Merge branch 'master' into xgboost_1.2

Author: chuyang-deng

CHANGELOG.md

@@ -1,5 +1,11 @@
 # Changelog
 
+## v2.10.0 (2020-09-23)
+
+### Features
+
+ * add inferentia pytorch inference container config
+
 ## v2.9.2 (2020-09-21)
 
 ### Bug Fixes and Other Changes

README.rst

@@ -164,7 +164,7 @@ Setup a Python environment, and install the dependencies listed in ``doc/require
     # conda
     conda create -n sagemaker python=3.7
     conda activate sagemaker
-    conda install --file doc/requirements.txt
+    conda install sphinx=3.1.1 sphinx_rtd_theme=0.5.0
 
     # pip
     pip install -r doc/requirements.txt
@@ -201,7 +201,7 @@ In order to host a SparkML model in SageMaker, it should be serialized with ``ML
 
 For more information on MLeap, see https://github.com/combust/mleap .
 
-Supported major version of Spark: 2.2 (MLeap version - 0.9.6)
+Supported major version of Spark: 2.4 (MLeap version - 0.9.6)
 
 Here is an example on how to create an instance of  ``SparkMLModel`` class and use ``deploy()`` method to create an
 endpoint which can be used to perform prediction against your trained SparkML Model.

VERSION

@@ -1 +1 @@
-2.9.3.dev0
+2.10.1.dev0

src/sagemaker/image_uri_config/inferentia-pytorch.json

@@ -0,0 +1,14 @@
+{
+    "processors": ["inf"],
+    "scope": ["inference"],
+    "versions": {
+        "1.5.1": {
+            "py_versions": ["py3"],
+            "registries": {
+                "us-east-1": "785573368785",
+                "us-west-2": "301217895009"
+            },
+            "repository": "sagemaker-neo-pytorch"
+        }
+    }
+}

src/sagemaker/image_uri_config/sparkml-serving.json

@@ -30,5 +30,34 @@
             },
             "repository": "sagemaker-sparkml-serving"
         }
-    }
+    },
+        "2.4": {
+            "registries": {
+                "af-south-1": "510948584623",
+                "ap-east-1": "651117190479",
+                "ap-northeast-1": "354813040037",
+                "ap-northeast-2": "366743142698",
+                "ap-south-1": "720646828776",
+                "ap-southeast-1": "121021644041",
+                "ap-southeast-2": "783357654285",
+                "ca-central-1": "341280168497",
+                "cn-north-1": "450853457545",
+                "cn-northwest-1": "451049120500",
+                "eu-central-1": "492215442770",
+                "eu-north-1": "662702820516",
+                "eu-west-1": "141502667606",
+                "eu-west-2": "764974769150",
+                "eu-west-3": "659782779980",
+                "eu-south-1": "978288397137",
+                "me-south-1": "801668240914",
+                "sa-east-1": "737474898029",
+                "us-east-1": "683313688378",
+                "us-east-2": "257758044811",
+                "us-gov-west-1": "414596584902",
+                "us-iso-east-1": "833128469047",
+                "us-west-1": "746614075791",
+                "us-west-2": "246618743249"
+            },
+            "repository": "sagemaker-sparkml-serving"
+        }
 }

src/sagemaker/sparkml/model.py

@@ -59,7 +59,7 @@ class SparkMLModel(Model):
     model .
     """
 
-    def __init__(self, model_data, role=None, spark_version=2.2, sagemaker_session=None, **kwargs):
+    def __init__(self, model_data, role=None, spark_version=2.4, sagemaker_session=None, **kwargs):
         """Initialize a SparkMLModel.
 
         Args:
@@ -73,7 +73,7 @@ def __init__(self, model_data, role=None, spark_version=2.2, sagemaker_session=N
                 artifacts. After the endpoint is created, the inference code
                 might use the IAM role, if it needs to access an AWS resource.
             spark_version (str): Spark version you want to use for executing the
-                inference (default: '2.2').
+                inference (default: '2.4').
             sagemaker_session (sagemaker.session.Session): Session object which
                 manages interactions with Amazon SageMaker APIs and any other
                 AWS services needed. If not specified, the estimator creates one

tests/conftest.py

@@ -44,6 +44,7 @@
     "coach_tensorflow",
     "inferentia_mxnet",
     "inferentia_tensorflow",
+    "inferentia_pytorch",
     "mxnet",
     "neo_mxnet",
     "neo_pytorch",

tests/integ/test_neo_mxnet.py

@@ -58,6 +58,9 @@ def mxnet_training_job(
 
 
 @pytest.mark.canary_quick
+@pytest.mark.skip(
+    reason="This test is failing because the image uri and the training script format has changed."
+)
 def test_attach_deploy(
     mxnet_training_job, sagemaker_session, cpu_instance_type, cpu_instance_family
 ):
@@ -86,6 +89,9 @@ def test_attach_deploy(
         predictor.predict(data)
 
 
+@pytest.mark.skip(
+    reason="This test is failing because the image uri and the training script format has changed."
+)
 def test_deploy_model(
     mxnet_training_job,
     sagemaker_session,

tests/integ/test_sparkml_serving.py

@@ -17,19 +17,18 @@
 
 import pytest
 
+from botocore.errorfactory import ClientError
+
 from sagemaker.sparkml.model import SparkMLModel
 from sagemaker.utils import sagemaker_timestamp
 from tests.integ import DATA_DIR
 from tests.integ.timeout import timeout_and_delete_endpoint_by_name
 
 
 @pytest.mark.canary_quick
-@pytest.mark.skip(
-    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_sparkml_model_deploy(sagemaker_session, cpu_instance_type):
-    # Uploads an MLeap serialized MLeap model to S3 and use that to deploy a SparkML model to perform inference
+    # Uploads an MLeap serialized MLeap model to S3 and use that to deploy
+    # a SparkML model to perform inference
     data_path = os.path.join(DATA_DIR, "sparkml_model")
     endpoint_name = "test-sparkml-deploy-{}".format(sagemaker_timestamp())
     model_data = sagemaker_session.upload_data(
@@ -59,7 +58,8 @@ def test_sparkml_model_deploy(sagemaker_session, cpu_instance_type):
         predictor = model.deploy(1, cpu_instance_type, endpoint_name=endpoint_name)
 
         valid_data = "1.0,C,38.0,71.5,1.0,female"
-        assert predictor.predict(valid_data) == "1.0,0.0,38.0,1.0,71.5,0.0,1.0"
+        assert predictor.predict(valid_data) == b"1.0,0.0,38.0,1.0,71.5,0.0,1.0"
 
         invalid_data = "1.0,28.0,C,38.0,71.5,1.0"
-        assert predictor.predict(invalid_data) is None
+        with pytest.raises(ClientError):
+            predictor.predict(invalid_data)

tests/unit/sagemaker/image_uris/test_neo.py

@@ -118,6 +118,10 @@ def test_inferentia_tensorflow(inferentia_tensorflow_version):
     _test_inferentia_framework_uris("tensorflow", inferentia_tensorflow_version)
 
 
+def test_inferentia_pytorch(inferentia_pytorch_version):
+    _test_inferentia_framework_uris("pytorch", inferentia_pytorch_version)
+
+
 def _expected_framework_uri(framework, version, region="us-west-2", processor="cpu"):
     return expected_uris.framework_uri(
         "sagemaker-{}".format(framework),

tests/unit/test_sparkml_serving.py

@@ -49,7 +49,7 @@ def sagemaker_session():
 
 def test_sparkml_model(sagemaker_session):
     sparkml = SparkMLModel(sagemaker_session=sagemaker_session, model_data=MODEL_DATA, role=ROLE)
-    assert sparkml.image_uri == image_uris.retrieve("sparkml-serving", REGION, version="2.2")
+    assert sparkml.image_uri == image_uris.retrieve("sparkml-serving", REGION, version="2.4")
 
 
 def test_predictor_type(sagemaker_session):