trainloop
Minimal PyTorch training loop with hooks for logging, checkpointing, and customization.
Docs: https://karimknaebel.github.io/trainloop/
Install
pip install trainloop
Basic example
import logging
import torch
import torch.nn as nn
from trainloop import BaseTrainer, CheckpointingHook, ProgressHook
logging.basicConfig(level=logging.INFO)
class MyTrainer(BaseTrainer):
def build_data_loader(self):
class ToyDataset(torch.utils.data.IterableDataset):
def __iter__(self):
while True:
data = torch.randn(784)
target = torch.randint(0, 10, (1,)).item()
yield data, target
return torch.utils.data.DataLoader(ToyDataset(), batch_size=32)
def build_model(self):
return nn.Sequential(
nn.Linear(784, 128),
nn.ReLU(),
nn.Linear(128, 10),
).to(self.device)
def build_optimizer(self):
return torch.optim.AdamW(self.model.parameters(), lr=3e-4)
def build_hooks(self):
return [
ProgressHook(interval=50, with_records=True),
CheckpointingHook(interval=500, keep_previous=2),
]
def forward(self, batch):
x, y = batch
x, y = x.to(self.device), y.to(self.device)
logits = self.model(x)
loss = nn.functional.cross_entropy(logits, y)
accuracy = (logits.argmax(1) == y).float().mean().item()
return loss, {"accuracy": accuracy}
trainer = MyTrainer(max_steps=2000, device="cpu", workspace="runs/demo")
trainer.train()
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