- Support passing kwargs to distributed.Adaptive, which now
  includes arguments like minimum and maximum [number of workers].
- Add an option workers argument to _retire_workers() to match
  dask/distributed#1797

- Support passing kwargs to distributed.Adaptive.__init__, which now
  takes keyword arguments like minimum and maximum [number of workers].
- Add an optional workers argument to _retire_workers() to match
  dask/distributed#1797 -- currently Adaptive
  raises a TypeError.

* Compatibility fixes with distributed 1.21.3

- Support passing kwargs to distributed.Adaptive.__init__, which now
  takes keyword arguments like minimum and maximum [number of workers].
- Add an optional workers argument to _retire_workers() to match
  dask/distributed#1797 -- currently Adaptive
  raises a TypeError.

* Adaptive memory resource compatibility fix for distributed==1.21.0

In dask/distributed#1594, the scheduler's
internal maps of task objects were changed from using their keys to
using TaskState objects. However, dask_drmaa.Adaptive was still
querying for keys, causing new workers to never find the memory
resource constraints for pending tasks and consequently tasks
to never find workers with sufficient resources. This was causing
the unit test test_adaptive_memory to wait indefinitely.

Try to fix this to support both distributed pre- and post- 1.21.0,
and un-skip test_adaptive_memory.

* basestring -> six.string_types

(Was testing on python2, switching to python2/3-compatible)

* Add six to requirements.txt

Also a couple of miscellaneous comments, including
Windows-specific comment for running docker-based tests.

* Undo windows comments (moving to a separate PR)

* Drop support for distributed < 1.21.0

Update requirements.txt to require distributed >= 1.21.0,
since there are some internal changes in the way tasks
are stored. Also drop the corresponding backwards-
compatibility fixes. Feel free to revert if
distributed 1.20.x support if desired.