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On this tutorial, we’ll learn to use nn.parallel.DistributedDataParallel for coaching our fashions in a number of GPUs. We’ll take a minimal instance of coaching a picture classifier and see how we are able to velocity up the coaching.
Let’s begin with some imports.
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.practical as F
import torch.optim as optim
import time
We’ll use the CIFAR10 in all our experiments with a batch dimension of 256.
def create_data_loader_cifar10():
remodel = transforms.Compose(
[
transforms.RandomCrop(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
batch_size = 256
trainset = torchvision.datasets.CIFAR10(root='./knowledge', prepare=True,
obtain=True, remodel=remodel)
trainloader = torch.utils.knowledge.DataLoader(trainset, batch_size=batch_size,
shuffle=True, num_workers=10, pin_memory=True)
testset = torchvision.datasets.CIFAR10(root='./knowledge', prepare=False,
obtain=True, remodel=remodel)
testloader = torch.utils.knowledge.DataLoader(testset, batch_size=batch_size,
shuffle=False, num_workers=10)
return trainloader, testloader
We’ll first prepare the mannequin on a single Nvidia A100 GPU for 1 epoch. Commonplace pytorch stuff right here, nothing new. The tutorial is predicated on the official tutorial from Pytorch’s docs.
def prepare(web, trainloader):
print("Begin coaching...")
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(web.parameters(), lr=0.001, momentum=0.9)
epochs = 1
num_of_batches = len(trainloader)
for epoch in vary(epochs):
running_loss = 0.0
for i, knowledge in enumerate(trainloader, 0):
inputs, labels = knowledge
pictures, labels = inputs.cuda(), labels.cuda()
optimizer.zero_grad()
outputs = web(pictures)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.merchandise()
print(f'[Epoch {epoch + 1}/{epochs}] loss: {running_loss / num_of_batches:.3f}')
print('Completed Coaching')
The check operate is equally outlined. The principle script will simply put every thing collectively:
if __name__ == '__main__':
begin = time.time()
PATH = './cifar_net.pth'
trainloader, testloader = create_data_loader_cifar10()
web = torchvision.fashions.resnet50(False).cuda()
start_train = time.time()
prepare(web, trainloader)
end_train = time.time()
torch.save(web.state_dict(), PATH)
check(web, PATH, testloader)
finish = time.time()
seconds = (finish - begin)
seconds_train = (end_train - start_train)
print(f"Whole elapsed time: {seconds:.2f} seconds,
Prepare 1 epoch {seconds_train:.2f} seconds")
We use a resnet50 to measure the efficiency of a decent-sized community.
Now let’s prepare the mannequin:
$ python -m train_1gpu
Accuracy of the community on the 10000 check pictures: 27 %
Whole elapsed time: 69.03 seconds, Prepare 1 epoch 13.08 seconds
Okay, time to get to optimization work.
Code is out there on GitHub. If you’re planning to solidify your Pytorch information, there are two wonderful books that we extremely suggest: Deep studying with PyTorch from Manning Publications and Machine Studying with PyTorch and Scikit-Be taught by Sebastian Raschka. You may all the time use the 35% low cost code blaisummer21 for all Manning’s merchandise.
torch.nn.DataParallel: no ache, no acquire
DataParallel is single-process, multi-thread, and solely works on a single machine. For every GPU, we use the identical mannequin to do the ahead move. We scatter the info all through the GPUs and carry out ahead passes in every considered one of them. Primarily, what occurs is that the batch dimension is split throughout the variety of staff.
On this use case, this performance supplied no acquire. That’s as a result of the system that I’m utilizing has a CPU and laborious disk bottleneck. Different machines which have very quick disk and CPU however battle with the GPU velocity (GPU bottleneck) might profit from this performance.
In follow, the one change it is advisable to do within the code is the next:
web = torchvision.fashions.resnet50(False)
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
web = nn.DataParallel(web)
When utilizing nn.DataParallel, the batch dimension needs to be divisible by the variety of GPUs.
nn.DataParallelsplits the batch and processes it independently in all of the accessible GPU’s. In every ahead move, the module is replicated on every GPU, which is a major overhead. Every reproduction handles a portion of the batch (batch_size / gpus). Throughout the backwards move, gradients from every reproduction are summed into the unique module.
Extra information on our earlier article on knowledge vs mannequin parallelism.
A superb follow when utilizing a number of GPUs is to outline prematurely the GPUs that your script is going to make use of:
import os
os.environ['CUDA_VISIBLE_DEVICES'] = "0,1"
This needs to be DONE earlier than some other import-related to CUDA.
Even from the Pytorch documentation it’s apparent that it is a very poor technique:
It’s endorsed to make use of
nn.DistributedDataParallel, as a substitute of this class, to do multi-GPU coaching, even when there may be solely a single node.
The reason being that DistributedDataParallel makes use of one course of per employee (GPU) whereas DataParallel encapsulates all the info communication in a single course of.
In accordance with the docs, the info might be on any system earlier than they’re handed into the mannequin.
In my experiment, DataParallel was slower than coaching on a single GPU. Even with 4 GPUs. After rising the variety of staff I decreased the time, however nonetheless worse than a single GPU. I measure and report the time required to coach the mannequin for one epoch, that’s 50K 32×32 pictures.
Remaining notice: to check the efficiency with a single GPU, I multiplied the batch dimension by the variety of staff, i.e. 4 for 4 GPUs. In any other case, it’s greater than 2X slower.
This brings us to the hardcore subject of Distributed Information-Parallel.
Code is out there on GitHub. You may all the time assist our work by social media sharing, making a donation, and shopping for our guide and e-course.
Pytorch Distributed Information-Parallel
Distributed knowledge parallel is multi-process and works for each single and multi-machine coaching. In pytorch, nn.parallel.DistributedDataParallel parallelizes the module by splitting the enter throughout the required units. This module is appropriate for multi-node,multi-GPU coaching as effectively. Right here, I solely experimented with a single node (1 machine with 4 GPUs).
The principle distinction right here is that every GPU is dealt with by a course of. Parameters are by no means broadcasted between processes, solely gradients.
The module is replicated on every machine and every system. Throughout the ahead move, every employee (GPU) processes the info and computes its personal gradient regionally. Throughout the backwards move, gradients from every node are averaged. Lastly, every employee performs a parameter replace and sends to all the opposite nodes the computed parameter replace.
The module performs an all-reduce step on gradients and assumes that they are going to be modified by the optimizer in all processes in the identical approach.
Under are the rules for changing your single GPU script to multi-GPU coaching.
Step 1: Initialize the distributed studying processes
def init_distributed():
dist_url = "env://"
rank = int(os.environ["RANK"])
world_size = int(os.environ['WORLD_SIZE'])
local_rank = int(os.environ['LOCAL_RANK'])
dist.init_process_group(
backend="nccl",
init_method=dist_url,
world_size=world_size,
rank=rank)
torch.cuda.set_device(local_rank)
dist.barrier()
This initialization works once we launch our script with torch.distributed.launch (Pytorch 1.7 and 1.8) or torch.run (Pytorch 1.9+) from every node (right here 1).
Step 2: Wrap the mannequin utilizing DDP
web = torchvision.fashions.resnet50(False).cuda()
web = nn.SyncBatchNorm.convert_sync_batchnorm(web)
local_rank = int(os.environ['LOCAL_RANK'])
web = nn.parallel.DistributedDataParallel(web, device_ids=[local_rank])
If every course of has the right native rank, tensor.cuda() or mannequin.cuda() might be referred to as appropriately all through the script.
Step 3: Use a DistributedSampler in your DataLoader
import torch
from torch.utils.knowledge.distributed import DistributedSampler
from torch.utils.knowledge import DataLoader
import torch.nn as nn
def create_data_loader_cifar10():
remodel = transforms.Compose(
[
transforms.RandomCrop(32),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
batch_size = 256
trainset = torchvision.datasets.CIFAR10(root='./knowledge', prepare=True,
obtain=True, remodel=remodel)
train_sampler = DistributedSampler(dataset=trainset, shuffle=True)
trainloader = torch.utils.knowledge.DataLoader(trainset, batch_size=batch_size,
sampler=train_sampler, num_workers=10, pin_memory=True)
testset = torchvision.datasets.CIFAR10(root='./knowledge', prepare=False,
obtain=True, remodel=remodel)
test_sampler =DistributedSampler(dataset=testset, shuffle=True)
testloader = torch.utils.knowledge.DataLoader(testset, batch_size=batch_size,
shuffle=False, sampler=test_sampler, num_workers=10)
return trainloader, testloader
In distributed mode, calling the data_loader.sampler.set_epoch() methodology initially of every epoch earlier than creating the DataLoader iterator is important to make shuffling work correctly throughout a number of epochs. In any other case, the identical ordering might be all the time used.
def prepare(web, trainloader):
print("Begin coaching...")
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(web.parameters(), lr=0.001, momentum=0.9)
epochs = 1
num_of_batches = len(trainloader)
for epoch in vary(epochs):
trainloader.sampler.set_epoch(epoch)
In a extra common kind:
for epoch in vary(epochs):
data_loader.sampler.set_epoch(epoch)
train_one_epoch(...)
Good practices for DDP
Any strategies that obtain knowledge needs to be remoted to the grasp course of. Any strategies that carry out file I/O needs to be remoted to the grasp course of.
import torch.distributed as dist
import torch
def is_dist_avail_and_initialized():
if not dist.is_available():
return False
if not dist.is_initialized():
return False
return True
def save_on_master(*args, **kwargs):
if is_main_process():
torch.save(*args, **kwargs)
def get_rank():
if not is_dist_avail_and_initialized():
return 0
return dist.get_rank()
def is_main_process():
return get_rank() == 0
Based mostly on this operate you’ll be able to make sure that some instructions are solely executed from the primary course of:
if is_main_process():
Launch script utilizing torch.distributed.launch or torch.run
$ python -m torch.distributed.launch --nproc_per_node=4 main_script.py
Errors will happen. You’ll want to kill any undesirable distributed coaching course of by:
$ kill $(ps aux | grep main_script.py | grep -v grep | awk '{print $2}')
Change main_script.py together with your script identify. One other extra easy possibility is $ kill -9 PID. In any other case you’ll be able to go to extra superior stuff, like killing all CUDA GPU associated processes when not proven in nvidia-smi
lsof /dev/nvidia* | awk '{print $2}' | xargs -I {} kill {}
That is just for the case that you simply can’t discover the PID of the method operating within the GPU.
An excellent guide on distributed coaching is Distributed Machine Studying with Python: Accelerating mannequin coaching and serving with distributed programs by Guanhua Wang.
Combined-precision coaching in Pytorch
Combined precision combines Floating Level (FP) 16 and FP 32 in numerous steps of the coaching. FP16 coaching is also called half-precision coaching, which comes with inferior efficiency. Automated mixed-precision is actually the most effective of each worlds: decreased coaching time with comparable efficiency to FP32.
In Combined Precision Coaching, all of the computational operations (ahead move, backward move, weight gradients) see the FP16 casted model. To take action, an FP32 copy of the load is important, in addition to computing the loss in FP32 after the ahead move in FP16 to keep away from over and underflows. The burden gradients are casted again to FP32 to replace the mannequin’s weights. Furthermore, the loss in FP32 is scaled as much as keep away from gradient underflow earlier than getting casted to FP16 to carry out the backward move. As compensation, the FP32 weights might be scaled down by the identical scalar earlier than the load replace.
Listed below are the adjustments within the prepare operate:
fp16_scaler = torch.cuda.amp.GradScaler(enabled=True)
for epoch in vary(epochs):
trainloader.sampler.set_epoch(epoch)
running_loss = 0.0
for i, knowledge in enumerate(trainloader, 0):
inputs, labels = knowledge
pictures, labels = inputs.cuda(), labels.cuda()
optimizer.zero_grad()
with torch.cuda.amp.autocast():
outputs = web(pictures)
loss = criterion(outputs, labels)
fp16_scaler.scale(loss).backward()
fp16_scaler.step(optimizer)
fp16_scaler.replace()
Outcomes and Sum up
In a utopian parallel world, N staff would give a speedup of N. Right here you see that you simply want 4 GPUs in DistributedDataParallel mode to get a speedup of 2X. Combined precision coaching usually supplies a considerable speedup however the A100 GPU and different Ampere-based GPU architectures have restricted positive aspects (so far as I’ve learn on-line).
Outcomes under report the time in seconds for 1 epoch on CIFAR10 with a resnet50 (batch dimension 256, NVidia A100 40GB GPU reminiscence):
| Time in seconds | |
| Single GPU (baseline) | 13.2 |
| DataParallel 4 GPUs | 19.1 |
| DistributedDataParallel 2 GPUs | 9.8 |
| DistributedDataParallel 4 GPUs | 6.1 |
| DistributedDataParallel 4 GPUs + Combined Precision | 6.5 |
A vital notice right here is that DistributedDataParallel makes use of an efficient batch dimension of 4*256=1024 so it makes fewer mannequin updates. That’s why I consider it scores a a lot decrease validation accuracy (14% in comparison with 27% within the baseline).
Code is out there on GitHub if you wish to mess around. The outcomes will fluctuate primarily based in your {hardware}. There’s all the time the case that I missed one thing in my experiments. In case you discover a flaw please let me know on our Discord server.
These findings would offer you a strong begin to coaching your fashions. I hope you discover them helpful. Helps us by social media sharing, making a donation, shopping for our guide or e-course. Your assist would assist us produce extra free content material and accessible AI content material. As all the time, thanks in your curiosity in our weblog.
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