def main(args):
model = PedalNet(args)
trainer = pl.Trainer(
max_epochs=args.max_epochs, gpus=args.gpus, row_log_interval=100
# The following line is for use with the Colab notebook when training on TPUs.
# Comment out the above line and uncomment the below line to use.
# max_epochs=args.max_epochs, tpu_cores=args.tpu_cores, gpus=args.gpus, row_log_interval=100
)
trainer.fit(model)
def main(args):
"""
This trains the PedalNet model to match the output data from the input data.
When you resume training from an existing model, you can override hparams such as
max_epochs, batch_size, or learning_rate. Note that changing num_channels,
dilation_depth, num_repeat, or kernel_size will change the shape of the WaveNet
model and is not advised.
"""
if args.resume_training != "":
model = PedalNet.load_from_checkpoint(args.resume_training)
# Check for any hparams overridden by user and update
for arg in sys.argv[1:]:
arg2 = arg.split("=")[0].split("--")[1]
if arg2 != "resume_training" and arg2 != "cpu" and arg2 != "tpu_cores":
arg3 = arg.split("=")[1]
if arg2 in model.hparams:
if arg2 == "learning_rate":
model.hparams[arg2] = float(arg3)
else:
model.hparams[arg2] = int(arg3)
print("Hparam overridden by user: "******"=", arg3, "\n")
if args.cpu == 0:
trainer = pl.Trainer(
resume_from_checkpoint=args.resume_training,
gpus=args.gpus,
log_every_n_steps=100,
max_epochs=args.max_epochs,
)
else:
trainer = pl.Trainer(resume_from_checkpoint=args.resume_training,
log_every_n_steps=100,
max_epochs=args.max_epochs)
print("\nHparams for continued model training:\n")
print(model.hparams, "\n")
else:
model = PedalNet(args)
if args.cpu == 0:
trainer = pl.Trainer(max_epochs=args.max_epochs,
gpus=args.gpus,
log_every_n_steps=100)
# The following line is for use with the Colab notebook when training on TPUs.
# Comment out the above line and uncomment the below line to use.
# max_epochs=args.max_epochs, tpu_cores=args.tpu_cores, gpus=args.gpus, log_every_n_steps=100
else:
trainer = pl.Trainer(max_epochs=args.max_epochs,
log_every_n_steps=100)
trainer.fit(model)
def main(args):
"""
This trains the PedalNet model to match the output data from the input data.
When you resume training from an existing model, you can override hparams such as
max_epochs, batch_size, or learning_rate. Note that changing num_channels,
dilation_depth, num_repeat, or kernel_size will change the shape of the WaveNet
model and is not advised.
"""
prepare(args)
model = PedalNet(vars(args))
trainer = pl.Trainer(
resume_from_checkpoint=args.model if args.resume else None,
gpus=None if args.cpu or args.tpu_cores else args.gpus,
tpu_cores=args.tpu_cores,
log_every_n_steps=100,
max_epochs=args.max_epochs,
)
trainer.fit(model)
trainer.save_checkpoint(args.model)
def main(args):
model = PedalNet(args)
trainer = pl.Trainer(max_epochs=args.max_epochs,
tpu_cores=args.tpu_cores,
row_log_interval=100)
trainer.fit(model)
def main(args):
model = PedalNet(vars(args))
trainer = pl.Trainer(
max_epochs=args.max_epochs, gpus=args.gpus, row_log_interval=100
)
trainer.fit(model)