TPU training with PyTorch Lightning¶
Author: PL team
License: CC BY-SA
Generated: 2022-08-15T09:28:51.879278
In this notebook, we’ll train a model on TPUs. Updating one Trainer flag is all you need for that. The most up to documentation related to TPU training can be found here.
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Setup¶
This notebook requires some packages besides pytorch-lightning.
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! pip install --quiet "torchvision" "pytorch-lightning>=1.4" "torch>=1.8" "torchmetrics>=0.7" "setuptools==59.5.0" "ipython[notebook]"
Install Colab TPU compatible PyTorch/TPU wheels and dependencies¶
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! pip install cloud-tpu-client==0.10 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.8-cp37-cp37m-linux_x86_64.whl
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import torch
import torch.nn.functional as F
from pytorch_lightning import LightningDataModule, LightningModule, Trainer
from pytorch_lightning.callbacks.progress import TQDMProgressBar
from torch import nn
from torch.utils.data import DataLoader, random_split
from torchmetrics.functional import accuracy
from torchvision import transforms
# Note - you must have torchvision installed for this example
from torchvision.datasets import MNIST
BATCH_SIZE = 1024
Defining The MNISTDataModule¶
Below we define MNISTDataModule. You can learn more about datamodules in docs.
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class MNISTDataModule(LightningDataModule):
def __init__(self, data_dir: str = "./"):
super().__init__()
self.data_dir = data_dir
self.transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))])
self.dims = (1, 28, 28)
self.num_classes = 10
def prepare_data(self):
# download
MNIST(self.data_dir, train=True, download=True)
MNIST(self.data_dir, train=False, download=True)
def setup(self, stage=None):
# Assign train/val datasets for use in dataloaders
if stage == "fit" or stage is None:
mnist_full = MNIST(self.data_dir, train=True, transform=self.transform)
self.mnist_train, self.mnist_val = random_split(mnist_full, [55000, 5000])
# Assign test dataset for use in dataloader(s)
if stage == "test" or stage is None:
self.mnist_test = MNIST(self.data_dir, train=False, transform=self.transform)
def train_dataloader(self):
return DataLoader(self.mnist_train, batch_size=BATCH_SIZE)
def val_dataloader(self):
return DataLoader(self.mnist_val, batch_size=BATCH_SIZE)
def test_dataloader(self):
return DataLoader(self.mnist_test, batch_size=BATCH_SIZE)
Defining the LitModel¶
Below, we define the model LitMNIST.
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class LitModel(LightningModule):
def __init__(self, channels, width, height, num_classes, hidden_size=64, learning_rate=2e-4):
super().__init__()
self.save_hyperparameters()
self.model = nn.Sequential(
nn.Flatten(),
nn.Linear(channels * width * height, hidden_size),
nn.ReLU(),
nn.Dropout(0.1),
nn.Linear(hidden_size, hidden_size),
nn.ReLU(),
nn.Dropout(0.1),
nn.Linear(hidden_size, num_classes),
)
def forward(self, x):
x = self.model(x)
return F.log_softmax(x, dim=1)
def training_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = F.nll_loss(logits, y)
self.log("train_loss", loss)
return loss
def validation_step(self, batch, batch_idx):
x, y = batch
logits = self(x)
loss = F.nll_loss(logits, y)
preds = torch.argmax(logits, dim=1)
acc = accuracy(preds, y)
self.log("val_loss", loss, prog_bar=True)
self.log("val_acc", acc, prog_bar=True)
def configure_optimizers(self):
optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
return optimizer
TPU Training¶
Lightning supports training on a single TPU core or 8 TPU cores.
The Trainer parameter devices defines how many TPU cores to train on (1 or 8) / Single TPU core to train on [1] along with accelerator=‘tpu’.
For Single TPU training, Just pass the TPU core ID [1-8] in a list. Setting devices=[5] will train on TPU core ID 5.
Train on TPU core ID 5 with devices=[5].
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# Init DataModule
dm = MNISTDataModule()
# Init model from datamodule's attributes
model = LitModel(*dm.size(), dm.num_classes)
# Init trainer
trainer = Trainer(
max_epochs=3,
callbacks=[TQDMProgressBar(refresh_rate=20)],
accelerator="tpu",
devices=[5],
)
# Train
trainer.fit(model, dm)
Train on single TPU core with devices=1.
[ ]:
# Init DataModule
dm = MNISTDataModule()
# Init model from datamodule's attributes
model = LitModel(*dm.dims, dm.num_classes)
# Init trainer
trainer = Trainer(
max_epochs=3,
accelerator="tpu",
devices=1,
callbacks=[TQDMProgressBar(refresh_rate=20)],
)
# Train
trainer.fit(model, dm)
Train on 8 TPU cores with accelerator='tpu' and devices=8. You might have to restart the notebook to run it on 8 TPU cores after training on single TPU core.
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# Init DataModule
dm = MNISTDataModule()
# Init model from datamodule's attributes
model = LitModel(*dm.dims, dm.num_classes)
# Init trainer
trainer = Trainer(
max_epochs=3,
callbacks=[TQDMProgressBar(refresh_rate=20)],
accelerator="tpu",
devices=8,
)
# Train
trainer.fit(model, dm)
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Congratulations on completing this notebook tutorial! If you enjoyed this and would like to join the Lightning movement, you can do so in the following ways!
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You can also contribute your own notebooks with useful examples !
Great thanks from the entire Pytorch Lightning Team for your interest !¶
