Roadmap

This document lists general directions that core team is interested to see developed in PyTorch-Ignite.

We are using Github Projects to define our different goals: releases, particular milestones etc.

Principal goals

• continue maintaining high-quality, well-tested and documented modules.
• provide distributed framework support via ignite.distributed: XLA (e.g. TPU), Horovod
• provide new higher-level API based on Engine to simplify the usage while keeping flexibility as a contrib module
• provide helper on data management via ignite.data: sampling, multi-dataloaders
• provide more intergrations with other tools to simplify Machine/Deep Learning end-to-end applications.
• visibility and communications

Codebase maintenance

• add typing to the whole package
• adapt the code and add mypy check
• merge contrib module into principal library ?

Pre-built Docker images

• Provide helper docker images to quick-start with a task
• https://hub.docker.com/orgs/pytorchignite

Distributed framework support

• XLA devices support via pytorch/xla
• Horovod
• Explore DDP + RPC

Higher-level API

• push-button contrib trainers with AMP, distributed etc
• automatic batch size via toma

Helper on data management

• better and simple coverage of multi-dataloaders use-cases, e.g. GAN, SSL, etc

Integrations

• Verify compatibility (if ignite is not blocking) writing applications for Federated Learning
• Verify compatibility (if ignite is not blocking) writing applications with Distributed RPC framework

Communications

• More applications and successful stories with PyTorch-Ignite
• Showcase via Trains Ignite server : https://app.ignite.trains.allegro.ai/projects
  • more experiments with Ignite from our users