fixed merge conflict

Author: Ivy Bazan

.github/PULL_REQUEST_TEMPLATE.md

@@ -12,7 +12,7 @@ _Put an `x` in the boxes that apply. You can also fill these out after creating
 
 - [ ] I have read the [CONTRIBUTING](https://github.com/aws/sagemaker-python-sdk/blob/master/CONTRIBUTING.md) doc
 - [ ] I used the commit message format described in [CONTRIBUTING](https://github.com/aws/sagemaker-python-sdk/blob/master/CONTRIBUTING.md#committing-your-change)
-- [ ] I have used the regional endpoint when creating S3 and/or STS clients (if appropriate)
+- [ ] I have passed the region in to any/all clients that I've initialized as part of this change.
 - [ ] I have updated any necessary documentation, including [READMEs](https://github.com/aws/sagemaker-python-sdk/blob/master/README.rst) and [API docs](https://github.com/aws/sagemaker-python-sdk/tree/master/doc) (if appropriate)
 
 #### Tests

CHANGELOG.md

@@ -1,5 +1,66 @@
 # Changelog
 
+## v1.55.0 (2020-03-31)
+
+### Features
+
+ * support cn-north-1 and cn-northwest-1
+
+## v1.54.0 (2020-03-31)
+
+### Features
+
+ * inferentia support
+
+## v1.53.0 (2020-03-30)
+
+### Features
+
+ * Allow setting S3 endpoint URL for Local Session
+
+### Bug Fixes and Other Changes
+
+ * Pass kwargs from create_model to Model constructors
+ * Warn if parameter server is used with multi-GPU instance
+
+## v1.52.1 (2020-03-26)
+
+### Bug Fixes and Other Changes
+
+ * Fix local _SageMakerContainer detached mode (aws#1374)
+
+## v1.52.0.post0 (2020-03-25)
+
+### Documentation Changes
+
+ * Add docs for debugger job support in operator
+
+## v1.52.0 (2020-03-24)
+
+### Features
+
+ * add us-gov-west-1 to neo supported regions
+
+## v1.51.4 (2020-03-23)
+
+### Bug Fixes and Other Changes
+
+ * Check that session is a LocalSession when using local mode
+ * add tflite to Neo-supported frameworks
+ * ignore tags with 'aws:' prefix when creating an EndpointConfig based on an existing one
+ * allow custom image when calling deploy or create_model with various frameworks
+
+### Documentation Changes
+
+ * fix description of default model_dir for TF
+ * add more details about PyTorch eia
+
+## v1.51.3 (2020-03-12)
+
+### Bug Fixes and Other Changes
+
+ * make repack_model only removes py file when new entry_point provided
+
 ## v1.51.2 (2020-03-11)
 
 ### Bug Fixes and Other Changes

VERSION

@@ -1 +1 @@
-1.51.3.dev0
+1.55.1.dev0

doc/using_pytorch.rst

@@ -118,10 +118,22 @@ to a certain filesystem path called ``model_dir``. This value is accessible thro
 After your training job is complete, SageMaker will compress and upload the serialized model to S3, and your model data
 will be available in the S3 ``output_path`` you specified when you created the PyTorch Estimator.
 
+If you are using Elastic Inference, you must convert your models to the TorchScript format and use ``torch.jit.save`` to save the model.
+For example:
+
+.. code:: python
+
+    import os
+    import torch
+
+    # ... train `model`, then save it to `model_dir`
+    model_dir = os.path.join(model_dir, "model.pt")
+    torch.jit.save(model, model_dir)
+
 Using third-party libraries
 ---------------------------
 
-When running your training script on SageMaker, it will have access to some pre-installed third-party libraries including ``torch``, ``torchvisopm``, and ``numpy``.
+When running your training script on SageMaker, it will have access to some pre-installed third-party libraries including ``torch``, ``torchvision``, and ``numpy``.
 For more information on the runtime environment, including specific package versions, see `SageMaker PyTorch Docker containers <#id4>`__.
 
 If there are other packages you want to use with your script, you can include a ``requirements.txt`` file in the same directory as your training script to install other dependencies at runtime. Both ``requirements.txt`` and your training script should be put in the same folder. You must specify this folder in ``source_dir`` argument when creating PyTorch estimator.
@@ -303,7 +315,8 @@ It loads the model parameters from a ``model.pth`` file in the SageMaker model d
 
 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:
+your ScriptModule as ``model.pt``. If you are implementing your own ``model_fn``, please use TorchScript and ``torch.jit.save``
+to save your ScriptModule, then load it in your ``model_fn`` with ``torch.jit.load``. 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
@@ -461,6 +474,23 @@ If you implement your own prediction function, you should take care to ensure th
    first argument to ``output_fn``. If you use the default
    ``output_fn``, this should be a torch.Tensor.
 
+The default Elastic Inference ``predict_fn`` is similar but runs the TorchScript model using ``torch.jit.optimized_execution``.
+If you are implementing your own ``predict_fn``, please also use the ``torch.jit.optimized_execution``
+block, for example:
+
+.. code:: python
+
+    import torch
+    import numpy as np
+
+    def predict_fn(input_data, model):
+        device = torch.device("cpu")
+        model = model.to(device)
+        input_data = data.to(device)
+        model.eval()
+        with torch.jit.optimized_execution(True, {"target_device": "eia:0"}):
+            output = model(input_data)
+
 Process Model Output
 ^^^^^^^^^^^^^^^^^^^^
 
@@ -671,6 +701,6 @@ The following are optional arguments. When you create a ``PyTorch`` object, you
 SageMaker PyTorch Docker Containers
 ***********************************
 
-For information about SageMaker PyTorch containers, see  `the SageMaker PyTorch containers repository <https://github.com/aws/sagemaker-pytorch-container>`_.
+For information about SageMaker PyTorch containers, see  `the SageMaker PyTorch container repository <https://github.com/aws/sagemaker-pytorch-container>`_ and `SageMaker PyTorch Serving container repository <https://github.com/aws/sagemaker-pytorch-serving-container>`__.
 
 For information about SageMaker PyTorch container dependencies, see `SageMaker PyTorch Containers <https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/pytorch#sagemaker-pytorch-docker-containers>`_.

doc/using_tf.rst

@@ -48,7 +48,7 @@ The training script is very similar to a training script you might run outside o
 
 * ``SM_MODEL_DIR``: A string that represents the local path where the training job writes the model artifacts to.
   After training, artifacts in this directory are uploaded to S3 for model hosting. This is different than the ``model_dir``
-  argument passed in your training script, which is an S3 location. ``SM_MODEL_DIR`` is always set to ``/opt/ml/model``.
+  argument passed in your training script, which can be an S3 location. ``SM_MODEL_DIR`` is always set to ``/opt/ml/model``.
 * ``SM_NUM_GPUS``: An integer representing the number of GPUs available to the host.
 * ``SM_OUTPUT_DATA_DIR``: A string that represents the path to the directory to write output artifacts to.
   Output artifacts might include checkpoints, graphs, and other files to save, but do not include model artifacts.
@@ -166,7 +166,7 @@ The following args are not permitted when using Script Mode:
 Where the S3 url is a path to your training data within Amazon S3.
 The constructor keyword arguments define how SageMaker runs your training script.
 
-For more information about the sagemaker.tensorflow.TensorFlow estimator, see `sagemaker.tensorflow.TensorFlow Class`_.
+For more information about the sagemaker.tensorflow.TensorFlow estimator, see `SageMaker TensorFlow Classes`_.
 
 Call the fit Method
 ===================
@@ -909,77 +909,11 @@ processing. There are 2 ways to do this:
 
 For more information, see: https://github.com/aws/sagemaker-tensorflow-serving-container#prepost-processing
 
-*************************************
-sagemaker.tensorflow.TensorFlow Class
-*************************************
+****************************
+SageMaker TensorFlow Classes
+****************************
 
-The following are the most commonly used ``TensorFlow`` constructor arguments.
-
-Required:
-
-- ``entry_point (str)`` Path (absolute or relative) to the Python file which
-  should be executed as the entry point to training.
-- ``role (str)`` An AWS IAM role (either name or full ARN). The Amazon
-  SageMaker training jobs and APIs that create Amazon SageMaker
-  endpoints use this role to access training data and model artifacts.
-  After the endpoint is created, the inference code might use the IAM
-  role, if accessing AWS resource.
-- ``train_instance_count (int)`` Number of Amazon EC2 instances to use for
-  training.
-- ``train_instance_type (str)`` Type of EC2 instance to use for training, for
-  example, 'ml.c4.xlarge'.
-
-Optional:
-
-- ``source_dir (str)`` Path (absolute or relative) to a directory with any
-  other training source code dependencies including the entry point
-  file. Structure within this directory will be preserved when training
-  on SageMaker.
-- ``dependencies (list[str])`` A list of paths to directories (absolute or relative) with
-  any additional libraries that will be exported to the container (default: ``[]``).
-  The library folders will be copied to SageMaker in the same folder where the entrypoint is copied.
-  If the ``source_dir`` points to S3, code will be uploaded and the S3 location will be used
-  instead. Example:
-
-  The following call
-
-  >>> TensorFlow(entry_point='train.py', dependencies=['my/libs/common', 'virtual-env'])
-
-  results in the following inside the container:
-
-  >>> opt/ml/code
-  >>>     ├── train.py
-  >>>     ├── common
-  >>>     └── virtual-env
-
-- ``hyperparameters (dict[str, ANY])`` Hyperparameters that will be used for training.
-  Will be made accessible as command line arguments.
-- ``train_volume_size (int)`` Size in GB of the EBS volume to use for storing
-  input data during training. Must be large enough to the store training
-  data.
-- ``train_max_run (int)`` Timeout in seconds for training, after which Amazon
-  SageMaker terminates the job regardless of its current status.
-- ``output_path (str)`` S3 location where you want the training result (model
-  artifacts and optional output files) saved. If not specified, results
-  are stored to a default bucket. If the bucket with the specific name
-  does not exist, the estimator creates the bucket during the ``fit``
-  method execution.
-- ``output_kms_key`` Optional KMS key ID to optionally encrypt training
-  output with.
-- ``base_job_name`` Name to assign for the training job that the ``fit``
-  method launches. If not specified, the estimator generates a default
-  job name, based on the training image name and current timestamp.
-- ``image_name`` An alternative docker image to use for training and
-  serving.  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``. Refer to: `SageMaker TensorFlow Docker containers <https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/tensorflow#sagemaker-tensorflow-docker-containers>`_ for details on what the official images support
-  and where to find the source code to build your custom image.
-- ``script_mode (bool)`` Whether to use Script Mode or not. Script mode is the only available training mode in Python 3,
-  setting ``py_version`` to ``py3`` automatically sets ``script_mode`` to True.
-- ``model_dir (str)`` Location where model data, checkpoint data, and TensorBoard checkpoints should be saved during training.
-  If not specified a S3 location will be generated under the training job's default bucket. And ``model_dir`` will be
-  passed in your training script as one of the command line arguments.
-- ``distributions (dict)`` Configure your distribution strategy with this argument.
+For information about the different TensorFlow-related classes in the SageMaker Python SDK, see https://sagemaker.readthedocs.io/en/stable/sagemaker.tensorflow.html.
 
 **************************************
 SageMaker TensorFlow Docker containers

src/sagemaker/amazon/amazon_estimator.py

@@ -281,7 +281,9 @@ def record_set(self, train, labels=None, channel="train", encrypt=False):
             RecordSet: A RecordSet referencing the encoded, uploading training
             and label data.
         """
-        s3 = self.sagemaker_session.boto_session.resource("s3")
+        s3 = self.sagemaker_session.boto_session.resource(
+            "s3", region_name=self.sagemaker_session.boto_region_name
+        )
         parsed_s3_url = urlparse(self.data_location)
         bucket, key_prefix = parsed_s3_url.netloc, parsed_s3_url.path
         key_prefix = key_prefix + "{}-{}/".format(type(self).__name__, sagemaker_timestamp())
@@ -467,9 +469,14 @@ def registry(region_name, algorithm=None):
     https://github.com/aws/sagemaker-python-sdk/tree/master/src/sagemaker/amazon
 
     Args:
-        region_name:
-        algorithm:
+        region_name (str): The region name for the account.
+        algorithm (str): The algorithm for the account.
+
+    Raises:
+        ValueError: If invalid algorithm passed in or if mapping does not exist for given algorithm
+            and region.
     """
+    region_to_accounts = {}
     if algorithm in [
         None,
         "pca",
@@ -482,7 +489,7 @@ def registry(region_name, algorithm=None):
         "object2vec",
         "ipinsights",
     ]:
-        account_id = {
+        region_to_accounts = {
             "us-east-1": "382416733822",
             "us-east-2": "404615174143",
             "us-west-2": "174872318107",
@@ -503,9 +510,11 @@ def registry(region_name, algorithm=None):
             "eu-west-3": "749696950732",
             "sa-east-1": "855470959533",
             "me-south-1": "249704162688",
-        }[region_name]
+            "cn-north-1": "390948362332",
+            "cn-northwest-1": "387376663083",
+        }
     elif algorithm in ["lda"]:
-        account_id = {
+        region_to_accounts = {
             "us-east-1": "766337827248",
             "us-east-2": "999911452149",
             "us-west-2": "266724342769",
@@ -521,9 +530,9 @@ def registry(region_name, algorithm=None):
             "eu-west-2": "644912444149",
             "us-west-1": "632365934929",
             "us-iso-east-1": "490574956308",
-        }[region_name]
+        }
     elif algorithm in ["forecasting-deepar"]:
-        account_id = {
+        region_to_accounts = {
             "us-east-1": "522234722520",
             "us-east-2": "566113047672",
             "us-west-2": "156387875391",
@@ -544,7 +553,9 @@ def registry(region_name, algorithm=None):
             "eu-west-3": "749696950732",
             "sa-east-1": "855470959533",
             "me-south-1": "249704162688",
-        }[region_name]
+            "cn-north-1": "390948362332",
+            "cn-northwest-1": "387376663083",
+        }
     elif algorithm in [
         "xgboost",
         "seq2seq",
@@ -553,7 +564,7 @@ def registry(region_name, algorithm=None):
         "object-detection",
         "semantic-segmentation",
     ]:
-        account_id = {
+        region_to_accounts = {
             "us-east-1": "811284229777",
             "us-east-2": "825641698319",
             "us-west-2": "433757028032",
@@ -574,15 +585,25 @@ def registry(region_name, algorithm=None):
             "eu-west-3": "749696950732",
             "sa-east-1": "855470959533",
             "me-south-1": "249704162688",
-        }[region_name]
+            "cn-north-1": "390948362332",
+            "cn-northwest-1": "387376663083",
+        }
     elif algorithm in ["image-classification-neo", "xgboost-neo"]:
-        account_id = NEO_IMAGE_ACCOUNT[region_name]
+        region_to_accounts = NEO_IMAGE_ACCOUNT
     else:
         raise ValueError(
             "Algorithm class:{} does not have mapping to account_id with images".format(algorithm)
         )
 
-    return get_ecr_image_uri_prefix(account_id, region_name)
+    if region_name in region_to_accounts:
+        account_id = region_to_accounts[region_name]
+        return get_ecr_image_uri_prefix(account_id, region_name)
+
+    raise ValueError(
+        "Algorithm ({algorithm}) is unsupported for region ({region_name}).".format(
+            algorithm=algorithm, region_name=region_name
+        )
+    )
 
 
 def get_image_uri(region_name, repo_name, repo_version=1):

src/sagemaker/chainer/estimator.py

@@ -203,6 +203,9 @@ def create_model(
             sagemaker.chainer.model.ChainerModel: A SageMaker ``ChainerModel``
             object. See :func:`~sagemaker.chainer.model.ChainerModel` for full details.
         """
+        if "image" not in kwargs:
+            kwargs["image"] = self.image_name
+
         return ChainerModel(
             self.model_data,
             role or self.role,
@@ -215,10 +218,10 @@ def create_model(
             py_version=self.py_version,
             framework_version=self.framework_version,
             model_server_workers=model_server_workers,
-            image=self.image_name,
             sagemaker_session=self.sagemaker_session,
             vpc_config=self.get_vpc_config(vpc_config_override),
             dependencies=(dependencies or self.dependencies),
+            **kwargs
         )
 
     @classmethod