Epyc

- Added results sets as keys in LabNotenook
- Added add-or-select method to LabNotebook
- Described Jupyter design pattern to avoid repeated computation of result sets
- Added better badges to README.rst
- Changed to Github workflows for CI
- Added basic command-line tool for manipulating notebooks
- Added exception propagation to Exeperiment.run()

- Fixed adding lists as results in HDF5 notebooks

- Added notebook-level locking
- Added loading notebooks directly from URLs
- Changed Experiment to handle lists of results dicts cleanly

- Back-ported to work with Python 3.6 and later, and therefore
with PyPy3

- Fixed issue with TypeMapping initialisation
- Addad ParallelLab for simpler multicore operation
- Updated tutorials to reflect new opportunities

- Fixed ClusterLab.updateResults() not to bail out completely in response
to a low-level crashed job (which can happen because of engine failure)
- Slightly more aggressive closing of connection to cluster, since connections
held open for a long time seem a lot more prone to failure
- Slight change to the semantics of lambda-expressions in Python3.8 broke
ClusterLab
- Dropped support for dill, replaced with automatic use of cloudpickle
- Fixed issue with handling RepeatedExperiments
- Reverted to LabNotebook.addResult() for all additions (splitting between
addResult() and addResults() caused confusion)
- Forced pending results job ids to strings, not bytes (needed by ipyparallel)
- Fixed slightly cavalier attitude towards default (missing) values in ResultSet
- Added epyc-specific exceptions in places where a programmatic response might
be appropriate
- Modified cancellation behaviour to record the job as cancelled rather than
silently dropping it, to maintain the audit trail
- Added locking of result sets to prevent further changes
- Added Travis CI integration