v3.16.0

v3.16.0 (2026-07-15)

New Features

• feat: actionable guidance for removed v2 interfaces (#6004)
• feat(serve): add SageMaker GenAI inference benchmarking and recommendation (#5874)
• feat(feature-store): add BatchWriteRecord and ListRecords to FeatureGroup (#5983)

Bug Fixes

• fix(iam): scope repo-level ECR actions to prevent false deny in preflight validation (#6024)
• fix: filter full recipe template from serverless train() (#6021)
• Fix sm-train unit tests + use single logger in base trainer (#6030)

Tests

• test(mlops): Skip non-PEP440 version keys in sklearn_latest_version (#6022)

v2.257.5

v2.257.5 (2026-07-14)

Bug Fixes

• Read the Docs build failure (#6023)

3.15.1

v3.15.1 (2026-07-09)

New Features

• feat: Add granular telemetry signals decorator params and error classification (#5963)

Bug Fixes

• fix: always apply evaluator identity keywords and allow explicit domain_id (#5989)
• fix: ModelBuilder resolves private hub artifacts correctly (#5985)
• fix: refresh LLMAsJudgeEvaluator allowed evaluator models (#5987)
• fix: define LAMBDA_ARN_REGEX in finetune_utils to fix RLVRTrainer NameError (#5988)
• fix: RLVR validation bugfix (#6000)
• fix: drop claude-sonnet-4-20250514 from evaluator allowlist (#6009)
• fix(serve): Invoke pip without shell in xgboost install_package (#5981)
• fix: Correct DJL-LMI ISO/ADC accounts + add THF/ISO-E (djl-lmi, huggingface-llm-neuronx) (#5980)

Documentation

• docs: Add AGENTS.md and llms.txt for AI agent v3 guidance (#5982)
• docs: Add SDK-first guidance to AGENTS.md and llms.txt (#5997)
• docs: Add Version Lifecycle page under Getting Started (#5994)
• docs: serve robots.txt opting V2 docs out of AI training crawls (#6003)

Other

• Add Triton Server v26.05 image URI config (#5999)
• Add sklearn 1.4-2-py312 and xgboost 3.2-0 image URI configs (#6008)
• test: Fix/v3 tests (#5996)
• test: wip nova hyperpod integ tests (#5990)

v2.257.4

v2.257.4 (2026-07-13)

Enhancements

• Add v2 -> v3 runtime migration warnings (#5978)
• Update SDK to use latest LMIv26 image for sdk v2.x (#5955)

Bug Fixes

• Harden S3 download path handling (#5984)

Documentation Changes

• Add noindex and canonical tags to deprecated V2 docs (#6001)
• Add deprecation banner to V2 docs (#5991)
• Add v2 -> v3 version lifecycle table to README (#5979)

Other Changes

• Mark capacity-flaky GPU integ tests as slow_test (#5998)
• Disable HF Xet/hf_transfer in serve integ tests (#5992)
• Fix slow tests in v2 (#5944)
• Fix canaries-v2 (#5932)

3.15.0

Nova Forge SDK Reconciliation (v3.15.0)

Release: v3.15.0 (2026-06-22)
Packages: sagemaker 3.15.0 · sagemaker-core 2.15.0 · sagemaker-train 1.15.0 · sagemaker-serve 1.15.0 · sagemaker-mlops 1.15.0

This release converges the standalone Nova Forge SDK into the SageMaker Python SDK, making Nova model customization (training, evaluation, deployment) first-class under the sagemaker.* namespace.

---

New Features

Training — Recipes

• feat(train): add 3-level recipe override support with get_resolved_recipe() (#2034)
• feat: recipe override handling (#2056)
• feat: add Nova-specific recipe validations (#2052)
• feat(train): auto-resolve HyperPod recipe from Hub (#2050)

Training — Compute / Infra

• feat(train): add Serverless / SMTJ / HyperPod support to trainers and evaluators (#2045)
• feat: add infra validation (#2049)

Training — Methods & Data

• feat(train): enable Data Mixing for Nova models (#2047, #2054)
• feat: add is_multimodal utils function (multimodal data auto-detection) (#2033)
• feat: RLVRTrainer Lambda ARN support (#2025)
• feat: RLVR reward Lambda validation (#2036)
• feat: Iterative training for SMTJ serverful and Hyperpod
• feat: New CPTTrainer class

Evaluation

• feat: InspectAI evaluator (#2039)
• feat: add Nova as a target for LLM-as-a-Judge (LLMAJ) (#2059)
• feat: support serverful training job checkpoint resolution in InspectAI evaluator (#2066)

Deploy / Setup / Validation

• feat: add Nova SMI config bounds validation to ModelBuilder (#2040)
• feat: Bedrock deployment from S3 checkpoint

---

Recipes

• fix: reject unknown recipe overrides (serverless + serverful) and untrusted IAM roles (#2071)
• fix: apply recipe overrides to hyperparameters in SMTJ serverful path (#2070)
• fix: recipe override errors in evaluator (#2067)
• fix: hub-content IAM perms, recipe dataset paths, and log markup escaping (#2065)

IAM / Security

• IAM role validations and util methods to create IAM roles

HyperPod / Compute

• fix: add HyperPod validation in train and evaluate (#2051)
• fix: evaluation on HyperPod (#2061)
• fix: resolve HyperPod training image from EKS payload template (#2069)
• fix: skip model_package_group validation when HyperPod compute is provided in CPTTrainer (#2068)
• fix: use compute param in get fine-tuning utils (#2060)

Data / Serverless

• fix: set Converse as S3DataType for Nova models in SMTJ Serverful for SFT and DPO (#2064)
• fix(train): use Converse S3DataType for Nova SFT/DPO in serverless flow (#2079)

Evaluation / Misc

• fix: MLflow error causing OSS model eval to fail (#2075)
• fix: RLVR setup and reward Lambda handling (#2076)

---

Full Changelog: v3.14.0...v3.15.0

3.14.0

What's Changed

• fix(integ): remove module-level env var from MTRL tests by @lucasjia-aws in #5929
• test: run integ-tests-us-east-1 for all submodules by @lucasjia-aws in #5934
• test: add nova tests by @lucasjia-aws in #5933
• chore: Run only the changed submodule's tests for test-only PRs by @lucasjia-aws in #5936
• test: fix canaries-v3 by @lucasjia-aws in #5940
• chore: deprecate Python 3.9 support by @mollyheamazon in #5941
• chore: Change GPU integ tests and Canaries schedule by @lucasjia-aws in #5938
• chore: Change GPU integ test schedule to 3h interval by @lucasjia-aws in #5945
• chore: Add unit/slow/localmode and unit-test-v3 jobs to CI Health workflow by @lucasjia-aws in #5949
• fix(ci): Use source-version-override so CI Health builds checkout the right branch by @lucasjia-aws in #5950
• fix:unskip and add mock test setup for mtrl integ tests by @rsareddy0329 in #5937
• chore: Register 'serial' pytest marker in mlops tox.ini by @lucasjia-aws in #5951
• test(serve): isolate Bedrock model-import integ tests behind import_model marker by @lucasjia-aws in #5952
• docs(ml_ops): Add Data Catalog Config notebook for Feature Store by @Vishakha263 in #5927
• fix(train): fall back to subscription recipes for models without base recipes by @aviruthen in #5946
• updated the SDK to use latest LMI image for sdk v3.x by @access2rohit in #5954
• Update training resource name in MTRL tests as previous MPG got cleanedup by @rsareddy0329 in #5953
• fix(serve): repair HuggingFace -> JumpStart redirect in ModelBuilder by @ZealSV in #5958
• fix(train): use latest model package in LLM-as-judge base model integ tests by @mollyheamazon in #5959
• Release 3.14.0 by @mollyheamazon in #5961

New Contributors

• @Vishakha263 made their first contribution in #5927

Full Changelog: v3.13.1...v3.14.0

v3.13.1

Features

• feat: add import job polling and provisioned throughput for Bedrock OSS deployments

Bug Fixes

• fix: Address MTRL Eval Hyperparameters issue

v3.13.0

New Features

• feat: Model customization - Add new finetuning Trainer - MultiTurnRLTrainer(Multi-Turn Reinforcement Learning)
• feat: Model customization - Add new evaluator - MultiTurnRLEvaluator
• feat: Deployment - Add MTRL support for BedrockModelBuilder and ModelBuilder.

Documentation

• Add details for MTRL trainer along with other finetuning interfaces under Model customization Section - https://sagemaker.readthedocs.io/en/stable/model_customization/model_customization.html
• Add details for MTRL evaluator along with existing evaluators.

Fixes

• fix: set sagemaker_config=None on mock session in test_from_jumpstart_config_applies_volume_size
• Restore BatchTransformInput.destination attribute in v3

v3.12.0

New Features

• SageMaker Token Generator (#5868): Embed the aws-sagemaker-token-generator library into sagemaker.core so users can generate SageMaker bearer tokens without installing a separate wheel. Usage: from sagemaker.core.aws_sagemaker_token_generator import provide_token
• Feature Processor - Spark 3.5 / Python 3.12 support (#5816): Dynamic Spark image resolution based on installed PySpark and Python versions. Supports Spark 3.1/3.2/3.3/3.5 with Python 3.9 and 3.12. Auto-installs sagemaker-feature-store-pyspark for Spark remote jobs.

Bug Fixes

• Networking vpc_config AttributeError and telemetry region fallback (#5839): Fix AttributeError on vpc_config in networking and telemetry region fallback for classmethods.
• Add CustomAttributes field to DefaultPayloadsModel (#5870): Add missing CustomAttributes field to DefaultPayloadsModel.
• sagemaker-core: Preserve falsy values in serialize() output (#5860): Fix bug where False, 0, and "" were silently dropped by serialize() due to truthy check. This caused issues like optimize_model=False being sent as True.
• serve: Prevent code injection in capture_dependencies path interpolation (#5792): Security fix — use repr() escaping to prevent code injection via crafted directory names in ModelBuilder with dependencies={"auto": True}. (CWE-94, P414309851)
• VolumeSizeInGB missing from v3 deploy for JumpStart models (#5847): Fix VolumeSizeInGB not being passed through when deploying models with inference_volume_size from JumpStart config.

v3.11.0

New Features

• Auto-detect subscription recipe hyperparameters in SFTTrainer for Nova Forge datamix support
• Create asymmetric ECDSA signing key in feature processor step compiler for remote function payload verification

Documentation

• Add Feature Store reference to Implement MLOps page
• Replace internal S3 URIs with user placeholders in SFT notebook