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fix for pytorch_lightning >= 1.0.0 #21

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fix for pytorch_lightning >= 1.0.0 #21

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7a449e9
fix import and named arguments for pl >= 1.1.12
GPla Dec 28, 2020
8cd69b5
fix for pytorch_ligthning >= 1.0.0
GPla Dec 28, 2020
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4 changes: 2 additions & 2 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -23,7 +23,7 @@ Here are the [results](https://github.com/AntixK/PyTorch-VAE/blob/master/README.
### Requirements
- Python >= 3.5
- PyTorch >= 1.3
- Pytorch Lightning >= 0.6.0 ([GitHub Repo](https://github.com/PyTorchLightning/pytorch-lightning/tree/deb1581e26b7547baf876b7a94361e60bb200d32))
- Pytorch Lightning >= 1.0.0 ([GitHub Repo](https://github.com/PyTorchLightning/pytorch-lightning/tree/deb1581e26b7547baf876b7a94361e60bb200d32))
- CUDA enabled computing device

### Installation
Expand Down Expand Up @@ -60,7 +60,7 @@ exp_params:

trainer_params:
gpus: 1
max_nb_epochs: 50
max_epochs: 50
gradient_clip_val: 1.5
.
.
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13 changes: 6 additions & 7 deletions run.py
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Expand Up @@ -6,7 +6,7 @@
from experiment import VAEXperiment
import torch.backends.cudnn as cudnn
from pytorch_lightning import Trainer
from pytorch_lightning.logging import TestTubeLogger
from pytorch_lightning.loggers import TestTubeLogger


parser = argparse.ArgumentParser(description='Generic runner for VAE models')
Expand Down Expand Up @@ -42,14 +42,13 @@
config['exp_params'])

runner = Trainer(default_save_path=f"{tt_logger.save_dir}",
min_nb_epochs=1,
min_epochs=1,
logger=tt_logger,
log_save_interval=100,
train_percent_check=1.,
val_percent_check=1.,
flush_logs_every_n_steps=100,
limit_train_batches=1.,
limit_val_batches=1.,
num_sanity_val_steps=5,
early_stop_callback = False,
**config['trainer_params'])

print(f"======= Training {config['model_params']['name']} =======")
runner.fit(experiment)
runner.fit(experiment)