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https://github.com/coqui-ai/trainer

🐸 - A general purpose model trainer, as flexible as it gets
https://github.com/coqui-ai/trainer

ai data-science deep-learning machine-learning pytorch

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🐸 - A general purpose model trainer, as flexible as it gets

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README 

        



# 👟 Trainer

An opinionated general purpose model trainer on PyTorch with a simple code base.



## Installation



From Github:



```console

git clone https://github.com/coqui-ai/Trainer

cd Trainer

make install

```



From PyPI:



```console

pip install trainer

```



Prefer installing from Github as it is more stable.



## 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, trainer.optimizer[0])



    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, trainer.optimizer[1])

    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.



## Anonymized Telemetry

We constantly seek to improve 🐸 for the community. To understand the community's needs better and address them accordingly, we collect stripped-down anonymized usage stats when you run the trainer.



Of course, if you don't want, you can opt out by setting the environment variable `TRAINER_TELEMETRY=0`.