Merge branch 'aws:master' into rsareddy-dev

Author: rsareddy0329

CHANGELOG.md

@@ -1,5 +1,43 @@
 # Changelog
 
+## v2.241.0 (2025-03-06)
+
+### Features
+
+ * Make DistributedConfig Extensible
+ * support training for JumpStart model references as part of Curated Hub Phase 2
+ * Allow ModelTrainer to accept hyperparameters file
+
+### Bug Fixes and Other Changes
+
+ * Skip tests with deprecated instance type
+ * Ensure Model.is_repack() returns a boolean
+ * Fix error when there is no session to call _create_model_request()
+ * Use sagemaker session's s3_resource in download_folder
+ * Added check for the presence of model package group before creating one
+ * Fix key error in _send_metrics()
+
+## v2.240.0 (2025-02-25)
+
+### Features
+
+ * Add support for TGI Neuronx 0.0.27 and HF PT 2.3.0 image in PySDK
+
+### Bug Fixes and Other Changes
+
+ * Remove main function entrypoint in ModelBuilder dependency manager.
+ * forbid extras in Configs
+ * altconfig hubcontent and reenable integ test
+ * Merge branch 'master-rba' into local_merge
+ * py_version doc fixes
+ * Add backward compatbility for RecordSerializer and RecordDeserializer
+ * update image_uri_configs  02-21-2025 06:18:10 PST
+ * update image_uri_configs  02-20-2025 06:18:08 PST
+
+### Documentation Changes
+
+ * Removed a line about python version requirements of training script which can misguide users.
+
 ## v2.239.3 (2025-02-19)
 
 ### Bug Fixes and Other Changes

VERSION

@@ -1 +1 @@
-2.239.4.dev0
+2.241.1.dev0

src/sagemaker/estimator.py

@@ -2550,7 +2550,6 @@ def _get_train_args(cls, estimator, inputs, experiment_config):
                 raise ValueError(
                     "File URIs are supported in local mode only. Please use a S3 URI instead."
                 )
-
         config = _Job._load_config(inputs, estimator)
 
         current_hyperparameters = estimator.hyperparameters()

src/sagemaker/experiments/_metrics.py

@@ -197,8 +197,8 @@ def _send_metrics(self, metrics):
             response = self._metrics_client.batch_put_metrics(**request)
             errors = response["Errors"] if "Errors" in response else None
             if errors:
-                message = errors[0]["Message"]
-                raise Exception(f'{len(errors)} errors with message "{message}"')
+                error_code = errors[0]["Code"]
+                raise Exception(f'{len(errors)} errors with error code "{error_code}"')
 
     def _construct_batch_put_metrics_request(self, batch):
         """Creates dictionary object used as request to metrics service."""

src/sagemaker/inputs.py

@@ -43,6 +43,8 @@ def __init__(
         attribute_names: Optional[List[Union[str, PipelineVariable]]] = None,
         target_attribute_name: Optional[Union[str, PipelineVariable]] = None,
         shuffle_config: Optional["ShuffleConfig"] = None,
+        hub_access_config: Optional[dict] = None,
+        model_access_config: Optional[dict] = None,
     ):
         r"""Create a definition for input data used by an SageMaker training job.
 
@@ -102,6 +104,13 @@ def __init__(
             shuffle_config (sagemaker.inputs.ShuffleConfig): If specified this configuration enables
                 shuffling on this channel. See the SageMaker API documentation for more info:
                 https://docs.aws.amazon.com/sagemaker/latest/dg/API_ShuffleConfig.html
+            hub_access_config (dict): Specify the HubAccessConfig of a
+                Model Reference for which a training job is being created for.
+            model_access_config (dict): For models that require a Model Access Config, specify True
+                or False for to indicate whether model terms of use have been accepted.
+                The `accept_eula` value must be explicitly defined as `True` in order to
+                accept the end-user license agreement (EULA) that some
+                models require. (Default: None).
         """
         self.config = {
             "DataSource": {"S3DataSource": {"S3DataType": s3_data_type, "S3Uri": s3_data}}
@@ -129,6 +138,27 @@ def __init__(
             self.config["TargetAttributeName"] = target_attribute_name
         if shuffle_config is not None:
             self.config["ShuffleConfig"] = {"Seed": shuffle_config.seed}
+        self.add_hub_access_config(hub_access_config)
+        self.add_model_access_config(model_access_config)
+
+    def add_hub_access_config(self, hub_access_config=None):
+        """Add Hub Access Config to the channel's configuration.
+
+        Args:
+            hub_access_config (dict): The HubAccessConfig to be added to the
+            channel's configuration.
+        """
+        if hub_access_config is not None:
+            self.config["DataSource"]["S3DataSource"]["HubAccessConfig"] = hub_access_config
+
+    def add_model_access_config(self, model_access_config=None):
+        """Add Model Access Config to the channel's configuration.
+
+        Args:
+            model_access_config (dict): Whether model terms of use have been accepted.
+        """
+        if model_access_config is not None:
+            self.config["DataSource"]["S3DataSource"]["ModelAccessConfig"] = model_access_config
 
 
 class ShuffleConfig(object):

src/sagemaker/job.py

@@ -65,6 +65,7 @@ def stop(self):
     @staticmethod
     def _load_config(inputs, estimator, expand_role=True, validate_uri=True):
         """Placeholder docstring"""
+        model_access_config, hub_access_config = _Job._get_access_configs(estimator)
         input_config = _Job._format_inputs_to_input_config(inputs, validate_uri)
         role = (
             estimator.sagemaker_session.expand_role(estimator.role)
@@ -95,19 +96,23 @@ def _load_config(inputs, estimator, expand_role=True, validate_uri=True):
             validate_uri,
             content_type="application/x-sagemaker-model",
             input_mode="File",
+            model_access_config=model_access_config,
+            hub_access_config=hub_access_config,
         )
         if model_channel:
             input_config = [] if input_config is None else input_config
             input_config.append(model_channel)
 
-        if estimator.enable_network_isolation():
-            code_channel = _Job._prepare_channel(
-                input_config, estimator.code_uri, estimator.code_channel_name, validate_uri
-            )
+        code_channel = _Job._prepare_channel(
+            input_config,
+            estimator.code_uri,
+            estimator.code_channel_name,
+            validate_uri,
+        )
 
-            if code_channel:
-                input_config = [] if input_config is None else input_config
-                input_config.append(code_channel)
+        if code_channel:
+            input_config = [] if input_config is None else input_config
+            input_config.append(code_channel)
 
         return {
             "input_config": input_config,
@@ -118,6 +123,23 @@ def _load_config(inputs, estimator, expand_role=True, validate_uri=True):
             "vpc_config": vpc_config,
         }
 
+    @staticmethod
+    def _get_access_configs(estimator):
+        """Return access configs from estimator object.
+
+        JumpStartEstimator uses access configs which need to be added to the model channel,
+        so they are passed down to the job level.
+
+        Args:
+            estimator (EstimatorBase): estimator object with access config field if applicable
+        """
+        model_access_config, hub_access_config = None, None
+        if hasattr(estimator, "model_access_config"):
+            model_access_config = estimator.model_access_config
+        if hasattr(estimator, "hub_access_config"):
+            hub_access_config = estimator.hub_access_config
+        return model_access_config, hub_access_config
+
     @staticmethod
     def _format_inputs_to_input_config(inputs, validate_uri=True):
         """Placeholder docstring"""
@@ -173,6 +195,8 @@ def _format_string_uri_input(
         input_mode=None,
         compression=None,
         target_attribute_name=None,
+        model_access_config=None,
+        hub_access_config=None,
     ):
         """Placeholder docstring"""
         s3_input_result = TrainingInput(
@@ -181,6 +205,8 @@ def _format_string_uri_input(
             input_mode=input_mode,
             compression=compression,
             target_attribute_name=target_attribute_name,
+            model_access_config=model_access_config,
+            hub_access_config=hub_access_config,
         )
         if isinstance(uri_input, str) and validate_uri and uri_input.startswith("s3://"):
             return s3_input_result
@@ -193,7 +219,11 @@ def _format_string_uri_input(
             )
         if isinstance(uri_input, str):
             return s3_input_result
-        if isinstance(uri_input, (TrainingInput, file_input, FileSystemInput)):
+        if isinstance(uri_input, (file_input, FileSystemInput)):
+            return uri_input
+        if isinstance(uri_input, TrainingInput):
+            uri_input.add_hub_access_config(hub_access_config=hub_access_config)
+            uri_input.add_model_access_config(model_access_config=model_access_config)
             return uri_input
         if is_pipeline_variable(uri_input):
             return s3_input_result
@@ -211,6 +241,8 @@ def _prepare_channel(
         validate_uri=True,
         content_type=None,
         input_mode=None,
+        model_access_config=None,
+        hub_access_config=None,
     ):
         """Placeholder docstring"""
         if not channel_uri:
@@ -226,7 +258,12 @@ def _prepare_channel(
                     raise ValueError("Duplicate channel {} not allowed.".format(channel_name))
 
         channel_input = _Job._format_string_uri_input(
-            channel_uri, validate_uri, content_type, input_mode
+            channel_uri,
+            validate_uri,
+            content_type,
+            input_mode,
+            model_access_config=model_access_config,
+            hub_access_config=hub_access_config,
         )
         channel = _Job._convert_input_to_channel(channel_name, channel_input)
 

src/sagemaker/jumpstart/artifacts/model_uris.py

@@ -29,6 +29,7 @@
     get_region_fallback,
     verify_model_region_and_return_specs,
 )
+from sagemaker.s3_utils import is_s3_url
 from sagemaker.session import Session
 from sagemaker.jumpstart.types import JumpStartModelSpecs
 
@@ -74,7 +75,7 @@ def _retrieve_hosting_artifact_key(model_specs: JumpStartModelSpecs, instance_ty
 def _retrieve_training_artifact_key(model_specs: JumpStartModelSpecs, instance_type: str) -> str:
     """Returns instance specific training artifact key or default one as fallback."""
     instance_specific_training_artifact_key: Optional[str] = (
-        model_specs.training_instance_type_variants.get_instance_specific_artifact_key(
+        model_specs.training_instance_type_variants.get_instance_specific_training_artifact_key(
             instance_type=instance_type
         )
         if instance_type
@@ -185,8 +186,8 @@ def _retrieve_model_uri(
         os.environ.get(ENV_VARIABLE_JUMPSTART_MODEL_ARTIFACT_BUCKET_OVERRIDE)
         or default_jumpstart_bucket
     )
-
-    model_s3_uri = f"s3://{bucket}/{model_artifact_key}"
+    if not is_s3_url(model_artifact_key):
+        model_s3_uri = f"s3://{bucket}/{model_artifact_key}"
 
     return model_s3_uri
 

src/sagemaker/jumpstart/estimator.py

@@ -41,6 +41,9 @@
     validate_model_id_and_get_type,
     resolve_model_sagemaker_config_field,
     verify_model_region_and_return_specs,
+    remove_env_var_from_estimator_kwargs_if_accept_eula_present,
+    get_model_access_config,
+    get_hub_access_config,
 )
 from sagemaker.utils import stringify_object, format_tags, Tags
 from sagemaker.model_monitor.data_capture_config import DataCaptureConfig
@@ -619,6 +622,10 @@ def _validate_model_id_and_get_type_hook():
         self._enable_network_isolation = estimator_init_kwargs.enable_network_isolation
         self.config_name = estimator_init_kwargs.config_name
         self.init_kwargs = estimator_init_kwargs.to_kwargs_dict(False)
+        # Access configs initialized to None, would be given a value when .fit() is called
+        # if applicable
+        self.model_access_config = None
+        self.hub_access_config = None
 
         super(JumpStartEstimator, self).__init__(**estimator_init_kwargs.to_kwargs_dict())
 
@@ -629,6 +636,7 @@ def fit(
         logs: Optional[str] = None,
         job_name: Optional[str] = None,
         experiment_config: Optional[Dict[str, str]] = None,
+        accept_eula: Optional[bool] = None,
     ) -> None:
         """Start training job by calling base ``Estimator`` class ``fit`` method.
 
@@ -679,8 +687,16 @@ def fit(
                 is built with :class:`~sagemaker.workflow.pipeline_context.PipelineSession`.
                 However, the value of `TrialComponentDisplayName` is honored for display in Studio.
                 (Default: None).
+            accept_eula (bool): For models that require a Model Access Config, specify True or
+                False to indicate whether model terms of use have been accepted.
+                The `accept_eula` value must be explicitly defined as `True` in order to
+                accept the end-user license agreement (EULA) that some
+                models require. (Default: None).
         """
-
+        self.model_access_config = get_model_access_config(accept_eula)
+        self.hub_access_config = get_hub_access_config(
+            hub_content_arn=self.init_kwargs.get("model_reference_arn", None)
+        )
         estimator_fit_kwargs = get_fit_kwargs(
             model_id=self.model_id,
             model_version=self.model_version,
@@ -695,7 +711,9 @@ def fit(
             tolerate_deprecated_model=self.tolerate_deprecated_model,
             sagemaker_session=self.sagemaker_session,
             config_name=self.config_name,
+            hub_access_config=self.hub_access_config,
         )
+        remove_env_var_from_estimator_kwargs_if_accept_eula_present(self.init_kwargs, accept_eula)
 
         return super(JumpStartEstimator, self).fit(**estimator_fit_kwargs.to_kwargs_dict())
 

src/sagemaker/jumpstart/factory/estimator.py

@@ -71,7 +71,6 @@
 from sagemaker.jumpstart.utils import (
     add_hub_content_arn_tags,
     add_jumpstart_model_info_tags,
-    get_eula_message,
     get_default_jumpstart_session_with_user_agent_suffix,
     get_top_ranked_config_name,
     update_dict_if_key_not_present,
@@ -265,6 +264,7 @@ def get_fit_kwargs(
     tolerate_deprecated_model: Optional[bool] = None,
     sagemaker_session: Optional[Session] = None,
     config_name: Optional[str] = None,
+    hub_access_config: Optional[Dict] = None,
 ) -> JumpStartEstimatorFitKwargs:
     """Returns kwargs required call `fit` on `sagemaker.estimator.Estimator` object."""
 
@@ -301,10 +301,32 @@ def get_fit_kwargs(
     estimator_fit_kwargs = _add_region_to_kwargs(estimator_fit_kwargs)
     estimator_fit_kwargs = _add_training_job_name_to_kwargs(estimator_fit_kwargs)
     estimator_fit_kwargs = _add_fit_extra_kwargs(estimator_fit_kwargs)
+    estimator_fit_kwargs = _add_hub_access_config_to_kwargs_inputs(
+        estimator_fit_kwargs, hub_access_config
+    )
 
     return estimator_fit_kwargs
 
 
+def _add_hub_access_config_to_kwargs_inputs(
+    kwargs: JumpStartEstimatorFitKwargs, hub_access_config=None
+):
+    """Adds HubAccessConfig to kwargs inputs"""
+
+    if isinstance(kwargs.inputs, str):
+        kwargs.inputs = TrainingInput(s3_data=kwargs.inputs, hub_access_config=hub_access_config)
+    elif isinstance(kwargs.inputs, TrainingInput):
+        kwargs.inputs.add_hub_access_config(hub_access_config=hub_access_config)
+    elif isinstance(kwargs.inputs, dict):
+        for k, v in kwargs.inputs.items():
+            if isinstance(v, str):
+                kwargs.inputs[k] = TrainingInput(s3_data=v, hub_access_config=hub_access_config)
+            elif isinstance(kwargs.inputs, TrainingInput):
+                kwargs.inputs[k].add_hub_access_config(hub_access_config=hub_access_config)
+
+    return kwargs
+
+
 def get_deploy_kwargs(
     model_id: str,
     model_version: Optional[str] = None,
@@ -668,18 +690,6 @@ def _add_env_to_kwargs(
             value,
         )
 
-    environment = getattr(kwargs, "environment", {}) or {}
-    if (
-        environment.get(SAGEMAKER_GATED_MODEL_S3_URI_TRAINING_ENV_VAR_KEY)
-        and str(environment.get("accept_eula", "")).lower() != "true"
-    ):
-        model_specs = kwargs.specs
-        if model_specs.is_gated_model():
-            raise ValueError(
-                "Need to define ‘accept_eula'='true' within Environment. "
-                f"{get_eula_message(model_specs, kwargs.region)}"
-            )
-
     return kwargs