Lightning supports running on TPUs. At this moment, TPUs are available on Google Cloud (GCP), Google Colab and Kaggle Environments. For more information on TPUs watch this video.
A TPU is a Tensor processing unit. Each TPU has 8 cores where each core is optimized for 128x128 matrix multiplies. In general, a single TPU is about as fast as 5 V100 GPUs!
A TPU pod hosts many TPUs on it. Currently, TPU pod v2 has 2048 cores! You can request a full pod from Google cloud or a “slice” which gives you some subset of those 2048 cores.
How to access TPUs¶
To access TPUs, there are three main ways.
Using Google Colab.
Using Google Cloud (GCP).
Colab is like a jupyter notebook with a free GPU or TPU hosted on GCP.
To get a TPU on colab, follow these steps:
Click “new notebook” (bottom right of pop-up).
Click runtime > change runtime settings. Select Python 3, and hardware accelerator “TPU”. This will give you a TPU with 8 cores.
Next, insert this code into the first cell and execute. This will install the xla library that interfaces between PyTorch and the TPU.
!curl https://raw.githubusercontent.com/pytorch/xla/master/contrib/scripts/env-setup.py -o pytorch-xla-env-setup.py !python pytorch-xla-env-setup.py --version nightly --apt-packages libomp5 libopenblas-dev
Once the above is done, install PyTorch Lightning (v 0.7.0+).
!pip install pytorch-lightning
Then set up your LightningModule as normal.
Lightning automatically inserts the correct samplers - no need to do this yourself!
Usually, with TPUs (and DDP), you would need to define a DistributedSampler to move the right chunk of data to the appropriate TPU. As mentioned, this is not needed in Lightning
Don’t add distributedSamplers. Lightning does this automatically
If for some reason you still need to, this is how to construct the sampler for TPU use
import torch_xla.core.xla_model as xm def train_dataloader(self): dataset = MNIST( os.getcwd(), train=True, download=True, transform=transforms.ToTensor() ) # required for TPU support sampler = None if use_tpu: sampler = torch.utils.data.distributed.DistributedSampler( dataset, num_replicas=xm.xrt_world_size(), rank=xm.get_ordinal(), shuffle=True ) loader = DataLoader( dataset, sampler=sampler, batch_size=32 ) return loader
Configure the number of TPU cores in the trainer. You can only choose 1 or 8. To use a full TPU pod skip to the TPU pod section.
import pytorch_lightning as pl my_model = MyLightningModule() trainer = pl.Trainer(tpu_cores=8) trainer.fit(my_model)
That’s it! Your model will train on all 8 TPU cores.
Single TPU core training¶
Lightning supports training on a single TPU core. Just pass the TPU core ID [1-8] in a list.
trainer = pl.Trainer(tpu_cores=)
Distributed Backend with TPU¶
`distributed_backend` option used for GPUs does not apply to TPUs.
TPUs work in DDP mode by default (distributing over each core)
To train on more than 8 cores, your code actually doesn’t change! All you need to do is submit the following command:
$ python -m torch_xla.distributed.xla_dist --tpu=$TPU_POD_NAME --conda-env=torch-xla-nightly -- python /usr/share/torch-xla-0.5/pytorch/xla/test/test_train_imagenet.py --fake_data
See this guide on how to set up the instance groups and VMs needed to run TPU Pods.
16 bit precision¶
Lightning also supports training in 16-bit precision with TPUs. By default, TPU training will use 32-bit precision. To enable 16-bit, set the 16-bit flag.
import pytorch_lightning as pl my_model = MyLightningModule() trainer = pl.Trainer(tpu_cores=8, precision=16) trainer.fit(my_model)
Under the hood the xla library will use the bfloat16 type.