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Join us in the hacking room at the TensorFlow Community Day https://conferences.oreilly.com/oscon/oscon-or/public/schedule/detail/70899 on July 17 in Portland, where we will have about 10 tables, of up to 10 people working on projects, or sharing technology skills and ideas. Each table will have one or two leaders.
If you are a hacking room leader, you will:

  • Present (briefly) to the attendees of the TensorFlow Community Day what the activity is at your hacking table – and the skills of the people you would like to join your table – and the goals for the day
  • Lead and work with your volunteer team (up to 10 people) through the day until 4pm
  • Present (briefly) the achievements of your hackers in the main tent

If you are a hacking room participant, you will:

  • Join a team and work with them as much as a possible through the day – Your activities could be coding, testing, documenting, creating a demo, brainstorming etc. You will have the opportunity to network with a wonderful set of people and build long-term technology relationships.

If you’re already registered for OSCON 2018, all you need to do is turn up. If you’re not registered, you can sign up for OSCON’s Expo Plus Pass on their registration page, and TensorFlow Day will be open to you. Refreshments and lunch will be provided for all registered participants.

Hacking tables so far (tentative):

  • R & TensorFlow – led by: Gabriela de Queroz & Augustina Ragwitz
  • Identifying Pokemons – led by: Al Kari
  • TensorFlow Build – led by Günhan Gülsoy and Thomas Truong:
    —- Bazel tutorial. Would be nice if we can get Bazel team or other Bazel experts to help here. If they are not available to attend in person, a video conference call would be very helpful.
    —- Review current issues/gaps with TensorFlow Bazel build. Perhaps team can knock out a few PRs on the spot 😉
    —- Identify and feed our requirements into the Bazel’s Q3/Q4 feature planning.

  • Maths Kernel Library (MKL) & TensorFlow – led by Clayne Robinson & Guozhong (GZ) Zhuang
    —- help people get started with building TensorFlow with MKL support and using existing public whls and containers
    —- get a closer look the issues that off-the-street users face when they are using TF+MKL out of the box
    —- brainstorm as to how we can better include community TF+MKL contributions that don’t come from Google and Intel.
    —- Help the OS community understand how MKL optimizations have been implemented.

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