| # |
| # 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]) |
| |
| 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))) |
| |
| for p in process: |
| p.start() |