MXNet-G is a deep learning framework designed on the basis of MXNet. It allows you to train models with a novel distributed SGD(Stochastic Gradient Descent ) algorithm named Grouping-SGD. A new parallelization scheme named GSP(Grouping Synchronous Parallel ) is used in Grouping-SGD for Distributed Deep Learning on Heterogeneous Cluster Systems. In Grouping-SGD, machines are partitioned into multiple groups, ensuring that networked machines in the same group have identical or similar hardware configurations and therefore similar performance. Machines in the same group update the models synchronously, as in Sync-SGD. Different groups update the models asynchronously, as in Async-SGD, and each group is treated as a virtual scheduling unit. To further improve the performance of Grouping-SGD, the parameter servers are arranged according to performance from high to low. As the average staleness of gradients increases with the increasing of the size of the cluster, we adopt a method to reduce the impact of stale gradients. In detail, each gradient is assigned a weighting factor WFg (WFg = 1/Yg), and Yg is the staleness of gradients. The greater the staleness Yg of gradient is, the smaller its weighting factor WFg is. We apply this method in Async-SGD, Stale-SGD and GroupingSGD. Grouping-SGD can converge faster than the other three algorithms including Sync-SGD, Async-SGD and Stale-SGD, while achieving accuracies very close to those of Sync-SGD.
The following installation instructions have been tested on Ubuntu 14.04
1. Prerequisites
Install the following NVIDIA libraries to setup MXNet-G with GPU support:
(1) Install CUDA 7.5 following the NVIDIA's guide ( http://developer.download.nvidia.com/compute/cuda/7.5/Prod/docs/sidebar/CUDA_Installation_Guide_Linux.pdf ).
(2) Install cuDNN 5 for CUDA 7.5 following the NVIDIA's guide ( https://developer.nvidia.com/rdp/cudnn-archive#a-collapse51a ). You may need to register with NVIDIA for downloading the cuDNN library. Note: Make sure to add CUDA install path to LD_LIBRARY_PATH. Example: export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:$LD_LIBRARY_PATH
2. Install and Build
Building MXNet-G from source is a 2 step process. Firstly, build the MXNet-G core shared library, libmxnet.so, from the C++ sources. Secondly, build the language specific bindings. Example - Python bindings
(1) Build the MXNet-G core shared library, libmxnet.so, from the C++ sources
- Install build tools and git.
$ sudo apt-get update
$ sudo apt-get install -y build-essential git- Install OpenBLAS, OpenCV.
sudo apt-get install -y libopenblas-dev liblapack-dev libopencv-dev- Download MXNet-G sources and build MXNet-G core shared library.
$ git clone --recursive https://github.com/CGCL-codes/MXNet-G
$ cd MXNet-G
$ make -j $(nproc)You can explore and use more compilation options in make/config.mk.
(2) Build the MXNet-G Python binding
- Install prerequisites
sudo apt-get install python-setuptools python-pip python-numpy python-scipy python-matplotlib- Install the MXNet-G Python binding.
cd python
sudo python setup.py install develop --user1. Set up password-less authentication between machines
If you don't already have an SSH key, generate one via
ssh-keygenYou'll then need to add your public key to each machine, by appending your public key file~/.ssh/id_rsa.pub to ~/.ssh/authorized_keys on each machine. If your home directory is on a shared filesystem visible to all machines, then simply run
cat ~/.ssh/id_rsa.pub >> ~/.ssh/authorized_keys2. Copy MXNet-G to other machines
(1) Change path
cd example/image-classification(2) Write IPs to hosts file. This hosts file contains IPs of the machines in the cluster. These machines should be able to communicate with each other without using passwords. An example of the contents of the hosts file would be:
10.10.10.21
10.10.10.22
10.10.10.23
(3) Send MXNet-G to other machines, simply run
python scp_mxnet.py3. Set the training data ratio for each worker node
Write training data ratio for each worker node to data_sharding file. Each line in data_sharding file has two digits, which indicate the start and end of the training data, respectively. An example of the contents of the data_sharding file would be (two workers):
0 0.3
0.3 1
4. Partition workers into multiple groups
Write the grouping results to groups file. Each line in groups file indicate one group, which may has one or more IPs of workers. An example of the contents of the groups file would be (three groups):
10.10.10.21
10.10.10.22 10.10.10.23
10.10.10.24 10.10.10.25 10.10.10.26
5. Start distributed training
You can start distributed training just run the *.sh in MXNet-G/example/image-classification. For example, if you want to run inception-bn small on Cifar10 using GSP, simply run:
time ./ gsp_cifar10_inception_small.shOther models, datasets, and parallelization schemes can be used in similar ways. In the running script, some important parameters are explained as follows:
-n: denotes the number of worker nodes to be launched.
-s: denotes the number of parameter server nodes to be launched.
-i: denotes the network interface to be used
--launcher: denotes the mode of communication, here you can use "ssh" mode
-H: denotes the hosts file to be used
--data-dir: denotes the location of the datasets
--batch-size: denotes the batch size of training data to be used
--lr: denotes the learning rate
--lr-factor: denotes the decrease factor of the learning rate
--num-epoch: denotes the epochs num
--num-examples: denotes the num of training examples in one epoch
--gpus: denotes the gpus to be used
--kv-store: denotes the parallelization scheme to be used. (BSP:dist_sync, ASP:dist_async, GSP:dist_gsync, SSP:dist_ssync)
--data-allocator: indicates whether to use data sharding strategy. (0:not use, 1:use)
--staleness: denotes the staleness of workers can't exceed this value
More information about MXNet can refer to: https://mxnet.incubator.apache.org/index.html
If you have any questions, please contact Geyan Ye ([email protected]).