Modify async to async_ to avoid reserved keyword clash.

datalab/utils/__init__.py

@@ -12,7 +12,7 @@
 
 """Google Cloud Platform library - Internal Helpers."""
 
-from ._async import async, async_function, async_method
+from ._async import async_, async_function, async_method
 from ._gcp_job import GCPJob
 from ._http import Http, RequestException
 from ._iterator import Iterator
@@ -24,7 +24,7 @@
 from ._utils import print_exception_with_last_stack, get_item, compare_datetimes, \
     pick_unused_port, is_http_running_on, gcs_copy_file
 
-__all__ = ['async', 'async_function', 'async_method', 'GCPJob', 'Http', 'RequestException',
+__all__ = ['async_', 'async_function', 'async_method', 'GCPJob', 'Http', 'RequestException',
            'Iterator', 'Job', 'JobError', 'JSONEncoder', 'LRUCache', 'LambdaJob', 'DataflowJob',
            'print_exception_with_last_stack', 'get_item', 'compare_datetimes', 'pick_unused_port',
            'is_http_running_on', 'gcs_copy_file']

datalab/utils/_async.py

@@ -23,7 +23,7 @@
 from future.utils import with_metaclass
 
 
-class async(with_metaclass(abc.ABCMeta, object)):
+class async_(with_metaclass(abc.ABCMeta, object)):
   """ Base class for async_function/async_method. Creates a wrapped function/method that will
       run the original function/method on a thread pool worker thread and return a Job instance
       for monitoring the status of the thread.
@@ -55,27 +55,27 @@ def __call__(self, *args, **kwargs):
     return _job.Job(future=self.executor.submit(self._call, *args, **kwargs))
 
 
-class async_function(async):
+class async_function(async_):
   """ This decorator can be applied to any static function that makes blocking calls to create
       a modified version that creates a Job and returns immediately; the original
       method will be called on a thread pool worker thread.
   """
 
   def _call(self, *args, **kwargs):
     # Call the wrapped method.
-    return self._function(*async._preprocess_args(*args), **async._preprocess_kwargs(**kwargs))
+    return self._function(*async_._preprocess_args(*args), **async_._preprocess_kwargs(**kwargs))
 
 
-class async_method(async):
+class async_method(async_):
   """ This decorator can be applied to any class instance method that makes blocking calls to create
       a modified version that creates a Job and returns immediately; the original method will be
       called on a thread pool worker thread.
   """
 
   def _call(self, *args, **kwargs):
     # Call the wrapped method.
-    return self._function(self.obj, *async._preprocess_args(*args),
-                          **async._preprocess_kwargs(**kwargs))
+    return self._function(self.obj, *async_._preprocess_args(*args),
+                          **async_._preprocess_kwargs(**kwargs))
 
   def __get__(self, instance, owner):
     # This is important for attribute inheritance and setting self.obj so it can be

datalab/utils/_lambda_job.py

@@ -30,7 +30,7 @@ def __init__(self, fn, job_id, *args, **kwargs):
       job_id: an optional ID for the job. If None, a UUID will be generated.
     """
     super(LambdaJob, self).__init__(job_id)
-    self._future = _async.async.executor.submit(fn, *args, **kwargs)
+    self._future = _async.async_.executor.submit(fn, *args, **kwargs)
 
   def __repr__(self):
     """Returns a representation for the job for showing in the notebook.

google/datalab/utils/__init__.py

@@ -12,7 +12,7 @@
 
 """Google Cloud Platform library - Internal Helpers."""
 
-from ._async import async, async_function, async_method
+from ._async import async_, async_function, async_method
 from ._http import Http, RequestException
 from ._iterator import Iterator
 from ._json_encoder import JSONEncoder
@@ -23,7 +23,7 @@
     pick_unused_port, is_http_running_on, gcs_copy_file, python_portable_string
 
 
-__all__ = ['async', 'async_function', 'async_method', 'Http', 'RequestException', 'Iterator',
+__all__ = ['async_', 'async_function', 'async_method', 'Http', 'RequestException', 'Iterator',
            'JSONEncoder', 'LRUCache', 'LambdaJob', 'DataflowJob',
            'print_exception_with_last_stack', 'get_item', 'compare_datetimes', 'pick_unused_port',
            'is_http_running_on', 'gcs_copy_file', 'python_portable_string']

google/datalab/utils/_async.py

@@ -23,7 +23,7 @@
 from future.utils import with_metaclass
 
 
-class async(with_metaclass(abc.ABCMeta, object)):
+class async_(with_metaclass(abc.ABCMeta, object)):
   """ Base class for async_function/async_method. Creates a wrapped function/method that will
       run the original function/method on a thread pool worker thread and return a Job instance
       for monitoring the status of the thread.
@@ -55,27 +55,27 @@ def __call__(self, *args, **kwargs):
     return Job(future=self.executor.submit(self._call, *args, **kwargs))
 
 
-class async_function(async):
+class async_function(async_):
   """ This decorator can be applied to any static function that makes blocking calls to create
       a modified version that creates a Job and returns immediately; the original
       method will be called on a thread pool worker thread.
   """
 
   def _call(self, *args, **kwargs):
     # Call the wrapped method.
-    return self._function(*async._preprocess_args(*args), **async._preprocess_kwargs(**kwargs))
+    return self._function(*async_._preprocess_args(*args), **async_._preprocess_kwargs(**kwargs))
 
 
-class async_method(async):
+class async_method(async_):
   """ This decorator can be applied to any class instance method that makes blocking calls to create
       a modified version that creates a Job and returns immediately; the original method will be
       called on a thread pool worker thread.
   """
 
   def _call(self, *args, **kwargs):
     # Call the wrapped method.
-    return self._function(self.obj, *async._preprocess_args(*args),
-                          **async._preprocess_kwargs(**kwargs))
+    return self._function(self.obj, *async_._preprocess_args(*args),
+                          **async_._preprocess_kwargs(**kwargs))
 
   def __get__(self, instance, owner):
     # This is important for attribute inheritance and setting self.obj so it can be

google/datalab/utils/_lambda_job.py

@@ -30,7 +30,7 @@ def __init__(self, fn, job_id, *args, **kwargs):
       job_id: an optional ID for the job. If None, a UUID will be generated.
     """
     super(LambdaJob, self).__init__(job_id)
-    self._future = _async.async.executor.submit(fn, *args, **kwargs)
+    self._future = _async.async_.executor.submit(fn, *args, **kwargs)
 
   def __repr__(self):
     """Returns a representation for the job for showing in the notebook.

setup.py

@@ -78,6 +78,7 @@
   classifiers=[
       "Programming Language :: Python",
       "Programming Language :: Python :: 2",
+      "Programming Language :: Python :: 3",
       "Development Status :: 4 - Beta",
       "Environment :: Other Environment",
       "Intended Audience :: Developers",

tox.ini

@@ -1,7 +1,7 @@
 [tox]
-# By default, we want to run tests for Python 2.7, Python 3.5, and run our
-# flake8 checks.
-envlist = py27,py35,flake8,coveralls
+# By default, we want to run tests for Python 2.7, Python 3.5, Python 3.7,
+# and run our flake8 checks.
+envlist = py27,py35,py37,flake8,coveralls
 # If an interpreter is missing locally, skip it.
 skip_missing_interpreters = true