Review for NeurIPS paper: Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning

Weaknesses: This work is most applicable on networks with many small kernels, which may not be of broad interest in all cases. Nonetheless, it does help with training MobileNet and similar networks on desktop or server GPUs. I also feel that some parts of the paper overstate the contribution, either by only evaluating on these networks or by leaving out some optimized baselines. The biggest issues here are: - For inference, you should compare against an optimized inference runtime such as TensorRT. This will likely do better than PyTorch or Caffe2 do out of the box, even with TorchScript.