PaperCandy

PaperCandy is a loosely coupled lightweight framework for deep learning papers. It provides a series of auxiliary tools for rapid rebuilding and writing papers. So far supports PyTorch as the only front-end framework.

Installation

NOTICE: This version is built with PyTorch. Consider reimplementing the abstract classes in package papercandy.core to adapt to the framework that you use if you are not a PyTorch user.

Optional Modules

COOTA

PaperCandy provides some extra functions based on COOTA, which are only available having installed COOTA. Documentation 👉here.

Check if COOTA is available:

from papercandy.core import optional_modules as om
print(om.coota_is_available())

Repository

Github | Pypi

Package

For Windows

pip install paper-candy

For Linux & macOS

pip3 install paper-candy

Quick Start

You can find classes and methods annotated under papercandy.core.

Some preparation needs to be done before the demo works.

1. Run this script to create the dataset.

   import os
   NUM_ITEMS = 6
   os.mkdir("./data")
   for i in range(1, NUM_ITEMS + 1):
       with open(f"./data/{i}.txt", "w") as f:
           f.write(f"[{2 * i - 1}, {2 * i}]")

2. Create file config.txt and leave it empty.

3. Make sure you have the structure like this:

   data

   --------1.txt

   --------2.txt

   --------3.txt

   --------4.txt

   --------5.txt

   --------6.txt

   config.txt

   main.py

from papercandy import *
from torch import nn


if __name__ == "main":
    CONFIG().CURRENT = new_config("./config.txt")
    dataset = ExampleDataset("./data")
    # `num_works`: the number of processes
    # `batch_size`: batch size
    dataloader = Dataloader(dataset, num_works=2, batch_size=4)
    trainer = Trainer(dataloder)
    
    torch_network = YOUR_NETWORK()
    network_container = NetworkC(torch_network)
    trainer.set_network(network_container)
    
    torch_loss_function = YOUR_LOSS_FUNCTION()
    loss_function_container = LossFunctionC(torch_loss_function)
    trainer.set_loss_function(loss_function_container)
    
    torch_optimizer = YOUR_OPTIMIZER()
    optimizer_container = OptimizerC(torch_optimizer)
    trainer.set_optimizer(optimizer_container)
    
    # the monitor is a callback interface for trainer
    tariner.train(monitor=TrainingMonitor())	# optional kwargs: `num_batches`, `monitor`
    
    drawer = draw(trainer, 1920, 1080)	# width, height
    drawer.save("./training_loss").show()

Predefined Configuration

Name	Required Type	Default Value	Usage
gpu_acceleration	papercandy.core.config.Bool	False	Whether to enable GPU acceleration in the training process.
device	int	0	The GPU device.

FAQ

1. macOS, Unexpected Ending

   Description

   The program unexpectedly ends.

   Cause

   It seems like PyTorch has some kind of bug on macOS that makes the program end when this happens when the network is forwarding.

   Solution

   There hasn't been a solution but to change to another system.

2. GPU Acceleration

   Description

   Cannot enable GPU acceleration even though gpu_acceleration has been set to True.

   Cause

   This is mostly because you didn't install the correct version of PyTorch. The CPU version will be automatically installed if any version of PyTorch was not found when you were installing PaperCandy, which is not capable of GPU acceleration, so the configuration will be compulsively changed to False.

   Solution

   Uninstall PyTorch and reinstall the correct version. See 👉here