Summary: fastai v1 — the rewrite of the deep learning library.

fastai_v1 is the codename for fastai deep learning library version 1.0. It’s the beginning of the new version of the library.

From the forums:

We’re doing a rewrite of the fastai library, with the following goals:

  • Support consistent API for classification, regression, localization, and generation, across all of: vision, NLP, tabular data, time series, and collaborative filtering
  • Clear and complete documentation for both new and experienced users
  • Well tested (both unit and integration tests)
  • Better structured code
  • Notebooks showing how and why the library is built as it is. has decided to create a new version of the library for the next course on October 2018. The current fastai v0.x will be maintained with the necessary fixes but won’t make any major change. do plan to have a library used outside of the course. With the changes in PyTorch on one hand, and the new features added as time goes, Jeremy felt he had to start again from scratch to create something more intuitive that ties all the existing APIs together.

Current Status#

At the moment (July 19, 2018), they’ve only just started. At this stage (Aug 4, 2018), nothing is functional. If you’re interested in contributing, join the discussion at dev forum. The development is happening in this GitHub repo.

UPDATE (2018-08-12): They’re in the process of incorporating all of the best practices used in the DAWNBench and “train ImageNet in 18 minutes” project directly into the fastai library, including automating the selection of hyper-parameters for fast and accurate training.

Why Follow Along?#

It’s early days, so I see them refactor the code every day. It would be nice to see the whole process, like for example, how and why certain decisions were made during development. I think we can learn a lot from that.

You can follow the dev commits in GitHub and see what’s happening, and welcome you to ask questions in fastai-dev forum or make suggestions.

It’s cool to see something being written from the ground up.