Integrate black into development process

buildspec-release.yml

@@ -9,6 +9,9 @@ phases:
       # run linters
       - tox -e flake8,pylint
 
+      # run format verification
+      - tox -e black-check
+
       # run package and docbuild checks
       - tox -e twine
       - tox -e sphinx

buildspec.yml

@@ -14,6 +14,9 @@ phases:
       - tox -e twine
       - tox -e sphinx
 
+      # run format verification
+      - tox -e black-check
+
       # run unit tests
       - AWS_ACCESS_KEY_ID= AWS_SECRET_ACCESS_KEY= AWS_SESSION_TOKEN=
         AWS_CONTAINER_CREDENTIALS_RELATIVE_URI= AWS_DEFAULT_REGION=

doc/conf.py

@@ -28,51 +28,65 @@ def __getattr__(cls, name):
             return MagicMock()
 
 
-MOCK_MODULES = ['tensorflow', 'tensorflow.core', 'tensorflow.core.framework', 'tensorflow.python',
-                'tensorflow.python.framework', 'tensorflow_serving', 'tensorflow_serving.apis',
-                'numpy', 'scipy', 'scipy.sparse']
+MOCK_MODULES = [
+    "tensorflow",
+    "tensorflow.core",
+    "tensorflow.core.framework",
+    "tensorflow.python",
+    "tensorflow.python.framework",
+    "tensorflow_serving",
+    "tensorflow_serving.apis",
+    "numpy",
+    "scipy",
+    "scipy.sparse",
+]
 sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES)
 
-project = u'sagemaker'
+project = u"sagemaker"
 version = pkg_resources.require(project)[0].version
 
 # Add any Sphinx extension module names here, as strings. They can be extensions
 # coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
-extensions = ['sphinx.ext.autodoc', 'sphinx.ext.doctest',
-              'sphinx.ext.intersphinx', 'sphinx.ext.todo',
-              'sphinx.ext.coverage', 'sphinx.ext.autosummary',
-              'sphinx.ext.napoleon']
+extensions = [
+    "sphinx.ext.autodoc",
+    "sphinx.ext.doctest",
+    "sphinx.ext.intersphinx",
+    "sphinx.ext.todo",
+    "sphinx.ext.coverage",
+    "sphinx.ext.autosummary",
+    "sphinx.ext.napoleon",
+]
 
 # Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
+templates_path = ["_templates"]
 
-source_suffix = '.rst'  # The suffix of source filenames.
-master_doc = 'index'  # The master toctree document.
+source_suffix = ".rst"  # The suffix of source filenames.
+master_doc = "index"  # The master toctree document.
 
-copyright = u'%s, Amazon' % datetime.now().year
+copyright = u"%s, Amazon" % datetime.now().year
 
 # The full version, including alpha/beta/rc tags.
 release = version
 
 # List of directories, relative to source directory, that shouldn't be searched
 # for source files.
-exclude_trees = ['_build']
+exclude_trees = ["_build"]
 
-pygments_style = 'default'
+pygments_style = "default"
 
 autoclass_content = "both"
-autodoc_default_flags = ['show-inheritance', 'members', 'undoc-members']
-autodoc_member_order = 'bysource'
+autodoc_default_flags = ["show-inheritance", "members", "undoc-members"]
+autodoc_member_order = "bysource"
 
-if 'READTHEDOCS' in os.environ:
-    html_theme = 'default'
+if "READTHEDOCS" in os.environ:
+    html_theme = "default"
 else:
-    html_theme = 'haiku'
+    html_theme = "haiku"
 html_static_path = []
-htmlhelp_basename = '%sdoc' % project
+htmlhelp_basename = "%sdoc" % project
 
 # Example configuration for intersphinx: refer to the Python standard library.
-intersphinx_mapping = {'http://docs.python.org/': None}
+intersphinx_mapping = {"http://docs.python.org/": None}
 
 # autosummary
 autosummary_generate = True

examples/cli/host/script.py

@@ -12,12 +12,12 @@ def model_fn(model_dir):
     :param: model_dir The directory where model files are stored.
     :return: a model (in this case a Gluon network)
     """
-    symbol = mx.sym.load('%s/model.json' % model_dir)
-    outputs = mx.symbol.softmax(data=symbol, name='softmax_label')
-    inputs = mx.sym.var('data')
-    param_dict = gluon.ParameterDict('model_')
+    symbol = mx.sym.load("%s/model.json" % model_dir)
+    outputs = mx.symbol.softmax(data=symbol, name="softmax_label")
+    inputs = mx.sym.var("data")
+    param_dict = gluon.ParameterDict("model_")
     net = gluon.SymbolBlock(outputs, inputs, param_dict)
-    net.load_params('%s/model.params' % model_dir, ctx=mx.cpu())
+    net.load_params("%s/model.params" % model_dir, ctx=mx.cpu())
     return net
 
 

examples/cli/train/download_training_data.py

@@ -2,8 +2,8 @@
 
 
 def download_training_data():
-    gluon.data.vision.MNIST('./data/training', train=True)
-    gluon.data.vision.MNIST('./data/training', train=False)
+    gluon.data.vision.MNIST("./data/training", train=True)
+    gluon.data.vision.MNIST("./data/training", train=False)
 
 
 if __name__ == "__main__":

examples/cli/train/script.py

@@ -15,13 +15,13 @@ def train(channel_input_dirs, hyperparameters, **kwargs):
     ctx = mx.cpu()
 
     # retrieve the hyperparameters we set in notebook (with some defaults)
-    batch_size = hyperparameters.get('batch_size', 100)
-    epochs = hyperparameters.get('epochs', 10)
-    learning_rate = hyperparameters.get('learning_rate', 0.1)
-    momentum = hyperparameters.get('momentum', 0.9)
-    log_interval = hyperparameters.get('log_interval', 100)
+    batch_size = hyperparameters.get("batch_size", 100)
+    epochs = hyperparameters.get("epochs", 10)
+    learning_rate = hyperparameters.get("learning_rate", 0.1)
+    momentum = hyperparameters.get("momentum", 0.9)
+    log_interval = hyperparameters.get("log_interval", 100)
 
-    training_data = channel_input_dirs['training']
+    training_data = channel_input_dirs["training"]
 
     # load training and validation data
     # we use the gluon.data.vision.MNIST class because of its built in mnist pre-processing logic,
@@ -35,8 +35,9 @@ def train(channel_input_dirs, hyperparameters, **kwargs):
     # Collect all parameters from net and its children, then initialize them.
     net.initialize(mx.init.Xavier(magnitude=2.24), ctx=ctx)
     # Trainer is for updating parameters with gradient.
-    trainer = gluon.Trainer(net.collect_params(), 'sgd',
-                            {'learning_rate': learning_rate, 'momentum': momentum})
+    trainer = gluon.Trainer(
+        net.collect_params(), "sgd", {"learning_rate": learning_rate, "momentum": momentum}
+    )
     metric = mx.metric.Accuracy()
     loss = gluon.loss.SoftmaxCrossEntropyLoss()
 
@@ -61,32 +62,34 @@ def train(channel_input_dirs, hyperparameters, **kwargs):
 
             if i % log_interval == 0 and i > 0:
                 name, acc = metric.get()
-                logger.info('[Epoch %d Batch %d] Training: %s=%f, %f samples/s' %
-                            (epoch, i, name, acc, batch_size / (time.time() - btic)))
+                logger.info(
+                    "[Epoch %d Batch %d] Training: %s=%f, %f samples/s"
+                    % (epoch, i, name, acc, batch_size / (time.time() - btic))
+                )
 
             btic = time.time()
 
         name, acc = metric.get()
-        logger.info('[Epoch %d] Training: %s=%f' % (epoch, name, acc))
+        logger.info("[Epoch %d] Training: %s=%f" % (epoch, name, acc))
 
         name, val_acc = test(ctx, net, val_data)
-        logger.info('[Epoch %d] Validation: %s=%f' % (epoch, name, val_acc))
+        logger.info("[Epoch %d] Validation: %s=%f" % (epoch, name, val_acc))
 
     return net
 
 
 def save(net, model_dir):
     # save the model
-    y = net(mx.sym.var('data'))
-    y.save('%s/model.json' % model_dir)
-    net.collect_params().save('%s/model.params' % model_dir)
+    y = net(mx.sym.var("data"))
+    y.save("%s/model.json" % model_dir)
+    net.collect_params().save("%s/model.params" % model_dir)
 
 
 def define_network():
     net = nn.Sequential()
     with net.name_scope():
-        net.add(nn.Dense(128, activation='relu'))
-        net.add(nn.Dense(64, activation='relu'))
+        net.add(nn.Dense(128, activation="relu"))
+        net.add(nn.Dense(64, activation="relu"))
         net.add(nn.Dense(10))
     return net
 
@@ -99,13 +102,18 @@ def input_transformer(data, label):
 def get_train_data(data_dir, batch_size):
     return gluon.data.DataLoader(
         gluon.data.vision.MNIST(data_dir, train=True, transform=input_transformer),
-        batch_size=batch_size, shuffle=True, last_batch='discard')
+        batch_size=batch_size,
+        shuffle=True,
+        last_batch="discard",
+    )
 
 
 def get_val_data(data_dir, batch_size):
     return gluon.data.DataLoader(
         gluon.data.vision.MNIST(data_dir, train=False, transform=input_transformer),
-        batch_size=batch_size, shuffle=False)
+        batch_size=batch_size,
+        shuffle=False,
+    )
 
 
 def test(ctx, net, val_data):

setup.py

@@ -24,46 +24,62 @@ def read(fname):
 
 
 def read_version():
-    return read('VERSION').strip()
+    return read("VERSION").strip()
 
 
 # Declare minimal set for installation
-required_packages = ['boto3>=1.9.169', 'numpy>=1.9.0', 'protobuf>=3.1', 'scipy>=0.19.0',
-                     'urllib3>=1.21, <1.25', 'protobuf3-to-dict>=0.1.5', 'docker-compose>=1.23.0',
-                     'requests>=2.20.0, <2.21']
+required_packages = [
+    "boto3>=1.9.169",
+    "numpy>=1.9.0",
+    "protobuf>=3.1",
+    "scipy>=0.19.0",
+    "urllib3>=1.21, <1.25",
+    "protobuf3-to-dict>=0.1.5",
+    "docker-compose>=1.23.0",
+    "requests>=2.20.0, <2.21",
+]
 
 # enum is introduced in Python 3.4. Installing enum back port
 if sys.version_info < (3, 4):
-    required_packages.append('enum34>=1.1.6')
+    required_packages.append("enum34>=1.1.6")
 
-setup(name="sagemaker",
-      version=read_version(),
-      description="Open source library for training and deploying models on Amazon SageMaker.",
-      packages=find_packages('src'),
-      package_dir={'': 'src'},
-      py_modules=[os.path.splitext(os.path.basename(path))[0] for path in glob('src/*.py')],
-      long_description=read('README.rst'),
-      author="Amazon Web Services",
-      url='https://github.com/aws/sagemaker-python-sdk/',
-      license="Apache License 2.0",
-      keywords="ML Amazon AWS AI Tensorflow MXNet",
-      classifiers=[
-          "Development Status :: 5 - Production/Stable",
-          "Intended Audience :: Developers",
-          "Natural Language :: English",
-          "License :: OSI Approved :: Apache Software License",
-          "Programming Language :: Python",
-          "Programming Language :: Python :: 2.7",
-          "Programming Language :: Python :: 3.6",
-      ],
-
-      install_requires=required_packages,
-
-      extras_require={
-          'test': ['tox', 'flake8', 'pytest==4.4.1', 'pytest-cov', 'pytest-rerunfailures',
-                   'pytest-xdist', 'mock', 'tensorflow>=1.3.0', 'contextlib2',
-                   'awslogs', 'pandas']},
-
-      entry_points={
-          'console_scripts': ['sagemaker=sagemaker.cli.main:main'],
-      })
+setup(
+    name="sagemaker",
+    version=read_version(),
+    description="Open source library for training and deploying models on Amazon SageMaker.",
+    packages=find_packages("src"),
+    package_dir={"": "src"},
+    py_modules=[os.path.splitext(os.path.basename(path))[0] for path in glob("src/*.py")],
+    long_description=read("README.rst"),
+    author="Amazon Web Services",
+    url="https://github.com/aws/sagemaker-python-sdk/",
+    license="Apache License 2.0",
+    keywords="ML Amazon AWS AI Tensorflow MXNet",
+    classifiers=[
+        "Development Status :: 5 - Production/Stable",
+        "Intended Audience :: Developers",
+        "Natural Language :: English",
+        "License :: OSI Approved :: Apache Software License",
+        "Programming Language :: Python",
+        "Programming Language :: Python :: 2.7",
+        "Programming Language :: Python :: 3.6",
+    ],
+    install_requires=required_packages,
+    extras_require={
+        "test": [
+            "tox",
+            "flake8",
+            "pytest==4.4.1",
+            "pytest-cov",
+            "pytest-rerunfailures",
+            "pytest-xdist",
+            "mock",
+            "tensorflow>=1.3.0",
+            "contextlib2",
+            "awslogs",
+            "pandas",
+            "black==19.3b0 ; python_version >= '3.6'",
+        ]
+    },
+    entry_points={"console_scripts": ["sagemaker=sagemaker.cli.main:main"]},
+)

src/sagemaker/__init__.py

@@ -18,15 +18,29 @@
 from sagemaker.amazon.kmeans import KMeans, KMeansModel, KMeansPredictor  # noqa: F401
 from sagemaker.amazon.pca import PCA, PCAModel, PCAPredictor  # noqa: F401
 from sagemaker.amazon.lda import LDA, LDAModel, LDAPredictor  # noqa: F401
-from sagemaker.amazon.linear_learner import LinearLearner, LinearLearnerModel, LinearLearnerPredictor  # noqa: F401
-from sagemaker.amazon.factorization_machines import FactorizationMachines, FactorizationMachinesModel  # noqa: F401
+from sagemaker.amazon.linear_learner import (  # noqa: F401
+    LinearLearner,
+    LinearLearnerModel,
+    LinearLearnerPredictor,
+)
+from sagemaker.amazon.factorization_machines import (  # noqa: F401
+    FactorizationMachines,
+    FactorizationMachinesModel,
+)
 from sagemaker.amazon.factorization_machines import FactorizationMachinesPredictor  # noqa: F401
 from sagemaker.amazon.ntm import NTM, NTMModel, NTMPredictor  # noqa: F401
-from sagemaker.amazon.randomcutforest import (RandomCutForest, RandomCutForestModel,  # noqa: F401
-                                              RandomCutForestPredictor)
+from sagemaker.amazon.randomcutforest import (  # noqa: F401
+    RandomCutForest,
+    RandomCutForestModel,
+    RandomCutForestPredictor,
+)
 from sagemaker.amazon.knn import KNN, KNNModel, KNNPredictor  # noqa: F401
 from sagemaker.amazon.object2vec import Object2Vec, Object2VecModel  # noqa: F401
-from sagemaker.amazon.ipinsights import IPInsights, IPInsightsModel, IPInsightsPredictor  # noqa: F401
+from sagemaker.amazon.ipinsights import (  # noqa: F401
+    IPInsights,
+    IPInsightsModel,
+    IPInsightsPredictor,
+)
 
 from sagemaker.algorithm import AlgorithmEstimator  # noqa: F401
 from sagemaker.analytics import TrainingJobAnalytics, HyperparameterTuningJobAnalytics  # noqa: F401
@@ -41,4 +55,4 @@
 from sagemaker.session import s3_input  # noqa: F401
 from sagemaker.session import get_execution_role  # noqa: F401
 
-__version__ = pkg_resources.require('sagemaker')[0].version
+__version__ = pkg_resources.require("sagemaker")[0].version