Merge pull request #1102 from NLGithubWP/add_train_mpi

Add the training script for models using MPI

examples/model_selection_psql/ms_mlp/train_mpi.py

@@ -0,0 +1,91 @@
+#
+# 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 singa import singa_wrap as singa
+from singa import opt
+from singa import tensor
+import argparse
+import train_cnn
+
+singa_dtype = {"float16": tensor.float16, "float32": tensor.float32}
+
+if __name__ == '__main__':
+    # Use argparse to get command config: max_epoch, model, data, etc., for single gpu training
+    parser = argparse.ArgumentParser(
+        description='Training using the autograd and graph.')
+    parser.add_argument('model',
+                        choices=['cnn', 'resnet', 'xceptionnet', 'mlp'],
+                        default='cnn')
+    parser.add_argument('data', choices=['mnist', 'cifar10', 'cifar100'], default='mnist')
+    parser.add_argument('-p',
+                        choices=['float32', 'float16'],
+                        default='float32',
+                        dest='precision')
+    parser.add_argument('-m',
+                        '--max-epoch',
+                        default=10,
+                        type=int,
+                        help='maximum epochs',
+                        dest='max_epoch')
+    parser.add_argument('-b',
+                        '--batch-size',
+                        default=64,
+                        type=int,
+                        help='batch size',
+                        dest='batch_size')
+    parser.add_argument('-l',
+                        '--learning-rate',
+                        default=0.005,
+                        type=float,
+                        help='initial learning rate',
+                        dest='lr')
+    parser.add_argument('-d',
+                        '--dist-option',
+                        default='plain',
+                        choices=['plain','half','partialUpdate','sparseTopK','sparseThreshold'],
+                        help='distibuted training options',
+                        dest='dist_option')  # currently partialUpdate support graph=False only
+    parser.add_argument('-s',
+                        '--sparsification',
+                        default='0.05',
+                        type=float,
+                        help='the sparsity parameter used for sparsification, between 0 to 1',
+                        dest='spars')
+    parser.add_argument('-g',
+                        '--disable-graph',
+                        default='True',
+                        action='store_false',
+                        help='disable graph',
+                        dest='graph')
+    parser.add_argument('-v',
+                        '--log-verbosity',
+                        default=0,
+                        type=int,
+                        help='logging verbosity',
+                        dest='verbosity')
+
+    args = parser.parse_args()
+
+    sgd = opt.SGD(lr=args.lr, momentum=0.9, weight_decay=1e-5, dtype=singa_dtype[args.precision])
+    sgd = opt.DistOpt(sgd)
+
+    train_cnn.run(sgd.global_rank, sgd.world_size, sgd.local_rank, args.max_epoch,
+              args.batch_size, args.model, args.data, sgd, args.graph,
+              args.verbosity, args.dist_option, args.spars, args.precision)