Releases: aws/sagemaker-python-sdk
Releases · aws/sagemaker-python-sdk
v1.50.5
Bug fixes and other changes
- Use serving_image_uri for Airflow
Documentation changes
- revise Processing docstrings for formatting and class links
- Add processing readthedocs
v1.50.4
Bug fixes and other changes
- Remove version number from default version comment
- remove remaining instances of python-dateutil pin
- upgrade boto3 and remove python-dateutil pin
Documentation changes
- Add issue templates and configure issue template chooser
- Update error type in delete_endpoint docstring
- add version requirement for using "requirements.txt" when serving an MXNet model
- update container dependency versions for MXNet and PyTorch
- Update supported versions of PyTorch
v1.50.3
Bug fixes and other changes
- ignore private Automatic Model Tuning hyperparameter when attaching AlgorithmEstimator
Documentation changes
- add Debugger API docs
v1.50.2
Bug fixes and other changes
- add tests to quick canary
- honor 'wait' flag when updating endpoint
- add default framework version warning message in Model classes
- Adding role arn explanation for sagemaker role
- allow predictor to be returned from AutoML.deploy()
- add PR checklist item about unique_name_from_base()
- use unique_name_from_base for multi-algo tuning test
- update copyright year in license header
Documentation changes
- add version requirement for using "requirement.txt" when serving a PyTorch model
- add SageMaker Debugger overview
- clarify requirements.txt usage for Chainer, MXNet, and Scikit-learn
- change "associate" to "create" for OpenID connector
- fix typo and improve clarity on installing packages via "requirements.txt"
v1.50.1
Bug fixes and other changes
- fix PyTorchModel deployment crash on Windows
- make PyTorch empty framework_version warning include the latest PyTorch version
v1.50.0
Features
- allow disabling debugger_hook_config
Bug fixes and other changes
- relax urllib3 and requests restrictions.
- Add uri as return statement for upload_string_as_file_body
- refactor logic in fw_utils and fill in docstrings
- increase poll from 5 to 30 for DescribeEndpoint lambda.
- fix test_auto_ml tests for regions without ml.c4.xlarge hosts.
- fix test_processing for regions without m4.xlarge instances.
- reduce test's describe frequency to eliminate throttling error.
- Increase number of retries when describing an endpoint since tf-2.0 has larger images and takes longer to start.
Documentation changes
- generalize Model Monitor documentation from SageMaker Studio tutorial
v1.49.0
Features
- Add support for TF-2.0.0.
- create ProcessingJob from ARN and from name
Bug fixes and other changes
- Make tf tests tf-1.15 and tf-2.0 compatible.
Documentation changes
- add Model Monitor documentation
- add link to Amazon algorithm estimator parent class to clarify **kwargs
v1.48.1
Bug fixes and other changes
- use name_from_base in auto_ml.py but unique_name_from_base in tests.
- make test's custom bucket include region and account name.
- add Keras to the list of Neo-supported frameworks
Documentation changes
- add link to parent classes to clarify **kwargs
- add link to framework-related parent classes to clarify **kwargs
v1.48.0
Features
- allow setting the default bucket in Session
Bug fixes and other changes
- set integration test parallelization to 512
- shorten base job name to avoid collision
- multi model integration test to create ECR repo with unique names to allow independent parallel executions