MXNet supports training with multiple CPUs and GPUs, which may be located on different physical machines.
By default, MXNet uses data parallelism to partition the workload over multiple devices. Assume there are n devices. Then each one will receive a copy of the complete model and train it on 1/n of the data. The results such as gradients and updated model are communicated across these devices.
MXNet also supports model parallelism. In this approach, each device holds onto only part of the model. This proves useful when the model is too large to fit onto a single device. As an example, see the following tutorial which shows how to use model parallelism for training a multi-layer LSTM model. In this tutorial, we'll focus on data parallelism.
By default, MXNet partitions a data batch evenly among the available GPUs. Assume a batch size b and assume there are k GPUs, then in one iteration each GPU will perform forward and backward on b/k examples. The gradients are then summed over all GPUs before updating the model.
To use GPUs, we need to compile MXNet with GPU support. For example, set
USE_CUDA=1inconfig.mkbeforemake. (see MXNet installation guide for more options).
If a machine has one or more GPU cards installed, then each card is labeled by a number starting from 0. To use a particular GPU, one can either specify the context context in code or pass --gpus at the command line. For example, to use GPU 0 and 2 in python, one can typically create a module with
import mxnet as mx module = mx.module.Module(context=[mx.gpu(0), mx.gpu(2)], ...)
while if the program accepts a --gpus flag (as seen in example/image-classification), then we can try
python train_mnist.py --gpus 0,2 ...
If the available GPUs are not all equally powerful, we can partition the workload accordingly. For example, if GPU 0 is 3 times faster than GPU 2, then we might use the workload option work_load_list=[3, 1], see Module for more details.
Training with multiple GPUs should yield the same results as training on a single GPU if all other hyper-parameters are the same. In practice, the results may exhibit small differences, owing to the randomness of I/O (random order or other augmentations), weight initialization with different seeds, and CUDNN.
We can control on which devices the gradient is aggregated and on which device the model is updated via KVStore, the MXNet module that supports data communication. One can either use mx.kvstore.create(type) to get an instance or use the program flag --kv-store type.
There are two commonly used types,
local: all gradients are copied to CPU memory and weights are updated there.device: both gradient aggregation and weight updates are run on GPUs. With this setting, the KVStore also attempts to use GPU peer-to-peer communication, potentially accelerating the communication. Note that this option may result in higher GPU memory usage.When using a large number of GPUs, e.g. >=4, we suggest using device for better performance.
KVStore also supports a number of options for running on multiple machines.
dist_sync behaves similarly to local but exhibits one major difference. With dist_sync, batch-size now means the batch size used on each machine. So if there are n machines and we use batch size b, then dist_sync behaves like local with batch size n*b.dist_device_sync is similar to dist_sync. The difference between them is that dist_device_sync aggregates gradients and updates weight on GPUs while dist_sync does so on CPU memory.dist_async performs asynchronous updates. The weight is updated whenever gradients are received from any machine. The update is atomic, i.e., no two updates happen on the same weight at the same time. However, the order is not guaranteed.To use distributed training, we need to compile with
USE_DIST_KVSTORE=1(see MXNet installation guide for more options).
Launching a distributed job is a bit different from running on a single machine. MXNet provides tools/launch.py to start a job by using ssh, mpi, sge, or yarn.
An easy way to set up a cluster of EC2 instances for distributed deep learning is using an AWS CloudFormation template. If you do not have a cluster, you can check the repository before you continue.
Assume we are at the directory mxnet/example/image-classification and want to train LeNet to classify MNIST images, as demonstrated here: train_mnist.py.
On a single machine, we can run:
python train_mnist.py --network lenet
Now, say we are given two ssh-able machines and MXNet is installed on both machines. We want to train LeNet on these two machines. First, we save the IPs (or hostname) of these two machines in file hosts, e.g.
$ cat hosts 172.30.0.172 172.30.0.171
Next, if the mxnet folder is accessible from both machines, e.g. on a network filesystem, then we can run:
python ../../tools/launch.py -n 2 --launcher ssh -H hosts python train_mnist.py --network lenet --kv-store dist_sync
Note that here we
launch.py to submit the job.ssh if all machines are ssh-able, mpi if mpirun is available, sge for Sun Grid Engine, and yarn for Apache Yarn.-n number of worker nodes to run on-H the host file which is required by ssh and mpi--kv-store use either dist_sync or dist_asyncNow consider if the mxnet folder is not accessible. We can first copy the MXNet library to this folder by
cp -r ../../python/mxnet . cp -r ../../lib/libmxnet.so mxnet
then ask launch.py to synchronize the current directory to all machines' /tmp/mxnet directory with --sync-dst-dir
python ../../tools/launch.py -n 2 -H hosts --sync-dst-dir /tmp/mxnet \ python train_mnist.py --network lenet --kv-store dist_sync
MXNet often chooses the first available network interface. But for machines that have multiple interfaces, we can specify which network interface to use for data communication by the environment variable DMLC_INTERFACE. For example, to use the interface eth0, we can
export DMLC_INTERFACE=eth0; python ../../tools/launch.py ...
SetPS_VERBOSE=1 to see the debug logging, e.g
export PS_VERBOSE=1; python ../../tools/launch.py ...
python ../../tools/launch.py -h