feature: Add support for PyTorch 1.2.0

README.rst

@@ -222,7 +222,7 @@ PyTorch SageMaker Estimators
 
 With PyTorch SageMaker Estimators, you can train and host PyTorch models on Amazon SageMaker.
 
-Supported versions of PyTorch: ``0.4.0``, ``1.0.0``, ``1.1.0``.
+Supported versions of PyTorch: ``0.4.0``, ``1.0.0``, ``1.1.0``, ``1.2.0``.
 
 We recommend that you use the latest supported version, because that's where we focus most of our development efforts.
 

src/sagemaker/fw_utils.py

@@ -67,6 +67,8 @@
     "tensorflow-serving-eia": "tensorflow-inference-eia",
     "mxnet": "mxnet-training",
     "mxnet-serving": "mxnet-inference",
+    "pytorch": "pytorch-training",
+    "pytorch-serving": "pytorch-inference",
     "mxnet-serving-eia": "mxnet-inference-eia",
 }
 
@@ -76,6 +78,8 @@
     "tensorflow-serving-eia": [1, 14, 0],
     "mxnet": [1, 4, 1],
     "mxnet-serving": [1, 4, 1],
+    "pytorch": [1, 2, 0],
+    "pytorch-serving": [1, 2, 0],
     "mxnet-serving-eia": [1, 4, 1],
 }
 
@@ -119,10 +123,15 @@ def _using_merged_images(region, framework, py_version, framework_version):
     is_gov_region = region in VALID_ACCOUNTS_BY_REGION
     is_py3 = py_version == "py3" or py_version is None
     is_merged_versions = _is_merged_versions(framework, framework_version)
+
     return (
         ((not is_gov_region) or region in ASIMOV_VALID_ACCOUNTS_BY_REGION)
         and is_merged_versions
-        and (is_py3 or _is_tf_14_or_later(framework, framework_version))
+        and (
+            is_py3
+            or _is_tf_14_or_later(framework, framework_version)
+            or _is_pt_12_or_later(framework, framework_version)
+        )
     )
 
 
@@ -140,6 +149,19 @@ def _is_tf_14_or_later(framework, framework_version):
     )
 
 
+def _is_pt_12_or_later(framework, framework_version):
+    """
+    Args:
+        framework: Name of the frameowork
+        framework_version: framework version
+    """
+    # Asimov team now owns PyTorch 1.2.0 py2 and py3
+    asimov_lowest_pt = [1, 2, 0]
+    version = [int(s) for s in framework_version.split(".")]
+    is_pytorch = framework in ("pytorch", "pytorch-serving")
+    return is_pytorch and version >= asimov_lowest_pt[0 : len(version)]
+
+
 def _registry_id(region, framework, py_version, account, framework_version):
     """
     Args:

src/sagemaker/pytorch/README.rst

@@ -4,7 +4,7 @@ SageMaker PyTorch Estimators and Models
 
 With PyTorch Estimators and Models, you can train and host PyTorch models on Amazon SageMaker.
 
-Supported versions of PyTorch: ``0.4.0``, ``1.0.0``, ``1.1.0``.
+Supported versions of PyTorch: ``0.4.0``, ``1.0.0``, ``1.1.0``, ``1.2.0``.
 
 We recommend that you use the latest supported version, because that's where we focus most of our development efforts.
 

src/sagemaker/pytorch/estimator.py

@@ -34,7 +34,7 @@ class PyTorch(Framework):
 
     __framework_name__ = "pytorch"
 
-    LATEST_VERSION = "1.1"
+    LATEST_VERSION = "1.2.0"
     """The latest version of PyTorch included in the SageMaker pre-built Docker images."""
 
     def __init__(

src/sagemaker/pytorch/model.py

@@ -14,6 +14,7 @@
 from __future__ import absolute_import
 
 import logging
+import pkg_resources
 
 import sagemaker
 from sagemaker.fw_utils import create_image_uri, model_code_key_prefix, python_deprecation_warning
@@ -53,6 +54,7 @@ class PyTorchModel(FrameworkModel):
     """
 
     __framework_name__ = "pytorch"
+    _LOWEST_MMS_VERSION = "1.2"
 
     def __init__(
         self,
@@ -122,22 +124,33 @@ def prepare_container_def(self, instance_type, accelerator_type=None):
             dict[str, str]: A container definition object usable with the
             CreateModel API.
         """
+        lowest_mms_version = pkg_resources.parse_version(self._LOWEST_MMS_VERSION)
+        framework_version = pkg_resources.parse_version(self.framework_version)
+        is_mms_version = framework_version >= lowest_mms_version
+
         deploy_image = self.image
         if not deploy_image:
             region_name = self.sagemaker_session.boto_session.region_name
+
+            framework_name = self.__framework_name__
+            if is_mms_version:
+                framework_name += "-serving"
+
             deploy_image = create_image_uri(
                 region_name,
-                self.__framework_name__,
+                framework_name,
                 instance_type,
                 self.framework_version,
                 self.py_version,
                 accelerator_type=accelerator_type,
             )
         deploy_key_prefix = model_code_key_prefix(self.key_prefix, self.name, deploy_image)
-        self._upload_code(deploy_key_prefix)
+        self._upload_code(deploy_key_prefix, repack=is_mms_version)
         deploy_env = dict(self.env)
         deploy_env.update(self._framework_env_vars())
 
         if self.model_server_workers:
             deploy_env[MODEL_SERVER_WORKERS_PARAM_NAME.upper()] = str(self.model_server_workers)
-        return sagemaker.container_def(deploy_image, self.model_data, deploy_env)
+        return sagemaker.container_def(
+            deploy_image, self.repacked_model_data or self.model_data, deploy_env
+        )

tests/integ/test_pytorch_train.py

@@ -54,6 +54,31 @@ def test_sync_fit_deploy(pytorch_training_job, sagemaker_session, cpu_instance_t
         assert output.shape == (batch_size, 10)
 
 
+@pytest.mark.local_mode
+def test_fit_deploy(sagemaker_local_session, pytorch_full_version):
+    pytorch = PyTorch(
+        entry_point=MNIST_SCRIPT,
+        role="SageMakerRole",
+        framework_version=pytorch_full_version,
+        py_version="py3",
+        train_instance_count=1,
+        train_instance_type="local",
+        sagemaker_session=sagemaker_local_session,
+    )
+
+    pytorch.fit({"training": "file://" + os.path.join(MNIST_DIR, "training")})
+
+    predictor = pytorch.deploy(1, "local")
+    try:
+        batch_size = 100
+        data = numpy.random.rand(batch_size, 1, 28, 28).astype(numpy.float32)
+        output = predictor.predict(data)
+
+        assert output.shape == (batch_size, 10)
+    finally:
+        predictor.delete_endpoint()
+
+
 def test_deploy_model(pytorch_training_job, sagemaker_session, cpu_instance_type):
     endpoint_name = "test-pytorch-deploy-model-{}".format(sagemaker_timestamp())
 

tests/unit/test_fw_utils.py

@@ -313,6 +313,28 @@ def test_create_image_uri_merged_gov_regions():
     )
 
 
+def test_create_image_uri_merged_pytorch():
+
+    image_uri = fw_utils.create_image_uri("us-west-2", "pytorch", "ml.p3.2xlarge", "1.2", "py2")
+    assert image_uri == "763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:1.2-gpu-py2"
+
+    image_uri = fw_utils.create_image_uri("us-west-2", "pytorch", "ml.p3.2xlarge", "1.1", "py2")
+    assert image_uri == "520713654638.dkr.ecr.us-west-2.amazonaws.com/sagemaker-pytorch:1.1-gpu-py2"
+
+    image_uri = fw_utils.create_image_uri(
+        "us-west-2", "pytorch-serving", "ml.c4.2xlarge", "1.2", "py2"
+    )
+    assert image_uri == "763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-inference:1.2-cpu-py2"
+
+    image_uri = fw_utils.create_image_uri(
+        "us-west-2", "pytorch-serving", "ml.c4.2xlarge", "1.1", "py2"
+    )
+    assert (
+        image_uri
+        == "520713654638.dkr.ecr.us-west-2.amazonaws.com/sagemaker-pytorch-serving:1.1-cpu-py2"
+    )
+
+
 def test_create_image_uri_accelerator_tf():
     image_uri = fw_utils.create_image_uri(
         MOCK_REGION, "tensorflow", "ml.p3.2xlarge", "1.0", "py3", accelerator_type="ml.eia1.medium"

tests/unit/test_pytorch.py

@@ -17,8 +17,7 @@
 import os
 import pytest
 import sys
-from mock import MagicMock, Mock
-from mock import patch
+from mock import ANY, MagicMock, Mock, patch
 
 from sagemaker.pytorch import defaults
 from sagemaker.pytorch import PyTorch
@@ -296,6 +295,42 @@ def test_model(sagemaker_session):
     assert isinstance(predictor, PyTorchPredictor)
 
 
+@patch("sagemaker.utils.create_tar_file", MagicMock())
+@patch("sagemaker.utils.repack_model")
+def test_mms_model(repack_model, sagemaker_session):
+    PyTorchModel(
+        MODEL_DATA,
+        role=ROLE,
+        entry_point=SCRIPT_PATH,
+        sagemaker_session=sagemaker_session,
+        framework_version="1.2",
+    ).deploy(1, GPU)
+
+    repack_model.assert_called_with(
+        dependencies=[],
+        inference_script=SCRIPT_PATH,
+        kms_key=None,
+        model_uri="s3://some/data.tar.gz",
+        repacked_model_uri=ANY,
+        sagemaker_session=sagemaker_session,
+        source_directory=None,
+    )
+
+
+@patch("sagemaker.utils.create_tar_file", MagicMock())
+@patch("sagemaker.utils.repack_model")
+def test_non_mms_model(repack_model, sagemaker_session):
+    PyTorchModel(
+        MODEL_DATA,
+        role=ROLE,
+        entry_point=SCRIPT_PATH,
+        sagemaker_session=sagemaker_session,
+        framework_version="1.1",
+    ).deploy(1, GPU)
+
+    repack_model.assert_not_called()
+
+
 @patch("sagemaker.fw_utils.tar_and_upload_dir", MagicMock())
 def test_model_image_accelerator(sagemaker_session):
     model = PyTorchModel(