Set of training loops for solving common deep learning problems using PyTorch Lightning workflow. Each subproject contains examples of the data format that is expected. The project aims to make prototyping models as easy as possible while benefiting from all the "training tricks" that every deep learning practitioner should use.
For every subproject the training loop is defined in train.py file. Its structure is similar in each case:
checkpoint_callback = ModelCheckpoint(
filepath=os.getcwd(),
save_top_k=2,
verbose=True,
monitor="val/loss",
mode="min",
)
experiment_name = ...
PROJECT_NAME = ...
logger = WandbLogger(name=experiment_name, project=PROJECT_NAME)
# And then actual training
pl.seed_everything(42)
trainer = Trainer(
max_epochs=40,
logger=logger,
gpus=1,
# precision=16,
deterministic=True,
accumulate_grad_batches=2,
callbacks=[EarlyStopping(monitor="val/loss")],
# resume_from_checkpoint = 'my_checkpoint.ckpt'
)
trainer.fit(model, dm)Defaultly we assume the user is logged into Wandb. To do so you need to run !wandb login your_personal_hash_generated_by_wandb in the terminal
This project is licensed under the MIT License - see the LICENSE file for details