Releases: aws/sagemaker-python-sdk
Releases · aws/sagemaker-python-sdk
v3.7.1
Features
- Telemetry: Added telemetry emitter to
ScriptProcessorandFrameworkProcessor, enabling SDK usage tracking for processing jobs via the telemetry attribution module (newPROCESSINGfeature enum added to telemetry constants)
Fixes
- ModelBuilder: Fixed
accept_eulahandling in ModelBuilder's LoRA deployment path — previously hardcoded toTrue, now respects the user-provided value and raises aValueErrorif not explicitly set toTrue - Evaluate: Fixed Lambda handler name derivation in the Evaluator — hardcoded the handler to
lambda_function.lambda_handlerinstead of deriving it from the source filename, which caused invocation failures when the source file had a non-default name
Full Changelog: v3.7.0...v3.7.1
v3.7.0
Fixes
- ModelBuilder: Sync Nova hosting configs with AGISageMakerInference
- Evaluate: Remove GPT OSS model evaluation restriction
Features
- AWS Batch: Add support for Quota Management job submission and job priority update
- AWS Batch: Extend list_jobs_by_share for quota_share_name
- Evaluate: Support IAM role for BaseEvaluator
- Telemetry: Add telemetry attribution module for SDK usage provenance
- MLflow: Metrics visualization, enhanced wait UI, and eval job links
Chores
- Updated SDK to use latest LMIv22 image for v3.x
- Migration guide update
- AWS Batch integ test resources are now uniquely named by test run
v3.6.0
Fixes
- HyperparameterTuner: Include sm_drivers channel in HyperparameterTuner jobs
- Pipeline: Fix handling of training step dependencies to allow successful pipeline creation
- ModelBuilder: Fix the bug in deploy from LORA finetuning job
Features
- Feature Processor: Port feature processor to v3
- Jumpstart: Add EUSC region config for JumpStart
v2.257.1
v3.5.0
Features
- Feature Store v3: New version of Feature Store functionality
- Batch job listing by share identifier: Added support for listing Batch jobs filtered by share identifier
- Stop condition for model customization trainers: Added stopping condition support to model customization trainers
- EMRStep smart output: Enhanced EMR step output handling with smart output capabilities
- Transform AMI version support: Added support for specifying AMI version in SageMaker transform jobs
Enhancements
- Inference pipeline notebook example: Added example notebook demonstrating inference pipeline usage
- Migration documentation: Added migration documentation
Bug Fixes
- Model Customization bugs: Fixed multiple issues in Model Customization functionality
- Default stopping condition removal: Removed default stopping condition for MC trainer to prevent conflicts
- Instance groups parameter handling: Fixed issue where default instance_type/instance_count were incorrectly applied when instance_groups was set
- JumpStart alt config resolution: Resolved alternative configuration resolution for JumpStart models
- Inference processor naming: Updated inference processor identifier from 'inf2' to 'neuronx'
- HuggingFace Neuronx PyTorch version: Corrected the PyTorch version for HuggingFace Neuronx
- License additions: Added license to sagemaker-mlops and sagemaker-serve packages
v3.4.1
Fixes
- Pipelines: Correct Tag class usage in pipeline creation (#5526)
- ModelTrainer: Support PipelineVariables in hyperparameters (#5519)
- HyperparameterTuner: Include ModelTrainer internal channels (#5516)
- Experiments: Don't apply default experiment config for pipelines in non-Eureka GA
regions (#5500)
Features
Chores
v2.257.0
v2.257.0 (2026-02-03)
Features
- Update image URIs for DJL 0.36.0 release
v3.4.0
Features
- feat: add emr-serverless step for SageMaker Pipelines
Bug fixes and Other Changes
- Add Nova recipe training support in ModelTrainer
- Add Partner-app Auth provider
- Add sagemaker dependency for remote function by default V3
v2.256.1
Bug fixes and Other Changes
- Bug fixes remote function
v3.3.1
Bug fixes and Other Changes
- ProcessingJob fix - Remove tags in Processor while Job creation
- Telemetry Updates
- sagemaker-mlops bug fix - Correct source code 'dependencies' parameter to 'requirements'
- aws_batch bug fix - remove experiment config parameter as it Estimator is deprecated.