Skip to content

v3.22.0

Choose a tag to compare

View all tags
@papriwal papriwal released this 15 Sep 05:37
v3.22.0
c796eb3
This commit was created on GitHub.com and signed with GitHub’s verified signature.
GPG key ID: B5690EEEBB952194
Verified
Learn about vigilant mode.

SageMaker Python SDK v3.22.0

Highlights

  • Added Instance Preferences for multi-instance-type training and processing, including resolved selected-instance fields.
  • Added Feature Store UpdateRecord and Standard_V2 storage support.
  • Added pre-trainer hyperparameter discovery with list_hyperparameters().
  • Added SageMaker Hub validation for raw base-model names.
  • Completed PipelineSession support for SFT, DPO, RLAIF, and RLVR trainers.
  • Improved private-Hub model and alias resolution for ModelTrainer.
  • Added and refreshed image URI configurations for supported inference and training frameworks.

Package versions

  • sagemaker-core==2.22.0
  • sagemaker-train==1.22.0
  • sagemaker-serve==1.22.0
  • sagemaker-mlops==1.22.0
  • sagemaker==3.22.0

Compatibility

  • sagemaker-core now requires boto3>=1.43.90,<2.0.0, ensuring botocore includes the public InstancePreferences API model.
  • Internal dependency lower bounds move to the versions released together in 3.22.0.

New features

  • feat(train): add list_hyperparameters() for pre-trainer hyperparameter discovery (#6149)
  • feat(train): validate raw base model names in SageMaker Hub (#6227)
  • feat(core,train): add InstancePreferences for multi-instance-type training and processing (#6249)
  • feat(feature-store): add UpdateRecord API and Standard_V2 storage type (#6247)

Bug fixes

  • fix(train): add PipelineSession support to SFT, DPO, RLAIF, and RLVR trainers (#6213)
  • fix(feature-store): register HubContent Dataset from DatasetBuilder CSV paths (#6212)
  • fix(local): detect Docker Compose v2+ when its version has no v prefix (#6231)
  • fix(train): resolve private Hub models and aliased references for ModelTrainer (#6201)
  • fix(core): resolve default training role from SageMaker configuration (#6228)
  • fix(train): validate evaluator models against the live supported-model list (#6217)
  • fix(train): complete PipelineSession support for SFT, DPO, RLAIF, and RLVR trainers (#6235)
  • fix(train): preserve training_plan_arn during serverful compute reconstruction (#6258)

Other changes

  • change(core): add image URI configs for DLC serving frameworks and Amazon Linux 2023 PyTorch (#6220)
  • change(core): add image URI configs for vLLM and SGLang (#6218)
  • ci(core): add botocore-sync workflows (#6226)
  • change(core): add TensorFlow inference 2.20 and training 2.21 image URI configs (#6230)
  • change(core): add Ray/llama-cpp CPU images and device-selectable DLC serving configs (#6229)
  • change(core): refresh generated image URI configs (55c2a9bd)
  • change(serve): emit the JumpStart model ID in ModelBuilder telemetry (#6234)

Tests and documentation

  • fix(ci,train): stop integration tests from rerunning the shallow suite (#6216)
  • docs(train): add guidance for maintaining shallow integration tests (#6219)
  • fix(train): refresh MTRL attached-job integration fixtures (#6259)
  • fix(train): add training_plan_arn to serverful test fixtures (#6270)
Assets 2
Loading