Merge pull request #859 from zlheui/sparsification_mnist
Adding MNIST sparsification to autograd
diff --git a/examples/cifar_distributed_cnn/autograd/sparsification_mnist.py b/examples/cifar_distributed_cnn/autograd/sparsification_mnist.py
new file mode 100644
index 0000000..315605a
--- /dev/null
+++ b/examples/cifar_distributed_cnn/autograd/sparsification_mnist.py
@@ -0,0 +1,45 @@
+#
+# 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.
+#
+
+from mnist_cnn import *
+import multiprocessing
+import sys
+
+if __name__ == '__main__':
+
+ # Generate a NCCL ID to be used for collective communication
+ nccl_id = singa.NcclIdHolder()
+
+ # Number of GPUs to be used
+ world_size = int(sys.argv[1])
+
+ # Use sparsification with parameters
+ topK = False # When topK = False, Sparsification based on a constant absolute threshold
+ corr = True # If True, uses local accumulate gradient for the correction
+ sparsThreshold = 0.05 # The constant absolute threshold for sparsification
+
+ process = []
+ for local_rank in range(0, world_size):
+ process.append(
+ multiprocessing.Process(target=train_mnist_cnn,
+ args=(True, local_rank, world_size, nccl_id,
+ sparsThreshold, topK, corr)))
+
+ for p in process:
+ p.start()
