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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/core/__init__.py
+2 -1
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@@ -90,7 +90,8 @@ Once you've defined the LightningModule, fit it using a trainer.
trainer.fit(model)
Check out this `COLAB <https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=HOk9c4_35FKg>`_
Check out this
`COLAB <https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=HOk9c4_35FKg>`_
for a live demo.
"""
pytorch_lightning/core/lightning.py
+4 -2
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@@ -257,9 +257,11 @@ class LightningModule(ABC, GradInformation, ModelIO, ModelHooks):
In this step you'd normally generate examples or calculate anything of interest such as accuracy.
Args:
batch (torch.nn.Tensor | (Tensor, Tensor) | [Tensor, Tensor]): The output of your dataloader. A tensor, tuple or list
batch (torch.nn.Tensor | (Tensor, Tensor) | [Tensor, Tensor]): The output of your dataloader.
A tensor, tuple or list
batch_idx (int): The index of this batch
dataloader_idx (int): The index of the dataloader that produced this batch (only if multiple val datasets used)
dataloader_idx (int): The index of the dataloader that produced this batch (only if multiple
val datasets used)
Return:
Dict or OrderedDict - passed to the validation_end step
pytorch_lightning/logging/__init__.py
-2
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@@ -114,6 +114,4 @@ try:
except ImportError:
pass
__all__ = all
pytorch_lightning/logging/neptune.py
+2 -1
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@@ -94,7 +94,8 @@ class NeptuneLogger(LightningLoggerBase):
Args:
api_key (str | None): Required in online mode. Neputne API token, found on https://neptune.ml.
Read how to get your API key https://docs.neptune.ml/python-api/tutorials/get-started.html#copy-api-token.
Read how to get your API key
https://docs.neptune.ml/python-api/tutorials/get-started.html#copy-api-token.
project_name (str): Required in online mode. Qualified name of a project in a form of
"namespace/project_name" for example "tom/minst-classification".
If None, the value of NEPTUNE_PROJECT environment variable will be taken.
pytorch_lightning/trainer/__init__.py
+3 -3
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@@ -1,7 +1,7 @@
"""
The trainer de-couples the engineering code (16-bit, early stopping, GPU distribution, etc...) from the
science code (GAN, BERT, your project, etc...). It uses many assumptions which are best practices in
science code (GAN, BERT, your project, etc...). It uses many assumptions which are best practices in
AI research today.
The trainer automates all parts of training except:
@@ -9,9 +9,9 @@ The trainer automates all parts of training except:
- what happens in training , test, val loop
- where the data come from
- which optimizers to use
- how to do the computations
- how to do the computations
The Trainer delegates those calls to your LightningModule which defines how to do those parts.
The Trainer delegates those calls to your LightningModule which defines how to do those parts.
This is the basic use of the trainer:
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
tests/test_trainer.py
+1 -1
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@@ -15,7 +15,7 @@ from pytorch_lightning.testing import (
LightningValidationMultipleDataloadersMixin,
LightningTestMultipleDataloadersMixin,
)
from pytorch_lightning.core.lightning import load_hparams_from_tags_csv
from pytorch_lightning.core.lightning import load_hparams_from_tags_csv
from pytorch_lightning.trainer.logging import TrainerLoggingMixin