lightning/docs/source/new-project.rst

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Quick Start
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To start a new project define two files, a LightningModule and a Trainer file.
To illustrate Lightning power and simplicity, here's an example of a typical research flow.
Case 1: BERT
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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
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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, use_amp=True)
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 all of the capabilities below (without coding or testing yourself).