Upgrade SKLearn to 1.2.1 (#163)

* Upgrade SKLearn to 1.2.1

* Delete buildspecs and refresh pickled models

* Correct local integration tests

---------

Co-authored-by: Malav Shastri <malavhs@amazon.com>

Author: malav-shastri

README.rst

@@ -184,7 +184,7 @@ If you want to run local integration tests, then use:
 
   # Example
   pytest test/integration --docker-base-name preprod-sklearn ``\``
-                                --tag 1.0-1 ``\``
+                                --tag 1.2-1 ``\``
                                 --py-version 3 ``\``
                                 --framework-version |FRAMEWORK_VERSION|
 
@@ -242,4 +242,4 @@ SageMaker Scikit-learn Container is licensed under the Apache 2.0 License. It is
 .com, Inc. or its affiliates. All Rights Reserved. The license is available at:
 http://aws.amazon.com/apache2.0/
 
-.. |FRAMEWORK_VERSION| replace:: 1.0-1
+.. |FRAMEWORK_VERSION| replace:: 1.2-1

docker/1.0-1/base/Dockerfile.cpu renamed to docker/1.2-1/base/Dockerfile.cpu

@@ -116,4 +116,4 @@ ENV PYTHONDONTWRITEBYTECODE=1 PYTHONUNBUFFERED=1 PYTHONIOENCODING=UTF-8 LANG=C.U
 # Install Scikit-Learn
 # Scikit-learn 0.20 was the last version to support Python 2.7 and Python 3.4.
 # Scikit-learn now requires Python 3.6 or newer.
-RUN python -m pip install --no-cache -I scikit-learn==1.0.2
+RUN python -m pip install --no-cache -I scikit-learn==1.2.1

docker/1.0-1/base/Dockerfile_arm.cpu renamed to docker/1.2-1/base/Dockerfile_arm.cpu

@@ -1,9 +1,9 @@
-FROM preprod-sklearn:1.0-1
+FROM preprod-sklearn:1.2-1
 
-RUN pip freeze | grep -q 'scikit-learn==1.0.2'; \
+RUN pip freeze | grep -q 'scikit-learn==1.2.1'; \
 	if [ $? -eq 0 ]; \
-		then echo 'scikit-learn version 1.0.2 requirement met'; \
-		else echo 'ERROR: Expected scikit-learn version is 1.0.2, check base images for scikit-learn version' && \
+		then echo 'scikit-learn version 1.2.1 requirement met'; \
+		else echo 'ERROR: Expected scikit-learn version is 1.2.1, check base images for scikit-learn version' && \
 			 exit 1; fi
 
 RUN pip install --upgrade --no-cache --no-deps sagemaker-scikit-learn-extension==2.5.0

docker/1.0-1/extension/Dockerfile.cpu renamed to docker/1.2-1/extension/Dockerfile.cpu

@@ -8,15 +8,15 @@ The SageMaker Scikit-learn Extension Container is built in 3 steps. The first 2
 
 The "base" Dockerfile encompass the installation of the framework and all of the dependencies needed.
 
-Tagging scheme is based on <Scikit-learn_version>-<SageMaker_version>-cpu-py<python_version>. (e.g. 1.0-1-cpu-py3)
+Tagging scheme is based on <Scikit-learn_version>-<SageMaker_version>-cpu-py<python_version>. (e.g. 1.2-1-cpu-py3)
 
 All "final" Dockerfiles build images using base images that use the tagging scheme above.
 
 ```
-docker build -t sklearn-base:1.0-1-cpu-py3 -f docker/1.0-1/base/Dockerfile.cpu .
+docker build -t sklearn-base:1.2-1-cpu-py3 -f docker/1.2-1/base/Dockerfile.cpu .
 ```
 
-Notice that this Dockerfile has the updated version of sklearn (1.0.2) installed.
+Notice that this Dockerfile has the updated version of sklearn (1.2.1) installed.
 
 ### Step 2: Final Image
 
@@ -38,7 +38,7 @@ python setup.py bdist_wheel
 Then build the final image, like in the sagemaker-sklearn-container
 
 ```
-docker build -t preprod-sklearn:1.0-1-cpu-py3 -f docker/1.0-1/final/Dockerfile.cpu .
+docker build -t preprod-sklearn:1.2-1-cpu-py3 -f docker/1.2-1/final/Dockerfile.cpu .
 ```
 
 ### Step 3: Build the extension image for SageMaker Scikit-learn Extension Container
@@ -47,10 +47,10 @@ The "extension" Dockerfiles encompass the installation of the SageMaker Autopilo
 
 The "extension" Dockerfiles use final images for building.
 
-Build the third additional Dockerfile needed for SageMaker Scikit-learn Extension Container. This Dockerfile specifies a hard dependency on a certain version of scikit-learn (i.e. v1.0.2).
+Build the third additional Dockerfile needed for SageMaker Scikit-learn Extension Container. This Dockerfile specifies a hard dependency on a certain version of scikit-learn (i.e. v1.2.1).
 
 Tagging scheme is based on extension-<Scikit-learn-Extension_version>-<SageMaker_version>-cpu-py<python_version>. (e.g. extension-2.5-1-cpu-py3). Make sure the "extension" image is tagged in accordance with the  `extension` (i.e. `extension-2.5-1-cpu-py3`).
 
 ```
-docker build -t preprod-sklearn-extension:2.5-1-cpu-py3 -f  docker/1.0-1/extension/Dockerfile.cpu .
+docker build -t preprod-sklearn-extension:2.5-1-cpu-py3 -f  docker/1.2-1/extension/Dockerfile.cpu .
 ```

docker/1.0-1/extension/README.md renamed to docker/1.2-1/extension/README.md

@@ -1,5 +1,5 @@
-FROM sklearn-base:1.0-1
-ENV SAGEMAKER_SKLEARN_VERSION 1.0-1
+FROM sklearn-base:1.2-1
+ENV SAGEMAKER_SKLEARN_VERSION 1.2-1
 
 LABEL com.amazonaws.sagemaker.capabilities.accept-bind-to-port=true
 

docker/1.0-1/final/Dockerfile.cpu renamed to docker/1.2-1/final/Dockerfile.cpu

@@ -14,7 +14,7 @@ retrying==1.3.3
 sagemaker-containers==2.8.6.post2
 sagemaker-inference==1.2.0
 sagemaker-training==4.0.1
-scikit-learn==1.0.2
+scikit-learn==1.2.1
 scipy==1.5.3
 six==1.15.0
 jinja2==3.0.3

docker/1.0-1/final/Dockerfile_arm.cpu renamed to docker/1.2-1/final/Dockerfile_arm.cpu

@@ -43,7 +43,7 @@ def pytest_addoption(parser):
     parser.addoption('--install-container-support', '-C', action='store_true')
     parser.addoption('--docker-base-name', default='sk-learn')
     parser.addoption('--region', default='us-west-2')
-    parser.addoption('--framework-version', default='1.0.2')
+    parser.addoption('--framework-version', default='1.2.1')
     parser.addoption('--py-version', choices=['2', '3'], default=str(sys.version_info.major))
     parser.addoption('--processor', choices=['cpu'], default='cpu')
     # If not specified, will default to {framework-version}-{processor}-py{py-version}

docker/1.0-1/resources/libffi7_3.3-6_arm64.deb renamed to docker/1.2-1/resources/libffi7_3.3-6_arm64.deb

@@ -150,15 +150,15 @@ def test_load_and_unload_model():
     res_json = json.loads(res)
     assert res_json['status'] == 'Workers scaled'
 
-    code, res = make_invocation_request('0.0, 0.0, 0.0, 0.0, 0.0, 0.0', model_name)
+    code, res = make_invocation_request('0.0, 0.0, 0.0, 0.0, 0.0', model_name)
     assert code == 200, res
 
     code, res = make_unload_model_request(model_name)
     assert code == 200, res
     res_json = json.loads(res)
     assert res_json['status'] == "Model \"{}\" unregistered".format(model_name), res
 
-    code, res = make_invocation_request('0.0, 0.0, 0.0, 0.0, 0.0, 0.0', model_name)
+    code, res = make_invocation_request('0.0, 0.0, 0.0, 0.0, 0.0', model_name)
     assert code == 404, res
     assert res['message'] == "Model not found: {}".format(model_name), res
 
@@ -184,10 +184,10 @@ def test_load_and_unload_two_models():
     res_json = json.loads(res)
     assert res_json['status'] == 'Workers scaled'
 
-    code, res = make_invocation_request('0.0, 0.0, 0.0, 0.0, 0.0, 0.0', model_name_0)
+    code, res = make_invocation_request('0.0, 0.0, 0.0, 0.0, 0.0', model_name_0)
     assert code == 200, res
 
-    code, res = make_invocation_request('0.0, 0.0, 0.0, 0.0, 0.0, 0.0', model_name_1)
+    code, res = make_invocation_request('0.0, 0.0, 0.0, 0.0, 0.0', model_name_1)
     assert code == 200, res
 
     code, res = make_unload_model_request(model_name_0)

docker/1.0-1/resources/mms/ExecutionParameters.java renamed to docker/1.2-1/resources/mms/ExecutionParameters.java

@@ -1,12 +1,12 @@
 [tox]
-envlist = {py3}-sklearn{1.0},flake8
+envlist = {py3}-sklearn{1.2},flake8
 
 [flake8]
 max-line-length = 120
 
 [testenv]
 deps =
-    sklearn1.0: scikit-learn==1.0.2
+    sklearn1.2: scikit-learn==1.2.1
     -r{toxinidir}/requirements.txt
     -r{toxinidir}/test-requirements.txt
 conda_deps=