Trainer fit stopped: max_epochs=1 reached. #14428
Description
🐛 Bug
`Trainer.fit` stopped: `max_epochs=1` reached.
Fails to run trainer fit
(py3.10) ➜ Deepgit:(main) ✗ python torch_nn.py
GPU available: False, used: False
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
HPU available: False, using: 0 HPUs
| Name | Type | Params
---------------------------------------
0 | encoder | Sequential | 50.4 K
1 | decoder | Sequential | 51.2 K
---------------------------------------
101 K Trainable params
0 Non-trainable params
101 K Total params
0.407 Total estimated model params size (MB)
/opt/miniconda3/envs/py3.10/lib/python3.10/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:225: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 16 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
rank_zero_warn(
Epoch 0: 100%|███████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 324.18it/s, loss=0.0663, v_num=13]
`Trainer.fit` stopped: `max_epochs=1` reached.
Epoch 0: 100%|███████████████████████████████████████████████████████████| 100/100 [00:00<00:00, 318.34it/s, loss=0.0663, v_num=13]
(py3.10) ➜ Deepgit:(main) ✗ To Reproduce
import os
from torch import optim, nn, utils, Tensor
from torchvision.datasets import MNIST
from torchvision.transforms import ToTensor
import pytorch_lightning as pl
# define any number of nn.Modules (or use your current ones)
encoder = nn.Sequential(nn.Linear(28 * 28, 64), nn.ReLU(), nn.Linear(64, 3))
decoder = nn.Sequential(nn.Linear(3, 64), nn.ReLU(), nn.Linear(64, 28 * 28))
# define the LightningModule
class LitAutoEncoder(pl.LightningModule):
def __init__(self, encoder, decoder):
super().__init__()
self.encoder = encoder
self.decoder = decoder
def training_step(self, batch, batch_idx):
# training_step defines the train loop.
# it is independent of forward
x, y = batch
x = x.view(x.size(0), -1)
z = self.encoder(x)
x_hat = self.decoder(z)
loss = nn.functional.mse_loss(x_hat, x)
# Logging to TensorBoard by default
self.log("train_loss", loss)
return loss
def configure_optimizers(self):
optimizer = optim.Adam(self.parameters(), lr=1e-3)
return optimizer
# init the autoencoder
autoencoder = LitAutoEncoder(encoder, decoder)
# setup data
dataset = MNIST(os.getcwd(), download=True, transform=ToTensor())
train_loader = utils.data.DataLoader(dataset)
# train the model (hint: here are some helpful Trainer arguments for rapid idea iteration)
trainer = pl.Trainer(limit_train_batches=100, max_epochs=1)
trainer.fit(model=autoencoder, train_dataloaders=train_loader)
Expected behavior
Environment
- Lightning Component (e.g. Trainer, LightningModule, LightningApp, LightningWork, LightningFlow):
- pytorch_lightning==1.7.3
- torch==1.12.1
- torchaudio==0.12.1
- torchvision==0.13.1
- PyTorch Lightning Version (e.g., 1.5.0):
- Lightning App Version (e.g., 0.5.2):
- PyTorch Version (e.g., 1.10):
- Python version (e.g., 3.9): 3.10
- OS (e.g., Linux): macOS Monterey
- CUDA/cuDNN version:
- GPU models and configuration:
- How you installed PyTorch (
conda,pip, source): pip - If compiling from source, the output of
torch.__config__.show(): - Running environment of LightningApp (e.g. local, cloud):
- Any other relevant information: