AWS Sagemaker API reference

Tags: Integration, MachineLearning, AWS

This package contains things that are useful when extending Flytekit.

AlgorithmName()	The algorithm name is used for deciding which pre-built image to point to.
AlgorithmSpecification([algorithm_name, ...])	Specifies the training algorithm to be used in the training job This object is mostly a pass-through, with a couple of exceptions include: (1) in Flyte, users don't need to specify TrainingImage; either use the built-in algorithm mode by using Flytekit's Simple Training Job and specifying an algorithm name and an algorithm version or (2) when users want to supply custom algorithms they should set algorithm_name field to CUSTOM.
CategoricalParameterRange(values)	param List[str] values list of strings representing categorical values
ContinuousParameterRange(max_value, ...)	param float max_value
DISTRIBUTED_TRAINING_CONTEXT_KEY	str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
DistributedProtocol()	The distribution framework is used for determining which underlying distributed training mechanism to use.
DistributedTrainingContext(current_host, ...)
HPOJob(max_number_of_training_jobs, ...[, ...])	HPOJob Configuration should be used to configure the HPO Job.
HyperparameterScalingType()
HyperparameterTuningJobConfig(...)	The specification of the hyperparameter tuning process https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-ex-tuning-job.html#automatic-model-tuning-ex-low-tuning-config
HyperparameterTuningObjective(...)	HyperparameterTuningObjective is a data structure that contains the target metric and the objective of the hyperparameter tuning.
HyperparameterTuningObjectiveType()
HyperparameterTuningStrategy()
InputContentType()	Specifies the type of content for input data.
InputMode()	When using FILE input mode, different SageMaker built-in algorithms require different file types of input data See https://docs.aws.amazon.com/sagemaker/latest/dg/cdf-training.html https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html
IntegerParameterRange(max_value, min_value, ...)	param int max_value
ParameterRangeOneOf(param)	Initializes a new ParameterRangeOneOf.
SagemakerCustomTrainingTask(*args, **kwargs)	Allows a custom training algorithm to be executed on Amazon Sagemaker.
SagemakerHPOTask(*args, **kwargs)	param task_type defines a unique task-type for every new extension. If a backend plugin is required then
SagemakerTrainingJobConfig(...[, ...])	Configuration for Running Training Jobs on Sagemaker.
TrainingJobEarlyStoppingType()
TrainingJobResourceConfig(instance_type, ...)	TrainingJobResourceConfig is a pass-through, specifying the instance type to use for the training job, the number of instances to launch, and the size of the ML storage volume the user wants to provision Refer to SageMaker official doc for more details: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingJob.html