pytorch-flame 0.1.2

A library based on Ignite to help you train and evaluate PyTorch neural networks more easily.

flame

Flame is a library that helps develop neural networks fast and flexibly. It is built on PyTorch Ignite, a high-level library in PyTorch Ecosystem.

Contents

• Why flame?
• Concepts
• Installation
• Get started
  • Usage
  • Run your first experiment

Why flame?

When developing neural networks people train and evaluate models a lot and repeat these works on many problems. Flame is created for solving two needs:

• Templates for doing experiments: flame provides templates for neural network development with common utilities like saving checkpoints periodically, resume training, logging, evaluating, etc.
• The way to add functionalities flexibly: depending on different problems developers have different requirements for the training and testing. They might want to stop the training progress if there is no improvement, plotting the results after each epoch or they just want a vanilla training loop. Now with flame, you can use any on-the-shelf metrics and handlers from Ignite or your own just by editing the configuration file.

Concepts

TBD

Installation

Create your new environment with Python 3 and install flame by pip:

pip install pytorch-flame

Get started

Usage

Flame provides two commands:

• Initialize a new project

  usage: flame init [-h] [-f] [directory]
  
  positional arguments:
    directory   Directory in which the new project is initialized. If not specified, it will be initialized in the current directory.
  
  optional arguments:
    -h, --help  show this help message and exit
    -f, --full  Whether to create a full template project or not.
  

• Run the training or testing

  usage: flame run [-h] file
  
  positional arguments:
    file        Config file
  
  optional arguments:
    -h, --help  show this help message and exit
  

Run your first experiment

Let's get started with a simple experiment: classification on the MNIST dataset.

1. Flame runs experiments with configs so you need to create configs first. Run commands

   mkdir mnist-classifcation && cd mnist-classification
   flame init
   

   or you can run just command

   flame init mnist-classification
   

   flame will create the folder and initialize in it. The folder created will have the structure:

   mnist-classification/
   └── configs
       ├── test.yml
       └── train.yml
   

   You can add -f or --full to init command for creating an extra file run.py in case you prefer running python run.py rather than flame run for some reason. Then the structure will be:

   mnist-classification/
   ├── configs
   │   ├── test.yml
   │   └── train.yml
   └── run.py
   

2. MNIST dataset and the model will be got from torchvision, so we need to install it.

   pip install torchvision
   

3. Now, you have all for the training. cd to mnist-classification and run it by

   flame run configs/train.yml
   

   To see how the training is going on, start Tensorboard

   tensorboard --logdir logs/
   

4. Checkpoints will be saved in checkpoints folder. Say the training is done and you want to evaluate the model checkpoints/best_model.pt, for example, change value checkpoint.loader.kwargs.path in configs/test.yml to checkpoints/best_model.pt.

   checkpoint:
     loader:
       module: flame.handlers
       name: CheckpointLoader
       kwargs:
         path: "'checkpoints/best_model.pt'"
   

   Run the following command to start evaluating the model:

   flame run configs/test.yml
   

That's it! You have just completed training and evaluating with flame.