* PyTorch 1.1.0 now required
  - Moved other dependencies to up-to-date versions as well
* Adapt LR scheduler to PyTorch 1.1 API changes:
  - Change lr_scheduler.step() calls to succeed validate calls,
    during training
  - Pass to lr_scheduler.step() caller both loss and top1
    (Resolves issue #240)
* Adapt thinning for PyTorch 1.1 semantic changes
  - **KNOWN ISSUE**: When a thinning recipe is applied, in certain
    cases PyTorch displays this warning:
    "UserWarning: non-inplace resize is deprecated".
    To be fixed later
* SummaryGraph: Workaround for new scope name issue from PyTorch 1.1.0
* Adapt to updated PyTest version:
  - Stop using deprecated 'message' parameter of pytest.raises(),
    use pytest.fail() instead
  - Make sure only a single test case per pytest.raises context
* Move PyTorch version check to root __init__.py 
  - This means the version each checked when Distiller is first
    imported. A RuntimeError is raised if the version is wrong.
* Updates to parameter_histograms notebook:
  - Replace deprecated normed argument with density
  - Add sparsity rate to plot title
  - Load model in CPU

* In PostTrainLinearQuantizer - moved 'clip_acts' and 'clip_n_stds'
  to overrides, removed 'no_clip_layers' parameter from __init__
* The 'no_clip_layers' command line argument REMAINS, handled in 
  PostTrainLinearQuantizer.from_args()
* Removed old code from comments, fixed warnings in 
  test_post_train_quant.py
* Updated tests
* Update post-train quant sample YAML

Also:
* Single worker limitation not needed anymore, been fixed in PyTorch
  since v0.4.0 (https://github.com/pytorch/pytorch/pull/4640)
* compress_classifier.py: If run in evaluation mode (--eval), enable
  deterministic mode.
* Call utils.set_deterministic at data loaders creation if
  deterministic argument is set (don't assume user calls it outside)
* Disable CUDNN benchmark mode in utils.set_deterministic
  (https://pytorch.org/docs/stable/notes/randomness.html#cudnn)

Not backward compatible - re-installation is required

* Fixes for PyTorch==1.0.0
* Refactoring folder structure
* Update installation section in docs

* For CIFAR-10 / ImageNet only
* Refactor data_loaders.py, reduce code duplication
* Implemented custom sampler
* Integrated in image classification sample
* Since we now shuffle the test set, had to update expected results
  in 2 full_flow_tests that do evaluation

* Asymmetric post-training quantization (only symmetric supported so until now)
* Quantization aware training for range-based (min-max) symmetric and asymmetric quantization
* Per-channel quantization support in both training and post-training
* Added tests and examples
* Updated documentation

* Fix issue #79

Change the default values so that the following scheduler meta-data keys
are always defined: 'starting_epoch', 'ending_epoch', 'frequency'

* compress_classifier.py: add a new argument

Allow the specification, from the command line arguments,  of the range of
pruning levels scanned when doing sensitivity analysis

* Add regression test for issue #79

* Added command line arguments for this and other post-training
  quantization settings in image classification sample.

* Adding PACT quantization method
* Move logic modifying the optimizer due to changes the quantizer makes into the Quantizer itself
* Updated documentation and tests

* Add Quantizer unit tests
* Require 'bits_overrides' to be OrderedDict to support overlapping
  patterns in a predictable manner + update documentation to reflect this
* Quantizer class cleanup
  * Use "public" nn.Module APIs instead of protected attributes
  * Call the builtins set/get/delattr instead of the class special methods
    (__***__)
  * Fix issues reported in #24
* Bug in RangeLinearQuantParamLayerWrapper - add explicit override of
  pre_quantized_forward accpeting single input (#15)
* Add DoReFa test to full_flow_tests