ptpt 0.0.7

PyTorch Personal Trainer: My personal framework for deep learning experiments

Alex's PyTorch Personal Trainer (ptpt)

(name subject to change)

This repository contains my personal lightweight framework for deep learning projects in PyTorch.

Disclaimer: this project is very much work-in-progress. Although technically useable, it is missing many features. Nonetheless, you may find some of the design patterns and code snippets to be useful in the meantime.

Installation

Install from pip by running pip install ptpt

You can also build from source. Simply run python -m build in the root of the repo, then run pip install on the resulting .whl file.

Usage

Import the library as with any other python library:

from ptpt.trainer import Trainer, TrainerConfig
from ptpt.log import debug, info, warning, error, critical

The core of the library is the trainer.Trainer class. In the simplest case, it takes the following as input:

net:            a `nn.Module` that is the model we wish to train.
loss_fn:        a function that takes a `nn.Module` and a batch as input.
                it returns the loss and optionally other metrics.
train_dataset:  the training dataset.
test_dataset:   the test dataset.
cfg:            a `TrainerConfig` instance that holds all
                hyperparameters.

Once this is instantiated, starting the training loop is as simple as calling trainer.train() where trainer is an instance of Trainer.

cfg stores most of the configuration options for Trainer. See the class definition of TrainerConfig for details on all options.

Examples

An example workflow would go like this:

Define your training and test datasets:

transform=transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])
train_dataset = datasets.MNIST('../data', train=True, download=True, transform=transform)
test_dataset = datasets.MNIST('../data', train=False, download=True, transform=transform)

Define your model:

# `Net` could be any `nn.Module`
net = Net()

Define your loss function that calls net, taking the full batch as input:

# minimising classification error
def loss_fn(net, batch):
    X, y = batch
    logits = net(X)
    loss = F.nll_loss(logits, y)

    pred = logits.argmax(dim=-1, keepdim=True)
    accuracy = 100. * pred.eq(y.view_as(pred)).sum().item() / y.shape[0]
    return loss, accuracy

Optionally create a configuration object:

# see class definition for full list of parameters
cfg = TrainerConfig(
    exp_name = 'mnist-conv',
    batch_size = 64,
    learning_rate = 4e-4,
    nb_workers = 4,
    save_outputs = False,
    metric_names = ['accuracy']
)

Initialise the Trainer class:

trainer = Trainer(
    net=net,
    loss_fn=loss_fn,
    train_dataset=train_dataset,
    test_dataset=test_dataset,
    cfg=cfg
)

Optionally, register some callback functions:

def callback_fn(_):
    info("Congratulations, you have completed an epoch!")
trainer.register_callback(CallbackType.TrainEpoch, callback_fn)

Call trainer.train() to begin the training loop

trainer.train() # Go!

See more examples here.

Motivation

I found myself repeating a lot of same structure in many of my deep learning projects. This project is the culmination of my efforts refining the typical structure of my projects into (what I hope to be) a wholly reusable and general-purpose library.

Additionally, there are many nice theoretical and engineering tricks that are available to deep learning researchers. Unfortunately, a lot of them are forgotten because they fall outside the typical workflow, despite them being very beneficial to include. Another goal of this project is to transparently include these tricks so they can be added and removed with minimal code change. Where it is sane to do so, some of these could be on by default.

Finally, I am guilty of forgetting to implement decent logging: both of standard output and of metrics. Logging of standard output is not hard, and is implemented using other libraries such as rich. However, metric logging is less obvious. I'd like to avoid larger dependencies such as tensorboard being an integral part of the project, so metrics will be logged to simple numpy arrays. The library will then provide functions to produce plots from these, or they can be used in another library.

TODO:

• Add arbitrary callback support at various points of execution
• Add more learning rate schedulers
• Add more optimizer options
• Add logging-to-file
• Add separate metric tracking
• Adds silent and simpler logging
• Support for distributed / multi-GPU operations
• Set of functions for producing visualisations from disk dumps
• General suite of useful functions

References

• rich by @willmcgugan

Citations