Nova training support

sagemaker-train/src/sagemaker/train/constants.py

@@ -62,3 +62,7 @@
     "mistral.mistral-large-2402-v1:0": ["us-west-2", "us-east-1", "eu-west-1"],
     "amazon.nova-pro-v1:0": ["us-east-1"]
 }
+
+SM_RECIPE = "recipe"
+SM_RECIPE_YAML = "recipe.yaml"
+SM_RECIPE_CONTAINER_PATH = f"/opt/ml/input/data/recipe/{SM_RECIPE_YAML}"

sagemaker-train/src/sagemaker/train/model_trainer.py

@@ -83,6 +83,9 @@
     SM_CODE_CONTAINER_PATH,
     SM_DRIVERS,
     SM_DRIVERS_LOCAL_PATH,
+    SM_RECIPE,
+    SM_RECIPE_YAML,
+    SM_RECIPE_CONTAINER_PATH,
     TRAIN_SCRIPT,
     DEFAULT_CONTAINER_ENTRYPOINT,
     DEFAULT_CONTAINER_ARGUMENTS,
@@ -100,7 +103,13 @@
 from sagemaker.core.telemetry.telemetry_logging import _telemetry_emitter
 from sagemaker.core.telemetry.constants import Feature
 from sagemaker.train import logger
-from sagemaker.train.sm_recipes.utils import _get_args_from_recipe, _determine_device_type
+from sagemaker.train.sm_recipes.utils import (
+    _get_args_from_recipe,
+    _determine_device_type,
+    _is_nova_recipe,
+    _is_llmft_recipe,
+    _load_base_recipe,
+)
 
 from sagemaker.core.jumpstart.configs import JumpStartConfig
 from sagemaker.core.jumpstart.document import get_hub_content_and_document
@@ -249,6 +258,8 @@ class ModelTrainer(BaseModel):
     _remote_debug_config: Optional[RemoteDebugConfig] = PrivateAttr(default=None)
     _metric_definitions: Optional[List[MetricDefinition]] = PrivateAttr(default=None)
 
+    _is_nova_recipe: Optional[bool] = PrivateAttr(default=None)
+    _is_llmft_recipe: Optional[bool] = PrivateAttr(default=None)
     # Private Attributes for Recipes
     _temp_recipe_train_dir: Optional[TemporaryDirectory] = PrivateAttr(default=None)
 
@@ -573,6 +584,23 @@ def _create_training_job_args(
 
             final_input_data_config = list(existing_channels.values()) + new_channels
 
+        if self._is_nova_recipe or self._is_llmft_recipe:
+            for input_data in final_input_data_config:
+                if input_data.channel_name == SM_RECIPE:
+                    raise ValueError(
+                        "Cannot use reserved channel name 'recipe' as an input channel name "
+                        " for Nova or LLMFT Recipe"
+                    )
+            recipe_file_path = os.path.join(self._temp_recipe_train_dir.name, SM_RECIPE_YAML)
+            recipe_channel = self.create_input_data_channel(
+                channel_name=SM_RECIPE,
+                data_source=recipe_file_path,
+                key_prefix=input_data_key_prefix,
+            )
+            final_input_data_config.append(recipe_channel)
+            if self._is_nova_recipe or self._is_llmft_recipe:
+                self.hyperparameters.update({"sagemaker_recipe_local_path": SM_RECIPE_CONTAINER_PATH})
+
         if final_input_data_config:
             final_input_data_config = self._get_input_data_config(
                 final_input_data_config, input_data_key_prefix
@@ -1039,6 +1067,7 @@ def from_recipe(
         checkpoint_config: Optional[shapes.CheckpointConfig] = None,
         training_input_mode: Optional[str] = "File",
         environment: Optional[Dict[str, str]] = None,
+        hyperparameters: Optional[Union[Dict[str, Any], str]] = {},
         tags: Optional[List[Tag]] = None,
         sagemaker_session: Optional[Session] = None,
         role: Optional[str] = None,
@@ -1136,12 +1165,20 @@ def from_recipe(
         if compute.instance_type is None:
             raise ValueError("Must set ``instance_type`` in Compute when using training recipes.")
         device_type = _determine_device_type(compute.instance_type)
-        if device_type == "cpu":
+        recipe = _load_base_recipe(
+            training_recipe=training_recipe, recipe_overrides=recipe_overrides
+        )
+        is_nova = _is_nova_recipe(recipe=recipe)
+        is_llmft = _is_llmft_recipe(recipe=recipe)
+        if device_type == "cpu" and not (is_nova or is_llmft):
             raise ValueError(
                 "Training recipes are not supported for CPU instances. "
                 "Please provide a GPU or Tranium instance type."
             )
 
+        if training_image is None and (is_nova or is_llmft):
+            raise ValueError("training_image must be provided when using recipe for Nova or LLMFT")
+
         if training_image_config and training_image is None:
             raise ValueError("training_image must be provided when using training_image_config.")
 
@@ -1154,16 +1191,29 @@ def from_recipe(
         # - distributed
         # - compute
         # - hyperparameters
-        model_trainer_args, recipe_train_dir = _get_args_from_recipe(
-            training_recipe=training_recipe,
+        model_trainer_args, tmp_dir = _get_args_from_recipe(
+            training_recipe=recipe,
             recipe_overrides=recipe_overrides,
             requirements=requirements,
             compute=compute,
             region_name=sagemaker_session.boto_region_name,
+            role=role,
         )
         if training_image is not None:
             model_trainer_args["training_image"] = training_image
 
+        if hyperparameters and not is_nova:
+            logger.warning(
+                "Hyperparameters are not supported for general and LLMFT training recipes. "
+                + "Ignoring hyperparameters input."
+            )
+        if is_nova:
+            if hyperparameters and isinstance(hyperparameters, str):
+                hyperparameters = cls._validate_and_load_hyperparameters_file(hyperparameters)
+                model_trainer_args["hyperparameters"].update(hyperparameters)
+            elif hyperparameters and isinstance(hyperparameters, dict):
+                model_trainer_args["hyperparameters"].update(hyperparameters)
+
         model_trainer = cls(
             sagemaker_session=sagemaker_session,
             role=role,
@@ -1180,7 +1230,9 @@ def from_recipe(
             **model_trainer_args,
         )
 
-        model_trainer._temp_recipe_train_dir = recipe_train_dir
+        model_trainer._is_nova_recipe = is_nova
+        model_trainer._is_llmft_recipe = is_llmft
+        model_trainer._temp_recipe_train_dir = tmp_dir
         return model_trainer
 
     @classmethod