Examples

Index > MachineLearning > Examples

Auto-generated documentation for MachineLearning type annotations stubs module types-boto3-machinelearning.

Client

Implicit type annotations

Can be used with types-boto3[machinelearning] package installed.

Write your MachineLearning code as usual, type checking and code completion should work out of the box.

# MachineLearningClient usage example

from boto3.session import Session


session = Session()

client = session.client("machinelearning")  # (1)
result = client.add_tags()  # (2)

1. client: MachineLearningClient
2. result: AddTagsOutputTypeDef

# DescribeBatchPredictionsPaginator usage example

from boto3.session import Session


session = Session()
client = session.client("machinelearning")  # (1)

paginator = client.get_paginator("describe_batch_predictions")  # (2)
for item in paginator.paginate(...):
    print(item)  # (3)

1. client: MachineLearningClient
2. paginator: DescribeBatchPredictionsPaginator
3. item: DescribeBatchPredictionsOutputTypeDef

# BatchPredictionAvailableWaiter usage example

from boto3.session import Session


session = Session()
client = session.client("machinelearning")  # (1)

waiter = client.get_waiter("batch_prediction_available")  # (2)
waiter.wait()

1. client: MachineLearningClient
2. waiter: BatchPredictionAvailableWaiter

Explicit type annotations

With types-boto3-lite[machinelearning] or a standalone types_boto3_machinelearning package, you have to explicitly specify client: MachineLearningClient type annotation.

All other type annotations are optional, as types should be discovered automatically. However, these type annotations can be helpful in your functions and methods.

# MachineLearningClient usage example with type annotations

from boto3.session import Session

from types_boto3_machinelearning.client import MachineLearningClient
from types_boto3_machinelearning.type_defs import AddTagsOutputTypeDef
from types_boto3_machinelearning.type_defs import AddTagsInputRequestTypeDef


session = Session()

client: MachineLearningClient = session.client("machinelearning")

kwargs: AddTagsInputRequestTypeDef = {...}
result: AddTagsOutputTypeDef = client.add_tags(**kwargs)

# DescribeBatchPredictionsPaginator usage example with type annotations

from boto3.session import Session

from types_boto3_machinelearning.client import MachineLearningClient
from types_boto3_machinelearning.paginator import DescribeBatchPredictionsPaginator
from types_boto3_machinelearning.type_defs import DescribeBatchPredictionsOutputTypeDef


session = Session()
client: MachineLearningClient = session.client("machinelearning")

paginator: DescribeBatchPredictionsPaginator = client.get_paginator("describe_batch_predictions")
for item in paginator.paginate(...):
    item: DescribeBatchPredictionsOutputTypeDef
    print(item)

# BatchPredictionAvailableWaiter usage example with type annotations

from boto3.session import Session

from types_boto3_machinelearning.client import MachineLearningClient
from types_boto3_machinelearning.waiter import BatchPredictionAvailableWaiter

session = Session()
client: MachineLearningClient = session.client("machinelearning")

waiter: BatchPredictionAvailableWaiter = client.get_waiter("batch_prediction_available")
waiter.wait()