Merge branch 'IvyBazan-master'

Author: Ivy Bazan

CHANGELOG.md

@@ -1,5 +1,49 @@
 # Changelog
 
+## v1.51.2 (2020-03-11)
+
+### Bug Fixes and Other Changes
+
+ * handle empty inputs/outputs in ProcessingJob.from_processing_name()
+ * use DLC images for GovCloud
+
+### Testing and Release Infrastructure
+
+ * generate test job name at test start instead of module start
+
+## v1.51.1 (2020-03-10)
+
+### Bug Fixes and Other Changes
+
+ * skip pytorch ei test in unsupported regions
+
+### Documentation Changes
+
+ * correct MultiString/MULTI_STRING docstring
+
+## v1.51.0 (2020-03-09)
+
+### Features
+
+ * pytorch 1.3.1 eia support
+
+### Documentation Changes
+
+ * Update Kubernetes Operator default tag
+ * improve docstring for tuner.best_estimator()
+
+## v1.50.18.post0 (2020-03-05)
+
+### Documentation Changes
+
+ * correct Estimator code_location default S3 path
+
+## v1.50.18 (2020-03-04)
+
+### Bug Fixes and Other Changes
+
+ * change default compile model max run to 15 mins
+
 ## v1.50.17.post0 (2020-03-03)
 
 ### Testing and Release Infrastructure

README.rst

@@ -176,7 +176,7 @@ TensorFlow SageMaker Estimators
 
 By using TensorFlow SageMaker Estimators, you can train and host TensorFlow models on Amazon SageMaker.
 
-Supported versions of TensorFlow: ``1.4.1``, ``1.5.0``, ``1.6.0``, ``1.7.0``, ``1.8.0``, ``1.9.0``, ``1.10.0``, ``1.11.0``, ``1.12.0``, ``1.13.1``, ``1.14.0``, ``1.15.0``, ``2.0.0``.
+Supported versions of TensorFlow: ``1.4.1``, ``1.5.0``, ``1.6.0``, ``1.7.0``, ``1.8.0``, ``1.9.0``, ``1.10.0``, ``1.11.0``, ``1.12.0``, ``1.13.1``, ``1.14.0``, ``1.15.0``, ``1.15.2``, ``2.0.0``, ``2.0.1``, ``2.1.0``.
 
 Supported versions of TensorFlow for Elastic Inference: ``1.11.0``, ``1.12.0``, ``1.13.1``, ``1.14.0``.
 
@@ -208,7 +208,9 @@ 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``, ``1.2.0``, ``1.3.1``.
+Supported versions of PyTorch: ``0.4.0``, ``1.0.0``, ``1.1.0``, ``1.2.0``, ``1.3.1``, ``1.4.0``.
+
+Supported versions of PyTorch for Elastic Inference: ``1.3.1``.
 
 We recommend that you use the latest supported version, because that's where we focus most of our development efforts.
 

VERSION

@@ -1 +1 @@
-1.50.18.dev0
+1.51.3.dev0

doc/amazon_sagemaker_operators_for_kubernetes.rst

@@ -349,7 +349,7 @@ that directory to your \ ``PATH``.
 
     export os="linux"
 
-    wget https://amazon-sagemaker-operator-for-k8s-us-east-1.s3.amazonaws.com/kubectl-smlogs-plugin/latest/${os}.amd64.tar.gz
+    wget https://amazon-sagemaker-operator-for-k8s-us-east-1.s3.amazonaws.com/kubectl-smlogs-plugin/v1/${os}.amd64.tar.gz
     tar xvzf ${os}.amd64.tar.gz
 
     # Move binaries to a directory in your homedir.
@@ -550,13 +550,13 @@ each region.
 +-------------+---------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------+
 | Region      | Controller Image                                                                            | Linux SMLogs                                                                                                           |
 +=============+=============================================================================================+========================================================================================================================+
-| us-east-1   | ``957583890962.dkr.ecr.us-east-1.amazonaws.com/amazon-sagemaker-operator-for-k8s:latest``   | https://amazon-sagemaker-operator-for-k8s-us-east-1.s3.amazonaws.com/kubectl-smlogs-plugin/latest/linux.amd64.tar.gz   |
+| us-east-1   | ``957583890962.dkr.ecr.us-east-1.amazonaws.com/amazon-sagemaker-operator-for-k8s:v1``       | https://amazon-sagemaker-operator-for-k8s-us-east-1.s3.amazonaws.com/kubectl-smlogs-plugin/v1/linux.amd64.tar.gz       |
 +-------------+---------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------+
-| us-east-2   | ``922499468684.dkr.ecr.us-east-2.amazonaws.com/amazon-sagemaker-operator-for-k8s:latest``   | https://amazon-sagemaker-operator-for-k8s-us-east-2.s3.amazonaws.com/kubectl-smlogs-plugin/latest/linux.amd64.tar.gz   |
+| us-east-2   | ``922499468684.dkr.ecr.us-east-2.amazonaws.com/amazon-sagemaker-operator-for-k8s:v1``       | https://amazon-sagemaker-operator-for-k8s-us-east-2.s3.amazonaws.com/kubectl-smlogs-plugin/v1/linux.amd64.tar.gz       |
 +-------------+---------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------+
-| us-west-2   | ``640106867763.dkr.ecr.us-west-2.amazonaws.com/amazon-sagemaker-operator-for-k8s:latest``   | https://amazon-sagemaker-operator-for-k8s-us-west-2.s3.amazonaws.com/kubectl-smlogs-plugin/latest/linux.amd64.tar.gz   |
+| us-west-2   | ``640106867763.dkr.ecr.us-west-2.amazonaws.com/amazon-sagemaker-operator-for-k8s:v1``       | https://amazon-sagemaker-operator-for-k8s-us-west-2.s3.amazonaws.com/kubectl-smlogs-plugin/v1/linux.amd64.tar.gz       |
 +-------------+---------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------+
-| eu-west-1   | ``613661167059.dkr.ecr.eu-west-1.amazonaws.com/amazon-sagemaker-operator-for-k8s:latest``   | https://amazon-sagemaker-operator-for-k8s-eu-west-1.s3.amazonaws.com/kubectl-smlogs-plugin/latest/linux.amd64.tar.gz   |
+| eu-west-1   | ``613661167059.dkr.ecr.eu-west-1.amazonaws.com/amazon-sagemaker-operator-for-k8s:v1``       | https://amazon-sagemaker-operator-for-k8s-eu-west-1.s3.amazonaws.com/kubectl-smlogs-plugin/v1/linux.amd64.tar.gz       |
 +-------------+---------------------------------------------------------------------------------------------+------------------------------------------------------------------------------------------------------------------------+
 
 

doc/using_pytorch.rst

@@ -6,6 +6,8 @@ With PyTorch Estimators and Models, you can train and host PyTorch models on Ama
 
 Supported versions of PyTorch: ``0.4.0``, ``1.0.0``, ``1.1.0``, ``1.2.0``, ``1.3.1``.
 
+Supported versions of PyTorch for Elastic Inference: ``1.3.1``.
+
 We recommend that you use the latest supported version, because that's where we focus most of our development efforts.
 
 You can visit the PyTorch repository at https://github.com/pytorch/pytorch.
@@ -250,6 +252,14 @@ You use the SageMaker PyTorch model server to host your PyTorch model when you c
 Estimator. The model server runs inside a SageMaker Endpoint, which your call to ``deploy`` creates.
 You can access the name of the Endpoint by the ``name`` property on the returned ``Predictor``.
 
+PyTorch on Amazon SageMaker has support for `Elastic Inference <https://docs.aws.amazon.com/sagemaker/latest/dg/ei.html>`_, which allows for inference acceleration to a hosted endpoint for a fraction of the cost of using a full GPU instance.
+In order to attach an Elastic Inference accelerator to your endpoint provide the accelerator type to ``accelerator_type`` to your ``deploy`` call.
+
+.. code:: python
+
+  predictor = pytorch_estimator.deploy(instance_type='ml.m4.xlarge',
+                                       initial_instance_count=1,
+                                       accelerator_type='ml.eia2.medium')
 
 The SageMaker PyTorch Model Server
 ==================================
@@ -291,6 +301,11 @@ It loads the model parameters from a ``model.pth`` file in the SageMaker model d
             model.load_state_dict(torch.load(f))
         return model
 
+However, if you are using PyTorch Elastic Inference, you do not have to provide a ``model_fn`` since the PyTorch serving
+container has a default one for you. But please note that if you are utilizing the default ``model_fn``, please save
+yor parameter file as ``model.pt`` instead of ``model.pth``. For more information on inference script, please refer to:
+`SageMaker PyTorch Default Inference Handler <https://github.com/aws/sagemaker-pytorch-serving-container/blob/master/src/sagemaker_pytorch_serving_container/default_inference_handler.py>`_.
+
 Serve a PyTorch Model
 ---------------------
 

src/sagemaker/algorithm.py

@@ -328,8 +328,8 @@ def transformer(
             instance_type (str): Type of EC2 instance to use, for example,
                 'ml.c4.xlarge'.
             strategy (str): The strategy used to decide how to batch records in
-                a single request (default: None). Valid values: 'MULTI_RECORD'
-                and 'SINGLE_RECORD'.
+                a single request (default: None). Valid values: 'MultiRecord'
+                and 'SingleRecord'.
             assemble_with (str): How the output is assembled (default: None).
                 Valid values: 'Line' or 'None'.
             output_path (str): S3 location for saving the transform result. If

src/sagemaker/estimator.py

@@ -823,8 +823,8 @@ def transformer(
             instance_type (str): Type of EC2 instance to use, for example,
                 'ml.c4.xlarge'.
             strategy (str): The strategy used to decide how to batch records in
-                a single request (default: None). Valid values: 'MULTI_RECORD'
-                and 'SINGLE_RECORD'.
+                a single request (default: None). Valid values: 'MultiRecord'
+                and 'SingleRecord'.
             assemble_with (str): How the output is assembled (default: None).
                 Valid values: 'Line' or 'None'.
             output_path (str): S3 location for saving the transform result. If
@@ -1470,7 +1470,7 @@ def __init__(
                 uploaded (default: None) - don't include a trailing slash since
                 a string prepended with a "/" is appended to ``code_location``. The code
                 file uploaded to S3 is 'code_location/job-name/source/sourcedir.tar.gz'.
-                If not specified, the default ``code location`` is s3://default_bucket/job-name/.
+                If not specified, the default ``code location`` is s3://output_bucket/job-name/.
             image_name (str): An alternate image name to use instead of the
                 official Sagemaker image for the framework. This is useful to
                 run one of the Sagemaker supported frameworks with an image
@@ -1895,8 +1895,8 @@ def transformer(
             instance_type (str): Type of EC2 instance to use, for example,
                 'ml.c4.xlarge'.
             strategy (str): The strategy used to decide how to batch records in
-                a single request (default: None). Valid values: 'MULTI_RECORD'
-                and 'SINGLE_RECORD'.
+                a single request (default: None). Valid values: 'MultiRecord'
+                and 'SingleRecord'.
             assemble_with (str): How the output is assembled (default: None).
                 Valid values: 'Line' or 'None'.
             output_path (str): S3 location for saving the transform result. If

src/sagemaker/fw_utils.py

@@ -53,9 +53,16 @@
 )
 
 VALID_PY_VERSIONS = ["py2", "py3"]
-VALID_EIA_FRAMEWORKS = ["tensorflow", "tensorflow-serving", "mxnet", "mxnet-serving"]
+VALID_EIA_FRAMEWORKS = [
+    "tensorflow",
+    "tensorflow-serving",
+    "mxnet",
+    "mxnet-serving",
+    "pytorch-serving",
+]
+PY2_RESTRICTED_EIA_FRAMEWORKS = ["pytorch-serving"]
 VALID_ACCOUNTS_BY_REGION = {"us-gov-west-1": "246785580436", "us-iso-east-1": "744548109606"}
-ASIMOV_VALID_ACCOUNTS_BY_REGION = {"us-iso-east-1": "886529160074"}
+ASIMOV_VALID_ACCOUNTS_BY_REGION = {"us-gov-west-1": "442386744353", "us-iso-east-1": "886529160074"}
 OPT_IN_ACCOUNTS_BY_REGION = {"ap-east-1": "057415533634", "me-south-1": "724002660598"}
 ASIMOV_OPT_IN_ACCOUNTS_BY_REGION = {"ap-east-1": "871362719292", "me-south-1": "217643126080"}
 DEFAULT_ACCOUNT = "520713654638"
@@ -71,6 +78,7 @@
     "mxnet-serving-eia": "mxnet-inference-eia",
     "pytorch": "pytorch-training",
     "pytorch-serving": "pytorch-inference",
+    "pytorch-serving-eia": "pytorch-inference-eia",
 }
 
 MERGED_FRAMEWORKS_LOWEST_VERSIONS = {
@@ -82,6 +90,7 @@
     "mxnet-serving-eia": [1, 4, 1],
     "pytorch": [1, 2, 0],
     "pytorch-serving": [1, 2, 0],
+    "pytorch-serving-eia": [1, 3, 1],
 }
 
 DEBUGGER_UNSUPPORTED_REGIONS = ["us-gov-west-1", "us-iso-east-1"]
@@ -124,25 +133,6 @@ def _is_dlc_version(framework, framework_version, py_version):
     return False
 
 
-def _use_dlc_image(region, framework, py_version, framework_version):
-    """Return if the DLC image should be used for the given framework,
-    framework version, Python version, and region.
-
-    Args:
-        region (str): The AWS region.
-        framework (str): The framework name, e.g. "tensorflow-scriptmode".
-        py_version (str): The Python version, e.g. "py3".
-        framework_version (str): The framework version.
-
-    Returns:
-        bool: Whether or not to use the corresponding DLC image.
-    """
-    is_gov_region = region in VALID_ACCOUNTS_BY_REGION
-    is_dlc_version = _is_dlc_version(framework, framework_version, py_version)
-
-    return ((not is_gov_region) or region in ASIMOV_VALID_ACCOUNTS_BY_REGION) and is_dlc_version
-
-
 def _registry_id(region, framework, py_version, account, framework_version):
     """Return the Amazon ECR registry number (or AWS account ID) for
     the given framework, framework version, Python version, and region.
@@ -159,7 +149,7 @@ def _registry_id(region, framework, py_version, account, framework_version):
             specific one for the framework, framework version, Python version,
             and region, then ``account`` is returned.
     """
-    if _use_dlc_image(region, framework, py_version, framework_version):
+    if _is_dlc_version(framework, framework_version, py_version):
         if region in ASIMOV_OPT_IN_ACCOUNTS_BY_REGION:
             return ASIMOV_OPT_IN_ACCOUNTS_BY_REGION.get(region)
         if region in ASIMOV_VALID_ACCOUNTS_BY_REGION:
@@ -207,6 +197,7 @@ def create_image_uri(
 
     if _accelerator_type_valid_for_framework(
         framework=framework,
+        py_version=py_version,
         accelerator_type=accelerator_type,
         optimized_families=optimized_families,
     ):
@@ -243,7 +234,7 @@ def create_image_uri(
         else:
             device_type = "cpu"
 
-    use_dlc_image = _use_dlc_image(region, framework, py_version, framework_version)
+    use_dlc_image = _is_dlc_version(framework, framework_version, py_version)
 
     if not py_version or (use_dlc_image and framework == "tensorflow-serving-eia"):
         tag = "{}-{}".format(framework_version, device_type)
@@ -259,21 +250,27 @@ def create_image_uri(
 
 
 def _accelerator_type_valid_for_framework(
-    framework, accelerator_type=None, optimized_families=None
+    framework, py_version, accelerator_type=None, optimized_families=None
 ):
     """
     Args:
         framework:
+        py_version:
         accelerator_type:
         optimized_families:
     """
     if accelerator_type is None:
         return False
 
+    if py_version == "py2" and framework in PY2_RESTRICTED_EIA_FRAMEWORKS:
+        raise ValueError(
+            "{} is not supported with Amazon Elastic Inference in Python 2.".format(framework)
+        )
+
     if framework not in VALID_EIA_FRAMEWORKS:
         raise ValueError(
             "{} is not supported with Amazon Elastic Inference. Currently only "
-            "Python-based TensorFlow and MXNet are supported.".format(framework)
+            "Python-based TensorFlow, MXNet, PyTorch are supported.".format(framework)
         )
 
     if optimized_families:

src/sagemaker/model.py

@@ -504,8 +504,8 @@ def transformer(
             instance_type (str): Type of EC2 instance to use, for example,
                 'ml.c4.xlarge'.
             strategy (str): The strategy used to decide how to batch records in
-                a single request (default: None). Valid values: 'MULTI_RECORD'
-                and 'SINGLE_RECORD'.
+                a single request (default: None). Valid values: 'MultiRecord'
+                and 'SingleRecord'.
             assemble_with (str): How the output is assembled (default: None).
                 Valid values: 'Line' or 'None'.
             output_path (str): S3 location for saving the transform result. If

src/sagemaker/pipeline.py

@@ -215,8 +215,8 @@ def transformer(
             instance_type (str): Type of EC2 instance to use, for example,
                 'ml.c4.xlarge'.
             strategy (str): The strategy used to decide how to batch records in
-                a single request (default: None). Valid values: 'MULTI_RECORD'
-                and 'SINGLE_RECORD'.
+                a single request (default: None). Valid values: 'MultiRecord'
+                and 'SingleRecord'.
             assemble_with (str): How the output is assembled (default: None).
                 Valid values: 'Line' or 'None'.
             output_path (str): S3 location for saving the transform result. If

src/sagemaker/processing.py

@@ -648,10 +648,9 @@ def from_processing_name(cls, sagemaker_session, processing_job_name):
         """
         job_desc = sagemaker_session.describe_processing_job(job_name=processing_job_name)
 
-        return cls(
-            sagemaker_session=sagemaker_session,
-            job_name=processing_job_name,
-            inputs=[
+        inputs = None
+        if job_desc.get("ProcessingInputs"):
+            inputs = [
                 ProcessingInput(
                     source=processing_input["S3Input"]["S3Uri"],
                     destination=processing_input["S3Input"]["LocalPath"],
@@ -664,19 +663,31 @@ def from_processing_name(cls, sagemaker_session, processing_job_name):
                     s3_compression_type=processing_input["S3Input"].get("S3CompressionType"),
                 )
                 for processing_input in job_desc["ProcessingInputs"]
-            ],
-            outputs=[
+            ]
+
+        outputs = None
+        if job_desc.get("ProcessingOutputConfig") and job_desc["ProcessingOutputConfig"].get(
+            "Outputs"
+        ):
+            outputs = [
                 ProcessingOutput(
-                    source=job_desc["ProcessingOutputConfig"]["Outputs"][0]["S3Output"][
-                        "LocalPath"
-                    ],
-                    destination=job_desc["ProcessingOutputConfig"]["Outputs"][0]["S3Output"][
-                        "S3Uri"
-                    ],
-                    output_name=job_desc["ProcessingOutputConfig"]["Outputs"][0]["OutputName"],
+                    source=processing_output["S3Output"]["LocalPath"],
+                    destination=processing_output["S3Output"]["S3Uri"],
+                    output_name=processing_output["OutputName"],
                 )
-            ],
-            output_kms_key=job_desc["ProcessingOutputConfig"].get("KmsKeyId"),
+                for processing_output in job_desc["ProcessingOutputConfig"]["Outputs"]
+            ]
+
+        output_kms_key = None
+        if job_desc.get("ProcessingOutputConfig"):
+            output_kms_key = job_desc["ProcessingOutputConfig"].get("KmsKeyId")
+
+        return cls(
+            sagemaker_session=sagemaker_session,
+            job_name=processing_job_name,
+            inputs=inputs,
+            outputs=outputs,
+            output_kms_key=output_kms_key,
         )
 
     @classmethod

src/sagemaker/pytorch/README.rst

@@ -6,6 +6,8 @@ With PyTorch Estimators and Models, you can train and host PyTorch models on Ama
 
 Supported versions of PyTorch: ``0.4.0``, ``1.0.0``, ``1.1.0``, ``1.2.0``, ``1.3.1``, ``1.4.0``.
 
+Supported versions of PyTorch for Elastic Inference: ``1.3.1``.
+
 We recommend that you use the latest supported version, because that's where we focus most of our development efforts.
 
 You can visit the PyTorch repository at https://github.com/pytorch/pytorch.