The latest changes to the logger caused the CI tests to fail,
because test assumes that the logging.conf file is present in the
same directory as the sample application script.
The sample application used cwd() instead, and did not find the
log configuration file.

Soon we will be reusing this function in other sample apps, so let's
move it to app_utils.

The 'master' branch now uses PyTorch 0.4, which has API changes that
are not backward compatible with PyTorch 0.3.

After we've upgraded Distiller's internal implementation to be
compatible with PyTorch 0.4, we've added a check that you are using
the correct PyTorch version.

Note that we only perform this check in the sample image classifier
compression application.

Eventually we will want to use this code in other sample applications,
so let's move the logger configuration code to a separate function.

There's a bit of ugly hacking in this current implementation because
I've added variable members to logging.logger.  These are actaully
config-once variables that convey the logging directory and filename.
I did not want to add more names to the global namespace, so I hacked
a temporary solution in which logging.logger is acts as a conveyor and
private namespace.  We'll get that cleaned up as we do more refactoring.

This is a niche feature, which lets you print the names of the modules
in a model, from the command-line.
Non-leaf nodes are excluded from this list.  Other caveats are documented
in the code.

Data parallel models may execute faster on multiple GPUs, but rendering
them creates visually complex and illegible graphs.
Therefore, when creating models for a PNG summary, we opt to use
non-parallel models.