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Update README.md

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@ -116,9 +116,17 @@ The rest of the code is automated by the [Trainer](https://pytorch-lightning.rea
![PT to PL](docs/source/_images/lightning_module/pt_trainer.png) ![PT to PL](docs/source/_images/lightning_module/pt_trainer.png)
## Testing Rigour ## Testing Rigour
All the automated code by the Trainer is [tested rigorously with every new PR](https://github.com/PyTorchLightning/pytorch-lightning/tree/master/tests). All the automated code by the Trainer is [tested rigorously with every new PR](https://github.com/PyTorchLightning/pytorch-lightning/tree/master/tests).
In fact, we also train a few models using a vanilla PyTorch loop and compare with the same model trained using the Trainer to make sure we achieve the EXACT same results. [Check out the parity tests here](https://github.com/PyTorchLightning/pytorch-lightning/tree/master/benchmarks). For every PR we test all combinations of:
- PyTorch 1.3, 1.4, 1.5
- Python 3.6, 3.7, 3.8
- Linux, OSX, Windows
- Multiple GPUs
**How does performance compare with vanilla PyTorch?**
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. Overall, Lightning guarantees rigorously tested, correct, modern best practices for the automated parts.
@ -329,7 +337,7 @@ Lightning has out-of-the-box integration with the popular logging/visualizing fr
## Running speed ## Running speed
Migrating to lightning does not mean compromising on speed! You can expect an overhead of about 600 ms per epoch comparing to pure PyTorch. Migrating to lightning does not mean compromising on speed! You can expect an overhead of about 300 ms per epoch compared with pure PyTorch.
## Examples ## Examples