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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.

* `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.