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@ -299,139 +299,6 @@ If you are one of these corporations, please feel free to reach out to will@pyto
--- ---
## FAQ
**Starting a new project?**
[Use our seed-project aimed at reproducibility!](https://github.com/PytorchLightning/pytorch-lightning-conference-seed)
**Why lightning?**
Although your research/production project might start simple, once you add things like GPU AND TPU training, 16-bit precision, etc, you end up spending more time engineering than researching. Lightning automates AND rigorously tests those parts for you.
Lightning has 3 goals in mind:
1. Maximal flexibility while abstracting out the common boilerplate across research projects.
2. Reproducibility. If all projects use the LightningModule template, it will be much much easier to understand what's going on and where to look! It will also mean every implementation follows a standard format.
3. Democratizing PyTorch power-user features. Distributed training? 16-bit? know you need them but don't want to take the time to implement? All good... these come built into Lightning.
**Who is Lightning for?**
- Professional researchers
- Ph.D. students
- Corporate production teams
If you're just getting into deep learning, we recommend you learn PyTorch first! Once you've implemented a few models, come back and use all the advanced features of Lightning :)
**What does lightning control for me?**
Everything in Blue!
This is how lightning separates the science (red) from engineering (blue).
![Overview](docs/source/_images/general/pl_overview.gif)
**How much effort is it to convert?**
If your code is not a huge mess you should be able to organize it into a LightningModule in less than 1 hour.
If your code IS a mess, then you needed to clean up anyhow ;)
[Check out this step-by-step guide](https://towardsdatascience.com/from-pytorch-to-pytorch-lightning-a-gentle-introduction-b371b7caaf09).
[Or watch this video](https://www.youtube.com/watch?v=QHww1JH7IDU).
**How flexible is it?**
As you see, you're just organizing your PyTorch code - there's no abstraction.
And for the stuff that the Trainer abstracts out, you can [override any part](https://pytorch-lightning.readthedocs.io/en/latest/introduction_guide.html#extensibility) you want to do things like implement your own distributed training, 16-bit precision, or even a custom backward pass.
For example, here you could do your own backward pass without worrying about GPUs, TPUs or 16-bit since we already handle it.
```python
class LitModel(LightningModule):
def optimizer_zero_grad(self, current_epoch, batch_idx, optimizer, opt_idx):
optimizer.zero_grad()
```
For anything else you might need, we have an extensive [callback system](https://pytorch-lightning.readthedocs.io/en/latest/introduction_guide.html#callbacks) you can use to add arbitrary functionality not implemented by our team in the Trainer.
**What types of research works?**
Anything! Remember, that this is just organized PyTorch code.
The Training step defines the core complexity found in the training loop.
##### Could be as complex as a seq2seq
```python
# define what happens for training here
def training_step(self, batch, batch_idx):
x, y = batch
# define your own forward and loss calculation
hidden_states = self.encoder(x)
# even as complex as a seq-2-seq + attn model
# (this is just a toy, non-working example to illustrate)
start_token = '<SOS>'
last_hidden = torch.zeros(...)
loss = 0
for step in range(max_seq_len):
attn_context = self.attention_nn(hidden_states, start_token)
pred = self.decoder(start_token, attn_context, last_hidden)
last_hidden = pred
pred = self.predict_nn(pred)
loss += self.loss(last_hidden, y[step])
#toy example as well
loss = loss / max_seq_len
return {'loss': loss}
```
##### Or as basic as CNN image classification
```python
# define what happens for validation here
def validation_step(self, batch, batch_idx):
x, y = batch
# or as basic as a CNN classification
out = self(x)
loss = my_loss(out, y)
return {'loss': loss}
```
**Does Lightning Slow my PyTorch?**
No! Lightning is meant for research/production cases that require high-performance.
We have tests to ensure we get the EXACT same results in under 600 ms difference per epoch. In reality, lightning adds about a 300 ms overhead per epoch.
[Check out the parity tests here](https://github.com/PyTorchLightning/pytorch-lightning/tree/master/benchmarks).
Overall, Lightning guarantees rigorously tested, correct, modern best practices for the automated parts.
**How does Lightning compare with Ignite and fast.ai?**
[Here's a thorough comparison](https://medium.com/@_willfalcon/pytorch-lightning-vs-pytorch-ignite-vs-fast-ai-61dc7480ad8a).
**Is this another library I have to learn?**
Nope! We use pure Pytorch everywhere and don't add unnecessary abstractions!
**Are there plans to support Python 2?**
Nope.
**Are there plans to support virtualenv?**
Nope. Please use anaconda or miniconda.
```bash
conda activate my_env
pip install pytorch-lightning
```
---
## Licence ## Licence
Please observe the Apache 2.0 license that is listed in this repository. In addition Please observe the Apache 2.0 license that is listed in this repository. In addition