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fix links to Docs (#744)

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* update log

* fix links

* formatting

* fixing docs path

* formatting
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Jirka Borovec authored and William Falcon committed 2020-01-26 08:38:01 -05:00
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README.md
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@@ -84,12 +84,12 @@ Lightning sets up all the boilerplate state-of-the-art training for you so you c
---
## How do I do use it?
Think about Lightning as refactoring your research code instead of using a new framework. The research code goes into a [LightningModule](https://pytorch-lightning.rtfd.io/en/latest/LightningModule/RequiredTrainerInterface/) which you fit using a Trainer.
Think about Lightning as refactoring your research code instead of using a new framework. The research code goes into a [LightningModule](https://pytorch-lightning.rtfd.io/en/latest/lightning-module.html) which you fit using a Trainer.
The LightningModule defines a *system* such as seq-2-seq, GAN, etc... It can ALSO define a simple classifier such as the example below.
To use lightning do 2 things:
1. [Define a LightningModule](https://pytorch-lightning.rtfd.io/en/latest/LightningModule/RequiredTrainerInterface/)
1. [Define a LightningModule](https://pytorch-lightning.rtfd.io/en/latest/lightning-module.html)
**WARNING:** This syntax is for version 0.5.0+ where abbreviations were removed.
```python
import os
@@ -165,7 +165,7 @@ To use lightning do 2 things:
# OPTIONAL
return DataLoader(MNIST(os.getcwd(), train=False, download=True, transform=transforms.ToTensor()), batch_size=32)
```
2. Fit with a [trainer](https://pytorch-lightning.rtfd.io/en/latest/Trainer/)
2. Fit with a [trainer](https://pytorch-lightning.rtfd.io/en/latest/pytorch_lightning.trainer.html)
```python
from pytorch_lightning import Trainer
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<text
id="PyTorch-Lightning"
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font-weight="normal"
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<tspan
x="53"
y="37"
id="tspan7">PyTorch Lightning</tspan>
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docs/source/conf.py
+5 -1
View File
@@ -318,7 +318,7 @@ def linkcode_resolve(domain, info):
obj = getattr(obj, part)
fname = inspect.getsourcefile(obj)
# https://github.com/rtfd/readthedocs.org/issues/5735
if any([s in fname for s in ('readthedocs', 'checkouts')]):
if any([s in fname for s in ('readthedocs', 'rtfd', 'checkouts')]):
# /home/docs/checkouts/readthedocs.org/user_builds/pytorch_lightning/checkouts/
# devel/pytorch_lightning/utilities/cls_experiment.py#L26-L176
path_top = os.path.abspath(os.path.join('..', '..', '..'))
@@ -338,6 +338,10 @@ def linkcode_resolve(domain, info):
# import subprocess
# tag = subprocess.Popen(['git', 'rev-parse', 'HEAD'], stdout=subprocess.PIPE,
# universal_newlines=True).communicate()[0][:-1]
branch = filename.split('/')[0]
# do mapping from latest tags to master
branch = {'latest': 'master', 'stable': 'master'}.get(branch, branch)
filename = '/'.join([branch] + filename.split('/')[1:])
return "https://github.com/%s/%s/blob/%s" \
% (github_user, github_repo, filename)
docs/source/examples.rst
+5 -5
View File
@@ -1,33 +1,33 @@
GAN
====
===
.. toctree::
:maxdepth: 3
pl_examples.domain_templates.gan
MNIST
====
=====
.. toctree::
:maxdepth: 3
pl_examples.basic_examples.lightning_module_template
Multi-node (ddp) MNIST
====
======================
.. toctree::
:maxdepth: 3
pl_examples.multi_node_examples.multi_node_ddp_demo
Multi-node (ddp2) MNIST
====
=======================
.. toctree::
:maxdepth: 3
pl_examples.multi_node_examples.multi_node_ddp2_demo
Imagenet
====
========
.. toctree::
:maxdepth: 3
pytorch_lightning/callbacks/pt_callbacks.py
+2 -1
View File
@@ -1,6 +1,7 @@
"""
Callbacks
====================================
=========
Callbacks supported by Lightning
"""
pytorch_lightning/core/__init__.py
+1
View File
@@ -85,6 +85,7 @@ Most methods are optional. Here's a minimal example.
Once you've defined the LightningModule, fit it using a trainer.
.. code-block:: python
trainer = pl.Trainer()
model = CoolModel()
pytorch_lightning/logging/__init__.py
+1
View File
@@ -3,6 +3,7 @@ Lightning supports most popular logging frameworks (Tensorboard, comet, weights
To use a logger, simply pass it into the trainer.
.. code-block:: python
from pytorch_lightning import logging
# lightning uses tensorboard by default
pytorch_lightning/logging/test_tube.py
+1 -1
View File
@@ -46,7 +46,7 @@ class TestTubeLogger(LightningLoggerBase):
`os.path.join(save_dir, name, version)`
Example
--------
-------
.. code-block:: python
pytorch_lightning/trainer/trainer.py
+48 -8
View File
@@ -96,6 +96,7 @@ class Trainer(TrainerIOMixin,
Args:
logger (:class:`.Logger`): Logger for experiment tracking.
Example::
from pytorch_lightning.logging import TensorBoardLogger
# default logger used by trainer
@@ -106,8 +107,10 @@ class Trainer(TrainerIOMixin,
)
Trainer(logger=logger)
checkpoint_callback (:class:`CheckpointCallback`): Callback for checkpointing.
Example::
from pytorch_lightning.callbacks import ModelCheckpoint
# default used by the Trainer
@@ -121,6 +124,7 @@ class Trainer(TrainerIOMixin,
)
trainer = Trainer(checkpoint_callback=checkpoint_callback)
early_stop_callback (:class:`.EarlyStopping`): Callback for early stopping. If
set to ``True``, then the default callback monitoring ``'val_loss'`` is created.
Will raise an error if ``'val_loss'`` is not found.
@@ -129,6 +133,7 @@ class Trainer(TrainerIOMixin,
If ``'val_loss'`` is not found will work as if early stopping is disabled.
Default: ``None``.
Example::
from pytorch_lightning.callbacks import EarlyStopping
# default used by the Trainer
@@ -141,25 +146,32 @@ class Trainer(TrainerIOMixin,
)
trainer = Trainer(early_stop_callback=early_stop_callback)
default_save_path (str): Default path for logs and weights when no logger/ckpt_callback passed
Example::
# default used by the Trainer
trainer = Trainer(default_save_path=os.getcwd())
gradient_clip_val (float): 0 means don't clip.
Example::
# default used by the Trainer
trainer = Trainer(gradient_clip_val=0.0)
gradient_clip (int):
.. deprecated:: 0.5.0
Use `gradient_clip_val` instead. Will remove 0.8.0.
process_position (int): orders the tqdm bar when running multiple models on same machine.
Example::
# default used by the Trainer
trainer = Trainer(process_position=0)
num_nodes (int): number of GPU nodes for distributed training.
Example::
# default used by the Trainer
trainer = Trainer(num_nodes=1)
@@ -172,6 +184,7 @@ class Trainer(TrainerIOMixin,
gpus (list|str|int): Which GPUs to train on.
Example::
# default used by the Trainer (ie: train on CPU)
trainer = Trainer(gpus=None)
@@ -192,6 +205,7 @@ class Trainer(TrainerIOMixin,
log_gpu_memory (str): None, 'min_max', 'all'. Might slow performance
because it uses the output of nvidia-smi.
Example::
# default used by the Trainer
trainer = Trainer(log_gpu_memory=None)
@@ -203,11 +217,13 @@ class Trainer(TrainerIOMixin,
show_progress_bar (bool): If true shows tqdm progress bar
Example::
# default used by the Trainer
trainer = Trainer(show_progress_bar=True)
overfit_pct (float): uses this much data of all datasets.
Example::
# default used by the Trainer
trainer = Trainer(overfit_pct=0.0)
@@ -216,14 +232,16 @@ class Trainer(TrainerIOMixin,
track_grad_norm (int): -1 no tracking. Otherwise tracks that norm
Example::
# default used by the Trainer
trainer = Trainer(track_grad_norm=-1)
# track the 2-norm
trainer = Trainer(track_grad_norm=2)
check_val_every_n_epoch (int): check val every n train epochs
check_val_every_n_epoch (int): Check val every n train epochs.
Example::
# default used by the Trainer
trainer = Trainer(check_val_every_n_epoch=1)
@@ -232,6 +250,7 @@ class Trainer(TrainerIOMixin,
fast_dev_run (bool): runs 1 batch of train, test and val to find any bugs (ie: a sort of unit test).
Example::
# default used by the Trainer
trainer = Trainer(fast_dev_run=False)
@@ -240,6 +259,7 @@ class Trainer(TrainerIOMixin,
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)
trainer = Trainer(accumulate_grad_batches=1)
@@ -249,8 +269,9 @@ class Trainer(TrainerIOMixin,
# no accumulation for epochs 1-4. accumulate 3 for epochs 5-10. accumulate 20 after that
trainer = Trainer(accumulate_grad_batches={5: 3, 10: 20})
max_epochs (int): Stop training once this number of epochs is reached
max_epochs (int): Stop training once this number of epochs is reached.
Example::
# default used by the Trainer
trainer = Trainer(max_epochs=1000)
@@ -260,6 +281,7 @@ class Trainer(TrainerIOMixin,
min_epochs (int): Force training for at least these many epochs
Example::
# default used by the Trainer
trainer = Trainer(min_epochs=1)
@@ -270,6 +292,7 @@ class Trainer(TrainerIOMixin,
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
trainer = Trainer(train_percent_check=1.0)
@@ -279,6 +302,7 @@ class Trainer(TrainerIOMixin,
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
trainer = Trainer(val_percent_check=1.0)
@@ -288,6 +312,7 @@ class Trainer(TrainerIOMixin,
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
trainer = Trainer(test_percent_check=1.0)
@@ -297,6 +322,7 @@ class Trainer(TrainerIOMixin,
val_check_interval (float|int): How often within one training epoch to check the validation set
If float, % of tng epoch. If int, check every n batch
Example::
# default used by the Trainer
trainer = Trainer(val_check_interval=1.0)
@@ -310,11 +336,13 @@ class Trainer(TrainerIOMixin,
log_save_interval (int): Writes logs to disk this often
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)
@@ -325,6 +353,7 @@ class Trainer(TrainerIOMixin,
distributed_backend (str): The distributed backend to use.
Options: 'dp', 'ddp', 'ddp2'.
Example::
# default used by the Trainer
trainer = Trainer(distributed_backend=None)
@@ -344,17 +373,20 @@ class Trainer(TrainerIOMixin,
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')
@@ -366,6 +398,7 @@ class Trainer(TrainerIOMixin,
weights_save_path (str): Where to save weights if specified.
Example::
# default used by the Trainer
trainer = Trainer(weights_save_path=os.getcwd())
@@ -383,6 +416,7 @@ class Trainer(TrainerIOMixin,
amp_level (str): The optimization level to use (O1, O2, etc...).
Check nvidia docs for level (https://nvidia.github.io/apex/amp.html#opt-levels)
Example::
# default used by the Trainer
trainer = Trainer(amp_level='O1')
@@ -390,6 +424,7 @@ class Trainer(TrainerIOMixin,
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.
Example::
# default used by the Trainer
trainer = Trainer(num_sanity_val_steps=5)
@@ -407,6 +442,7 @@ class Trainer(TrainerIOMixin,
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)
@@ -415,15 +451,18 @@ class Trainer(TrainerIOMixin,
resume_from_checkpoint (str): To resume training from a specific checkpoint pass in the path here.k
Example::
# default used by the Trainer
trainer = Trainer(resume_from_checkpoint=None)
# 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`
"""
#
# .. warning:: Following arguments become deprecated and they will be removed in v0.8.0:
# - `nb_sanity_val_steps`
# Transfer params
# Backward compatibility
@@ -660,8 +699,10 @@ class Trainer(TrainerIOMixin,
def tng_tqdm_dic(self):
"""Read-only for tqdm metrics.
.. warning:: Deprecated in v0.5.0. use training_tqdm_dict instead.
:return:
:return: dictionary
.. deprecated:: 0.5.0
Use `training_tqdm_dict` instead. Will remove 0.8.0.
"""
warnings.warn("`tng_tqdm_dic` has renamed to `training_tqdm_dict` since v0.5.0"
" and will be removed in v0.8.0", DeprecationWarning)
@@ -718,7 +759,6 @@ class Trainer(TrainerIOMixin,
return 1
def init_optimizers(self, optimizers):
# single optimizer
if isinstance(optimizers, Optimizer):
return [optimizers], []