-
----
-
-## Key Features
-
-* Scale your models to run on any hardware (CPU, GPUs, TPUs) without changing your model
-* Making code more readable by decoupling the research code from the engineering
-* Easier to reproduce
-* Less error prone by automating most of the training loop and tricky engineering
-* Keeps all the flexibility (LightningModules are still PyTorch modules), but removes a ton of boilerplate
-* Lightning has out-of-the-box integration with the popular logging/visualizing frameworks ([Tensorboard](https://pytorch.org/docs/stable/tensorboard.html), [MLFlow](https://mlflow.org/), [Neptune.ai](https://neptune.ai/), [Comet.ml](https://www.comet.ml/site/), [Wandb](https://www.wandb.com/)).
-* [Tested rigorously with every new PR](https://github.com/PyTorchLightning/pytorch-lightning/tree/master/tests). We test every combination of PyTorch and Python supported versions, every OS, multi GPUs and even TPUs.
-* Minimal running speed overhead (about 300 ms per epoch compared with pure PyTorch).
-
-### Lightning automates 40+ parts of DL/ML research
-- GPU training
-- Distributed GPU (cluster) training
-- TPU training
-- EarlyStopping
-- Logging/Visualizing
-- Checkpointing
-- Experiment management
-- [Full list here](https://pytorch-lightning.readthedocs.io/en/latest/#common-use-cases)
-
----
-
## How To Use
#### Setup step: Install
@@ -214,6 +171,50 @@ trainer = Trainer(tpu_cores=8)
trainer = Trainer(tpu_cores=[1])
```
+----
+
+
+## [PyTorch Lightning Masterclass](https://www.youtube.com/watch?v=DbESHcCoWbM&list=PLaMu-SDt_RB5NUm67hU2pdE75j6KaIOv2)
+### [New lessons weekly!](https://www.youtube.com/watch?v=DbESHcCoWbM&list=PLaMu-SDt_RB5NUm67hU2pdE75j6KaIOv2)
+
+
+
+---
+
+## Key Features
+
+* Scale your models to run on any hardware (CPU, GPUs, TPUs) without changing your model
+* Making code more readable by decoupling the research code from the engineering
+* Easier to reproduce
+* Less error prone by automating most of the training loop and tricky engineering
+* Keeps all the flexibility (LightningModules are still PyTorch modules), but removes a ton of boilerplate
+* Lightning has out-of-the-box integration with the popular logging/visualizing frameworks ([Tensorboard](https://pytorch.org/docs/stable/tensorboard.html), [MLFlow](https://mlflow.org/), [Neptune.ai](https://neptune.ai/), [Comet.ml](https://www.comet.ml/site/), [Wandb](https://www.wandb.com/)).
+* [Tested rigorously with every new PR](https://github.com/PyTorchLightning/pytorch-lightning/tree/master/tests). We test every combination of PyTorch and Python supported versions, every OS, multi GPUs and even TPUs.
+* Minimal running speed overhead (about 300 ms per epoch compared with pure PyTorch).
+
+### Lightning automates 40+ parts of DL/ML research
+- GPU training
+- Distributed GPU (cluster) training
+- TPU training
+- EarlyStopping
+- Logging/Visualizing
+- Checkpointing
+- Experiment management
+- [Full list here](https://pytorch-lightning.readthedocs.io/en/latest/#common-use-cases)
+
---
### Docs
-
- 
-
-