TypedDicts in Python

Thu, 13 Jan 2022 17:11:28 +0000

In Python 3.8+, you can create a dictionary with type annotations with the TypedDict class in the typing module. This is a regular dictionary that allows you to define it with type hints, allowing for better documentation of simple objects like dictionaries. To use them, create a class that inherits from typing.TypedDict and define your expected keys and their types like so:

from typing import TypedDict

class ModelMetadata(TypedDict):
    audience_id: str
    training_correlation: List[float]
    validation_correlation: List[float]
    final_sample_count: int
    final_accounts: List[AccountMetadata]

Input and Output Channels for Sagemaker Training Jobs

Wed, 27 Oct 2021 19:08:01 +0000

Inputs

With SageMaker training jobs, you specify an “input channel” and configure it in the job definition. Each channel is a dictionary that specifies the data split. Each of these items will be made available on the instance in /opt/ml/input/data/<stage>.

JSON Example

{
 "train": {
 "ContentType": "trainingContentType",
 "TrainingInputMode": "File",
 "S3DistributionType": "FullyReplicated",
 "RecordWrapperType": "None"
 },
 "evaluation": {
 "ContentType": "evalContentType",
 "TrainingInputMode": "File",
 "S3DistributionType": "FullyReplicated",
 "RecordWrapperType": "None"
 },
 "validation": {
 "TrainingInputMode": "File",
 "S3DistributionType": "FullyReplicated",
 "RecordWrapperType": "None"
 }
}

CDK Example

Source

Outputs