128 lines
4.1 KiB
Markdown
128 lines
4.1 KiB
Markdown
Lightning makes multi-gpu training and 16 bit training trivial.
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*Note:*
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None of the flags below require changing anything about your lightningModel definition.
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---
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#### Choosing a backend
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Lightning supports two backends. DataParallel and DistributedDataParallel. Both can be used for single-node multi-GPU training.
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For multi-node training you must use DistributedDataParallel.
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You can toggle between each mode by setting this flag.
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``` {.python}
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# DEFAULT uses DataParallel
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trainer = Trainer(distributed_backend='dp')
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# change to distributed data parallel
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trainer = Trainer(distributed_backend='ddp')
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```
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If you request multiple nodes, the back-end will auto-switch to ddp.
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We recommend you use DistributedDataparallel even for single-node multi-GPU training. It is MUCH faster than DP but *may*
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have configuration issues depending on your cluster.
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For a deeper understanding of what lightning is doing, feel free to read [this guide](https://medium.com/@_willfalcon/9-tips-for-training-lightning-fast-neural-networks-in-pytorch-8e63a502f565).
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---
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#### CUDA flags
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CUDA flags make certain GPUs visible to your script.
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Lightning sets these for you automatically, there's NO NEED to do this yourself.
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```python
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# lightning will set according to what you give the trainer
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# os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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# os.environ["CUDA_VISIBLE_DEVICES"] = "0"
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```
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---
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#### 16-bit mixed precision
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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.
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First, install apex (if install fails, look [here](https://github.com/NVIDIA/apex)):
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```bash
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$ git clone https://github.com/NVIDIA/apex
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$ cd apex
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$ pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
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```
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then set this use_amp to True.
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``` {.python}
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# DEFAULT
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trainer = Trainer(amp_level='O2', use_amp=False)
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```
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---
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#### Single-gpu
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Make sure you're on a GPU machine.
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```python
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# DEFAULT
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trainer = Trainer(gpus=[0])
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```
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---
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#### multi-gpu
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Make sure you're on a GPU machine. You can set as many GPUs as you want.
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In this setting, the model will run on all 8 GPUs at once using DataParallel under the hood.
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```python
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# to use DataParallel (default)
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trainer = Trainer(gpus=[0,1,2,3,4,5,6,7], distributed_backend='dp')
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# RECOMMENDED use DistributedDataParallel
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trainer = Trainer(gpus=[0,1,2,3,4,5,6,7], distributed_backend='ddp')
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```
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---
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#### Multi-node
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Multi-node training is easily done by specifying these flags.
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```python
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# train on 12*8 GPUs
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trainer = Trainer(gpus=[0,1,2,3,4,5,6,7], nb_gpu_nodes=12)
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```
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In addition, make sure to set up your SLURM job correctly via the [SlurmClusterObject](https://williamfalcon.github.io/test-tube/hpc/SlurmCluster/). In particular, specify the number of tasks per node correctly.
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```python
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cluster = SlurmCluster(
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hyperparam_optimizer=test_tube.HyperOptArgumentParser(),
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log_path='/some/path/to/save',
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)
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# OPTIONAL FLAGS WHICH MAY BE CLUSTER DEPENDENT
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# which interface your nodes use for communication
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cluster.add_command('export NCCL_SOCKET_IFNAME=^docker0,lo')
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# see output of the NCCL connection process
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# NCCL is how the nodes talk to each other
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cluster.add_command('export NCCL_DEBUG=INFO')
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# setting a master port here is a good idea.
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cluster.add_command(f'export MASTER_PORT={PORT}')
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# good to load the latest NCCL version
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cluster.load_modules(['NCCL/2.4.7-1-cuda.10.0'])
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# configure cluster
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cluster.per_experiment_nb_nodes = 12
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cluster.per_experiment_nb_gpus = 8
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cluster.add_slurm_cmd(cmd='ntasks-per-node', value=8, comment='1 task per gpu')
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```
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Finally, make sure to add a distributed sampler to your dataset. The distributed sampler copies a
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portion of your dataset onto each GPU. (World_size = gpus_per_node * nb_nodes).
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```python
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# ie: this:
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dataset = myDataset()
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dataloader = Dataloader(dataset)
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# becomes:
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dataset = myDataset()
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dist_sampler = torch.utils.data.distributed.DistributedSampler(dataset)
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dataloader = Dataloader(dataset, sampler=dist_sampler)
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```
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---
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#### Self-balancing architecture
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Here lightning distributes parts of your module across available GPUs to optimize for speed and memory.
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COMING SOON.
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