coqui-tts-trainer 0.3.3
General purpose model trainer for PyTorch that is more flexible than it should be, by 🐸Coqui.
License: Apache-2.0
Requires Python: <3.15,>=3.10

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# 👟 Trainer

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An opinionated general purpose model trainer on PyTorch with a simple code base.
Fork of the [original, unmaintained repository](https://github.com/coqui-ai/Trainer). New PyPI package: [coqui-tts-trainer](https://pypi.org/project/coqui-tts-trainer)

## Installation

From PyPI:

```console
pip install coqui-tts-trainer
```

From Github:

```console
git clone https://github.com/idiap/coqui-ai-Trainer
cd coqui-ai-Trainer
pip install -e .
```

## Implementing a model
Subclass and overload the functions in the [```TrainerModel()```](trainer/model.py)


## Training a model with auto-optimization
See the [MNIST example](examples/train_mnist.py).


## Training a model with advanced optimization
With 👟 you can define the whole optimization cycle as you want as the in GAN example below. It enables more
under-the-hood control and flexibility for more advanced training loops.

You just have to use the ```scaled_backward()``` function to handle mixed precision training.

```python
...

def optimize(self, batch, trainer):
    imgs, _ = batch

    # sample noise
    z = torch.randn(imgs.shape[0], 100)
    z = z.type_as(imgs)

    # train discriminator
    imgs_gen = self.generator(z)
    logits = self.discriminator(imgs_gen.detach())
    fake = torch.zeros(imgs.size(0), 1)
    fake = fake.type_as(imgs)
    loss_fake = trainer.criterion(logits, fake)

    valid = torch.ones(imgs.size(0), 1)
    valid = valid.type_as(imgs)
    logits = self.discriminator(imgs)
    loss_real = trainer.criterion(logits, valid)
    loss_disc = (loss_real + loss_fake) / 2

    # step dicriminator
    self.scaled_backward(loss_disc, None, trainer)

    if trainer.total_steps_done % trainer.grad_accum_steps == 0:
        trainer.optimizer[0].step()
        trainer.optimizer[0].zero_grad()

    # train generator
    imgs_gen = self.generator(z)

    valid = torch.ones(imgs.size(0), 1)
    valid = valid.type_as(imgs)

    logits = self.discriminator(imgs_gen)
    loss_gen = trainer.criterion(logits, valid)

    # step generator
    self.scaled_backward(loss_gen, None, trainer)
    if trainer.total_steps_done % trainer.grad_accum_steps == 0:
        trainer.optimizer[1].step()
        trainer.optimizer[1].zero_grad()
    return {"model_outputs": logits}, {"loss_gen": loss_gen, "loss_disc": loss_disc}

...
```

See the [GAN training example](examples/train_simple_gan.py) with Gradient Accumulation


## Training with Batch Size Finder
see the test script [here](tests/test_train_batch_size_finder.py) for training with batch size finder.


The batch size finder starts at a default BS(defaults to 2048 but can also be user defined) and searches for the largest batch size that can fit on your hardware. you should expect for it to run multiple trainings until it finds it. to use it instead of calling ```trainer.fit()``` youll call ```trainer.fit_with_largest_batch_size(starting_batch_size=2048)``` with ```starting_batch_size``` being the batch the size you want to start the search with. very useful if you are wanting to use as much gpu mem as possible.

## Training with DDP

```console
$ python -m trainer.distribute --script path/to/your/train.py --gpus "0,1"
```

We don't use ```.spawn()``` to initiate multi-gpu training since it causes certain limitations.

- Everything must the pickable.
- ```.spawn()``` trains the model in subprocesses and the model in the main process is not updated.
- DataLoader with N processes gets really slow when the N is large.

## Training with [Accelerate](https://huggingface.co/docs/accelerate/index)

Setting `use_accelerate` in `TrainingArgs` to `True` will enable training with Accelerate.

You can also use it for multi-gpu or distributed training.

```console
CUDA_VISIBLE_DEVICES="0,1,2" accelerate launch --multi_gpu --num_processes 3 train_recipe_autoregressive_prompt.py
```

See the [Accelerate docs](https://huggingface.co/docs/accelerate/basic_tutorials/launch).

## Adding a callback
👟 Supports callbacks to customize your runs. You can either set callbacks in your model implementations or give them
explicitly to the Trainer.

Please check `trainer.utils.callbacks` to see available callbacks.

Here is how you provide an explicit call back to a 👟Trainer object for weight reinitialization.

```python
def my_callback(trainer):
    print(" > My callback was called.")

trainer = Trainer(..., callbacks={"on_init_end": my_callback})
trainer.fit()
```

## Profiling example

- Create the torch profiler as you like and pass it to the trainer.
    ```python
    import torch
    profiler = torch.profiler.profile(
        activities=[
            torch.profiler.ProfilerActivity.CPU,
            torch.profiler.ProfilerActivity.CUDA,
        ],
        schedule=torch.profiler.schedule(wait=1, warmup=1, active=3, repeat=2),
        on_trace_ready=torch.profiler.tensorboard_trace_handler("./profiler/"),
        record_shapes=True,
        profile_memory=True,
        with_stack=True,
    )
    prof = trainer.profile_fit(profiler, epochs=1, small_run=64)
    then run Tensorboard
    ```
- Run the tensorboard.
    ```console
    tensorboard --logdir="./profiler/"
    ```

## Supported Experiment Loggers
- [Tensorboard](https://www.tensorflow.org/tensorboard) - actively maintained
- [ClearML](https://clear.ml/) - actively maintained
- [MLFlow](https://mlflow.org/)
- [Aim](https://aimstack.io/)
- [WandDB](https://wandb.ai/)

To add a new logger, you must subclass [BaseDashboardLogger](trainer/logging/base_dash_logger.py) and overload its functions.