Learnings from TensorFlow Dev Summit 2019

It was a rainy day in Sunnyvale but many people tweeted photos of rainbows. We were happily waiting in the rain for check-in, and were very excited about the Summit.

Even though I knew the recordings will be made available later, I still took notes during talks because it helps me to learn. I also love taking photos with people I met so that I can remember them. There were so many new announcements and people to meet at the Summit, I could barely keep up.

One of the biggest announcements from the Summit was TensorFlow 2.0 Alpha. Give it a try:

Recently I also shared my notes on TF 2.0 from trying out the preview.

TensorFlow 2.0 is great for both beginners and experts; for both researchers and developers. It focuses on usability, clarity and flexibility:

During breaks I got to hang out with the TensorFlow team and community.

It was great honor to meet Francois Chollet, creator of Keras. I got a new Keras sticker from him and I enjoyed chatting with him about Keras.

Lots of changes planned for 2019 focusing on the four areas:

I’m very excited about Swift for TensorFlow, the next generation ML framework. Keras makes training easier but Swift is taking it further:

We saw a walkthrough of a image classification code which looks a lot like Keras. Swift for TensorFlow has seamless Python Interoperability. The great integration between Swift for TensorFlow and Python for TensorFlow should enable easier transition to Swift for TensorFlow.

TensorFlow Javascript is a library for training and deploying ML models in the browser:

New courses were announced at the Summit from Udacity, Coursera and Fast.ai. Before the Summit MIT also made a new Deep Learning course with TensorFlow 2.0.

I went to a few more talks on the second day of the Summit.

TensorFlow On-Device: Compressing, Quantizing, and Distributing

This was a popular session. The room was so crowded that I managed to find a corner to stand and even the overflow room was filled.

There were 2 talks: first one was “TensorFlow Lite on Android” — about how to get started with TensorFlow Lite. Second talk was “TensorFlow lite: Quantization”. After that there was Q&As with the TensorFlow Lite team.

Research and Future talks:

Global docs sprint planning

Before I headed over to the airport, Paige and I chatted about planning for a global docs sprint to help improve the documentation of TensorFlow.

I summarized the announcements and learnings from the Summit in sketchnotes. Trying to fit my notes from the Summit on a single piece of paper really helps me to visualize the key take-aways.

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