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This commit is contained in:
William Falcon
2020-01-17 05:03:31 -05:00
parent f02d0bcbb9
commit 57db191028
7 changed files with 53 additions and 50 deletions
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pytorch_lightning/trainer/trainer.py
+41 -40
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@@ -108,7 +108,7 @@ class Trainer(TrainerIOMixin,
Trainer(logger=logger)
checkpoint_callback (:class:`CheckpointCallback`): Callback for checkpointing.
Example::
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.callbacks import ModelCheckpoint
# default used by the Trainer
checkpoint_callback = ModelCheckpoint(
@@ -123,7 +123,7 @@ class Trainer(TrainerIOMixin,
trainer = Trainer(checkpoint_callback=checkpoint_callback)
early_stop_callback (:class:`.EarlyStopping`): Callback for early stopping
Example::
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning.callbacks import EarlyStopping
# default used by the Trainer
early_stop_callback = EarlyStopping(
@@ -158,11 +158,11 @@ class Trainer(TrainerIOMixin,
# to train on 8 nodes
trainer = Trainer(num_nodes=8)
nb_gpu_nodes (int):
.. deprecated:: 0.5.0
Use `num_nodes` instead. Will remove 0.8.0.
gpus (list|str|int): Which GPUs to train on.
Example::
# default used by the Trainer (ie: train on CPU)
@@ -206,7 +206,7 @@ class Trainer(TrainerIOMixin,
# use only 1% of the train, test, val datasets
trainer = Trainer(overfit_pct=0.01)
track_grad_norm (int): -1 no tracking. Otherwise tracks that norm
Example::
# default used by the Trainer
@@ -230,7 +230,7 @@ class Trainer(TrainerIOMixin,
# runs 1 train, val, test batch and program ends
trainer = Trainer(fast_dev_run=True)
accumulate_grad_batches (int|dict): Accumulates grads every k batches or as set up in the dict.
Example::
# default used by the Trainer (no accumulation)
@@ -246,7 +246,7 @@ class Trainer(TrainerIOMixin,
Example::
# default used by the Trainer
trainer = Trainer(max_epochs=1000)
max_nb_epochs (int):
.. deprecated:: 0.5.0
Use `max_epochs` instead. Will remove 0.8.0.
@@ -255,12 +255,12 @@ class Trainer(TrainerIOMixin,
Example::
# default used by the Trainer
trainer = Trainer(min_epochs=1)
min_nb_epochs (int):
.. deprecated:: 0.5.0
Use `min_nb_epochs` instead. Will remove 0.8.0.
train_percent_check (int): How much of training dataset to check.
train_percent_check (int): How much of training dataset to check.
Useful when debugging or testing something that happens at the end of an epoch.
Example::
# default used by the Trainer
@@ -269,7 +269,7 @@ class Trainer(TrainerIOMixin,
# run through only 25% of the training set each epoch
trainer = Trainer(train_percent_check=0.25)
val_percent_check (int): How much of validation dataset to check.
val_percent_check (int): How much of validation dataset to check.
Useful when debugging or testing something that happens at the end of an epoch.
Example::
# default used by the Trainer
@@ -278,7 +278,7 @@ class Trainer(TrainerIOMixin,
# run through only 25% of the validation set each epoch
trainer = Trainer(val_percent_check=0.25)
test_percent_check (int): How much of test dataset to check.
test_percent_check (int): How much of test dataset to check.
Useful when debugging or testing something that happens at the end of an epoch.
Example::
# default used by the Trainer
@@ -305,12 +305,12 @@ class Trainer(TrainerIOMixin,
Example::
# default used by the Trainer
trainer = Trainer(log_save_interval=100)
row_log_interval (int): How often to add logging rows (does not write to disk)
Example::
# default used by the Trainer
trainer = Trainer(row_log_interval=10)
add_row_log_interval (int):
.. deprecated:: 0.5.0
Use `row_log_interval` instead. Will remove 0.8.0.
@@ -324,8 +324,8 @@ class Trainer(TrainerIOMixin,
# dp = DataParallel (split a batch onto k gpus on same machine).
trainer = Trainer(gpus=2, distributed_backend='dp')
# ddp = DistributedDataParallel
# Each gpu trains by itself on a subset of the data.
# ddp = DistributedDataParallel
# Each gpu trains by itself on a subset of the data.
# Gradients sync across all gpus and all machines.
trainer = Trainer(gpus=2, num_nodes=2, distributed_backend='ddp')
@@ -334,29 +334,29 @@ class Trainer(TrainerIOMixin,
# syncs gradients across nodes like ddp
# useful for things like increasing the number of negative samples
trainer = Trainer(gpus=2, num_nodes=2, distributed_backend='ddp2')
use_amp (bool): If true uses apex for 16bit precision
Example::
# default used by the Trainer
trainer = Trainer(use_amp=False)
print_nan_grads (bool): Prints gradients with nan values
Example::
# default used by the Trainer
trainer = Trainer(print_nan_grads=False)
weights_summary (str): Prints a summary of the weights when training begins.
Options: 'full', 'top', None.
Example::
# default used by the Trainer (ie: print all weights)
trainer = Trainer(weights_summary='full')
# print only the top level modules
trainer = Trainer(weights_summary='top')
# don't print a summary
trainer = Trainer(weights_summary=None)
weights_save_path (str): Where to save weights if specified.
Example::
# default used by the Trainer
@@ -369,7 +369,7 @@ class Trainer(TrainerIOMixin,
# **NOTE: this saves weights to some/path NOT my/path
checkpoint_callback = ModelCheckpoint(filepath='some/path')
trainer = Trainer(
checkpoint_callback=checkpoint_callback,
checkpoint_callback=checkpoint_callback,
weights_save_path='my/path'
)
@@ -378,7 +378,7 @@ class Trainer(TrainerIOMixin,
Example::
# default used by the Trainer
trainer = Trainer(amp_level='O1')
num_sanity_val_steps (int): Sanity check runs n batches of val before starting the training routine.
This catches any bugs in your validation without having to wait for the first validation check.
The Trainer uses 5 steps by default. Turn it off or modify it here.
@@ -392,12 +392,13 @@ class Trainer(TrainerIOMixin,
nb_sanity_val_steps (int):
.. deprecated:: 0.5.0
Use `num_sanity_val_steps` instead. Will remove 0.8.0.
truncated_bptt_steps (int): Truncated back prop breaks performs backprop every k steps of a much longer sequence
If this is enabled, your batches will automatically get truncated
and the trainer will apply Truncated Backprop to it. Make sure your batches have a sequence dimension.
(`Williams et al. "An efficient gradient-based algorithm for on-line training of recurrent network trajectories."
<http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.56.7941&rep=rep1&type=pdf>`_)
truncated_bptt_steps (int): Truncated back prop breaks performs backprop every k steps of
a much longer sequence If this is enabled, your batches will automatically get truncated
and the trainer will apply Truncated Backprop to it. Make sure your batches have a sequence
dimension. (`Williams et al. "An efficient gradient-based algorithm for on-line training of
recurrent network trajectories."
<http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.56.7941&rep=rep1&type=pdf>`_)
Example::
# default used by the Trainer (ie: disabled)
trainer = Trainer(truncated_bptt_steps=None)
@@ -413,7 +414,7 @@ class Trainer(TrainerIOMixin,
# resume from a specific checkpoint
trainer = Trainer(resume_from_checkpoint='some/path/to/my_checkpoint.ckpt')
"""
#
#
# .. warning:: Following arguments become deprecated and they will be removed in v0.8.0:
# - `nb_sanity_val_steps`
@@ -665,13 +666,13 @@ class Trainer(TrainerIOMixin,
def fit(self, model):
r"""
Runs the full optimization routine.
Example::
trainer = Trainer()
model = LightningModule()
trainer.fit()
trainer.fit()
"""
# when using multi-node or DDP within a node start each module in a separate process
if self.use_ddp2:
@@ -829,27 +830,27 @@ class Trainer(TrainerIOMixin,
def test(self, model=None):
r"""
Separates from fit to make sure you never run on your test set until you want to.
Args:
model (LightningModule): The model to test.
Example::
# Option 1
# run test after fitting
trainer = Trainer()
model = LightningModule()
trainer.fit()
trainer.test()
# Option 2
# run test from a loaded model
model = LightningModule.load_from_checkpoint('path/to/checkpoint.ckpt')
trainer = Trainer()
trainer.test(model)
"""
self.testing = True