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lightning/docs/index.md

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###### New project Quick Start
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
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.
```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_nb):
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
But if you wanted to try something **completely** different, you'd define a new module for that.
```python
class CoolerNotBERT(pl.LightningModule):
def __init__(self):
self.net = ...
def training_step(self, batch, batch_nb):
# 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.
```python
if use_bert:
model = BERT()
else:
model = CoolerNotBERT()
trainer = Trainer(gpus=[0, 1, 2, 3], 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).
---
###### Templates
1. [MNIST LightningModule](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#minimal-example)
2. [Trainer](https://williamfalcon.github.io/pytorch-lightning/Trainer/)
- [Basic CPU Trainer Template](https://github.com/williamFalcon/pytorch-lightning/blob/master/examples/new_project_templates/single_cpu_template.py)
- [Multi-GPU Trainer Template](https://github.com/williamFalcon/pytorch-lightning/blob/master/examples/new_project_templates/single_gpu_node_template.py)
- [GPU cluster Trainer Template](https://github.com/williamFalcon/pytorch-lightning/blob/master/examples/new_project_templates/multi_node_cluster_template.py)
###### Docs shortcuts
- [LightningModule](LightningModule/RequiredTrainerInterface/)
- [Trainer](Trainer/)
###### Quick start examples
- [CPU example](examples/Examples/#cpu-hyperparameter-search)
- [Hyperparameter search on single GPU](examples/Examples/#hyperparameter-search-on-a-single-or-multiple-gpus)
- [Hyperparameter search on multiple GPUs on same node](examples/Examples/#hyperparameter-search-on-a-single-or-multiple-gpus)
- [Hyperparameter search on a SLURM HPC cluster](examples/Examples/#Hyperparameter search on a SLURM HPC cluster)
###### Checkpointing
- [Model saving](https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#model-saving)
- [Model loading](https://williamfalcon.github.io/pytorch-lightning/LightningModule/methods/#load-from-metrics)
- [Restoring training session](https://williamfalcon.github.io/pytorch-lightning/Trainer/Checkpointing/#restoring-training-session)
###### Computing cluster (SLURM)
- [Running grid search on a cluster](https://williamfalcon.github.io/pytorch-lightning/Trainer/SLURM%20Managed%20Cluster#running-grid-search-on-a-cluster)
- [Walltime auto-resubmit](https://williamfalcon.github.io/pytorch-lightning/Trainer/SLURM%20Managed%20Cluster#walltime-auto-resubmit)
###### Debugging
- [Fast dev run](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#fast-dev-run)
- [Inspect gradient norms](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#inspect-gradient-norms)
- [Log GPU usage](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#Log-gpu-usage)
- [Make model overfit on subset of data](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#make-model-overfit-on-subset-of-data)
- [Print the parameter count by layer](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#print-the-parameter-count-by-layer)
- [Pring which gradients are nan](https://williamfalcon.github.io/pytorch-lightning/Trainer/debugging/#print-which-gradients-are-nan)
- [Print input and output size of every module in system](https://williamfalcon.github.io/pytorch-lightning/LightningModule/properties/#example_input_array)
###### Distributed training
- [16-bit mixed precision](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#16-bit-mixed-precision)
- [Multi-GPU](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#Multi-GPU)
- [Multi-node](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#Multi-node)
- [Single GPU](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#single-gpu)
- [Self-balancing architecture](https://williamfalcon.github.io/pytorch-lightning/Trainer/Distributed%20training/#self-balancing-architecture)
###### Experiment Logging
- [Display metrics in progress bar](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#display-metrics-in-progress-bar)
- [Log metric row every k batches](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#log-metric-row-every-k-batches)
- [Process position](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#process-position)
- [Tensorboard support](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#tensorboard-support)
- [Save a snapshot of all hyperparameters](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#save-a-snapshot-of-all-hyperparameters)
- [Snapshot code for a training run](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#snapshot-code-for-a-training-run)
- [Write logs file to csv every k batches](https://williamfalcon.github.io/pytorch-lightning/Trainer/Logging/#write-logs-file-to-csv-every-k-batches)
###### Training loop
- [Accumulate gradients](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#accumulated-gradients)
- [Force training for min or max epochs](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-training-for-min-or-max-epochs)
- [Force disable early stop](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#force-disable-early-stop)
- [Gradient Clipping](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#gradient-clipping)
- [Hooks](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/)
- [Learning rate scheduling](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers)
- [Use multiple optimizers (like GANs)](https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#configure_optimizers)
- [Set how much of the training set to check (1-100%)](https://williamfalcon.github.io/pytorch-lightning/Trainer/Training%20Loop/#set-how-much-of-the-training-set-to-check)
- [Step optimizers at arbitrary intervals](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/#optimizer_step)
###### Validation loop
- [Check validation every n epochs](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#check-validation-every-n-epochs)
- [Hooks](https://williamfalcon.github.io/pytorch-lightning/Trainer/hooks/)
- [Set how much of the validation set to check](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-how-much-of-the-validation-set-to-check)
- [Set how much of the test set to check](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-how-much-of-the-test-set-to-check)
- [Set validation check frequency within 1 training epoch](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-validation-check-frequency-within-1-training-epoch)
- [Set the number of validation sanity steps](https://williamfalcon.github.io/pytorch-lightning/Trainer/Validation%20loop/#set-the-number-of-validation-sanity-steps)
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