Merge branch 'master' into dependabot/pip/tests/data/serve_resources/mlflow/pytorch/torch-2.2.0

Author: benieric

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/image_uri_config/huggingface-llm.json

@@ -717,7 +717,7 @@
                 "tag_prefix": "2.3.0-tgi2.2.0",
                 "repository": "huggingface-pytorch-tgi-inference",
                 "container_version": {
-                    "gpu": "cu121-ubuntu22.04"
+                    "gpu": "cu121-ubuntu22.04-v2.0"
                 }
             }
         }

src/sagemaker/model.py

@@ -549,7 +549,10 @@ def register(
             model_package_group_name = utils.base_name_from_image(
                 self.image_uri, default_base_name=ModelPackage.__name__
             )
-        if model_package_group_name is not None:
+        if (
+            model_package_group_name is not None
+            and model_type is not JumpStartModelType.PROPRIETARY
+        ):
             container_def = self.prepare_container_def(accept_eula=accept_eula)
             container_def = update_container_with_inference_params(
                 framework=framework,
@@ -2466,32 +2469,55 @@ def update_model_card(self, model_card: Union[ModelCard, ModelPackageModelCard])
         desc_model_package = sagemaker_session.sagemaker_client.describe_model_package(
             ModelPackageName=self.model_package_arn
         )
+        if hasattr(model_card, "model_package_details"):
+            model_card.model_package_details = None
         update_model_card_req = model_card._create_request_args()
-        if update_model_card_req["ModelCardStatus"] is not None:
-            if (
-                desc_model_package["ModelCard"]["ModelCardStatus"]
-                == update_model_card_req["ModelCardStatus"]
-            ):
-                del update_model_card_req["ModelCardStatus"]
-
         if update_model_card_req.get("ModelCardName") is not None:
             del update_model_card_req["ModelCardName"]
-        if update_model_card_req.get("Content") is not None:
-            previous_content_hash = _hash_content_str(
-                desc_model_package["ModelCard"]["ModelCardContent"]
-            )
-            current_content_hash = _hash_content_str(update_model_card_req["Content"])
-            if (
-                previous_content_hash == current_content_hash
-                or update_model_card_req.get("Content") == "{}"
-                or update_model_card_req.get("Content") == "null"
-            ):
-                del update_model_card_req["Content"]
-            else:
-                update_model_card_req["ModelCardContent"] = update_model_card_req["Content"]
-                del update_model_card_req["Content"]
-        update_model_package_args = {
-            "ModelPackageArn": self.model_package_arn,
-            "ModelCard": update_model_card_req,
-        }
-        sagemaker_session.sagemaker_client.update_model_package(**update_model_package_args)
+        if update_model_card_req["Content"] is not None:
+            if "model_package_details" in update_model_card_req["Content"]:
+                update_model_card_req["Content"].pop("model_package_details", None)
+            update_model_card_req["ModelCardContent"] = update_model_card_req["Content"]
+            del update_model_card_req["Content"]
+
+        if "ModelCard" in desc_model_package:
+            if update_model_card_req["ModelCardStatus"] is not None:
+                if (
+                    desc_model_package["ModelCard"]["ModelCardStatus"]
+                    != update_model_card_req["ModelCardStatus"]
+                ):
+                    new_mc_mp_req = update_model_card_req
+                    del new_mc_mp_req["ModelCardContent"]
+                    update_model_package_args = {
+                        "ModelPackageArn": self.model_package_arn,
+                        "ModelCard": new_mc_mp_req,
+                    }
+                    sagemaker_session.sagemaker_client.update_model_package(
+                        **update_model_package_args
+                    )
+
+            if update_model_card_req.get("ModelCardContent") is not None:
+                previous_content_hash = _hash_content_str(
+                    desc_model_package["ModelCard"]["ModelCardContent"]
+                )
+                current_content_hash = _hash_content_str(update_model_card_req["ModelCardContent"])
+                if not (
+                    previous_content_hash == current_content_hash
+                    or update_model_card_req.get("ModelCardContent") == "{}"
+                    or update_model_card_req.get("ModelCardContent") == "null"
+                ):
+                    new_mc_mp_req = update_model_card_req
+                    del new_mc_mp_req["ModelCardStatus"]
+                    update_model_package_args = {
+                        "ModelPackageArn": self.model_package_arn,
+                        "ModelCard": new_mc_mp_req,
+                    }
+                    sagemaker_session.sagemaker_client.update_model_package(
+                        **update_model_package_args
+                    )
+        else:
+            update_model_package_args = {
+                "ModelPackageArn": self.model_package_arn,
+                "ModelCard": update_model_card_req,
+            }
+            sagemaker_session.sagemaker_client.update_model_package(**update_model_package_args)

tests/integ/test_byo_estimator.py

@@ -12,14 +12,20 @@
 # language governing permissions and limitations under the License.
 from __future__ import absolute_import
 
+import io
 import json
 import os
 
+import numpy as np
+
 import pytest
+import sagemaker.amazon.common as smac
+
 
 import sagemaker
 from sagemaker import image_uris
 from sagemaker.estimator import Estimator
+from sagemaker.s3 import S3Uploader
 from sagemaker.serializers import SimpleBaseSerializer
 from sagemaker.utils import unique_name_from_base
 from tests.integ import DATA_DIR, TRAINING_DEFAULT_TIMEOUT_MINUTES, datasets
@@ -102,6 +108,60 @@ def test_byo_estimator(sagemaker_session, region, cpu_instance_type, training_se
             assert prediction["score"] is not None
 
 
+@pytest.mark.release
+def test_estimator_register_publish_training_details(sagemaker_session, region):
+
+    bucket = sagemaker_session.default_bucket()
+    prefix = "model-card-sample-notebook"
+
+    raw_data = (
+        (0.5, 0),
+        (0.75, 0),
+        (1.0, 0),
+        (1.25, 0),
+        (1.50, 0),
+        (1.75, 0),
+        (2.0, 0),
+        (2.25, 1),
+        (2.5, 0),
+        (2.75, 1),
+        (3.0, 0),
+        (3.25, 1),
+        (3.5, 0),
+        (4.0, 1),
+        (4.25, 1),
+        (4.5, 1),
+        (4.75, 1),
+        (5.0, 1),
+        (5.5, 1),
+    )
+    training_data = np.array(raw_data).astype("float32")
+    labels = training_data[:, 1]
+
+    # upload data to S3 bucket
+    buf = io.BytesIO()
+    smac.write_numpy_to_dense_tensor(buf, training_data, labels)
+    buf.seek(0)
+    s3_train_data = f"s3://{bucket}/{prefix}/train"
+    S3Uploader.upload_bytes(b=buf, s3_uri=s3_train_data, sagemaker_session=sagemaker_session)
+    output_location = f"s3://{bucket}/{prefix}/output"
+    container = image_uris.retrieve("linear-learner", region)
+    estimator = Estimator(
+        container,
+        role="SageMakerRole",
+        instance_count=1,
+        instance_type="ml.m4.xlarge",
+        output_path=output_location,
+        sagemaker_session=sagemaker_session,
+    )
+    estimator.set_hyperparameters(
+        feature_dim=2, mini_batch_size=10, predictor_type="binary_classifier"
+    )
+    estimator.fit({"train": s3_train_data})
+    print(f"Training job name: {estimator.latest_training_job.name}")
+    estimator.register()
+
+
 def test_async_byo_estimator(sagemaker_session, region, cpu_instance_type, training_set):
     image_uri = image_uris.retrieve("factorization-machines", region)
     endpoint_name = unique_name_from_base("byo")

tests/unit/sagemaker/image_uris/test_huggingface_llm.py

@@ -42,7 +42,7 @@
         "2.0.0": "2.1.1-tgi2.0.0-gpu-py310-cu121-ubuntu22.04",
         "2.0.1": "2.1.1-tgi2.0.1-gpu-py310-cu121-ubuntu22.04",
         "2.0.2": "2.3.0-tgi2.0.2-gpu-py310-cu121-ubuntu22.04",
-        "2.2.0": "2.3.0-tgi2.2.0-gpu-py310-cu121-ubuntu22.04",
+        "2.2.0": "2.3.0-tgi2.2.0-gpu-py310-cu121-ubuntu22.04-v2.0",
     },
     "inf2": {
         "0.0.16": "1.13.1-optimum0.0.16-neuronx-py310-ubuntu22.04",

tests/unit/test_estimator.py

@@ -4402,7 +4402,7 @@ def test_register_default_image_without_instance_type_args(sagemaker_session):
     framework = "TENSORFLOW"
     framework_version = "2.9"
     nearest_model_name = "resnet50"
-
+    model_card = {"ModelCardStatus": ModelCardStatusEnum.DRAFT, "ModelCardContent": "{}"}
     estimator.register(
         content_types=content_types,
         response_types=response_types,
@@ -4425,6 +4425,7 @@ def test_register_default_image_without_instance_type_args(sagemaker_session):
         "marketplace_cert": False,
         "sample_payload_url": sample_payload_url,
         "task": task,
+        "model_card": model_card,
     }
     sagemaker_session.create_model_package_from_containers.assert_called_with(
         **expected_create_model_package_request
@@ -4454,6 +4455,7 @@ def test_register_inference_image(sagemaker_session):
     framework = "TENSORFLOW"
     framework_version = "2.9"
     nearest_model_name = "resnet50"
+    model_card = {"ModelCardStatus": ModelCardStatusEnum.DRAFT, "ModelCardContent": "{}"}
 
     estimator.register(
         content_types=content_types,
@@ -4480,6 +4482,7 @@ def test_register_inference_image(sagemaker_session):
         "marketplace_cert": False,
         "sample_payload_url": sample_payload_url,
         "task": task,
+        "model_card": model_card,
     }
     sagemaker_session.create_model_package_from_containers.assert_called_with(
         **expected_create_model_package_request