Files
pytorch-lightning/docs/source/new-project.rst
T
d4a31f02e0 Enable TPU support (#868)
* added tpu docs

* added tpu flags

* add tpu docs + init training call

* amp

* amp

* amp

* amp

* optimizer step

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* fix test pkg create (#873)

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added test return and print

* added test return and print

* added test return and print

* added test return and print

* added test return and print

* Update pytorch_lightning/trainer/trainer.py

Co-Authored-By: Luis Capelo <luiscape@gmail.com>

* Fix segmentation example (#876)

* removed torchvision model and added custom model

* minor fix

* Fixed relative imports issue

* Fix/typo (#880)

* Update greetings.yml

* Update greetings.yml

* Changelog (#869)

* Create CHANGELOG.md

* Update CHANGELOG.md

* Update CHANGELOG.md

* Update PULL_REQUEST_TEMPLATE.md

* Update PULL_REQUEST_TEMPLATE.md

* Add PR links to Version 0.6.0 in CHANGELOG.md

* Add PR links for Unreleased in CHANGELOG.md

* Update PULL_REQUEST_TEMPLATE.md

* Fixing Function Signatures (#871)

* added tpu docs

* added tpu flags

* add tpu docs + init training call

* amp

* amp

* amp

* amp

* optimizer step

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added auto data transfer to TPU

* added test return and print

* added test return and print

* added test return and print

* added test return and print

* added test return and print

* added test return and print

* added test return and print

* added test return and print

Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: Luis Capelo <luiscape@gmail.com>
Co-authored-by: Akshay Kulkarni <akshayk.vnit@gmail.com>
Co-authored-by: Ethan Harris <ewah1g13@soton.ac.uk>
Co-authored-by: Shikhar Chauhan <xssChauhan@users.noreply.github.com>
2020-02-17 16:01:20 -05:00

73 lines
2.1 KiB
ReStructuredText

Quick Start
===========
| To start a new project define two files, a LightningModule and a Trainer file.
| To illustrate the power of Lightning and its simplicity, here's an example of a typical research flow.
Case 1: BERT
------------
| Let's say you're working on something like BERT but want to try different ways of training or even different networks.
| You would define a single LightningModule and use flags to switch between your different ideas.
.. code-block:: python
class BERT(pl.LightningModule):
def __init__(self, model_name, task):
self.task = task
if model_name == 'transformer':
self.net = Transformer()
elif model_name == 'my_cool_version':
self.net = MyCoolVersion()
def training_step(self, batch, batch_idx):
if self.task == 'standard_bert':
# do standard bert training with self.net...
# return loss
if self.task == 'my_cool_task':
# do my own version with self.net
# return loss
Case 2: COOLER NOT BERT
-----------------------
But if you wanted to try something **completely** different, you'd define a new module for that.
.. code-block:: python
class CoolerNotBERT(pl.LightningModule):
def __init__(self):
self.net = ...
def training_step(self, batch, batch_idx):
# do some other cool task
# return loss
Rapid research flow
-------------------
Then you could do rapid research by switching between these two and using the same trainer.
.. code-block:: python
if use_bert:
model = BERT()
else:
model = CoolerNotBERT()
trainer = Trainer(gpus=4, precision=16)
trainer.fit(model)
**Notice a few things about this flow:**
1. You're writing pure PyTorch... no unnecessary abstractions or new libraries to learn.
2. You get free GPU and 16-bit support without writing any of that code in your model.
3. You also get early stopping, multi-gpu training, 16-bit and MUCH more without coding anything!