Merge branch 'master' into feat/jumpstart-model-url

Author: evakravi

.githooks/pre-push

@@ -12,5 +12,5 @@ start_time=`date +%s`
 tox -e sphinx,doc8 --parallel all
 ./ci-scripts/displaytime.sh 'sphinx,doc8' $start_time
 start_time=`date +%s`
-tox -e py36,py37,py38 --parallel all -- tests/unit
-./ci-scripts/displaytime.sh 'py36,py37,py38 unit' $start_time
+tox -e py36,py37,py38,py39 --parallel all -- tests/unit
+./ci-scripts/displaytime.sh 'py36,py37,py38,py39 unit' $start_time

CHANGELOG.md

@@ -1,5 +1,62 @@
 # Changelog
 
+## v2.83.0 (2022-04-04)
+
+### Features
+
+ * Hugging Face Transformers 4.17 for TF 2.6
+
+### Bug Fixes and Other Changes
+
+ * IOC image version select issue
+
+## v2.82.2 (2022-04-01)
+
+### Bug Fixes and Other Changes
+
+ * Revert "fix: Fix Pipeline variables related customer issues (#2959)"
+ * Refactor repack_model script injection, fixes tar.gz error
+
+## v2.82.1 (2022-03-31)
+
+### Bug Fixes and Other Changes
+
+ * Update Inferentia Image URI Config
+ * Fix Pipeline variables related customer issues
+ * more logging info for static pipeline test data setup
+
+## v2.82.0 (2022-03-30)
+
+### Features
+
+ * pluggable instance fallback mechanism, add CapacityError
+ * support passing Env Vars to local mode training
+
+## v2.81.1 (2022-03-29)
+
+### Bug Fixes and Other Changes
+
+ * Update black-check version, add support for Spark 3.1 Processing
+
+## v2.81.0 (2022-03-26)
+
+### Features
+
+ * Retrieve data configuration
+ * enable EnableInterContainerTrafficEncryption for model monitoring
+ * Hugging Face Transformers 4.17 for PT 1.10
+
+### Bug Fixes and Other Changes
+
+ * remove `new` from serverless
+ * temporarily skip tests impacted by data inconsistency
+ * Implement override solution for pipeline variables
+
+### Documentation Changes
+
+ * add documentation for image_uri serverless use case
+ * minor fixes for smddp 1.4.0 doc
+
 ## v2.80.0 (2022-03-18)
 
 ### Features

MANIFEST.in

@@ -1,6 +1,7 @@
 recursive-include src/sagemaker *.py
 
 include src/sagemaker/image_uri_config/*.json
+recursive-include requirements *
 
 include VERSION
 include LICENSE.txt

VERSION

@@ -1 +1 @@
-2.80.1.dev0
+2.83.1.dev0

doc/overview.rst

@@ -1226,6 +1226,28 @@ to configure or manage the underlying infrastructure. After you trained a model,
 Serverless endpoint and then invoke the endpoint with the model to get inference results back. More information about
 SageMaker Serverless Inference can be found in the `AWS documentation <https://docs.aws.amazon.com/sagemaker/latest/dg/serverless-endpoints.html>`__.
 
+For using SageMaker Serverless Inference, if you plan to use any of the SageMaker-provided container or Bring Your Own Container
+model, you will need to pass ``image_uri``. An example to use ``image_uri`` for creating MXNet model:
+
+.. code:: python
+
+    from sagemaker.mxnet import MXNetModel
+    import sagemaker
+
+    role = sagemaker.get_execution_role()
+
+    # create MXNet Model Class
+    mxnet_model = MXNetModel(
+        model_data="s3://my_bucket/pretrained_model/model.tar.gz", # path to your trained sagemaker model
+        role=role, # iam role with permissions to create an Endpoint
+        entry_point="inference.py",
+        image_uri="763104351884.dkr.ecr.us-west-2.amazonaws.com/mxnet-inference:1.4.1-cpu-py3" # image wanted to use
+    )
+
+For more Amazon SageMaker provided algorithms and containers image paths, please check this page: `Amazon SageMaker provided
+algorithms and Deep Learning Containers <https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html>`_.
+After creating model using ``image_uri``, you can then follow the steps below to create serverless endpoint.
+
 To deploy serverless endpoint, you will need to create a ``ServerlessInferenceConfig``.
 If you create ``ServerlessInferenceConfig`` without specifying its arguments, the default ``MemorySizeInMB`` will be **2048** and
 the default ``MaxConcurrency`` will be **5** :
@@ -1235,14 +1257,14 @@ the default ``MaxConcurrency`` will be **5** :
     from sagemaker.serverless import ServerlessInferenceConfig
 
     # Create an empty ServerlessInferenceConfig object to use default values
-    serverless_config = new ServerlessInferenceConfig()
+    serverless_config = ServerlessInferenceConfig()
 
 Or you can specify ``MemorySizeInMB`` and ``MaxConcurrency`` in ``ServerlessInferenceConfig`` (example shown below):
 
 .. code:: python
 
     # Specify MemorySizeInMB and MaxConcurrency in the serverless config object
-    serverless_config = new ServerlessInferenceConfig(
+    serverless_config = ServerlessInferenceConfig(
       memory_size_in_mb=4096,
       max_concurrency=10,
     )
@@ -1254,6 +1276,14 @@ Then use the ``ServerlessInferenceConfig`` in the estimator's ``deploy()`` metho
     # Deploys the model that was generated by fit() to a SageMaker serverless endpoint
     serverless_predictor = estimator.deploy(serverless_inference_config=serverless_config)
 
+Or directly using model's ``deploy()`` method to deploy a serverless endpoint:
+
+.. code:: python
+
+    # Deploys the model to a SageMaker serverless endpoint
+    serverless_predictor = model.deploy(serverless_inference_config=serverless_config)
+
+
 After deployment is complete, you can use predictor's ``predict()`` method to invoke the serverless endpoint just like
 real-time endpoints:
 

requirements/extras/local_requirements.txt

@@ -0,0 +1,4 @@
+urllib3==1.26.8
+docker-compose==1.29.2
+docker~=5.0.0
+PyYAML==5.4.1

requirements/extras/scipy_requirements.txt

@@ -0,0 +1 @@
+scipy==1.5.4

requirements/extras/test_requirements.txt

@@ -0,0 +1,20 @@
+tox==3.24.5
+flake8==4.0.1
+pytest==6.0.2
+pytest-cov==3.0.0
+pytest-rerunfailures==10.2
+pytest-timeout==2.1.0
+pytest-xdist==2.4.0
+coverage>=5.2, <6.2
+mock==4.0.3
+contextlib2==21.6.0
+awslogs==0.14.0
+black==22.3.0
+stopit==1.1.2
+apache-airflow==2.2.4
+apache-airflow-providers-amazon==3.0.0
+attrs==20.3.0
+fabric==2.6.0
+requests==2.27.1
+sagemaker-experiments==0.1.35
+Jinja2==3.0.3

requirements/tox/doc8_requirements.txt

@@ -0,0 +1,2 @@
+doc8==0.10.1
+Pygments==2.11.2

requirements/tox/docstyle_requirements.txt

@@ -0,0 +1 @@
+pydocstyle==6.1.1

requirements/tox/flake8_requirements.txt

@@ -0,0 +1,2 @@
+flake8==4.0.1
+flake8-future-import==0.4.6

requirements/tox/mypy_requirements.txt

@@ -0,0 +1 @@
+mypy==0.942

requirements/tox/pydocstyle_requirements.txt

@@ -0,0 +1 @@
+pydocstyle==6.1.1

requirements/tox/pylint_requirements.txt

@@ -0,0 +1,2 @@
+pylint==2.6.2
+astroid==2.4.2

requirements/tox/spelling_requirements.txt

@@ -0,0 +1,2 @@
+pyenchant==3.2.2
+pylint==2.6.2

requirements/tox/twine_requirements.txt

@@ -0,0 +1 @@
+twine==3.8.0

setup.py

@@ -16,7 +16,7 @@
 import os
 from glob import glob
 
-from setuptools import setup, find_packages
+from setuptools import find_packages, setup
 
 
 def read(fname):
@@ -31,6 +31,20 @@ def read_version():
     return read("VERSION").strip()
 
 
+def read_requirements(filename):
+    """Reads requirements file which lists package dependencies.
+
+    Args:
+        filename: type(str) Relative file path of requirements.txt file
+
+    Returns:
+        list of dependencies extracted from file
+    """
+    with open(os.path.abspath(filename)) as fp:
+        deps = [line.strip() for line in fp.readlines()]
+    return deps
+
+
 # Declare minimal set for installation
 required_packages = [
     "attrs==20.3.0",
@@ -47,42 +61,15 @@ def read_version():
 ]
 
 # Specific use case dependencies
+# Keep format of *_requirements.txt to be tracked by dependabot
 extras = {
-    "local": [
-        "urllib3==1.26.8",
-        "docker-compose==1.29.2",
-        "docker~=5.0.0",
-        "PyYAML==5.4.1",  # PyYAML version has to match docker-compose requirements
-    ],
-    "scipy": ["scipy==1.5.4"],
+    "local": read_requirements("requirements/extras/local_requirements.txt"),
+    "scipy": read_requirements("requirements/extras/scipy_requirements.txt"),
 }
 # Meta dependency groups
 extras["all"] = [item for group in extras.values() for item in group]
 # Tests specific dependencies (do not need to be included in 'all')
-extras["test"] = (
-    [
-        extras["all"],
-        "tox==3.24.5",
-        "flake8==4.0.1",
-        "pytest==6.0.2",
-        "pytest-cov==3.0.0",
-        "pytest-rerunfailures==10.2",
-        "pytest-timeout==2.1.0",
-        "pytest-xdist==2.4.0",
-        "coverage>=5.2, <6.2",
-        "mock==4.0.3",
-        "contextlib2==21.6.0",
-        "awslogs==0.14.0",
-        "black==22.1.0",
-        "stopit==1.1.2",
-        "apache-airflow==2.2.3",
-        "apache-airflow-providers-amazon==3.0.0",
-        "attrs==20.3.0",
-        "fabric==2.6.0",
-        "requests==2.27.1",
-        "sagemaker-experiments==0.1.35",
-    ],
-)
+extras["test"] = (extras["all"] + read_requirements("requirements/extras/test_requirements.txt"),)
 
 setup(
     name="sagemaker",

src/sagemaker/chainer/model.py

@@ -99,10 +99,14 @@ 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``.
-            image_uri (str): A Docker image URI (default: None). If not specified, a
-                default image for Chainer will be used. If ``framework_version``
-                or ``py_version`` are ``None``, then ``image_uri`` is required. If
-                also ``None``, then a ``ValueError`` will be raised.
+            image_uri (str): A Docker image URI (default: None). In serverless
+                inferece, it is required. More image information can be found in
+                `Amazon SageMaker provided algorithms and Deep Learning Containers
+                <https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html>`_.
+                In instance based inference, if not specified, a default image for
+                Chainer will be used. If ``framework_version`` or ``py_version``
+                are ``None``, then ``image_uri`` is required. If also ``None``,
+                then a ``ValueError`` will be raised.
             framework_version (str): Chainer version you want to use for
                 executing your model training code. Defaults to ``None``. Required
                 unless ``image_uri`` is provided.

src/sagemaker/estimator.py

@@ -74,9 +74,7 @@
     get_config_value,
     name_from_base,
 )
-from sagemaker.workflow.entities import Expression
-from sagemaker.workflow.parameters import Parameter
-from sagemaker.workflow.properties import Properties
+from sagemaker.workflow.entities import PipelineVariable
 
 logger = logging.getLogger(__name__)
 
@@ -602,7 +600,7 @@ def _json_encode_hyperparameters(hyperparameters: Dict[str, Any]) -> Dict[str, A
         current_hyperparameters = hyperparameters
         if current_hyperparameters is not None:
             hyperparameters = {
-                str(k): (v if isinstance(v, (Parameter, Expression, Properties)) else json.dumps(v))
+                str(k): (v.to_string() if isinstance(v, PipelineVariable) else json.dumps(v))
                 for (k, v) in current_hyperparameters.items()
             }
         return hyperparameters
@@ -1813,7 +1811,7 @@ def _get_train_args(cls, estimator, inputs, experiment_config):
         current_hyperparameters = estimator.hyperparameters()
         if current_hyperparameters is not None:
             hyperparameters = {
-                str(k): (v if isinstance(v, (Parameter, Expression, Properties)) else str(v))
+                str(k): (v.to_string() if isinstance(v, PipelineVariable) else str(v))
                 for (k, v) in current_hyperparameters.items()
             }
 

src/sagemaker/exceptions.py

@@ -23,6 +23,10 @@ def __init__(self, message, allowed_statuses, actual_status):
         super(UnexpectedStatusException, self).__init__(message)
 
 
+class CapacityError(UnexpectedStatusException):
+    """Raised when resource status is not expected and fails with a reason of CapacityError"""
+
+
 class AsyncInferenceError(Exception):
     """The base exception class for Async Inference exceptions."""
 

src/sagemaker/huggingface/model.py

@@ -133,7 +133,11 @@ def __init__(
             py_version (str): Python version you want to use for executing your
                 model training code. Defaults to ``None``. Required unless
                 ``image_uri`` is provided.
-            image_uri (str): A Docker image URI. Defaults to None. If not specified, a
+            image_uri (str): A Docker image URI. Defaults to None. For serverless
+                inferece, it is required. More image information can be found in
+                `Amazon SageMaker provided algorithms and Deep Learning Containers
+                <https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html>`_.
+                For instance based inference, if not specified, a
                 default image for PyTorch will be used. If ``framework_version``
                 or ``py_version`` are ``None``, then ``image_uri`` is required. If
                 also ``None``, then a ``ValueError`` will be raised.