examples/cifar_distributed_cnn/README.md

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# Image Classification using Convolutional Neural Networks

Examples inside this folder show how to train CNN models using 
SINGA for image classification. The data augmentation is done 
only once before the training.

* `data` includes the scripts for preprocessing image datasets.
  Currently, MNIST, CIFAR10 and CIFAR100 are included.

* `model` includes the CNN model construction codes by creating
  a subclass of `Module` to wrap the neural network operations 
  of each model. Then computational graph is enabled to optimized 
  the memory and efficiency.

* `autograd` includes the codes to train CNN models by calling the
  [neural network operations](../../python/singa/autograd.py) imperatively. 
  The computational graph is not created.

* `train_cnn.py` is the training script, which controls the training flow by
  doing BackPropagation and SGD update.

* `train_multiprocess.py` is the script for distributed training on a single
  node with multiple GPUs; it uses Python's multiprocessing module and NCCL.

* `train_mpi.py` is the script for distributed training (among multiple nodes) 
  using MPI and NCCL for communication.

* `benchmark.py` tests the training throughput using `ResNet50` as the workload.