fix(jumpstart): ignore unknown fields in HubContentDocument to handle…

… hub schema additions (#5849)

Update version, changelog, and pyproject files for release of 3.10.1 (#…

…5838)

Release 2.257.3 (#5826)

chore: Bump version to 3.10.0 for release (#5821)

* chore: Bump version to 3.10.0 for release

---------

Co-authored-by: Mohamed Zeidan <zeidmo@amazon.com>

chore: Bump version to 3.9.0 for release (#5793)

fix: bug: ModelBuilder overwrites user-provided HF_MODEL_ID for DJL S…

…erving, preventi (5529) (#5734)

* fix: bug: ModelBuilder overwrites user-provided HF_MODEL_ID for DJL Serving, preventi (5529)

* fix: address review comments (iteration #1)

* fix: address review comments (iteration #1)

* fix: address review comments (iteration #1)

* fix: address review comments (iteration #2)

chore: Bump version to 3.8.0 for release (#5768)

* chore: Bump version to 3.8.0 for release

Update all VERSION files and pyproject.toml dependency bounds
to reflect the 3.8.0 release. Add v3.8.0 changelog entry with
new features (Feature Group Manager, Image Upgrades) and bug
fixes (MLFlowConfig, docker compose v2, HuggingFace, Pytorch).

* docs: Add v3.8.0 changelog entries to submodules

Add release notes to sagemaker-core (v2.8.0), sagemaker-serve
(v1.8.0), sagemaker-train (v1.8.0), and sagemaker-mlops (v1.8.0)
changelogs with changes mapped to their respective submodules.

chore: Updated changelog, version and pyproject.toml for release (#5706)

fix(evaluate): Remove ModelPackageConfig from EvaluateBaseModel steps (…

…#5635)

When evaluate_base_model=True, the EvaluateBaseModel step in both
DETERMINISTIC_TEMPLATE and CUSTOM_SCORER_TEMPLATE incorrectly included
ModelPackageConfig with SourceModelPackageArn, causing the base model
evaluation to load fine-tuned model weights instead of using only the
base model from the public hub. This made both evaluations identical,
leading users to believe fine-tuning had no effect.

Remove ModelPackageConfig from the EvaluateBaseModel step in both
templates so it only uses BaseModelArn from ServerlessJobConfig. The
EvaluateCustomModel step retains ModelPackageConfig to correctly load
fine-tuned weights. This is consistent with the fix already applied to
the LLMAJ_TEMPLATE.

---
X-AI-Prompt: Fix BenchMarkEvaluator evaluate_base_model bug from D406780217
X-AI-Tool: Kiro

sim: https://t.corp.amazon.com/D406780217

Update CHANGELOG 3.6.0 (#5649)

* Update CHANGELOG.md sagemaker-core

* Update VERSION sagemaker-core

* Update CHANGELOG.md sagemaker-train

* Update VERSION sagemaker-train

* Update pyproject.toml sagemaker-train

* Update CHANGELOG.md sagemaker-serve

* Update VERSION sagemaker-serve

* Update pyproject.toml sagemaker-serve

* Update CHANGELOG.md sagemaker-mlops

* Update VERSION sagemaker-mlops

* Update pyproject.toml sagemaker-mlops

* Update VERSION meta

* Update CHANGELOG.md meta

* Update pyproject.toml meta