Pembelajaran Terdistribusi dengan Apache MXNet dan Horovod

Terjemahan artikel disiapkan pada malam dimulainya kursus "ML Industri tentang Big Data"

---

, . , , . , , , .

Apache MXNet Horovod. Horovod , MXNet , Horovod.

Apache MXNet

Apache MXNet – , , . MXNet , , , API , Python, C++, Clojure, Java, Julia, R, Scala .

MXNet

MXNet (parameter server). , . – . , . , , «--» .

Horovod

Horovod – , Uber. GPU , NVIDIA Collective Communications Library (NCCL) Message Passing Interface (MPI) . . , MXNet, Tensorflow, Keras, PyTorch.

MXNet Horovod

MXNet Horovod API , Horovod. Horovod API horovod.broadcast(), horovod.allgather() horovod.allreduce() MXNet, . , MXNet, - . distributed optimizer, Horovod horovod.DistributedOptimizer Optimizer MXNet , API Horovod . .

MNIST MXNet Horovod MacBook.

mxnet horovod PyPI:

pip install mxnet
pip install horovod

: pip install horovod, , MACOSX_DEPLOYMENT_TARGET=10.vv, vv – MacOS, , MacOSX Sierra MACOSX_DEPLOYMENT_TARGET=10.12 pip install horovod

OpenMPI .

mxnet_mnist.py MacBook :

mpirun -np 2 -H localhost:2 -bind-to none -map-by slot python mxnet_mnist.py

. :

INFO:root:Epoch[0] Batch [0-50] Speed: 2248.71 samples/sec      accuracy=0.583640
INFO:root:Epoch[0] Batch [50-100] Speed: 2273.89 samples/sec      accuracy=0.882812
INFO:root:Epoch[0] Batch [50-100] Speed: 2273.39 samples/sec      accuracy=0.870000

ResNet50-v1 ImageNet 64 GPU p3.16xlarge EC2, 8 GPU NVIDIA Tesla V100 AWS cloud, 45000 / (.. ). 44 90 75.7%.

MXNet 8, 16, 32 64 GPU 1 1 2 1 . 1 . y , ( ) y . . , 38% 64 GPU. Horovod .

1. MXNet Horovod

1 64 GPU. MXNet Horovod .

1. Horovod 2 1.

, MXNet Horovod. MXNet .

1

MXNet 1.4.0 Horovod 0.16.0 , . GPU. Ubuntu 16.04 Linux, GPU Driver 396.44, CUDA 9.2, cuDNN 7.2.1, NCCL 2.2.13 OpenMPI 3.1.1. Amazon Deep Learning AMI, .

2

MXNet API Horovod. MXNet Gluon API . , , , . , Horovod:

• Horovod ( 8), , .
• ( 18), , .
• Horovod DistributedOptimizer ( 25), .

, Horovod-MXNet MNIST ImageNet.

1  import mxnet as mx
2  import horovod.mxnet as hvd
3
4  # Horovod: initialize Horovod
5  hvd.init()
6
7  # Horovod: pin a GPU to be used to local rank
8  context = mx.gpu(hvd.local_rank())
9
10 # Build model
11 model = ...
12
13 # Initialize parameters
14 model.initialize(initializer, ctx=context)
15 params = model.collect_params()
16
17 # Horovod: broadcast parameters
18 hvd.broadcast_parameters(params, root_rank=0)
19
20 # Create optimizer
21 optimizer_params = ...
22 opt = mx.optimizer.create('sgd', **optimizer_params)
23
24 # Horovod: wrap optimizer with DistributedOptimizer
25 opt = hvd.DistributedOptimizer(opt)
26
27 # Create trainer and loss function
28 trainer = mx.gluon.Trainer(params, opt, kvstore=None)
29 loss_fn = ...
30
31 # Train model
32 for epoch in range(num_epoch):
33    ...

3

MPI. , 4 GPU , 16 GPU . (SGD) :

• mini-batch size: 256
• learning rate: 0.1
• momentum: 0.9
• weight decay: 0.0001

GPU 64 GPU GPU ( 0,1 1 GPU 6,4 64 GPU), , GPU, 256 ( 256 1 GPU 16 384 64 GPU). weight decay momentum GPU. float16 float32 , float16, GPU NVIDIA Tesla.

$ mpirun -np 16 \
    -H server1:4,server2:4,server3:4,server4:4 \
    -bind-to none -map-by slot \
    -mca pml ob1 -mca btl ^openib \
    python mxnet_imagenet_resnet50.py

Apache MXNet Horovod. ImageNet, ResNet50-v1. , , Horovod.

MXNet , MXNe, MXNet. MXNet in 60 minutes, .

MXNet Horovod, Horovod, MXNet MNIST ImageNet.

* AWS EC2

---

« ML »

---