endpoint is created, client applications can send requests to obtain inferences. The endpoint configuration is created using the CreateEndpointConfig API.

Parameters:

• endpoint_name (str) – Name of the Amazon SageMaker Endpoint being created.

• config_name (str) – Name of the Amazon SageMaker endpoint configuration to deploy.

• wait (bool) – Whether to wait for the endpoint deployment to complete before returning (default: True).

• tags (Optional[Tags]) – A list of key-value pairs for tagging the endpoint (default: None).

Returns:

Name of the Amazon SageMaker Endpoint created.

Return type:

str

Raises:

• botocore.exceptions.ClientError – If Sagemaker throws an exception while creating

• endpoint. –

create_inference_component(inference_component_name: str, endpoint_name: str, variant_name: str, specification: Dict[str, Any], runtime_config: Dict[str, Any] | None = None, tags: List[Dict[str, str | PipelineVariable]] | Dict[str, str | PipelineVariable] | None = None, wait: bool = True)

Create an Amazon SageMaker Inference Component.

Parameters:

• inference_component_name (str) – Name of the Amazon SageMaker inference component to create.

• endpoint_name (str) – Name of the Amazon SageMaker endpoint that the inference component will deploy to.

• variant_name (str) – Name of the Amazon SageMaker variant that the inference component will deploy to.

• specification (Dict[str, Any]) – The inference component specification.

• runtime_config (Optional[Dict[str, Any]]) – Optional. The inference component runtime configuration. (Default: None).

• tags (Optional[Tags]) – Optional. Either a dictionary or a list of dictionaries containing key-value pairs. (Default: None).

• wait (bool) – Optional. Wait for the inference component to finish being created before returning a value. (Default: True).

Returns:

Name of the Amazon SageMaker InferenceComponent if created.

Return type:

str

create_inference_recommendations_job(role: str, sample_payload_url: str, supported_content_types: List[str], job_name: str | None = None, job_type: str = 'Default', model_name: str | None = None, model_package_version_arn: str | None = None, job_duration_in_seconds: int | None = None, nearest_model_name: str | None = None, supported_instance_types: List[str] | None = None, framework: str | None = None, framework_version: str | None = None, endpoint_configurations: List[Dict[str, any]] | None = None, traffic_pattern: Dict[str, any] | None = None, stopping_conditions: Dict[str, any] | None = None, resource_limit: Dict[str, any] | None = None)

Creates an Inference Recommendations Job

Parameters:

• role (str) – An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifactså. You must grant sufficient permissions to this role.

• sample_payload_url (str) – The S3 path where the sample payload is stored.

• supported_content_types (List[str]) – The supported MIME types for the input data.

• model_name (str) – Name of the Amazon SageMaker Model to be used.

• model_package_version_arn (str) – The Amazon Resource Name (ARN) of a versioned model package.

• job_name (str) – The name of the job being run.

• job_type (str) – The type of job being run. Must either be Default or Advanced.

• job_duration_in_seconds (int) – The maximum job duration that a job can run for. Will be used for Advanced jobs.

• nearest_model_name (str) – The name of a pre-trained machine learning model benchmarked by Amazon SageMaker Inference Recommender that matches your model.

• supported_instance_types (List[str]) – A list of the instance types that are used to generate inferences in real-time.

• framework (str) – The machine learning framework of the Image URI.

• framework_version (str) – The framework version of the Image URI.

• endpoint_configurations (List[Dict[str, any]]) – Specifies the endpoint configurations to use for a job. Will be used for Advanced jobs.

• traffic_pattern (Dict[str, any]) – Specifies the traffic pattern for the job. Will be used for Advanced jobs.

• stopping_conditions (Dict[str, any]) – A set of conditions for stopping a recommendation job. If any of the conditions are met, the job is automatically stopped. Will be used for Advanced jobs.

• resource_limit (Dict[str, any]) – Defines the resource limit for the job. Will be used for Advanced jobs.

Returns:

The name of the job created. In the form of SMPYTHONSDK-<timestamp>

Return type:

str

create_model(name, role=None, container_defs=None, vpc_config=None, enable_network_isolation=None, primary_container=None, tags=None)

Create an Amazon SageMaker Model.

Specify the S3 location of the model artifacts and Docker image containing the inference code. Amazon SageMaker uses this information to deploy the model in Amazon SageMaker. This method can also be used to create a Model for an Inference Pipeline if you pass the list of container definitions through the containers parameter.

Parameters:

• name (str) – Name of the Amazon SageMaker Model to create.

• role (str) – An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts. You must grant sufficient permissions to this role.

• container_defs (list[dict[str, str]] or [dict[str, str]]) – A single container definition or a list of container definitions which will be invoked sequentially while performing the prediction. If the list contains only one container, then it’ll be passed to SageMaker Hosting as the PrimaryContainer and otherwise, it’ll be passed as Containers.You can also specify the return value of sagemaker.get_container_def() or sagemaker.pipeline_container_def(), which will used to create more advanced container configurations, including model containers which need artifacts from S3.

• vpc_config (dict[str, list[str]]) – The VpcConfig set on the model (default: None) * ‘Subnets’ (list[str]): List of subnet ids. * ‘SecurityGroupIds’ (list[str]): List of security group ids.

• enable_network_isolation (bool) – Whether the model requires network isolation or not.

• primary_container (str or dict[str, str]) – Docker image which defines the inference code. You can also specify the return value of sagemaker.container_def(), which is used to create more advanced container configurations, including model containers which need artifacts from S3. This field is deprecated, please use container_defs instead.

• tags (Optional[Tags]) – Optional. The list of tags to add to the model.

Example

>>> tags = [{'Key': 'tagname', 'Value': 'tagvalue'}]
For more information about tags, see https://boto3.amazonaws.com/v1/documentation            /api/latest/reference/services/sagemaker.html#SageMaker.Client.add_tags

Returns:

Name of the Amazon SageMaker Model created.

Return type:

str

create_model_package_from_algorithm(name, description, algorithm_arn, model_data)

Create a SageMaker Model Package from the results of training with an Algorithm Package.

Parameters:

• name (str) – ModelPackage name

• description (str) – Model Package description

• algorithm_arn (str) – arn or name of the algorithm used for training.

• model_data (str or dict[str, Any]) – s3 URI or a dictionary representing a

• training (ModelDataSource to the model artifacts produced by)

default_bucket()

Return the name of the default bucket to use in relevant Amazon SageMaker interactions.

This function will create the s3 bucket if it does not exist.

Returns:

The name of the default bucket. If the name was not explicitly specified through

the Session or sagemaker_config, the bucket will take the form: sagemaker-{region}-{AWS account ID}.

Return type:

str

delete_endpoint(endpoint_name)

Delete an Amazon SageMaker Endpoint.

Parameters:

endpoint_name (str) – Name of the Amazon SageMaker Endpoint to delete.

delete_endpoint_config(endpoint_config_name)

Delete an Amazon SageMaker endpoint configuration.

Parameters:

endpoint_config_name (str) – Name of the Amazon SageMaker endpoint configuration to delete.

delete_model(model_name)

Delete an Amazon SageMaker Model.

Parameters:

model_name (str) – Name of the Amazon SageMaker model to delete.

describe_inference_component(inference_component_name)

Describe an Amazon SageMaker InferenceComponent

Parameters:

inference_component_name (str) – Name of the Amazon SageMaker InferenceComponent.

Returns:

Inference component details.

Return type:

dict[str,str]

determine_bucket_and_prefix(bucket: str | None = None, key_prefix: str | None = None, sagemaker_session=None)

Helper function that returns the correct S3 bucket and prefix to use depending on the inputs.

Parameters:

• bucket (Optional[str]) – S3 Bucket to use (if it exists)

• key_prefix (Optional[str]) – S3 Object Key Prefix to use or append to (if it exists)

• sagemaker_session (sagemaker.core.session.Session) – Session to fetch a default bucket and prefix from, if bucket doesn’t exist. Expected to exist

Returns: The correct S3 Bucket and S3 Object Key Prefix that should be used

download_data(path, bucket, key_prefix='', extra_args=None)

Download file or directory from S3. :param path: Local path where the file or directory should be downloaded to. :type path: str :param bucket: Name of the S3 Bucket to download from. :type bucket: str :param key_prefix: Optional S3 object key name prefix. :type key_prefix: str :param extra_args: Optional extra arguments that may be passed to the

download operation. Please refer to the ExtraArgs parameter in the boto3 documentation here: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/s3-example-download-file.html

Returns:

List of local paths of downloaded files

Return type:

list[str]

endpoint_from_production_variants(name, production_variants, tags=None, kms_key=None, wait=True, data_capture_config_dict=None, async_inference_config_dict=None, explainer_config_dict=None, live_logging=False, vpc_config=None, enable_network_isolation=None, role=None)

Create an SageMaker Endpoint from a list of production variants.

Parameters:

• name (str) – The name of the Endpoint to create.

• production_variants (list[dict[str, str]]) – The list of production variants to deploy.

• tags (Optional[Tags]) – A list of key-value pairs for tagging the endpoint (default: None).

• kms_key (str) – The KMS key that is used to encrypt the data on the storage volume attached to the instance hosting the endpoint.

• wait (bool) – Whether to wait for the endpoint deployment to complete before returning (default: True).

• data_capture_config_dict (dict) – Specifies configuration related to Endpoint data capture for use with Amazon SageMaker Model Monitoring. Default: None.

• async_inference_config_dict (dict) – specifies configuration related to async endpoint. Use this configuration when trying to create async endpoint and make async inference (default: None)

• explainer_config_dict (dict) – Specifies configuration related to explainer. Use this configuration when trying to use online explainability. (default: None).

• (dict[str (vpc_config) – The VpcConfig set on the model (default: None). * ‘Subnets’ (list[str]): List of subnet ids. * ‘SecurityGroupIds’ (list[str]): List of security group ids.

• list[str]] – The VpcConfig set on the model (default: None). * ‘Subnets’ (list[str]): List of subnet ids. * ‘SecurityGroupIds’ (list[str]): List of security group ids.

• enable_network_isolation (Boolean) – Default False. If True, enables network isolation in the endpoint, isolating the model container. No inbound or outbound network calls can be made to or from the model container.

• role (str) – An AWS IAM role (either name or full ARN). The Amazon SageMaker training jobs and APIs that create Amazon SageMaker endpoints use this role to access training data and model artifacts. After the endpoint is created, the inference code might use the IAM role if it needs to access some AWS resources. (default: None).

Returns:

The name of the created Endpoint.

Return type:

str

endpoint_in_service_or_not(endpoint_name: str)

Check whether an Amazon SageMaker Endpoint` is in IN_SERVICE status.

Raise any exception that is not recognized as “not found”.

Parameters:

• endpoint_name (str) – Name of the Amazon SageMaker Endpoint to

• status. (check)

Returns:

True if Endpoint is IN_SERVICE, False if Endpoint not exists or it’s in other status.

Return type:

bool

Raises:

expand_role(role)

Expand an IAM role name into an ARN.

If the role is already in the form of an ARN, then the role is simply returned. Otherwise we retrieve the full ARN and return it.

Parameters:

role (str) – An AWS IAM role (either name or full ARN).

Returns:

The corresponding AWS IAM role ARN.

Return type:

str

expected_bucket_owner_id_bucket_check(bucket_name, s3, expected_bucket_owner_id)

Checks if the bucket belongs to a particular owner and throws a Client Error if it is not

Parameters:

• bucket_name (str) – Name of the S3 bucket

• s3 (str) – S3 object from boto session

• expected_bucket_owner_id (str) – Owner ID string

general_bucket_check_if_user_has_permission(bucket_name, s3, bucket, region, bucket_creation_date_none)

Checks if the person running has the permissions to the bucket

If there is any other error that comes up with calling head bucket, it is raised up here If there is no bucket , it will create one

Parameters:

• bucket_name (str) – Name of the S3 bucket

• s3 (str) – S3 object from boto session

• region (str) – The region in which to create the bucket.

• bucket_creation_date_none (bool) – Indicating whether S3 bucket already exists or not

generate_default_sagemaker_bucket_name(boto_session)

Generates a name for the default sagemaker S3 bucket.

Parameters:

boto_session (boto3.session.Session) – The underlying Boto3 session which AWS service

get_caller_identity_arn()

Returns the ARN user or role whose credentials are used to call the API.

Returns:

The ARN user or role

Return type:

str

list_s3_files(bucket, key_prefix)

Lists the S3 files given an S3 bucket and key. :param bucket: Name of the S3 Bucket to download from. :type bucket: str :param key_prefix: S3 object key name prefix. :type key_prefix: str

Returns:

The list of files at the S3 path.

Return type:

[str]

read_s3_file(bucket, key_prefix)

Read a single file from S3.

Parameters:

• bucket (str) – Name of the S3 Bucket to download from.

• key_prefix (str) – S3 object key name prefix.

Returns:

The body of the s3 file as a string.

Return type:

str

update_endpoint(endpoint_name, endpoint_config_name, wait=True)

Update an Amazon SageMaker Endpoint , Raise an error endpoint_name does not exist.

Parameters:

• endpoint_name (str) – Name of the Amazon SageMaker Endpoint to update.

• endpoint_config_name (str) – Name of the Amazon SageMaker endpoint configuration to deploy.

• wait (bool) – Whether to wait for the endpoint deployment to complete before returning (default: True).

Returns:

Name of the Amazon SageMaker Endpoint being updated.

Return type:

str

Raises:

• - ValueError – if the endpoint does not already exist

• - botocore.exceptions.ClientError – If SageMaker throws an error while

• creating endpoint config, describing endpoint or updating endpoint –

update_inference_component(inference_component_name, specification=None, runtime_config=None, wait=True)

Update an Amazon SageMaker InferenceComponent

Parameters:

• inference_component_name (str) – Name of the Amazon SageMaker InferenceComponent.

• specification ([dict[str,int]]) – Resource configuration. Optional. Example: { “MinMemoryRequiredInMb”: 1024, “NumberOfCpuCoresRequired”: 1, “NumberOfAcceleratorDevicesRequired”: 1, “MaxMemoryRequiredInMb”: 4096, },

• runtime_config ([dict[str,int]]) – Number of copies. Optional. Default: { “copyCount”: 1 }

• wait – Wait for inference component to be created before return. Optional. Default is True.

Returns:

inference component name

Return type:

str

Raises:

ValueError – If the inference_component_name does not exist.

upload_data(path, bucket=None, key_prefix='data', callback=None, extra_args=None)

Upload local file or directory to S3.

If a single file is specified for upload, the resulting S3 object key is {key_prefix}/{filename} (filename does not include the local path, if any specified). If a directory is specified for upload, the API uploads all content, recursively, preserving relative structure of subdirectories. The resulting object key names are: {key_prefix}/{relative_subdirectory_path}/filename.

Parameters:

• path (str) – Path (absolute or relative) of local file or directory to upload.

• bucket (str) – Name of the S3 Bucket to upload to (default: None). If not specified, the default bucket of the Session is used (if default bucket does not exist, the Session creates it).

• key_prefix (str) – Optional S3 object key name prefix (default: ‘data’). S3 uses the prefix to create a directory structure for the bucket content that it display in the S3 console.

• extra_args (dict) – Optional extra arguments that may be passed to the upload operation. Similar to ExtraArgs parameter in S3 upload_file function. Please refer to the ExtraArgs parameter documentation here: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/s3-uploading-files.html#the-extraargs-parameter

Returns:

The S3 URI of the uploaded file(s). If a file is specified in the path argument,

the URI format is: s3://{bucket name}/{key_prefix}/{original_file_name}. If a directory is specified in the path argument, the URI format is s3://{bucket name}/{key_prefix}.

Return type:

str

upload_string_as_file_body(body, bucket, key, kms_key=None)

Upload a string as a file body. :param body: String representing the body of the file. :type body: str :param bucket: Name of the S3 Bucket to upload to (default: None). If not specified, the

default bucket of the Session is used (if default bucket does not exist, the Session creates it).

Parameters:

• key (str) – S3 object key. This is the s3 path to the file.

• kms_key (str) – The KMS key to use for encrypting the file.

Returns:

The S3 URI of the uploaded file.

The URI format is: s3://{bucket name}/{key}.

Return type:

str

wait_for_endpoint(endpoint, poll=30, live_logging=False)

Wait for an Amazon SageMaker endpoint deployment to complete.

Parameters:

• endpoint (str) – Name of the Endpoint to wait for.

• poll (int) – Polling interval in seconds (default: 30).

Raises:

• exceptions.CapacityError – If the endpoint creation job fails with CapacityError.

• exceptions.UnexpectedStatusException – If the endpoint creation job fails.

Returns:

Return value from the DescribeEndpoint API.

Return type:

dict

wait_for_inference_component(inference_component_name, poll=20)

Wait for an Amazon SageMaker Inference Component deployment to complete.

Parameters:

• inference_component_name (str) – Name of the Inference Component to wait for.

• poll (int) – Polling interval in seconds (default: 20).

Raises:

• exceptions.CapacityError – If the inference component creation fails with CapacityError.

• exceptions.UnexpectedStatusException – If the inference component creation fails.

Returns:

Return value from the DescribeInferenceComponent API.

Return type:

dict

wait_for_inference_recommendations_job(job_name: str, poll: int = 120, log_level: str = 'Verbose') → Dict[str, Any]

Wait for an Amazon SageMaker Inference Recommender job to complete.

Parameters:

• job_name (str) – Name of the Inference Recommender job to wait for.

• poll (int) – Polling interval in seconds (default: 120).

• log_level (str) – The level of verbosity for the logs.

• (default (Can be "Quiet" or "Verbose") – “Quiet”).

Returns:

Return value from the DescribeInferenceRecommendationsJob API.

Return type:

(dict)

Raises:

• exceptions.CapacityError – If the Inference Recommender job fails with CapacityError.

• exceptions.UnexpectedStatusException – If the Inference Recommender job fails.

wait_for_optimization_job(job, poll=5)

Wait for an Amazon SageMaker Optimization job to complete.

Parameters:

• job (str) – Name of optimization job to wait for.

• poll (int) – Polling interval in seconds (default: 5).

Returns:

Return value from the DescribeOptimizationJob API.

Return type:

(dict)

Raises:

• exceptions.ResourceNotFound – If optimization job fails with CapacityError.

• exceptions.UnexpectedStatusException – If optimization job fails.

sagemaker.core.helper.session_helper.botocore_resolver()

Get the DNS suffix for the given region.

Parameters:

region (str) – AWS region name

Returns:

the DNS suffix

Return type:

str

sagemaker.core.helper.session_helper.container_def(image_uri, model_data_url=None, env=None, container_mode=None, image_config=None, accept_eula=None, additional_model_data_sources=None, model_reference_arn=None)

Create a definition for executing a container as part of a SageMaker model.

Parameters:

• image_uri (str) – Docker image URI to run for this container.

• model_data_url (str or dict[str, Any]) – S3 location of model data required by this container, e.g. SageMaker training job model artifacts. It can either be a string representing S3 URI of model data, or a dictionary representing a ModelDataSource object. (default: None).

• env (dict[str, str]) – Environment variables to set inside the container (default: None).

• container_mode (str) – The model container mode. Valid modes: * MultiModel: Indicates that model container can support hosting multiple models * SingleModel: Indicates that model container can support hosting a single model This is the default model container mode when container_mode = None

• image_config (dict[str, str]) – Specifies whether the image of model container is pulled from ECR, or private registry in your VPC. By default it is set to pull model container image from ECR. (default: None).

• accept_eula (bool) – For models that require a Model Access Config, specify True or False to indicate whether model terms of use have been accepted. The accept_eula value must be explicitly defined as True in order to accept the end-user license agreement (EULA) that some models require. (Default: None).

• additional_model_data_sources (PipelineVariable or dict) – Additional location of SageMaker model data (default: None).

Returns:

A complete container definition object usable with the CreateModel API if passed via PrimaryContainers field.

Return type:

dict[str, str]

sagemaker.core.helper.session_helper.expand_role(self, role)

Expand an IAM role name into an ARN.

If the role is already in the form of an ARN, then the role is simply returned. Otherwise we retrieve the full ARN and return it.

Parameters:

role (str) – An AWS IAM role (either name or full ARN).

Returns:

The corresponding AWS IAM role ARN.

Return type:

str

sagemaker.core.helper.session_helper.get_add_model_package_inference_args(model_package_arn, name, containers=None, content_types=None, response_types=None, inference_instances=None, transform_instances=None, description=None)

Get request dictionary for UpdateModelPackage API for additional inference.

Parameters:

• model_package_arn (str) – Arn for the model package.

• name (str) – Name to identify the additional inference specification

• containers (dict) – The Amazon ECR registry path of the Docker image that contains the inference code.

• image_uris (List[str]) – The ECR path where inference code is stored.

• description (str) – Description for the additional inference specification

• content_types (list[str]) – The supported MIME types for the input data.

• response_types (list[str]) – The supported MIME types for the output data.

• inference_instances (list[str]) – A list of the instance types that are used to generate inferences in real-time (default: None).

• transform_instances (list[str]) – A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed (default: None).

sagemaker.core.helper.session_helper.get_execution_role(sagemaker_session=None, use_default=False)

Return the role ARN whose credentials are used to call the API.

Throws an exception if role doesn’t exist.

Parameters:

• sagemaker_session (Session) – Current sagemaker session.

• use_default (bool) – Use a default role if get_caller_identity_arn does not return a correct role. This default role will be created if needed. Defaults to False.

Returns:

The role ARN

Return type:

(str)

sagemaker.core.helper.session_helper.get_log_events_for_inference_recommender(cw_client, log_group_name, log_stream_name)

Retrieves log events from the specified CloudWatch log group and log stream.

Parameters:

• cw_client (boto3.client) – A boto3 CloudWatch client.

• log_group_name (str) – The name of the CloudWatch log group.

• log_stream_name (str) – The name of the CloudWatch log stream.

Returns:

A dictionary containing log events from CloudWatch log group and log stream.

Return type:

(dict)

sagemaker.core.helper.session_helper.get_update_model_package_inference_args(model_package_arn, containers=None, content_types=None, response_types=None, inference_instances=None, transform_instances=None)

Get request dictionary for UpdateModelPackage API for inference specification.

Parameters:

• model_package_arn (str) – Arn for the model package.

• containers (dict) – The Amazon ECR registry path of the Docker image that contains the inference code.

• content_types (list[str]) – The supported MIME types for the input data.

• response_types (list[str]) – The supported MIME types for the output data.

• inference_instances (list[str]) – A list of the instance types that are used to generate inferences in real-time (default: None).

• transform_instances (list[str]) – A list of the instance types on which a transformation job can be run or on which an endpoint can be deployed (default: None).

sagemaker.core.helper.session_helper.production_variant(model_name=None, instance_type=None, initial_instance_count=None, variant_name='AllTraffic', initial_weight=1, accelerator_type=None, serverless_inference_config=None, volume_size=None, model_data_download_timeout=None, container_startup_health_check_timeout=None, managed_instance_scaling=None, routing_config=None, inference_ami_version=None)

Create a production variant description suitable for use in a ProductionVariant list.

This is also part of a CreateEndpointConfig request.

Parameters:

• model_name (str) – The name of the SageMaker model this production variant references.

• instance_type (str) – The EC2 instance type for this production variant. For example, ‘ml.c4.8xlarge’.

• initial_instance_count (int) – The initial instance count for this production variant (default: 1).

• variant_name (string) – The VariantName of this production variant (default: ‘AllTraffic’).

• initial_weight (int) – The relative InitialVariantWeight of this production variant (default: 1).

• accelerator_type (str) – Type of Elastic Inference accelerator for this production variant. For example, ‘ml.eia1.medium’. For more information: https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html

• serverless_inference_config (dict) – Specifies configuration dict related to serverless endpoint. The dict is converted from sagemaker.model_monitor.ServerlessInferenceConfig object (default: None)

• volume_size (int) – The size, in GB, of the ML storage volume attached to individual inference instance associated with the production variant. Currenly only Amazon EBS gp2 storage volumes are supported.

• model_data_download_timeout (int) – The timeout value, in seconds, to download and extract model data from Amazon S3 to the individual inference instance associated with this production variant.

• container_startup_health_check_timeout (int) – The timeout value, in seconds, for your inference container to pass health check by SageMaker Hosting. For more information about health check see: https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-inference-code.html #your-algorithms -inference-algo-ping-requests

Returns:

An SageMaker ProductionVariant description

Return type:

dict[str, str]

sagemaker.core.helper.session_helper.s3_path_join(*args, with_end_slash: bool = False)

Returns the arguments joined by a slash (“/”), similar to os.path.join() (on Unix).

Behavior of this function: - If the first argument is “s3://”, then that is preserved. - The output by default will have no slashes at the beginning or end. There is one exception

(see with_end_slash). For example, s3_path_join(“/foo”, “bar/”) will yield “foo/bar” and s3_path_join(“foo”, “bar”, with_end_slash=True) will yield “foo/bar/”

• Any repeat slashes will be removed in the output (except for “s3://” if provided at the

  beginning). For example, s3_path_join(“s3://”, “//foo/”, “/bar///baz”) will yield “s3://foo/bar/baz”.

• Empty or None arguments will be skipped. For example

  s3_path_join(“foo”, “”, None, “bar”) will yield “foo/bar”

Alternatives to this function that are NOT recommended for S3 paths: - os.path.join(…) will have different behavior on Unix machines vs non-Unix machines - pathlib.PurePosixPath(…) will apply potentially unintended simplification of single

dots (“.”) and root directories. (for example pathlib.PurePosixPath(“foo”, “/bar/./”, “baz”) would yield “/bar/baz”)

• “{}/{}/{}”.format(…) and similar may result in unintended repeat slashes

Parameters:

• *args – The strings to join with a slash.

• with_end_slash (bool) – (default: False) If true and if the path is not empty, appends a “/” to the end of the path

Returns:

The joined string, without a slash at the end unless with_end_slash is True.

Return type:

str

sagemaker.core.helper.session_helper.sts_regional_endpoint(region)

Get the AWS STS endpoint specific for the given region.

We need this function because the AWS SDK does not yet honor the region_name parameter when creating an AWS STS client.

For the list of regional endpoints, see https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_temp_enable-regions.html#id_credentials_region-endpoints.

Parameters:

region (str) – AWS region name

Returns:

AWS STS regional endpoint

Return type:

str

sagemaker.core.helper.session_helper.update_args(args: Dict[str, Any], **kwargs)

Updates the request arguments dict with the value if populated.

This is to handle the case that the service API doesn’t like NoneTypes for argument values.

Parameters:

• request_args (Dict[str, Any]) – the request arguments dict

• kwargs – key, value pairs to update the args dict