text: The paper proposes a deep learning framework called DeePa that supports multiple dimensions of parallelism in computation to accelerate training of convolutional neural networks. Whereas the majority of work on parallel or distributed deep learning partitions training over bootstrap samples of training data (called image parallelism in the paper), DeePa is able to additionally partition the operations over image height, width and channel. This gives more options to parallelize different parts of the neural network. For example, the best DeePa configurations studied in the paper for AlexNet, VGG-16, and Inception-v3 typically use image parallelism for the initial layers, reduce GPU utilization for the deeper layers to reduce data transfer overhead, and use model parallelism on a smaller number of GPUs for fully connected layers. The net is that DeePa allows such configurations to be created that provide an increase in training throughput and lower data transfer in practice for training these networks. These configurations for parellism are not easily programmed in other frameworks like TensorFlow and PyTorch. The paper can potentially be improved in a few ways. One is to explore more demanding training workloads that require larger-scale distribution and parallelism. The ImageNet 22-K would be a good example and would really highlight the benefits of the DeePa in practice. Beyond that, more complex workloads like 3D CNNs for video modeling would also provide a strong motivation for having multiple dimensions of the data for partitioning operations.