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ood
주피터 노트북에서 PyTorch 모델을 객체지향으로 다루기 위한 얇은 보조 라이브러리.
모델은 유저가 PyTorch로 직접 작성한다. 이 라이브러리는 그 주변만 담당한다 — 하이퍼파라미터 자동 저장, 셀 간 메서드 추가, 학습 중 손실 곡선 라이브 렌더링, 디바이스 자동 선택, 체크포인트.
설치
pip install ood-dl
배포 이름은 ood-dl, import 이름은 ood 다. PyPI 에 ood 이름이 이미 쓰이고 있어서다.
import ood as od
이 저장소에서 직접 개발하려면:
uv sync
퀵스타트
import torch
from torch import nn
from torch.nn import functional as F
import ood as od
class SyntheticRegression(od.DataModule):
def __init__(self, n=200, batch_size=32):
super().__init__()
self.save_hyperparameters()
torch.manual_seed(0)
self.X = torch.randn(n, 2)
self.y = self.X @ torch.tensor([[2.0], [-3.4]]) + 4.2
def get_dataloader(self, train):
idx = slice(0, 160) if train else slice(160, None)
return self.get_tensorloader((self.X, self.y), train, idx)
class LinearRegression(od.Module):
def __init__(self, lr=0.03):
super().__init__()
self.save_hyperparameters()
self.net = nn.LazyLinear(1)
다음 셀에서 메서드를 덧붙인다. 클래스를 다시 정의할 필요가 없다.
@od.add_to_class(LinearRegression)
def loss(self, y_hat, y):
return F.mse_loss(y_hat, y)
@od.add_to_class(LinearRegression)
def configure_optimizers(self):
return torch.optim.SGD(self.parameters(), lr=self.lr)
학습을 돌리면 손실 곡선이 셀 출력에 실시간으로 갱신된다.
trainer = od.Trainer(max_epochs=20)
trainer.fit(LinearRegression(), SyntheticRegression())
trainer.save_checkpoint("linreg.pt")
전체 예제는 examples/quickstart.ipynb 참고.
API
| 이름 | 역할 |
|---|---|
od.add_to_class(Class) |
데코레이트한 함수를 Class 의 메서드로 등록 |
od.HyperParameters |
save_hyperparameters() 로 __init__ 인자를 속성 + hparams 로 저장 |
od.DataModule |
get_dataloader(train) 하나만 구현하면 되는 데이터 규약 |
od.Module |
forward/loss/configure_optimizers 를 채우는 모델 규약 |
od.Trainer |
fit(model, data), save_checkpoint, load_checkpoint, history |
od.ProgressBoard |
라이브 손실 곡선. Trainer(plot=True) 가 자동으로 만든다 |
od.default_device() |
cuda → mps → cpu |
개발
uv run pytest
라이센스
MIT. LICENSE 참고.
설계는 d2l-ai/d2l-en의 d2l/torch.py를 참고했다.
해당 샘플 코드는 modified MIT(LICENSE-SAMPLECODE)로 배포된다.
1 maintainer
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