Recap of the 2020 TensorFlow Dev Summit

Posted by Laurence Moroney, Developer Advocate

Thanks to everyone who joined our virtual TensorFlow Dev Summit 2020 livestream! While we couldn’t meet in person, we hope we were able to make the event more accessible than ever.
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TensorFlow 2.2

After the release of TensorFlow 2.0 at last year’s Developer Summit, we have been working hard with our users and community to bring more enhancements and features, and this week posted a release candidate of TensorFlow 2.2.
. And, we've increased the compatibility of the TensorFlow ecosystem, including key libraries like section on GitHub for details.

The TensorFlow Ecosystem

TensorFlow 2.2 is just one part of a much bigger, and growing ecosystem of libraries and extensions that help you accomplish your machine learning goals. There’s a tool for all kinds of developers!
. Here, the T5 model, pretrained on the C4 dataset (available in The T5 model uses the latest in transfer learning to convert every language problem into a text-to-text format
In addition, we saw hundreds of papers and posters at NeurIPS last year that used TensorFlow.

We’re always looking to improve the experience for researchers, so we’re highlighting some important features.

First, we’ve gotten positive feedback from researchers on TensorBoard.dev, a tool we launched last year that lets you upload and share experiment results by URL. The URL allows for quickly visualizing hyperparameter sweeps. Second, we introduced a new Performance Profiler tool via TensorBoard that provides consistent monitoring of model performance.
in the tf.data API were created with this in mind to give researchers quick access to state of the art datasets. Whether you use these or personal datasets, the underlying TFRecord-based architecture allows you to build parallelization with and from Google Research are composable with TensorFlow, such as using TensorFlow data pipelines to input data into JAX. For those of you who are really heading towards the cutting edge of computing, check out and encourage you to take a look. We're also working on a set of new, lighter-weight preprocessing layers, now in , Sandeep spoke about how we're continuing to expand is a hosted Python notebook environment that gives you access to GPU and TPU resources that can be used for training TensorFlow models. In addition to the free tier, we recently launched the new . At the Dev Summit Tim Novikoff gave a fun talk on (TFX). An example high-level architecture of a production system built using TFX is show below:
. These are designed to make it easy for you to build end-to-end production pipelines like the one we show above, built on top of , a custom build of TensorFlow with many optimizations and enterprise grade long term support. We also have a very soon, that will enable simple drag and drop into android studio - and then automatically generate the Java classes for the TF Lite model with just a few clicks. We've also made it so model authors can provide a metadata spec when creating and converting models, making it easier for users of the model to understand how it works and use it in production. We also have our new , we continue to make it easier than ever for JavaScript developers and web developers to build and use machine learning. We announced several new models for face and hand tracking and NLP that are ready to use in your web or Node.js application. We also launched performance improvements, new backend, and more seamless integration with TensorFlow 2.0 model training.

Finally, we previewed the new TensorFlow Runtime (TFRT) that speeds up core loops, and will be available in open source soon. We also discussed are a rapidly growing community! In just 7 months since we launched, there are 73 User groups globally, the biggest in Korea with 46,000+ members. After the TensorFlow Roadshows in 2019, we launched the first 2 TFUGs in LATAM, in Brazil and Argentina. We’re always looking to encourage more, so, if you want to form one in your area, please reach out at dedicated to a variety of topics. Our latest addition, on Graphics will be available at the end of March. To learn more about SIGs and how they work, check out the , who actively support developers, companies, and tech communities by speaking at events, publishing content
If you’re interested in challenges, we’re running a new to learn more.

Education

If you are a university or other education organization we have a program to help you create educational content to teach machine learning with TensorFlow. We’ll support you with a variety of resources to help you succeed. We have published a request for proposals for financial sponsorship to help with widening access to traditionally underrepresented communities in AI, too. Details can be found on the specialization at Coursera with deeplearning.ai, giving you an introduction to how to build models for Computer Vision, NLP, Sequence Modelling and more.

We followed this up with for TensorFlow 2.0.

We've published an .

TensorFlow Developer Certificate

We’re excited to launch an assessment of your TensorFlow coding and model creation skills that will lead to an official certificate of your prowess as a TensorFlow Developer! In an effort to widen access to people of diverse backgrounds, experiences, and perspectives, we're offering a limited number of stipends for the educational material and/or the exam cost. Details are now available at specialization at Coursera will help you prepare for the assessment. Once you've earned the certificate you can share it on LinkedIn, GitHub and the new , read the , and don’t forget to subscribe to our on YouTube. For your convenience here are direct links to each of the sessions also:

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• TensorFlow Quantum: A software platform for hybrid quantum-classical ML