aws · Oct 17, 2023 · Oct 19, 2023 · Oct 19, 2023 · Oct 19, 2023 · Oct 20, 2023
diff --git a/.flake8 b/.flake8
@@ -1,3 +1,3 @@
 [flake8]
-application_import_names = image_utils, integration, local_mode_utils, sagemaker_pytorch_container, test, timeout, utils
+application_import_names = image_utils, integration, local_mode_utils, sagemaker_inference, sagemaker_pytorch_serving_container, test, timeout, utils
 import-order-style = google
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -1,5 +1,41 @@
 # Changelog
 
+## v2.0.25 (2024-10-07)
+
+### Bug Fixes and Other Changes
+
+ * Disabling token auth and enabling model API by default
+
+## v2.0.24 (2024-05-31)
+
+### Bug Fixes and Other Changes
+
+ * Ignore zombie processes when detecting TorchServe status
+
+## v2.0.23 (2024-03-15)
+
+### Bug Fixes and Other Changes
+
+ * Improve error logging when invoking custom handler methods
+
+## v2.0.22 (2023-12-18)
+
+### Bug Fixes and Other Changes
+
+ * Fix dependency versions
+
+## v2.0.21 (2023-10-23)
+
+### Bug Fixes and Other Changes
+
+ * Cleanup to remove EIA integration tests and other unused files/code
+
+## v2.0.20 (2023-10-19)
+
+### Bug Fixes and Other Changes
+
+ * Migrate base inference toolkit scripts and unit tests
+
 ## v2.0.19 (2023-10-17)
 
 ### Bug Fixes and Other Changes

diff --git a/MANIFEST.in b/MANIFEST.in
@@ -1,4 +1,5 @@
 recursive-include src/sagemaker_pytorch_serving_container *
+recursive-include src/sagemaker_inference *
 
 include VERSION
 include LICENSE

diff --git a/README.rst b/README.rst
@@ -4,8 +4,7 @@ SageMaker PyTorch Inference Toolkit
 
 SageMaker PyTorch Inference Toolkit is an open-source library for serving PyTorch models on Amazon SageMaker.
 This library provides default pre-processing, predict and postprocessing for certain PyTorch model types and
-utilizes the `SageMaker Inference Toolkit <https://github.com/aws/sagemaker-inference-toolkit>`__
-for starting up the model server, which is responsible for handling inference requests.
+is responsible for starting up the TorchServe model server on SageMaker, which is responsible for handling inference requests.
 
 For training, see `SageMaker PyTorch Training Toolkit <https://github.com/aws/sagemaker-pytorch-training-toolkit>`__.
 

diff --git a/VERSION b/VERSION
@@ -1 +1 @@
-2.0.19
+2.0.26.dev0
diff --git a/artifacts/changehostname.c b/artifacts/changehostname.c
diff --git a/artifacts/config.properties b/artifacts/config.properties
diff --git a/artifacts/start_with_right_hostname.sh b/artifacts/start_with_right_hostname.sh
diff --git a/artifacts/ts-entrypoint.py b/artifacts/ts-entrypoint.py
diff --git a/buildspec-container-pr.yml b/buildspec-container-pr.yml
diff --git a/buildspec.yml b/buildspec.yml
@@ -2,11 +2,9 @@ version: 0.2
 
 env:
   variables:
-    FRAMEWORK_VERSIONS: '2.0.0 2.0.1'
-    # EIA_FRAMEWORK_VERSION: '1.3.1'
+    FRAMEWORK_VERSIONS: '2.1.0 2.2.0'
     CPU_INSTANCE_TYPE: 'ml.c4.xlarge'
     GPU_INSTANCE_TYPE: 'ml.g4dn.12xlarge'
-    # EIA_ACCELERATOR_TYPE: 'ml.eia2.medium'
     ECR_REPO: 'sagemaker-test'
     GITHUB_REPO: 'sagemaker-pytorch-serving-container'
     DLC_ACCOUNT: '763104351884'
@@ -35,9 +33,6 @@ phases:
       # run unit tests
       - tox -e py38,py39,py310 test/unit
 
-      # define EIA tag
-      # - DLC_EIA_TAG="$EIA_FRAMEWORK_VERSION-dlc-eia-$BUILD_ID"
-
       # run local CPU integration tests (build and push the image to ECR repo)
       - |
         for FRAMEWORK_VERSION in $FRAMEWORK_VERSIONS; 
@@ -48,18 +43,18 @@ phases:
             docker system prune --all --force;
           done
 
-      # launch remote GPU instance with Deep Learning AMI GPU PyTorch 1.9 (Ubuntu 20.04)
       - prefix='ml.'
       - instance_type=${GPU_INSTANCE_TYPE#"$prefix"}
-
-      # build DLC GPU image because the base DLC image is too big and takes too long to build as part of the test
       - python3 setup.py sdist
       - $(aws ecr get-login --registry-ids $DLC_ACCOUNT --no-include-email --region $AWS_DEFAULT_REGION)
       - create-key-pair
+
+      # launch remote GPU instance with Deep Learning AMI GPU PyTorch 2.2 (Ubuntu 20.04)
+      # build DLC GPU image because the base DLC image is too big and takes too long to build as part of the test
       - |
         for FRAMEWORK_VERSION in $FRAMEWORK_VERSIONS;
           do
-            launch-ec2-instance --instance-type $instance_type --ami-name ami-03e3ef8c92fdb39ad;
+            launch-ec2-instance --instance-type $instance_type --ami-name ami-081c4092fbff425f0;
             DLC_GPU_TAG="$FRAMEWORK_VERSION-dlc-gpu-$BUILD_ID";
             build_dir="test/container/$FRAMEWORK_VERSION";
             docker build -f "$build_dir/Dockerfile.dlc.gpu" -t $PREPROD_IMAGE:$DLC_GPU_TAG --build-arg region=$AWS_DEFAULT_REGION .;
@@ -96,10 +91,6 @@ phases:
             docker system prune --all --force;
           done
 
-      # run EIA sagemaker integration tests
-      # - test_cmd="IGNORE_COVERAGE=- tox -e py38 -- test/integration/sagemaker --build-image --push-image --dockerfile-type dlc.eia --region $AWS_DEFAULT_REGION --docker-base-name $ECR_REPO --aws-id $ACCOUNT --framework-version $EIA_FRAMEWORK_VERSION --processor cpu --instance-type $CPU_INSTANCE_TYPE --accelerator-type $EIA_ACCELERATOR_TYPE --tag $DLC_EIA_TAG"
-      # - execute-command-if-has-matching-changes "$test_cmd" "test/" "src/*.py" "setup.py" "setup.cfg"
-
     finally:
 
       # remove ECR image
@@ -111,5 +102,3 @@ phases:
             aws ecr batch-delete-image --repository-name $ECR_REPO --region $AWS_DEFAULT_REGION --image-ids imageTag=$DLC_CPU_TAG;
             aws ecr batch-delete-image --repository-name $ECR_REPO --region $AWS_DEFAULT_REGION --image-ids imageTag=$DLC_GPU_TAG;
           done
-      
-      # - aws ecr batch-delete-image --repository-name $ECR_REPO --region $AWS_DEFAULT_REGION --image-ids imageTag=$DLC_EIA_TAG
diff --git a/docker/1.1.0/base/Dockerfile.cpu b/docker/1.1.0/base/Dockerfile.cpu
diff --git a/docker/1.1.0/base/Dockerfile.gpu b/docker/1.1.0/base/Dockerfile.gpu
diff --git a/docker/1.1.0/final/Dockerfile.cpu b/docker/1.1.0/final/Dockerfile.cpu
diff --git a/docker/1.1.0/final/Dockerfile.gpu b/docker/1.1.0/final/Dockerfile.gpu