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How to launch a distributed training |
If you have multiple GPUs, the most reliable way to use all of them for training is to use the distributed package from pytorch. To help you, there is a distributed module in fastai that has helper functions to make it really easy.
Distributed training doesn't work in a notebook, so first, clean up your experiments notebook and prepare a script to run the training. For instance, here is a minimal script that trains a wide resnet on CIFAR10.
from fastai.vision import *
from fastai.vision.models.wrn import wrn_22
path = untar_data(URLs.CIFAR)
ds_tfms = ([*rand_pad(4, 32), flip_lr(p=0.5)], [])
data = ImageDataBunch.from_folder(path, valid='test', ds_tfms=ds_tfms, bs=128).normalize(cifar_stats)
learn = Learner(data, wrn_22(), metrics=accuracy)
learn.fit_one_cycle(10, 3e-3, wd=0.4, div_factor=10, pct_start=0.5)Your script is going to be executed in a different process that will each happen on a different GPU. To make this work properly, add the following introduction between your imports and the rest of your code.
from fastai.distributed import *
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--local_rank", type=int)
args = parser.parse_args()
torch.cuda.set_device(arg.local_rank)
torch.distributed.init_process_group(backend='nccl', init_method='env://')What we do here is that we import the necessary stuff from fastai (for later), we create an argument parser that will intercept an argument named local_rank (which will contain the name of the GPU to use), then we set our GPU accordingly. The last line is what pytorch needs to set things up properly and know that this process is part of a larger group.
You then have to add one thing to your learner before fitting it to tell it it's going to execute a distributed training:
learn = learn.to_distributed(arg.local_rank)This will add the additional callbacks that will make sure your model and your data loaders are properly setups.
Now you can save your scriptn here is what the full example looks like:
from fastai.vision import *
from fastai.vision.models.wrn import wrn_22
from fastai.distributed import *
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--local_rank", type=int)
args = parser.parse_args()
torch.cuda.set_device(arg.local_rank)
torch.distributed.init_process_group(backend='nccl', init_method='env://')
path = untar_data(URLs.CIFAR)
ds_tfms = ([*rand_pad(4, 32), flip_lr(p=0.5)], [])
data = ImageDataBunch.from_folder(path, valid='test', ds_tfms=ds_tfms, bs=128).normalize(cifar_stats)
learn = Learner(data, wrn_22(), metrics=accuracy).to_distributed(arg.local_rank)
learn.fit_one_cycle(10, 3e-3, wd=0.4, div_factor=10, pct_start=0.5)In your terminal, type the following line (adapt num_gpus and script_name to the number of GPUs you want to use and your script name ending with .py).
python -m torch.distributed.launch --nproc_per_node={num_gpus} {script_name}
What will happen is that the same model will be copied on all your available GPUs. During training, the full dataset will randomly be split between the GPUs (that will change at each epoch). Each GPU will grab a batch (on that fractioned dataset), pass it through the model, compute the loss then back-propagate the gradients. Then they will share their results and average them, which means like your training is the equivalent of a training with a batch size of batch_size x num_gpus (where batch_size is what you used in your script).
Since they all have the same gradients at this stage, they will al perform the same update, so the models will still be the same after this step. Then training continues with the next batch, until the number of desired iterations is done.