Merge branch 'master' into master

Author: brunopistone

.github/workflows/codeql.yml

@@ -0,0 +1,35 @@
+name: "CodeQL"
+on:
+  push:
+    branches: [ "master" ]
+  pull_request:
+    branches: [ "master" ]
+  schedule:
+    - cron: '30 8 * * *'
+jobs:
+  analyze:
+    name: Analyze (${{ matrix.language }})
+    runs-on: ${{ 'ubuntu-latest' }}
+    permissions:
+      security-events: write
+      packages: read
+
+    strategy:
+      matrix:
+        include:
+          - language: python
+            build-mode: none
+          - language: java-kotlin
+            build-mode: none
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@6ccd57f4c5d15bdc2fef309bd9fb6cc9db2ef1c6
+      - name: Initialize CodeQL
+        uses: github/codeql-action/init@4b1d7da102ff94aca014c0245062b1a463356d72
+        with:
+          languages: ${{ matrix.language }}
+          build-mode: ${{ matrix.build-mode }}
+      - name: Perform CodeQL Analysis
+        uses: github/codeql-action/analyze@4b1d7da102ff94aca014c0245062b1a463356d72
+        with:
+          category: "/language:${{matrix.language}}"

CHANGELOG.md

@@ -1,5 +1,44 @@
 # Changelog
 
+## v2.226.1 (2024-07-17)
+
+## v2.226.0 (2024-07-12)
+
+### Features
+
+ * Curated hub improvements
+ * InferenceSpec support for MMS and testing
+
+### Bug Fixes and Other Changes
+
+ * ModelBuilder not passing HF_TOKEN to model.
+ * update image_uri_configs  07-10-2024 07:18:04 PST
+
+## v2.225.0 (2024-07-10)
+
+### Features
+
+ * model optimization
+
+### Bug Fixes and Other Changes
+
+ * fix integ test
+ * update uris for v1.1.1
+ * update image_uri_configs  07-04-2024 07:17:24 PST
+
+## v2.224.4 (2024-07-04)
+
+### Bug Fixes and Other Changes
+
+ * allow for inf spec and server override to be passed
+
+## v2.224.3 (2024-07-03)
+
+### Bug Fixes and Other Changes
+
+ * Upgrade local dependencies
+ * Improve docstrings for estimator tags
+
 ## v2.224.2 (2024-06-27)
 
 ### Bug Fixes and Other Changes

VERSION

@@ -1 +1 @@
-2.224.3.dev0
+2.226.2.dev0

requirements/extras/local_requirements.txt

@@ -1,3 +1,3 @@
 urllib3>=1.26.8,<3.0.0
-docker>=5.0.2,<7.0.0
+docker>=5.0.2,<8.0.0
 PyYAML>=5.4.1,<7

requirements/extras/test_requirements.txt

@@ -38,3 +38,4 @@ accelerate>=0.24.1,<=0.27.0
 schema==0.7.5
 tensorflow>=2.1,<=2.16
 mlflow>=2.12.2,<2.13
+huggingface_hub>=0.23.4

setup.py

@@ -49,7 +49,7 @@ def read_requirements(filename):
 # Declare minimal set for installation
 required_packages = [
     "attrs>=23.1.0,<24",
-    "boto3>=1.33.3,<2.0",
+    "boto3>=1.34.142,<2.0",
     "cloudpickle==2.2.1",
     "google-pasta",
     "numpy>=1.9.0,<2.0",

src/sagemaker/accept_types.py

@@ -82,6 +82,7 @@ def retrieve_default(
     tolerate_deprecated_model: bool = False,
     sagemaker_session: Session = DEFAULT_JUMPSTART_SAGEMAKER_SESSION,
     model_type: JumpStartModelType = JumpStartModelType.OPEN_WEIGHTS,
+    config_name: Optional[str] = None,
 ) -> str:
     """Retrieves the default accept type for the model matching the given arguments.
 
@@ -105,6 +106,7 @@ def retrieve_default(
             object, used for SageMaker interactions. If not
             specified, one is created using the default AWS configuration
             chain. (Default: sagemaker.jumpstart.constants.DEFAULT_JUMPSTART_SAGEMAKER_SESSION).
+        config_name (Optional[str]): Name of the JumpStart Model config to apply. (Default: None).
     Returns:
         str: The default accept type to use for the model.
 
@@ -125,4 +127,5 @@ def retrieve_default(
         tolerate_deprecated_model=tolerate_deprecated_model,
         sagemaker_session=sagemaker_session,
         model_type=model_type,
+        config_name=config_name,
     )

src/sagemaker/content_types.py

@@ -82,6 +82,7 @@ def retrieve_default(
     tolerate_deprecated_model: bool = False,
     sagemaker_session: Session = DEFAULT_JUMPSTART_SAGEMAKER_SESSION,
     model_type: JumpStartModelType = JumpStartModelType.OPEN_WEIGHTS,
+    config_name: Optional[str] = None,
 ) -> str:
     """Retrieves the default content type for the model matching the given arguments.
 
@@ -105,6 +106,7 @@ def retrieve_default(
             object, used for SageMaker interactions. If not
             specified, one is created using the default AWS configuration
             chain. (Default: sagemaker.jumpstart.constants.DEFAULT_JUMPSTART_SAGEMAKER_SESSION).
+        config_name (Optional[str]): Name of the JumpStart Model config to apply. (Default: None).
     Returns:
         str: The default content type to use for the model.
 
@@ -125,6 +127,7 @@ def retrieve_default(
         tolerate_deprecated_model=tolerate_deprecated_model,
         sagemaker_session=sagemaker_session,
         model_type=model_type,
+        config_name=config_name,
     )
 
 

src/sagemaker/deserializers.py

@@ -102,6 +102,7 @@ def retrieve_default(
     tolerate_deprecated_model: bool = False,
     sagemaker_session: Session = DEFAULT_JUMPSTART_SAGEMAKER_SESSION,
     model_type: JumpStartModelType = JumpStartModelType.OPEN_WEIGHTS,
+    config_name: Optional[str] = None,
 ) -> BaseDeserializer:
     """Retrieves the default deserializer for the model matching the given arguments.
 
@@ -125,6 +126,7 @@ def retrieve_default(
             object, used for SageMaker interactions. If not
             specified, one is created using the default AWS configuration
             chain. (Default: sagemaker.jumpstart.constants.DEFAULT_JUMPSTART_SAGEMAKER_SESSION).
+        config_name (Optional[str]): Name of the JumpStart Model config to apply. (Default: None).
     Returns:
         BaseDeserializer: The default deserializer to use for the model.
 
@@ -146,4 +148,5 @@ def retrieve_default(
         tolerate_deprecated_model=tolerate_deprecated_model,
         sagemaker_session=sagemaker_session,
         model_type=model_type,
+        config_name=config_name,
     )

src/sagemaker/enums.py

@@ -40,3 +40,12 @@ class RoutingStrategy(Enum):
     """The endpoint routes requests to the specific instances that have
     more capacity to process them.
     """
+
+
+class Tag(str, Enum):
+    """Enum class for tag keys to apply to models."""
+
+    OPTIMIZATION_JOB_NAME = "sagemaker-sdk:optimization-job-name"
+    SPECULATIVE_DRAFT_MODEL_PROVIDER = "sagemaker-sdk:speculative-draft-model-provider"
+    FINE_TUNING_MODEL_PATH = "sagemaker-sdk:fine-tuning-model-path"
+    FINE_TUNING_JOB_NAME = "sagemaker-sdk:fine-tuning-job-name"

src/sagemaker/environment_variables.py

@@ -37,6 +37,7 @@ def retrieve_default(
     sagemaker_session: Session = DEFAULT_JUMPSTART_SAGEMAKER_SESSION,
     instance_type: Optional[str] = None,
     script: JumpStartScriptScope = JumpStartScriptScope.INFERENCE,
+    config_name: Optional[str] = None,
 ) -> Dict[str, str]:
     """Retrieves the default container environment variables for the model matching the arguments.
 
@@ -68,6 +69,7 @@ def retrieve_default(
             variables specific for the instance type.
         script (JumpStartScriptScope): The JumpStart script for which to retrieve environment
             variables.
+        config_name (Optional[str]): Name of the JumpStart Model config to apply. (Default: None).
     Returns:
         dict: The variables to use for the model.
 
@@ -91,4 +93,5 @@ def retrieve_default(
         sagemaker_session=sagemaker_session,
         instance_type=instance_type,
         script=script,
+        config_name=config_name,
     )

src/sagemaker/estimator.py

@@ -68,6 +68,7 @@
 from sagemaker.interactive_apps import SupportedInteractiveAppTypes
 from sagemaker.interactive_apps.tensorboard import TensorBoardApp
 from sagemaker.instance_group import InstanceGroup
+from sagemaker.model_card.model_card import ModelCard, TrainingDetails
 from sagemaker.utils import instance_supports_kms
 from sagemaker.job import _Job
 from sagemaker.jumpstart.utils import (
@@ -1797,8 +1798,17 @@ def register(
         else:
             if "model_kms_key" not in kwargs:
                 kwargs["model_kms_key"] = self.output_kms_key
-            model = self.create_model(image_uri=image_uri, **kwargs)
+            model = self.create_model(image_uri=image_uri, name=model_name, **kwargs)
         model.name = model_name
+        if self.model_data is not None and model_card is None:
+            training_details = TrainingDetails.from_model_s3_artifacts(
+                model_artifacts=[self.model_data], sagemaker_session=self.sagemaker_session
+            )
+            model_card = ModelCard(
+                name="estimator_card",
+                training_details=training_details,
+                sagemaker_session=self.sagemaker_session,
+            )
         return model.register(
             content_types,
             response_types,

src/sagemaker/huggingface/llm_utils.py

@@ -13,7 +13,9 @@
 """Functions for generating ECR image URIs for pre-built SageMaker Docker images."""
 from __future__ import absolute_import
 
+import os
 from typing import Optional
+import importlib.util
 
 import urllib.request
 from urllib.error import HTTPError, URLError
@@ -123,3 +125,26 @@ def get_huggingface_model_metadata(model_id: str, hf_hub_token: Optional[str] =
             "Did not find model metadata for the following HuggingFace Model ID %s" % model_id
         )
     return hf_model_metadata_json
+
+
+def download_huggingface_model_metadata(
+    model_id: str, model_local_path: str, hf_hub_token: Optional[str] = None
+) -> None:
+    """Downloads the HuggingFace Model snapshot via HuggingFace API.
+
+    Args:
+        model_id (str): The HuggingFace Model ID
+        model_local_path (str): The local path to save the HuggingFace Model snapshot.
+        hf_hub_token (str): The HuggingFace Hub Token
+
+    Raises:
+        ImportError: If huggingface_hub is not installed.
+    """
+    if not importlib.util.find_spec("huggingface_hub"):
+        raise ImportError("Unable to import huggingface_hub, check if huggingface_hub is installed")
+
+    from huggingface_hub import snapshot_download
+
+    os.makedirs(model_local_path, exist_ok=True)
+    logger.info("Downloading model %s from Hugging Face Hub to %s", model_id, model_local_path)
+    snapshot_download(repo_id=model_id, local_dir=model_local_path, token=hf_hub_token)

src/sagemaker/hyperparameters.py

@@ -37,6 +37,7 @@ def retrieve_default(
     tolerate_vulnerable_model: bool = False,
     tolerate_deprecated_model: bool = False,
     sagemaker_session: Session = DEFAULT_JUMPSTART_SAGEMAKER_SESSION,
+    config_name: Optional[str] = None,
 ) -> Dict[str, str]:
     """Retrieves the default training hyperparameters for the model matching the given arguments.
 
@@ -69,6 +70,7 @@ def retrieve_default(
             object, used for SageMaker interactions. If not
             specified, one is created using the default AWS configuration
             chain. (Default: sagemaker.jumpstart.constants.DEFAULT_JUMPSTART_SAGEMAKER_SESSION).
+        config_name (Optional[str]): Name of the JumpStart Model config to apply. (Default: None).
     Returns:
         dict: The hyperparameters to use for the model.
 
@@ -90,6 +92,7 @@ def retrieve_default(
         tolerate_vulnerable_model=tolerate_vulnerable_model,
         tolerate_deprecated_model=tolerate_deprecated_model,
         sagemaker_session=sagemaker_session,
+        config_name=config_name,
     )