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Simplest Pytorch Lightning Example
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import pytorch_lightning as pl | |
import numpy as np | |
import torch | |
from torch.nn import MSELoss | |
from torch.optim import Adam | |
from torch.utils.data import DataLoader, Dataset | |
import torch.nn as nn | |
class SimpleDataset(Dataset): | |
def __init__(self): | |
X = np.arange(10000) | |
y = X * 2 | |
X = [[_] for _ in X] | |
y = [[_] for _ in y] | |
self.X = torch.Tensor(X) | |
self.y = torch.Tensor(y) | |
def __len__(self): | |
return len(self.y) | |
def __getitem__(self, idx): | |
return {"X": self.X[idx], "y": self.y[idx]} | |
class MyModel(pl.LightningModule): | |
def __init__(self): | |
super().__init__() | |
self.fc = nn.Linear(1, 1) | |
self.criterion = MSELoss() | |
def forward(self, inputs_id, labels=None): | |
outputs = self.fc(inputs_id) | |
loss = 0 | |
if labels is not None: | |
loss = self.criterion(outputs, labels) | |
return loss, outputs | |
def train_dataloader(self): | |
dataset = SimpleDataset() | |
return DataLoader(dataset, batch_size=1000) | |
def training_step(self, batch, batch_idx): | |
input_ids = batch["X"] | |
labels = batch["y"] | |
loss, outputs = self(input_ids, labels) | |
return {"loss": loss} | |
def configure_optimizers(self): | |
optimizer = Adam(self.parameters()) | |
return optimizer | |
if __name__ == '__main__': | |
model = MyModel() | |
trainer = pl.Trainer(max_epochs=20, gpus=1) | |
trainer.fit(model) | |
X = torch.Tensor([[1.0], [51.0], [89.0]]) | |
_, y = model(X) | |
print(y) |
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# pytorch lightning with wandb | |
import pytorch_lightning as pl | |
import numpy as np | |
import torch | |
from pytorch_lightning.loggers import WandbLogger | |
from torch.nn import MSELoss | |
from torch.optim import Adam | |
from torch.utils.data import DataLoader, Dataset | |
import torch.nn as nn | |
class SimpleDataset(Dataset): | |
def __init__(self): | |
X = np.arange(10000) | |
y = X * 2 | |
X = [[_] for _ in X] | |
y = [[_] for _ in y] | |
self.X = torch.Tensor(X) | |
self.y = torch.Tensor(y) | |
def __len__(self): | |
return len(self.y) | |
def __getitem__(self, idx): | |
return {"X": self.X[idx], "y": self.y[idx]} | |
class MyModel(pl.LightningModule): | |
def __init__(self): | |
super().__init__() | |
self.fc = nn.Linear(1, 1) | |
self.criterion = MSELoss() | |
def forward(self, inputs_id, labels=None): | |
outputs = self.fc(inputs_id) | |
loss = 0 | |
if labels is not None: | |
loss = self.criterion(outputs, labels) | |
self.log('mse_loss', loss) | |
return loss, outputs | |
def train_dataloader(self): | |
dataset = SimpleDataset() | |
return DataLoader(dataset, batch_size=1000, num_workers=12) | |
def training_step(self, batch, batch_idx): | |
input_ids = batch["X"] | |
labels = batch["y"] | |
loss, outputs = self(input_ids, labels) | |
return {"loss": loss} | |
def configure_optimizers(self): | |
optimizer = Adam(self.parameters()) | |
return optimizer | |
if __name__ == '__main__': | |
wandb_logger = WandbLogger(project='hugging-face') | |
model = MyModel() | |
trainer = pl.Trainer(max_epochs=600, gpus=1, logger=wandb_logger) | |
trainer.fit(model) | |
X = torch.Tensor([[1.0], [51.0], [89.0]]) | |
_, y = model(X) | |
print(y) |
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