Merge pull request #1169 from wangsheng1001/dev-postgresql

Add README.md for the cnn ms example

examples/cnn_ms/README.md

@@ -0,0 +1,44 @@
+<!--
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+      http://www.apache.org/licenses/LICENSE-2.0
+    Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+-->
+
+# 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.