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Jirka Borovec
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* upgrade req. * move MkDocs * create Sphinx * init Sphinx * move md from MkDocs to Sphinx * CI: build docs * build Sphinx formatting move docs from MD to docstring in particular package/modules formatting add Sphinx ext. rename root_module to core drop implicit name "_logger" drop duplicate name "overwrite" fix imports use pytorch theme add sample link mapping try fix RTD build use forked template fix some docs warnings fix paths add deprecation warnings fix flake8 fix paths revert refactor revert MLFlowLogger * revert example import * update link * Update lightning_module_template.py
61 lines
1.9 KiB
Markdown
61 lines
1.9 KiB
Markdown
## New project Quick Start
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To start a new project define two files, a LightningModule and a Trainer file.
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To illustrate Lightning power and simplicity, here's an example of a typical research flow.
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### 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.
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You would define a single LightningModule and use flags to switch between your different ideas.
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```python
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class BERT(pl.LightningModule):
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def __init__(self, model_name, task):
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self.task = task
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if model_name == 'transformer':
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self.net = Transformer()
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elif model_name == 'my_cool_version':
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self.net = MyCoolVersion()
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def training_step(self, batch, batch_nb):
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if self.task == 'standard_bert':
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# do standard bert training with self.net...
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# return loss
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if self.task == 'my_cool_task':
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# do my own version with self.net
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# return loss
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```
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### 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.
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```python
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class CoolerNotBERT(pl.LightningModule):
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def __init__(self):
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self.net = ...
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def training_step(self, batch, batch_nb):
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# do some other cool task
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# return loss
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```
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### Rapid research flow
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Then you could do rapid research by switching between these two and using the same trainer.
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```python
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if use_bert:
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model = BERT()
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else:
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model = CoolerNotBERT()
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trainer = Trainer(gpus=4, use_amp=True)
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trainer.fit(model)
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```
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Notice a few things about this flow:
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1. You're writing pure PyTorch... no unnecessary abstractions or new libraries to learn.
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2. You get free GPU and 16-bit support without writing any of that code in your model.
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3. You also get all of the capabilities below (without coding or testing yourself).
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