empty meta_tags.csv
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search the docs.

If you still can't find what you need:
What is your question?
For loading data, I should write down path for 'meta_tags.csv'.
However, in the 'meta_tags.csv', there is nothing.
Code
Please paste a code snippet if your question requires it!
Same get_started code with checkpoint callbacks

import os
import torch
from torch.nn import functional as F
from torch.utils.data import DataLoader
from torchvision.datasets import MNIST
import torchvision.transforms as transforms

import pytorch_lightning as pl

class CoolSystem(pl.LightningModule):

    def __init__(self):
        super(CoolSystem, self).__init__()
        # not the best model...
        self.l1 = torch.nn.Linear(28 * 28, 10)

    def forward(self, x):
        return torch.relu(self.l1(x.view(x.size(0), -1)))

    def training_step(self, batch, batch_nb):
        # REQUIRED
        x, y = batch
        y_hat = self.forward(x)
        return {'loss': F.cross_entropy(y_hat, y)}

    def validation_step(self, batch, batch_nb):
        # OPTIONAL
        x, y = batch
        y_hat = self.forward(x)
        return {'val_loss': F.cross_entropy(y_hat, y)}

    def validation_end(self, outputs):
        # OPTIONAL
        avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
        return {'avg_val_loss': avg_loss}

    def configure_optimizers(self):
        # REQUIRED
        # can return multiple optimizers and learning_rate schedulers
        return torch.optim.Adam(self.parameters(), lr=0.02)

    @pl.data_loader
    def tng_dataloader(self):
        # REQUIRED
        return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)

    @pl.data_loader
    def val_dataloader(self):
        # OPTIONAL
        return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)

    @pl.data_loader
    def test_dataloader(self):
        # OPTIONAL
        return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)

from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
from test_tube import Experiment

model = CoolSystem()

checkpoint_callback = ModelCheckpoint(
    filepath='./model_ckpt/weights6.ckpt',
    save_best_only=True,
    verbose=True,
    monitor='val_loss',
    mode='auto'
)

early_stopping = EarlyStopping(
    monitor='val_loss',
    patience=5,
    verbose=True,
    mode='auto'
)

exp = Experiment(save_dir=os.getcwd(), version=24)
trainer = Trainer(experiment=exp, max_nb_epochs=1000, train_percent_check=0.1, gpus=[1], checkpoint_callback=checkpoint_callback, early_stop_callback=early_stopping)



What have you tried?
I tried on my model and this base tutorial code. But both of them are failed.
I just started using this API today, but I've faced a lot of difficulties..
Sometimes, I need to import exit from sys.
Sometimes, loss doesn't decrease. When I restart jupyter notebook multiple times, suddenly loss gets decreasing.
Sometimes, memory can't be allocated during training (about 81 epochs).
And I can't tag something like 'Losses/train_loss', 'Losses/valid_loss' for tensorboard by using test-tube... The error said 'loss' is referenced before assignments.
I can tolerate these difficulties because I can still progress my training.
But I don't have any solution with loading models from empty file.
What's your environment?


conda version (no venv)
no conda


PyTorch version
torch==1.2.0+cu92
torchvision==0.4.0


Lightning version
pytorch-lightning==0.4.7


Test-tube version
test-tube==0.6.9