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Lightning makes multi-gpu training and 16 bit training trivial.
Note:
None of the flags below require changing anything about your lightningModel definition.
Choosing a backend
Lightning supports two backends. DataParallel and DistributedDataParallel. Both can be used for single-node multi-GPU training. For multi-node training you must use DistributedDataParallel.
You can toggle between each mode by setting this flag.
# DEFAULT uses DataParallel
trainer = Trainer(distributed_backend='dp')
# change to distributed data parallel
trainer = Trainer(distributed_backend='ddp')
If you request multiple nodes, the back-end will auto-switch to ddp. We recommend you use DistributedDataparallel even for single-node multi-GPU training. It is MUCH faster than DP but may have configuration issues depending on your cluster.
For a deeper understanding of what lightning is doing, feel free to read this guide.
CUDA flags
CUDA flags make certain GPUs visible to your script. Lightning sets these for you automatically, there's NO NEED to do this yourself.
# lightning will set according to what you give the trainer
# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
# os.environ["CUDA_VISIBLE_DEVICES"] = "0"
16-bit mixed precision
16 bit precision can cut your memory footprint by half. If using volta architecture GPUs it can give a dramatic training speed-up as well.
First, install apex (if install fails, look here):
$ git clone https://github.com/NVIDIA/apex
$ cd apex
$ pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
then set this use_amp to True.
# DEFAULT
trainer = Trainer(amp_level='O2', use_amp=False)
Single-gpu
Make sure you're on a GPU machine.
# DEFAULT
trainer = Trainer(gpus=[0])
multi-gpu
Make sure you're on a GPU machine. You can set as many GPUs as you want. In this setting, the model will run on all 8 GPUs at once using DataParallel under the hood.
# to use DataParallel (default)
trainer = Trainer(gpus=[0,1,2,3,4,5,6,7], distributed_backend='dp')
# RECOMMENDED use DistributedDataParallel
trainer = Trainer(gpus=[0,1,2,3,4,5,6,7], distributed_backend='ddp')
Multi-node
Multi-node training is easily done by specifying these flags.
# train on 12*8 GPUs
trainer = Trainer(gpus=[0,1,2,3,4,5,6,7], nb_gpu_nodes=12)
In addition, make sure to set up your SLURM job correctly via the SlurmClusterObject. In particular, specify the number of tasks per node correctly.
cluster = SlurmCluster(
hyperparam_optimizer=test_tube.HyperOptArgumentParser(),
log_path='/some/path/to/save',
)
# OPTIONAL FLAGS WHICH MAY BE CLUSTER DEPENDENT
# which interface your nodes use for communication
cluster.add_command('export NCCL_SOCKET_IFNAME=^docker0,lo')
# see output of the NCCL connection process
# NCCL is how the nodes talk to each other
cluster.add_command('export NCCL_DEBUG=INFO')
# setting a master port here is a good idea.
cluster.add_command('export MASTER_PORT=%r' % PORT)
# good to load the latest NCCL version
cluster.load_modules(['NCCL/2.4.7-1-cuda.10.0'])
# configure cluster
cluster.per_experiment_nb_nodes = 12
cluster.per_experiment_nb_gpus = 8
cluster.add_slurm_cmd(cmd='ntasks-per-node', value=8, comment='1 task per gpu')
Finally, make sure to add a distributed sampler to your dataset. The distributed sampler copies a portion of your dataset onto each GPU. (World_size = gpus_per_node * nb_nodes).
# ie: this:
dataset = myDataset()
dataloader = Dataloader(dataset)
# becomes:
dataset = myDataset()
dist_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
dataloader = Dataloader(dataset, sampler=dist_sampler)
Self-balancing architecture
Here lightning distributes parts of your module across available GPUs to optimize for speed and memory.
COMING SOON.