# forck.live — 2–8 May 2022

> AI lab announcements, news reports and podcasts.

forck.live follows what companies announce about their own products. Press may
corroborate an official story; it never leads and never appears as an item of its
own. A missing date stays missing. A title is the publisher's words — where a
company does not publish in English, an English rendering is listed separately
as `Title in English` and named as this site's translation, never as theirs.

This file is one issue of the weekly archive: every confirmed
first-party announcement of ISO week 2022-W18, 2–8 May 2022, newest
first. The window never moves, so this URL always names the same seven days —
but a post published inside them and detected later is added when it arrives,
so a closed week can still gain entries.

Every entry is one first-party announcement, and carries the same eight fields
in this order before any others:

    Company · Announced · Category · Coverage · Announcement · Group · Source · Record

`Source` is the company's own page the announcement was traced to and `Record`
is this site's page for it. Both are safe to cite.

`Coverage` is a number of outlets, or `not counted` — which means this record
reached no count of its own, never that nobody wrote about it.

`Category` is this tracker's own analysis, and is authoritative wherever it is
stated. `not stated` means either that nothing has analysed the post yet or
that the analysis placed it outside this taxonomy; neither is a verdict about
the announcement.

`Announcement` is whether the post is news of something the company did, rather
than a how-to, a customer story, a changelog listing or a staffing note.

`Group` is which of this record's groups the entry falls in, carried on the
entry itself so it holds where the groups are headings and where they are not.
One of: `models`, `covered`, `announcements`, `routine`.

Anything after `Record` is held detail that only some announcements carry.

- This week's page: https://forck.live/week/2022-W18
- The current issue, and the catalogue of the other weeks: https://forck.live/briefing.md
- Catalogue: https://forck.live/llms.txt

## Announcements

### Hugging Face — Welcome fastai to the Hugging Face Hub

- Company: Hugging Face (huggingface.co)
- Announced: 2022-05-06
- Category: capability-change
- Coverage: not counted
- Announcement: yes
- Group: announcements
- Source: https://huggingface.co/blog/fastai
- Record: https://forck.live/items/2205-welcome-fastai-to-the-hugging-face-hub
- Subject: Platform

Hugging Face announces integration of fastai with the Hugging Face Hub, allowing fastai users to share and upload models to the Hub with a single line of Python.

### OpenAI — OpenAI leadership team update

- Company: OpenAI (openai.com)
- Announced: 2022-05-05T07:00:00+00:00
- Category: not stated
- Coverage: not counted
- Announcement: no
- Group: routine
- Source: https://openai.com/index/leadership-team-update
- Record: https://forck.live/items/910-openai-leadership-team-update
- Subject: GPT / ChatGPT / API

OpenAI announces changes to its executive team to maintain progress toward upcoming milestones.

### Hugging Face — An Introduction to Deep Reinforcement Learning

- Company: Hugging Face (huggingface.co)
- Announced: 2022-05-04
- Category: not stated
- Coverage: not counted
- Announcement: no
- Group: routine
- Source: https://huggingface.co/blog/deep-rl-intro
- Record: https://forck.live/items/2206-an-introduction-to-deep-reinforcement-learning
- Subject: Platform

Hugging Face published an introductory article on deep reinforcement learning, which serves as the first unit of a free Deep Reinforcement Learning Class. The article covers basic concepts, frameworks, and practical exercises including training a lunar lander agent.

### Hugging Face — Accelerate Large Model Training using PyTorch Fully Sharded Data Parallel

- Company: Hugging Face (huggingface.co)
- Announced: 2022-05-02
- Category: not stated
- Coverage: not counted
- Announcement: no
- Group: routine
- Source: https://huggingface.co/blog/pytorch-fsdp
- Record: https://forck.live/items/2207-accelerate-large-model-training-using-pytorch-fully-sharded-data-parallel
- Subject: Platform

To use PyTorch's FullyShardedDataParallel (FSDP) with the Accelerate library to train large models, demonstrating with GPT-2 Large and GPT-2 XL that FSDP enables larger batch sizes and avoids out-of-memory errors compared to Distributed Data Parallel.