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Roadmap

vfdev edited this page Sep 11, 2020 · 16 revisions

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

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