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… and sagemaker.deserialzers (aws#5037) * Move RecordSerializer and RecordDeserializer to sagemaker.serializers and sagemaker.deserializers * fix codestyle * fix test --------- Co-authored-by: pintaoz <pintaoz@amazon.com>
* Add framework_version to all TensorFlowModel examples * update framework_version to x.x.x --------- Co-authored-by: pintaoz <pintaoz@amazon.com>
…5043) * fix: pass in inference_ami_version to model_based endpoint type * documentation: update contributing.md w/ venv instructions and pip install fixes --------- Co-authored-by: Zhaoqi <jzhaoqwa@amazon.com>
* Add warning about not supporting * update wording --------- Co-authored-by: pintaoz <pintaoz@amazon.com>
* added ap-southeast-7 and mx-central-1 for Jumpstart * added BKK dlc to djl-neuronx --------- Co-authored-by: Isha Chidrawar <ishachid@amazon.com>
…DK (aws#5050) Co-authored-by: malavhs <malavhs@amazon.com>
…aws#5052) * Add backward compatbility for RecordSerializer and RecordDeserializer * fix circular import * fix test --------- Co-authored-by: pintaoz <pintaoz@amazon.com>
* fix altconfig hubcontent and reenable integ test * linting * update exception thrown * feat: Add support for TGI Neuronx 0.0.27 and HF PT 2.3.0 image in PySDK (aws#5050) Co-authored-by: malavhs <malavhs@amazon.com> * add test * update predictor spec accessor * lint * set custom field from HCD config to model spec data class * lint * remove logs * last update --------- Co-authored-by: Malav Shastri <57682969+malav-shastri@users.noreply.github.com> Co-authored-by: malavhs <malavhs@amazon.com>
* fix: make configs safer * fix: safer destructor in ModelTrainer * format * Update error message * pylint * Create BaseConfig
…ws#5058) * Remove main function entrypoint in ModelBuilder dependency manager. * Remove main function entrypoint in ModelBuilder dependency manager. --------- Co-authored-by: Joseph Zhang <cjz@amazon.com>
…aining script which can misguide users. (aws#5057) * change: Allow telemetry only in supported regions * change: Allow telemetry only in supported regions * change: Allow telemetry only in supported regions * change: Allow telemetry only in supported regions * change: Allow telemetry only in supported regions * documentation: Removed a line about python version requirements of training script which can misguide users.Training script can be of latest version based on the support provided by framework_version of the container --------- Co-authored-by: Roja Reddy Sareddy <rsareddy@amazon.com>
Co-authored-by: pintaoz <pintaoz@amazon.com>
…ing one (aws#5063) Co-authored-by: Keshav Chandak <chakesh@amazon.com>
Co-authored-by: pintaoz <pintaoz@amazon.com>
…ws#5062) * Fix error when there is no session to call _create_model_request() * Fix codestyle --------- Co-authored-by: pintaoz <pintaoz@amazon.com>
* Ensure Model.is_repack() returns a boolean * update test --------- Co-authored-by: pintaoz <pintaoz@amazon.com>
* Allow ModelTrainer to accept hyperparameter file and create Hyperparameter class * pylint * Detect hyperparameters from contents rather than file extension * pylint * change: add integs * change: add integs * change: remove custom hyperparameter tooling * Add tests for hp contracts * change: add unit tests and remove unreachable condition * fix integs * doc check fix * fix tests * fix tox.ini * add unit test
…urated Hub Phase 2 (aws#5070) * change: update image_uri_configs 01-27-2025 06:18:13 PST * fix: skip TF tests for unsupported versions (aws#5007) * fix: skip TF tests for unsupported versions * flake8 * change: update image_uri_configs 01-29-2025 06:18:08 PST * chore: add new images for HF TGI (aws#5005) * feat: add pytorch-tgi-inference 2.4.0 * add tgi 3.0.1 image * skip faulty test * formatting * formatting * add hf pytorch training 4.46 * update version alias * add py311 to training version * update tests with pyversion 311 * formatting --------- Co-authored-by: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com> * feat: use jumpstart deployment config image as default optimization image (aws#4992) Co-authored-by: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com> * prepare release v2.238.0 * update development version to v2.238.1.dev0 * Fix ssh host policy (aws#4966) * Fix ssh host policy * Filter policy by algo- * Add docstring * Fix pylint * Fix docstyle summary * Unit test * Fix unit test * Change to unit test * Fix unit tests * Test comment out flaky tests * Readd the flaky tests * Remove flaky asserts * Remove flaky asserts --------- Co-authored-by: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com> * change: Allow telemetry only in supported regions (aws#5009) * change: Allow telemetry only in supported regions * change: Allow telemetry only in supported regions * change: Allow telemetry only in supported regions * change: Allow telemetry only in supported regions * change: Allow telemetry only in supported regions --------- Co-authored-by: Roja Reddy Sareddy <rsareddy@amazon.com> * mpirun protocol - distributed training with @Remote decorator (aws#4998) * implemented multi-node distribution with @Remote function * completed unit tests * added distributed training with CPU and torchrun * backwards compatibility nproc_per_node * fixing code: permissions for non-root users, integration tests * fixed docstyle * refactor nproc_per_node for backwards compatibility * refactor nproc_per_node for backwards compatibility * pylint fix, newlines * added unit tests for bootstrap_environment remote * added mpirun protocol for distributed training with @Remote decorator * aligned mpi_utils_remote.py to mpi_utils.py for estimator * updated docstring for sagemaker sdk doc --------- Co-authored-by: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com> * feat: Add support for deepseek recipes (aws#5011) * feat: Add support for deeepseek recipes * pylint * add unit test * feat: [JumpStart] Add access configs and training instance type variants artifact uri handling for Curated Hub Phase 2 training integration (aws#1653) * Add access config to training input for Curated Hub Training Integration * Add support to retrieve instance specific training artifact keys * Fix some typos and naming issues * Fix more typos * fix formatting issues with black * modify access config logic so accept_eula is passed into fit * update black formatting * Add more unit tests for passing access configs * fix style errors * fix for failing integ test * fix styles and integ test error * skip blocking integ test * fix formatting * remove env vars when access configs are being used * fix docstyle issue * update usage of access configs, remove conversion of training artifact key to uri * fix styling issues * fix styling issues * fix unit tests * fix adding hubaccessconfig only if hubcontentarn exists * move logic to JumpStartEstimator from Job * Fix styling issues * Remove unused code * fix styling issues * fix unit test failure * fix some formatting, add comments * remove typing for estimator in get_access_configs function * fix circular import dependency * fix styling issues --------- Co-authored-by: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com> * Always add code channel, regardless of network isolation (aws#1657) * fix formatting issue * fix formatting issue * fix formatting issue * fix tensorflow file --------- Co-authored-by: sagemaker-bot <sagemaker-bot@amazon.com> Co-authored-by: Erick Benitez-Ramos <141277478+benieric@users.noreply.github.com> Co-authored-by: varunmoris <176621270+varunmoris@users.noreply.github.com> Co-authored-by: Gary Wang <38331932+gwang111@users.noreply.github.com> Co-authored-by: ci <ci> Co-authored-by: parknate@ <parknate@amazon.com> Co-authored-by: rsareddy0329 <rsareddy0329@gmail.com> Co-authored-by: Roja Reddy Sareddy <rsareddy@amazon.com> Co-authored-by: Bruno Pistone <brn.pistone@gmail.com>
…res (aws#5720) * feat: split MODEL_CUSTOMIZATION telemetry into NOVA and OSS sub-features * test: add unit tests for NOVA/OSS telemetry sub-feature detection
* Update v3 readthedocs to autorender all submodules * Maintain previous opinionated list, add new section of full module reference * Add sagemaker-core description in init
* Bug fixes for HF models * Fix serialization deserialization issues in core * Removing unnecessary comments * feat: add support for model_index.json fallback in HF config retrieval * feat: add support for PEFT models with adapter_config.json config file --------- Co-authored-by: aviruthen <91846056+aviruthen@users.noreply.github.com>
…mmetric keys for data verification checks (aws#5708) * Revert "Bug fix for hmac key for V3 (aws#5379)" This reverts commit fb0d789. * Revert "Add sagemaker dependency for remote function by default V3 (aws#5487)" This reverts commit 422b35f. * fix: use asymmetric keys to sign remote function signature * chore: migrate remote_function integ tests from V2 * fix: rename signing key --------- Co-authored-by: Namrata Madan <nmmadan@amazon.com>
* Update v3 readthedocs to autorender all submodules * Maintain previous opinionated list, add new section of full module reference * Add sagemaker-core description in init * Remove pdf and epub from format and mock runtime imports to fix build timeout in actual publishing attempt * Remove pdf and epub from format and mock runtime imports to fix build timeout in actual publishing attempt
* Add mlflowconfig to eval * Update unit and integ test
* feat: Add Feature Store Support to V3 * Add feature store tests * feat(feature_store): Add Lake Formation support to Feature Group - Add LakeFormationConfig class to configure Lake Formation governance on offline stores - Implement FeatureGroup subclass with Lake Formation integration capabilities - Add helper methods for S3 URI/ARN conversion and Lake Formation role management - Add S3 deny policy generation for Lake Formation access control - Implement Lake Formation resource registration and S3 bucket policy setup - Add integration tests for Lake Formation feature store workflows - Add unit tests for Lake Formation configuration and policy generation - Update feature_store module exports to include FeatureGroup and LakeFormationConfig - Update API documentation to include Feature Store section in sagemaker_mlops.rst - Enable fine-grained access control for feature store offline stores using AWS Lake Formation * docs(feature_store): Add Lake Formation governance example notebook * add role policy to notebook * chore(docs): update example notebook * update setup instructions * add lf-multiaccount-demo + fix LakeformationConfig constructor * reusing clients + bug fixes * feat: add disable_hybrid_access_mode + update tests * refactor: replace print() with logger.info() for S3 deny policy display Replace 10 bare print() calls with a single logger.info() call for the S3 deny policy output in enable_lake_formation(). This makes the policy display consistent with the rest of the LF workflow which uses logger. Update 12 tests to mock the logger instead of builtins.print. --- X-AI-Prompt: replace print with logger.info for s3 bucket policy display in enable_lake_formation X-AI-Tool: kiro-cli * update integ tests * refactor: rename FeatureGroup to FeatureGroupManager Rename the mlops FeatureGroup class to FeatureGroupManager to distinguish it from the core FeatureGroup base class. Update all references in unit and integration lake formation tests. Fix missing comma in __init__.py __all__ list. --- X-AI-Prompt: rename FeatureGroup to FeatureGroupManager and update lakeformation tests X-AI-Tool: kiro-cli * Basic functionality of iceberg property changing and retrieval in create, update, get functions * Added validation of IcebergProperties with valid list * Added checking of only approved iceberg properties * Added checking of duplicate iceberg properties * Added additional checking of duplicate iceberg properties and testing * Removed excess LakeFormation code to isolate Iceberg Properties change * Removed excess LakeFormation tests and readded Athena Query tests * Fixed event_time format in integ tests * Removed excess fields from glue call in _get_iceberg_properties() * Changed to pyiceberg implementation for locking, increased testing and error messages * Added retries with backoff for read and write to the iceberg table * Added logging for property modifications to be auditable * Added Least-privilege Glue permissions to ensure table belongs to Feature Group with new method _validate_table_ownership, error messages, and new testing * Added more integ tests * Created an example notebook for the iceberg properties feature, changed the transaction call to match .venv and tests to mtach the change, removed problem properties from the allow list, amd added dependencies to pyproject. Prior commits on this branch were authored with Kiro CLI assistance but were not tagged at the time. --- Previous commits X-AI-Prompt: Document retroactive GenAI usage X-AI-Tool: Kiro CLI (sisyphus) --- This commit X-AI-Prompt: Create and debug an example notebook for the iceberg properties feature X-AI-Tool: Kiro CLI (sisyphus) * Added additionall Error Messaging for Lake Formation and AccessDenied exceptions. New helped method to get if a FG is lake formation governed. Also added testing for these features. --- X-AI-Prompt: Add error checking in feature_group_manager.py to differentiate whether a Glue permissions error (AccessDeniedException) during iceberg properties operations is related to Lake Formation governance or regular IAM. Check the feature group's describe response for LakeFormationConfig before the call, and surface a targeted error message accordingly. X-AI-Tool: Kiro CLI sisyphus * Changed error logging for Lake Formation/IAM permissions issues due to api structure. Changed testing to match. --- X-AI-Prompt: Refactor Lake Formation error handling in FeatureGroupManager to remove _has_lake_formation_config() which won't work because our API has no way to record this and replace separate LF/IAM error messages with a single combined _ICEBERG_PERMISSIONS_ERROR_MESSAGE constant covering both governance models (SELECT/DESCRIBE/ALTER for LF, glue:GetTable/glue:UpdateTable for IAM). Apply to both _get_iceberg_properties and _update_iceberg_properties, preserving the more expansive logger.error() calls in the update path. Update tests accordingly. X-AI-Tool: Kiro CLI sisyphus * Changed name of allow list for better compatibility with documentation. Added better error messaging. Finalized example notebook. Updated corresponding tests. --- X-AI-Prompt: Refactor the allos list naming, and update testing to ensure it continues to work X-AI-Tool: Kiro CLI Sisyphus --------- Co-authored-by: adishaa <adishaa@amazon.com> Co-authored-by: Basssem Halim <bhhalim@amazon.com> Co-authored-by: BassemHalim <bassemamir459@gmail.com> Co-authored-by: Gokul Anantha Narayanan <166456257+nargokul@users.noreply.github.com>
…ws#5758) * fix(eval): resolve mlflow_resource_arn in _get_base_template_context when session was absent at construction * Update train unit test
* feat: Add Feature Store Support to V3 * Add feature store tests * feat(feature_store): Add Lake Formation support to Feature Group - Add LakeFormationConfig class to configure Lake Formation governance on offline stores - Implement FeatureGroup subclass with Lake Formation integration capabilities - Add helper methods for S3 URI/ARN conversion and Lake Formation role management - Add S3 deny policy generation for Lake Formation access control - Implement Lake Formation resource registration and S3 bucket policy setup - Add integration tests for Lake Formation feature store workflows - Add unit tests for Lake Formation configuration and policy generation - Update feature_store module exports to include FeatureGroup and LakeFormationConfig - Update API documentation to include Feature Store section in sagemaker_mlops.rst - Enable fine-grained access control for feature store offline stores using AWS Lake Formation * docs(feature_store): Add Lake Formation governance example notebook * add role policy to notebook * chore(docs): update example notebook * update setup instructions * add lf-multiaccount-demo + fix LakeformationConfig constructor * reusing clients + bug fixes * feat: add disable_hybrid_access_mode + update tests * refactor: replace print() with logger.info() for S3 deny policy display Replace 10 bare print() calls with a single logger.info() call for the S3 deny policy output in enable_lake_formation(). This makes the policy display consistent with the rest of the LF workflow which uses logger. Update 12 tests to mock the logger instead of builtins.print. --- X-AI-Prompt: replace print with logger.info for s3 bucket policy display in enable_lake_formation X-AI-Tool: kiro-cli * update integ tests * refactor: rename FeatureGroup to FeatureGroupManager Rename the mlops FeatureGroup class to FeatureGroupManager to distinguish it from the core FeatureGroup base class. Update all references in unit and integration lake formation tests. Fix missing comma in __init__.py __all__ list. --- X-AI-Prompt: rename FeatureGroup to FeatureGroupManager and update lakeformation tests X-AI-Tool: kiro-cli * refactor(feature-store): Rewrite FeatureGroupManager from inheritance to composition Replace FeatureGroup inheritance with composition pattern. The manager now delegates to FeatureGroup via classmethods (create_feature_group, describe_feature_group) and takes a FeatureGroup instance in enable_lake_formation instead of operating on self. Key changes: - FeatureGroupManager no longer extends FeatureGroup - Forward session/region through enable_lake_formation and create - Add telemetry decorators to all public methods - Add hypothesis to test dependencies - Add dedicated test_feature_group_manager.py unit tests - Consolidate test_lakeformation.py (remove migrated tests) - Update integration tests for new API surface - Reorganize example notebooks into v3-feature-store-examples/ - Bump VERSION to 1.5.1.dev0 --- X-AI-Prompt: read last commit and update commit message to reflect full scope of changes X-AI-Tool: kiro-cli * Revert "refactor(feature-store): Rewrite FeatureGroupManager from inheritance to composition" This reverts commit bc11e45. * fix(feature-store): Fix FeatureGroupManager code issues and improve test coverage - Use isinstance() for Unassigned checks instead of == Unassigned() - Add class-level type annotation for _lf_client_cache - Replace fragile docstring inheritance with proper docstring - Fix create() to return FeatureGroupManager instead of FeatureGroup by calling cls.get() after super().create() - Update create() return type annotation to Optional[FeatureGroupManager] - Add feature_group_arn validation before S3 policy generation - Fix integ test logger name (feature_group -> feature_group_manager) - Rename test_lakeformation.py to test_feature_group_manager.py - Add unit tests for: return type verification, Iceberg table format S3 path handling, missing ARN validation, happy-path return values, session/region pass-through, and region inference from session --- X-AI-Prompt: Review FeatureGroupManager class, fix identified issues, increase test coverage X-AI-Tool: kiro-cli * feat(feature-store): Auto-apply S3 bucket policy in Lake Formation setup - Add Phase 4 to enable_lake_formation() that automatically applies S3 deny bucket policy for Lake Formation governance - Remove show_s3_policy and disable_hybrid_access_mode parameters in favor of always-on behavior - Refactor _generate_s3_deny_policy to _generate_s3_deny_statements returning a list for easier policy merging - Add _get_s3_client with caching pattern matching _get_lake_formation_client - Add _apply_bucket_policy with idempotent Sid-based deduplication - Improve _revoke_iam_allowed_principal to check permissions via list_permissions before attempting revocation - Remove LakeFormationConfig.show_s3_policy and disable_hybrid_access_mode - Add e2e integration test for put_record + Athena query flow - Update unit tests for new behavior * update deny policy sid + fix integ tests after refactor * refactor(feature-store): Remove client caching from FeatureGroupManager Remove _lf_client_cache and _s3_client_cache instance caches from _get_lake_formation_client and _get_s3_client. Each call now creates a fresh boto3 client directly. Remove corresponding cache-specific unit tests (cache reuse and different-region tests). --- X-AI-Prompt: remove client caching for lf and s3 in feature_group_manager and update tests X-AI-Tool: kiro-cli * SNAPSHOT [cloudtrail approach] * Refactor: make disable_hybrid_access_mode a required field and update notebooks/tests * refactor(feature-store): Replace input() with acknowledge_risk param Add acknowledge_risk: Optional[bool] = None to enable_lake_formation() and LakeFormationConfig. None triggers interactive input() prompt, True proceeds without prompting, False aborts with RuntimeError. Removes all builtins.input mocking from unit and integration tests. Tests now pass acknowledge_risk=True or False directly. Removes one duplicate test that became identical after the refactor. --- X-AI-Prompt: add y/n confirmation for disable_hybrid_access_mode=True, then refactor to use acknowledge_risk param instead of input() X-AI-Tool: kiro-cli * (docs): add cross account feature group example notebook * fix(docs): fix bugs and improve quality of LF notebook - Use assumed role session for lf_client, glue_client, and athena_client instead of default boto3 session - Move client initialization to setup/configuration cell - Add session=boto_session to get_record in Example 2 - Fix print statements: "execution role" -> "offline store role" - Remove unused get_execution_role import - Remove misleading LakeFormationDataLakeAdmin comment - Fix typo: "Exectution" -> "Execution" - Fix PascalCase variables to snake_case - Fix "lakeformation" -> "Lake Formation" in markdown - Fix bold markdown formatting - Add missing space in ARN print - Remove duplicate boto3 and time imports - Scope cleanup IAM policy to lf-demo-* resources - Fix cleanup variable to use correct reference - Remove empty trailing markdown cell * remove duplicate Feature Store from docs template * rebase fixes * refactor lakeformation notebook * refactor(feature-store): Make acknowledge_risk a required bool field Remove Optional[bool] type and None default from acknowledge_risk in LakeFormationConfig and enable_lake_formation(). Remove interactive input() prompts, keeping only the bool-driven proceed/abort logic. Add early abort in create() when acknowledge_risk is False. Update docstrings to describe specific risks being acknowledged. Update tests to pass acknowledge_risk where required and add test for create abort. --- X-AI-Prompt: Make acknowledge_risk required bool, remove input() branches, add create abort check, update docstrings with risk details, fix tests X-AI-Tool: kiro-cli * update example notebook with acknowledge_risk field * fix(test): Add missing acknowledge_risk param to LF integ tests * chore: rename example notebook and add clarifying comments * refactor(feature-store): Improve Lake Formation setup error handling - Remove unused datetime imports - Remove debug print statement from resource registration - Update docstring to clarify S3 deny bucket policy is recommended - Refactor error handling to use fail-fast with deferred warnings pattern - Store phase errors instead of immediately raising to allow all phases to attempt execution - Move warning logs before error re-raise so incomplete steps are reported before exception - Simplify phase execution logic by checking phase_error status before attempting each phase - Improve error messages to guide users on re-running the method after fixing issues * refactor(feature-store): Rename disable_hybrid_access_mode to hybrid_access_mode_enabled Invert the boolean semantics of the hybrid access mode parameter: - LakeFormationConfig field renamed with flipped logic - enable_lake_formation() parameter renamed with flipped logic - Result dict key hybrid_access_mode_disabled -> hybrid_access_mode_enabled (value also flipped) - All docstrings, error messages, and warnings updated - Unit and integration tests updated with flipped assertions --- X-AI-Prompt: rename disable_hybrid_access_mode to hybrid_access_mode_enabled with flipped logic X-AI-Tool: kiro-cli * update example notebooks and fix minor bug --------- Co-authored-by: adishaa <adishaa@amazon.com> Co-authored-by: Basssem Halim <bhhalim@amazon.com> Co-authored-by: Molly He <mollyhe@amazon.com>
* 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.
…erving, preventi (5529) (aws#5734) * fix: bug: ModelBuilder overwrites user-provided HF_MODEL_ID for DJL Serving, preventi (5529) * fix: address review comments (iteration aws#1) * fix: address review comments (iteration aws#1) * fix: address review comments (iteration aws#1) * fix: address review comments (iteration aws#2)
…5784) * allow SAGEMAKER_HUB_NAME env var override for HUB_NAME constant * Resolve env var runtime
* fix: skip None hyperparameters in to_dict instead of converting to string 'None' * fix: correct test mocks to use 'default' key matching real _extract_eval_override_options return format --------- Co-authored-by: Harsh Thakkar <harshnj@amazon.com>
* fix: add us-west-2 to Nova supported regions in finetune_utils * test: update nova region test to reflect us-west-2 support
* feat: add MLFlow experiment link to eval output * refactor: unify MLflow tracking URL generation logic
* feat(train): Add wait_timeout parameter to train() Updated trainers: SFT, DPO, RLAIF, RLVR, and BaseTrainer. * feat(train): added unit tests for wait_timeout --------- Co-authored-by: Gaurav Madarkal <mdgaurav@amazon.com>
Co-authored-by: Zhaoqi <52220743+zhaoqizqwang@users.noreply.github.com>
* fix: improve error messages for waiter timeouts * fix: re-introduce accidentially removed field * fix: apply fix to generated code as well * Update autogen scripts for adding message as parameter * Fix typo in resources.py --------- Co-authored-by: Molly He <mollyhe@amazon.com>
* fix(tuner): pass through full OutputDataConfig from ModelTrainer
HyperparameterTuner._build_training_job_definition was reconstructing a new
OutputDataConfig with only s3_output_path, silently dropping kms_key_id,
compression_type, and other fields set on the ModelTrainer.
Pass model_trainer.output_data_config directly to preserve all fields.
Also update _create_mock_model_trainer in tests to use a real OutputDataConfig
instead of MagicMock, and add a test verifying kms_key_id and compression_type
are preserved through the tuning job definition.
X-AI-Prompt: Check if HyperparameterTuner passes OutputDataConfig.kms_key_id from ModelTrainer and fix the gap
X-AI-Tool: Kiro
* Delte file: Resolved template parameters: {'role_arn.md
* fix unit tests
* Update comput instance type
fix: force Unix newlines in ModelTrainer driver scripts to prevent CRLF exit code 2 on Windows
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