Network compression can reduce the memory footprint of a neural network, increase its inference speed and save energy. Distiller provides a [PyTorch](http://pytorch.org/) environment for prototyping and analyzing compression algorithms, such as sparsity-inducing methods and low-precision arithmetic.
<details><summary><b>What's New in October?</b></summary>
<p>
We've added collection of activation statistics!
Activation statistics can be leveraged to make pruning and quantization decisions, and so
we added support to collect these data.
Two types of activation statistics are supported: summary statistics, and detailed records
per activation.
Currently we support the following summaries:
- Average activation sparsity, per layer
- Average L1-norm for each activation channel, per layer
- Average sparsity for each activation channel, per layer
For the detailed records we collect some statistics per activation and store it in a record.
Using this collection method generates more detailed data, but consumes more time, so
Beware.
* You can collect activation data for the different training phases: training/validation/test.
* You can access the data directly from each module that you chose to collect stats for.
* You can also create an Excel workbook with the stats.
