apeforest commented on a change in pull request #14286: Add examples of running MXNet with Horovod URL: https://github.com/apache/incubator-mxnet/pull/14286#discussion_r267976954
########## File path: example/distributed_training-horovod/README.md ########## @@ -0,0 +1,201 @@ +<!--- 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. --> + +# Distributed Training using MXNet with Horovod +[Horovod](https://github.com/horovod/horovod) is a distributed training framework that demonstrates +excellent scaling efficiency for dense models running on a large number of nodes. It currently +supports mainstream deep learning frameworks such as MXNet, TensorFlow, Keras, and PyTorch. +It is created at Uber and currently hosted by the [Linux Foundation Deep Learning](https://lfdl.io)(LF DL). + +MXNet is supported in Horovod 0.16.0 [release](https://eng.uber.com/horovod-pyspark-apache-mxnet-support/). + +## What's New? +Compared with the standard distributed training script in MXNet which uses parameter server to +distribute and aggregate parameters, Horovod uses ring allreduce and/or tree-based allreduce algorithm +to communicate parameters between workers. There is no dedicated server and the communication data size +between workers does not depend on the number of workers. Therefore, it scales well in the case where +there are a large number of workers and network bandwidth is the bottleneck. + +# Install +## Install MXNet +```bash +$ pip install mxnet +``` +**Note**: There is a [known issue](https://github.com/horovod/horovod/issues/884) when running Horovod with MXNet on a Linux system with GCC version 5.X and above. We recommend users to build MXNet from source following this [guide](https://mxnet.incubator.apache.org/install/build_from_source.html) as a workaround for now. Also mxnet-mkl package in 1.4.0 release does not support Horovod. Review comment: Which platform are you installing? The following steps in the README work for me on both MacOS and Amazon Linux and Centos 7 (all gcc4) ``` pip install mxnet pip install horovod ``` ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
