Neural network pruning allows for impressive theoretical reduction of models sizes and complexity. However it usually offers little practical benefits as it is most often limited to just zeroing out weights, without actually removing the pruned parameters. This precludes from actual advantages provided by sparsification methods. We propose Simplify, a PyTorch compatible library for achieving effective model simplification. Simplified models benefit of both a smaller memory footprint and a lower inference time, making their deployment to embedded or mobile devices much more efficient.