So you want to train neural nets with PyTorch? Here are your options:

Enter torchtraining - we try to get what's best from both worlds while adding: explicitness, functional approach, easy extensions and freedom to structure your code!

All of that using single ** piping operator!

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Version Documentation Tests codecov codebeat PyPI Python PyTorch Docker LOC

Tutorials

See tutorials to get a grasp of what's the fuss is all about:

  • Introduction - quick tour around functionalities with CIFAR100 classification and tensorboard.
  • GAN training - more advanced example and creating you own pipeline components.

Installation

See documentation for full list of extras (e.g. installation with integrations like horovod).

To just start you can install via pip:

pip install --user torchtraining

Why torchtraining?

There are a lot of training libraries around for a lot of frameworks. Why would you choose this one?

torchtraining fits you, not the other way around

We think it's impossible to squeeze user's code in an overly strict API. We are not trying to fit everything into a single... .fit() method (or Trainer god class, see 40! arguments in PyTorch-Lightning trainer). This approach has shown time and time again it does not work for more complicated use cases as one cannot foresee the endless possibilities of training neural network and data generation user might require. torchtraining gives you building blocks to calculate metrics, log results, distribute training instead.

Implement single forward instead of 40 methods

Implementing forward with data argument is all you will ever need (okay, accumulators also need calculate, but that's it), we add thin __call__. Compare that to PyTorch-Lightning's LightningModule (source code here)

  • training_step
  • training_step_end
  • training_epoch_end (repeat all the above for validation and test)
  • validation_end, test_end
  • configure_sync_batchnorm
  • configure_ddp
  • init_ddp_connection
  • configure_apex
  • configure_optimizers
  • optimizer_step
  • optimizer_zero_grad
  • tbptt_split_batch (?)
  • prepare_data
  • train_dataloader
  • tng_dataloader
  • test_dataloader
  • val_dataloader

This list could go on (and probably will grow even bigger as time passes). We believe in functional approach and using only what you need (a lot of decoupled building blocks instead of gigantic god classes trying to do everything). Once again: we can't foresee future and won't squash everything into single class.

Explicitness

You are offered building blocks and it's up to you what you want to use. Still, you are explicit about everything going on in your code, for example:

  • when, where and what to log to tensorboard
  • when and how often to run optimization
  • what neural network(s) go into what step
  • what data you choose to accumulate and how often
  • which component of your pipeline should log via loguru
  • and how to log (e.g. to stdout and file or maybe over the web?)

See introduction tutorial to see how it's done

Neural network != training

We don't think your neural network source code should be polluted with training. We think it's better to have data preparation in data.py module, optimizers in optimizers.py and so on. With torchtraining you don't have to crunch any functionalities into single god class.

Nothing under the hood (almost)

~3000 lines of code (including comet-ml, neptune and horovod integration) and short functions/classes allow you to quickly dig into the source if you find something odd/not working. It's leverages what exists instead of reinventing the wheel.

PyTorch first

We don't force you to jump into and from numpy as most of the tasks can already be done in PyTorch. We are pytorch first. Unless we have to integrate third party tool... In that case you don't pay for this feature if you don't use it!

Easy integration with other tools

If we don't provide an integration out of the box, you can request it via issues or make your own PR. Any code you want can almost always be integrated via following steps:

  • make a new module (say amazing.py)
  • create new classes inheriting from torchtraining.Operation
  • implement forward for each operation which takes single argument data which can be anything (Tuple, List, torch.Tensor, str, whatever really)
  • process this data in forward and return results
  • you have your own operator compatible with **!

Other tools integrate components by trying to squash them into their predefined APIs and/or trying to be smart and guess what the user does (which often fails). Here's how we do:

Example of integration of neptune image logging:

import torchtraining as tt

class Image(tt.Operation):
    def __init__(
        self,
        experiment,
        log_name: str,
        image_name: str = None,
        description: str = None,
        timestamp=None,
        experiment=None,
    ):
        super().__init__()
        self.experiment = experiment
        self.log_name = log_name
        self.image_name = image_name
        self.description = description
        self.timestamp = timestamp

    # Always forward some data so it can be reused
    def forward(self, data):
        self.experiment.log_image(
            self.log_name, data, self.image_name, self.description, self.timestamp
        )
        return data

Contributing

This project is currently in it's infancy and we would love to get some help from you! You can find current ideas inside issues tagged by [DISCUSSION] (see here).

Also feel free to make your own feature requests and give us your thoughts in issues!

Remember: It's only 0.0.1 version, direction is there but you can be sure to encounter a lot of bugs along the way at the moment

Why ** as an operator?

Indeed, operators like |, >> or > would be way more intuitive, but:

  • Those are left associative and would require users to explicitly uses parentheses around pipes
  • > cannot be piped as easily
  • Way more complicated code on our side to handle >> or |

Currently ** seems like a reasonable trade-off, still it may be subject to change in future.

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