breaking: require framework_version, py_version for pytorch

doc/frameworks/pytorch/using_pytorch.rst

@@ -154,7 +154,8 @@ directories ('train' and 'test').
     pytorch_estimator = PyTorch('pytorch-train.py',
                                 train_instance_type='ml.p3.2xlarge',
                                 train_instance_count=1,
-                                framework_version='1.0.0',
+                                framework_version='1.5.0',
+                                py_version='py3',
                                 hyperparameters = {'epochs': 20, 'batch-size': 64, 'learning-rate': 0.1})
     pytorch_estimator.fit({'train': 's3://my-data-bucket/path/to/my/training/data',
                            'test': 's3://my-data-bucket/path/to/my/test/data'})
@@ -247,7 +248,8 @@ operation.
     pytorch_estimator = PyTorch(entry_point='train_and_deploy.py',
                                 train_instance_type='ml.p3.2xlarge',
                                 train_instance_count=1,
-                                framework_version='1.0.0')
+                                framework_version='1.5.0',
+                                py_version='py3')
     pytorch_estimator.fit('s3://my_bucket/my_training_data/')
 
     # Deploy my estimator to a SageMaker Endpoint and get a Predictor

src/sagemaker/pytorch/estimator.py

@@ -19,9 +19,9 @@
 from sagemaker.fw_utils import (
     framework_name_from_image,
     framework_version_from_tag,
-    empty_framework_version_warning,
-    python_deprecation_warning,
     is_version_equal_or_higher,
+    python_deprecation_warning,
+    validate_version_or_image_args,
 )
 from sagemaker.pytorch import defaults
 from sagemaker.pytorch.model import PyTorchModel
@@ -40,10 +40,10 @@ class PyTorch(Framework):
     def __init__(
         self,
         entry_point,
+        framework_version=None,
+        py_version=None,
         source_dir=None,
         hyperparameters=None,
-        py_version=defaults.PYTHON_VERSION,
-        framework_version=None,
         image_name=None,
         **kwargs
     ):
@@ -69,6 +69,13 @@ def __init__(
                 file which should be executed as the entry point to training.
                 If ``source_dir`` is specified, then ``entry_point``
                 must point to a file located at the root of ``source_dir``.
+            framework_version (str): PyTorch version you want to use for
+                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#pytorch-sagemaker-estimators.
+            py_version (str): Python version you want to use for executing your
+                model training code. One of 'py2' or 'py3'. Defaults to ``None``. Required
+                unless ``image_name`` is 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
@@ -80,12 +87,6 @@ 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'.
-            framework_version (str): PyTorch version you want to use for
-                executing your model training code. List of supported versions
-                https://github.com/aws/sagemaker-python-sdk#pytorch-sagemaker-estimators.
-                If not specified, this will default to 0.4.
             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
@@ -95,6 +96,9 @@ def __init__(
                     * ``123412341234.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.
 
@@ -104,28 +108,25 @@ def __init__(
             :class:`~sagemaker.estimator.Framework` and
             :class:`~sagemaker.estimator.EstimatorBase`.
         """
-        if framework_version is None:
+        validate_version_or_image_args(framework_version, py_version, image_name)
+        if py_version == "py2":
             logger.warning(
-                empty_framework_version_warning(defaults.PYTORCH_VERSION, self.LATEST_VERSION)
+                python_deprecation_warning(self.__framework_name__, defaults.LATEST_PY2_VERSION)
             )
-        self.framework_version = framework_version or defaults.PYTORCH_VERSION
+        self.framework_version = framework_version
+        self.py_version = py_version
 
         if "enable_sagemaker_metrics" not in kwargs:
             # enable sagemaker metrics for PT v1.3 or greater:
-            if is_version_equal_or_higher([1, 3], self.framework_version):
+            if self.framework_version and is_version_equal_or_higher(
+                [1, 3], self.framework_version
+            ):
                 kwargs["enable_sagemaker_metrics"] = True
 
         super(PyTorch, self).__init__(
             entry_point, source_dir, hyperparameters, image_name=image_name, **kwargs
         )
 
-        if py_version == "py2":
-            logger.warning(
-                python_deprecation_warning(self.__framework_name__, defaults.LATEST_PY2_VERSION)
-            )
-
-        self.py_version = py_version
-
     def create_model(
         self,
         model_server_workers=None,
@@ -177,12 +178,12 @@ def create_model(
             self.model_data,
             role or self.role,
             entry_point or self.entry_point,
+            framework_version=self.framework_version,
+            py_version=self.py_version,
             source_dir=(source_dir or self._model_source_dir()),
             enable_cloudwatch_metrics=self.enable_cloudwatch_metrics,
             container_log_level=self.container_log_level,
             code_location=self.code_location,
-            py_version=self.py_version,
-            framework_version=self.framework_version,
             model_server_workers=model_server_workers,
             sagemaker_session=self.sagemaker_session,
             vpc_config=self.get_vpc_config(vpc_config_override),
@@ -210,15 +211,19 @@ class constructor
         image_name = init_params.pop("image")
         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
 
-        init_params["py_version"] = py_version
-        init_params["framework_version"] = framework_version_from_tag(tag)
-
         training_job_name = init_params["base_job_name"]
 
         if framework != cls.__framework_name__:

src/sagemaker/pytorch/model.py

@@ -21,7 +21,7 @@
     create_image_uri,
     model_code_key_prefix,
     python_deprecation_warning,
-    empty_framework_version_warning,
+    validate_version_or_image_args,
 )
 from sagemaker.model import FrameworkModel, MODEL_SERVER_WORKERS_PARAM_NAME
 from sagemaker.pytorch import defaults
@@ -66,9 +66,9 @@ def __init__(
         model_data,
         role,
         entry_point,
-        image=None,
-        py_version=defaults.PYTHON_VERSION,
         framework_version=None,
+        py_version=None,
+        image=None,
         predictor_cls=PyTorchPredictor,
         model_server_workers=None,
         **kwargs
@@ -87,12 +87,18 @@ 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): PyTorch 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. Defaults to ``None``. Required unless
+                ``image`` is provided.
             image (str): A Docker image URI (default: None). If not specified, a
                 default image for PyTorch will be used.
-            py_version (str): Python version you want to use for executing your
-                model training code (default: 'py3').
-            framework_version (str): PyTorch 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
@@ -109,22 +115,18 @@ def __init__(
             :class:`~sagemaker.model.FrameworkModel` and
             :class:`~sagemaker.model.Model`.
         """
-        super(PyTorchModel, self).__init__(
-            model_data, image, role, entry_point, predictor_cls=predictor_cls, **kwargs
-        )
-
-        if py_version == "py2":
+        validate_version_or_image_args(framework_version, py_version, image)
+        if py_version and py_version == "py2":
             logger.warning(
                 python_deprecation_warning(self.__framework_name__, defaults.LATEST_PY2_VERSION)
             )
+        self.framework_version = framework_version
+        self.py_version = py_version
 
-        if framework_version is None:
-            logger.warning(
-                empty_framework_version_warning(defaults.PYTORCH_VERSION, defaults.LATEST_VERSION)
-            )
+        super(PyTorchModel, self).__init__(
+            model_data, image, role, entry_point, predictor_cls=predictor_cls, **kwargs
+        )
 
-        self.py_version = py_version
-        self.framework_version = framework_version or defaults.PYTORCH_VERSION
         self.model_server_workers = model_server_workers
 
     def prepare_container_def(self, instance_type, accelerator_type=None):

tests/conftest.py

@@ -173,6 +173,11 @@ def pytorch_version(request):
     return request.param
 
 
+@pytest.fixture(scope="module", params=["py2", "py3"])
+def pytorch_py_version(request):
+    return request.param
+
+
 @pytest.fixture(scope="module", params=["0.20.0"])
 def sklearn_version(request):
     return request.param

tests/integ/test_airflow_config.py

@@ -614,7 +614,8 @@ def test_pytorch_airflow_config_uploads_data_source_to_s3_when_inputs_not_provid
         estimator = PyTorch(
             entry_point=PYTORCH_MNIST_SCRIPT,
             role=ROLE,
-            framework_version="1.1.0",
+            framework_version="1.3.1",
+            py_version="py3",
             train_instance_count=2,
             train_instance_type=cpu_instance_type,
             hyperparameters={"epochs": 6, "backend": "gloo"},
@@ -639,6 +640,7 @@ def test_pytorch_12_airflow_config_uploads_data_source_to_s3_when_inputs_not_pro
             entry_point=PYTORCH_MNIST_SCRIPT,
             role=ROLE,
             framework_version="1.2.0",
+            py_version="py3",
             train_instance_count=2,
             train_instance_type=cpu_instance_type,
             hyperparameters={"epochs": 6, "backend": "gloo"},

tests/integ/test_git.py

@@ -56,6 +56,9 @@ def test_github(sagemaker_local_session):
     script_path = "mnist.py"
     data_path = os.path.join(DATA_DIR, "pytorch_mnist")
     git_config = {"repo": GIT_REPO, "branch": BRANCH, "commit": COMMIT}
+
+    # TODO: fails for newer pytorch versions when using MNIST from torchvision due to missing dataset
+    # "algo-1-v767u_1  | RuntimeError: Dataset not found. You can use download=True to download it"
     pytorch = PyTorch(
         entry_point=script_path,
         role="SageMakerRole",

tests/integ/test_pytorch_train.py

@@ -98,11 +98,9 @@ def test_fit_deploy(sagemaker_local_session, pytorch_full_version):
         predictor.delete_endpoint()
 
 
-@pytest.mark.skipif(
-    PYTHON_VERSION == "py2",
-    reason="Python 2 is supported by PyTorch {} and lower versions.".format(LATEST_PY2_VERSION),
-)
-def test_deploy_model(pytorch_training_job, sagemaker_session, cpu_instance_type):
+def test_deploy_model(
+    pytorch_training_job, sagemaker_session, cpu_instance_type, pytorch_full_version
+):
     endpoint_name = "test-pytorch-deploy-model-{}".format(sagemaker_timestamp())
 
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
@@ -114,6 +112,8 @@ def test_deploy_model(pytorch_training_job, sagemaker_session, cpu_instance_type
             model_data,
             "SageMakerRole",
             entry_point=MNIST_SCRIPT,
+            framework_version=pytorch_full_version,
+            py_version="py3",
             sagemaker_session=sagemaker_session,
         )
         predictor = model.deploy(1, cpu_instance_type, endpoint_name=endpoint_name)
@@ -125,10 +125,6 @@ def test_deploy_model(pytorch_training_job, sagemaker_session, cpu_instance_type
         assert output.shape == (batch_size, 10)
 
 
-@pytest.mark.skipif(
-    PYTHON_VERSION == "py2",
-    reason="Python 2 is supported by PyTorch {} and lower versions.".format(LATEST_PY2_VERSION),
-)
 def test_deploy_packed_model_with_entry_point_name(sagemaker_session, cpu_instance_type):
     endpoint_name = "test-pytorch-deploy-model-{}".format(sagemaker_timestamp())
 
@@ -139,6 +135,7 @@ def test_deploy_packed_model_with_entry_point_name(sagemaker_session, cpu_instan
             "SageMakerRole",
             entry_point="mnist.py",
             framework_version="1.4.0",
+            py_version="py3",
             sagemaker_session=sagemaker_session,
         )
         predictor = model.deploy(1, cpu_instance_type, endpoint_name=endpoint_name)
@@ -160,8 +157,9 @@ def test_deploy_model_with_accelerator(sagemaker_session, cpu_instance_type):
     pytorch = PyTorchModel(
         model_data,
         "SageMakerRole",
-        framework_version="1.3.1",
         entry_point=EIA_SCRIPT,
+        framework_version="1.3.1",
+        py_version="py3",
         sagemaker_session=sagemaker_session,
     )
     with timeout_and_delete_endpoint_by_name(endpoint_name, sagemaker_session):
@@ -193,7 +191,7 @@ def _get_pytorch_estimator(
         entry_point=entry_point,
         role="SageMakerRole",
         framework_version=pytorch_full_version,
-        py_version=PYTHON_VERSION,
+        py_version="py3",
         train_instance_count=1,
         train_instance_type=instance_type,
         sagemaker_session=sagemaker_session,

tests/integ/test_source_dirs.py

@@ -19,6 +19,7 @@
 import tests.integ.lock as lock
 from tests.integ import DATA_DIR, PYTHON_VERSION
 
+from sagemaker.pytorch.defaults import PYTORCH_VERSION
 from sagemaker.pytorch.estimator import PyTorch
 
 
@@ -30,11 +31,14 @@ def test_source_dirs(tmpdir, sagemaker_local_session):
     with open(lib, "w") as f:
         f.write("def question(to_anything): return 42")
 
+    # TODO: fails on newer versions of pytorch in call to np.load(BytesIO(stream.read()))
+    # "ValueError: Cannot load file containing pickled data when allow_pickle=False"
     estimator = PyTorch(
         entry_point="train.py",
         role="SageMakerRole",
         source_dir=source_dir,
         dependencies=[lib],
+        framework_version=PYTORCH_VERSION,
         py_version=PYTHON_VERSION,
         train_instance_count=1,
         train_instance_type="local",

tests/integ/test_transformer.py

@@ -178,7 +178,7 @@ def test_transform_pytorch_vpc_custom_model_bucket(
         entry_point=os.path.join(data_dir, "mnist.py"),
         role="SageMakerRole",
         framework_version=pytorch_full_version,
-        py_version=PYTHON_VERSION,
+        py_version="py3",
         sagemaker_session=sagemaker_session,
         vpc_config={"Subnets": subnet_ids, "SecurityGroupIds": [security_group_id]},
         code_location="s3://{}".format(custom_bucket_name),

tests/integ/test_tuner.py

@@ -819,15 +819,16 @@ def test_tuning_chainer(sagemaker_session, cpu_instance_type):
     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_attach_tuning_pytorch(sagemaker_session, cpu_instance_type):
+def test_attach_tuning_pytorch(sagemaker_session, cpu_instance_type, pytorch_full_version):
     mnist_dir = os.path.join(DATA_DIR, "pytorch_mnist")
     mnist_script = os.path.join(mnist_dir, "mnist.py")
 
     estimator = PyTorch(
         entry_point=mnist_script,
         role="SageMakerRole",
         train_instance_count=1,
-        py_version=PYTHON_VERSION,
+        framework_version=pytorch_full_version,
+        py_version="py3",
         train_instance_type=cpu_instance_type,
         sagemaker_session=sagemaker_session,
     )