breaking: require framework_version, py_version for sklearn

doc/frameworks/sklearn/using_sklearn.rst

@@ -31,7 +31,7 @@ To train a Scikit-learn model by using the SageMaker Python SDK:
 Prepare a Scikit-learn Training Script
 ======================================
 
-Your Scikit-learn training script must be a Python 2.7 or 3.6 compatible source file.
+Your Scikit-learn training script must be a Python 3.6 compatible source file.
 
 The training script is similar to a training script you might run outside of SageMaker, but you
 can access useful properties about the training environment through various environment variables.
@@ -465,8 +465,10 @@ The following code sample shows how to do this, using the ``SKLearnModel`` class
 
 .. code:: python
 
-    sklearn_model = SKLearnModel(model_data="s3://bucket/model.tar.gz", role="SageMakerRole",
-        entry_point="transform_script.py")
+    sklearn_model = SKLearnModel(model_data="s3://bucket/model.tar.gz",
+                                 role="SageMakerRole",
+                                 entry_point="transform_script.py",
+                                 framework_version="0.20.0")
 
     predictor = sklearn_model.deploy(instance_type="ml.c4.xlarge", initial_instance_count=1)
 

src/sagemaker/sklearn/estimator.py

@@ -19,8 +19,8 @@
 from sagemaker.fw_registry import default_framework_uri
 from sagemaker.fw_utils import (
     framework_name_from_image,
-    empty_framework_version_warning,
-    python_deprecation_warning,
+    framework_version_from_tag,
+    validate_version_or_image_args,
 )
 from sagemaker.sklearn import defaults
 from sagemaker.sklearn.model import SKLearnModel
@@ -37,10 +37,10 @@ class SKLearn(Framework):
     def __init__(
         self,
         entry_point,
-        framework_version=defaults.SKLEARN_VERSION,
+        framework_version=None,
+        py_version="py3",
         source_dir=None,
         hyperparameters=None,
-        py_version="py3",
         image_name=None,
         **kwargs
     ):
@@ -68,8 +68,13 @@ def __init__(
                 If ``source_dir`` is specified, then ``entry_point``
                 must point to a file located at the root of ``source_dir``.
             framework_version (str): Scikit-learn version you want to use for
-                executing your model training code. List of supported versions
+                executing your model training code. Defaults to ``None``. Required
+                unless ``image_name`` is provided. List of supported versions:
                 https://github.com/aws/sagemaker-python-sdk#sklearn-sagemaker-estimators
+            py_version (str): Python version you want to use for executing your
+                model training code (default: 'py3'). Currently, 'py3' is the only
+                supported version. If ``None`` is passed in, ``image_name`` must be
+                provided.
             source_dir (str): Path (absolute, relative or an S3 URI) to a directory
                 with any other training source code dependencies aside from the entry
                 point file (default: None). If ``source_dir`` is an S3 URI, it must
@@ -81,15 +86,18 @@ def __init__(
                 SageMaker. For convenience, this accepts other types for keys
                 and values, but ``str()`` will be called to convert them before
                 training.
-            py_version (str): Python version you want to use for executing your
-                model training code (default: 'py3'). One of 'py2' or 'py3'.
             image_name (str): If specified, the estimator will use this image
                 for training and hosting, instead of selecting the appropriate
                 SageMaker official image based on framework_version and
                 py_version. It can be an ECR url or dockerhub image and tag.
+
                 Examples:
                     123.dkr.ecr.us-west-2.amazonaws.com/my-custom-image:1.0
                     custom-image:latest.
+
+                If ``framework_version`` or ``py_version`` are ``None``, then
+                ``image_name`` is required. If also ``None``, then a ``ValueError``
+                will be raised.
             **kwargs: Additional kwargs passed to the
                 :class:`~sagemaker.estimator.Framework` constructor.
 
@@ -99,6 +107,14 @@ def __init__(
             :class:`~sagemaker.estimator.Framework` and
             :class:`~sagemaker.estimator.EstimatorBase`.
         """
+        validate_version_or_image_args(framework_version, py_version, image_name)
+        if py_version and py_version != "py3":
+            raise AttributeError(
+                "Scikit-learn image only supports Python 3. Please use 'py3' for py_version."
+            )
+        self.framework_version = framework_version
+        self.py_version = py_version
+
         # SciKit-Learn does not support distributed training or training on GPU instance types.
         # Fail fast.
         train_instance_type = kwargs.get("train_instance_type")
@@ -112,6 +128,7 @@ def __init__(
                     "Please remove the 'train_instance_count' argument or set "
                     "'train_instance_count=1' when initializing SKLearn."
                 )
+
         super(SKLearn, self).__init__(
             entry_point,
             source_dir,
@@ -120,19 +137,6 @@ def __init__(
             **dict(kwargs, train_instance_count=1)
         )
 
-        if py_version == "py2":
-            logger.warning(
-                python_deprecation_warning(self.__framework_name__, defaults.LATEST_PY2_VERSION)
-            )
-
-        self.py_version = py_version
-
-        if framework_version is None:
-            logger.warning(
-                empty_framework_version_warning(defaults.SKLEARN_VERSION, defaults.SKLEARN_VERSION)
-            )
-        self.framework_version = framework_version or defaults.SKLEARN_VERSION
-
         if image_name is None:
             image_tag = "{}-{}-{}".format(framework_version, "cpu", py_version)
             self.image_name = default_framework_uri(
@@ -216,28 +220,40 @@ class constructor
         Args:
             job_details: the returned job details from a describe_training_job
                 API call.
-            model_channel_name:
+            model_channel_name (str): Name of the channel where pre-trained
+                model data will be downloaded (default: None).
 
         Returns:
             dictionary: The transformed init_params
         """
-        init_params = super(SKLearn, cls)._prepare_init_params_from_job_description(job_details)
-
+        init_params = super(SKLearn, cls)._prepare_init_params_from_job_description(
+            job_details, model_channel_name
+        )
         image_name = init_params.pop("image")
-        framework, py_version, _, _ = framework_name_from_image(image_name)
+        framework, py_version, tag, _ = framework_name_from_image(image_name)
+
+        if tag is None:
+            framework_version = None
+        else:
+            framework_version = framework_version_from_tag(tag)
+        init_params["framework_version"] = framework_version
         init_params["py_version"] = py_version
 
+        if not framework:
+            # If we were unable to parse the framework name from the image it is not one of our
+            # officially supported images, in this case just add the image to the init params.
+            init_params["image_name"] = image_name
+            return init_params
+
+        training_job_name = init_params["base_job_name"]
+
         if framework and framework != cls.__framework_name__:
-            training_job_name = init_params["base_job_name"]
             raise ValueError(
                 "Training job: {} didn't use image for requested framework".format(
                     training_job_name
                 )
             )
-        if not framework:
-            # If we were unable to parse the framework name from the image it is not one of our
-            # officially supported images, in this case just add the image to the init params.
-            init_params["image_name"] = image_name
+
         return init_params
 
 

src/sagemaker/sklearn/model.py

@@ -18,8 +18,8 @@
 from sagemaker import fw_utils
 
 import sagemaker
-from sagemaker.fw_utils import model_code_key_prefix, python_deprecation_warning
 from sagemaker.fw_registry import default_framework_uri
+from sagemaker.fw_utils import model_code_key_prefix, validate_version_or_image_args
 from sagemaker.model import FrameworkModel, MODEL_SERVER_WORKERS_PARAM_NAME
 from sagemaker.predictor import RealTimePredictor, npy_serializer, numpy_deserializer
 from sagemaker.sklearn import defaults
@@ -62,9 +62,9 @@ def __init__(
         model_data,
         role,
         entry_point,
-        image=None,
+        framework_version=None,
         py_version="py3",
-        framework_version=defaults.SKLEARN_VERSION,
+        image=None,
         predictor_cls=SKLearnPredictor,
         model_server_workers=None,
         **kwargs
@@ -83,12 +83,19 @@ def __init__(
                 file which should be executed as the entry point to model
                 hosting. If ``source_dir`` is specified, then ``entry_point``
                 must point to a file located at the root of ``source_dir``.
+            framework_version (str): Scikit-learn version you want to use for
+                executing your model training code. Defaults to ``None``. Required
+                unless ``image`` is provided.
+            py_version (str): Python version you want to use for executing your
+                model training code (default: 'py3'). Currently, 'py3' is the only
+                supported version. If ``None`` is passed in, ``image`` must be
+                provided.
             image (str): A Docker image URI (default: None). If not specified, a
                 default image for Scikit-learn will be used.
-            py_version (str): Python version you want to use for executing your
-                model training code (default: 'py3').
-            framework_version (str): Scikit-learn version you want to use for
-                executing your model training code.
+
+                If ``framework_version`` or ``py_version`` are ``None``, then
+                ``image`` is required. If also ``None``, then a ``ValueError``
+                will be raised.
             predictor_cls (callable[str, sagemaker.session.Session]): A function
                 to call to create a predictor with an endpoint name and
                 SageMaker ``Session``. If specified, ``deploy()`` returns the
@@ -105,17 +112,18 @@ def __init__(
             :class:`~sagemaker.model.FrameworkModel` and
             :class:`~sagemaker.model.Model`.
         """
+        validate_version_or_image_args(framework_version, py_version, image)
+        if py_version and py_version != "py3":
+            raise AttributeError(
+                "Scikit-learn image only supports Python 3. Please use 'py3' for py_version."
+            )
+        self.framework_version = framework_version
+        self.py_version = py_version
+
         super(SKLearnModel, self).__init__(
             model_data, image, role, entry_point, predictor_cls=predictor_cls, **kwargs
         )
 
-        if py_version == "py2":
-            logger.warning(
-                python_deprecation_warning(self.__framework_name__, defaults.LATEST_PY2_VERSION)
-            )
-
-        self.py_version = py_version
-        self.framework_version = framework_version
         self.model_server_workers = model_server_workers
 
     def prepare_container_def(self, instance_type, accelerator_type=None):

tests/integ/test_git.py

@@ -173,6 +173,7 @@ def test_private_github_with_2fa(sagemaker_local_session, sklearn_full_version):
                 model_data,
                 "SageMakerRole",
                 entry_point=script_path,
+                framework_version=sklearn_full_version,
                 source_dir=source_dir,
                 sagemaker_session=sagemaker_local_session,
                 git_config=git_config,

tests/integ/test_sklearn_train.py

@@ -36,7 +36,7 @@ def sklearn_training_job(sagemaker_session, sklearn_full_version, cpu_instance_t
     sagemaker_session.boto_region_name
 
 
-@pytest.mark.skipif(PYTHON_VERSION != "py3", reason="Scikit-learn image supports only python 3.")
+@pytest.mark.skipif(PYTHON_VERSION != "py3", reason="Scikit-learn image supports only Python 3.")
 def test_training_with_additional_hyperparameters(
     sagemaker_session, sklearn_full_version, cpu_instance_type
 ):
@@ -66,7 +66,7 @@ def test_training_with_additional_hyperparameters(
         return sklearn.latest_training_job.name
 
 
-@pytest.mark.skipif(PYTHON_VERSION != "py3", reason="Scikit-learn image supports only python 3.")
+@pytest.mark.skipif(PYTHON_VERSION != "py3", reason="Scikit-learn image supports only Python 3.")
 def test_training_with_network_isolation(
     sagemaker_session, sklearn_full_version, cpu_instance_type
 ):
@@ -121,7 +121,9 @@ def test_attach_deploy(sklearn_training_job, sagemaker_session, cpu_instance_typ
     reason="This test has always failed, but the failure was masked by a bug. "
     "This test should be fixed. Details in https://github.com/aws/sagemaker-python-sdk/pull/968"
 )
-def test_deploy_model(sklearn_training_job, sagemaker_session, cpu_instance_type):
+def test_deploy_model(
+    sklearn_training_job, sagemaker_session, cpu_instance_type, sklearn_full_version
+):
     endpoint_name = "test-sklearn-deploy-model-{}".format(sagemaker_timestamp())
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
         desc = sagemaker_session.sagemaker_client.describe_training_job(
@@ -133,6 +135,7 @@ def test_deploy_model(sklearn_training_job, sagemaker_session, cpu_instance_type
             model_data,
             "SageMakerRole",
             entry_point=script_path,
+            framework_version=sklearn_full_version,
             sagemaker_session=sagemaker_session,
         )
         predictor = model.deploy(1, cpu_instance_type, endpoint_name=endpoint_name)