Announcing the winners of the #PoweredByTF 2.0 Dev Post Challenge

Published in
3 min readJun 21, 2019


Posted by the TensorFlow team

At the 2019 TensorFlow Dev Summit we announced the Powered by TF Challenge on DevPost specifically for users to create and share their latest and greatest with TensorFlow 2.0.

Thank you to everyone who joined the challenge, we had over 600 participants. We loved seeing what you built, ranging from an app that shows memes and asserts how you feel and try to cheer you up, to using Convolutional Neural Networks to predict noise in lab-rat MRI signals.

Today we’re excited to announce our winners. We’ll be featuring their work on our blog over the next month, so make sure you check back in to learn more about how they hacked on 2.0.

In the meantime, continue hacking on newly released TensorFlow 2.0 beta and provide feedback! For more on TensorFlow 2.0, join our developers mailing list, file issues with the 2.0 tag, and check out our docs.

  1. Huskarl: a Deep Reinforcement Learning framework, built with Keras

Huskarl is a framework for deep reinforcement learning focused on research and fast prototyping. It’s built on TensorFlow 2.0 and uses the tf.keras API when possible for conciseness and readability.

Huskarl makes it easy to parallelize computation of environment dynamics across multiple CPUs. This is useful for speeding up on-policy learning algorithms that benefit from multiple concurrent sources of experience such as A2C or PPO. It is especially useful for computationally intensive environments such as physics-based ones.

The Huskarl A2C agent learning to balance a cartpole using 16 environment instances simultaneously. The thicker blue line shows the reward obtained using the greedy, target policy. A gaussian epsilon-greedy policy is used when acting in the other 15 environments, with epsilon mean varying from 0 to 1.

2. a Python 3 package for generating N-body simulations

A python 3 package for generating N-body simulations, computing transit timing variations (TTV) and retrieving orbit parameters and uncertainties from TTV measurements within a Bayesian framework. Machine learning is used to estimate the orbit parameters and constraints priors before running a retrieval to model orbital perturbations.

Top Left: plots of the orbit positions for each object. Top Middle: Radial velocity semi-amplitude (m/s) for the star. Top Right: Lomb-Scargle periodogram of the RV semi-amplitude signal. Bottom Left: Table of simulation parameters. Bottom Middle: The difference (or residuals) between the observed transit times and a calculated linear ephemeris (O-C). Bottom Righ:t Lomb-Scargle periodogram of the O-C signal for each planet.

3. HandTrack.js: a library for prototyping hand gestures in the browser

Handtrack.js is a library for prototyping real time hand detection (bounding box), directly in the browser. Underneath, it uses a trained convolutional neural network that provides bounding box predictions for the location of hands in an image. The convolutional neural network (ssdlite, mobilenetv2) is trained using the TensorFlow object detection api.

4. DeepPavlov: an open-source library for end-to-end dialog systems and chatbots

The DeepPavlov team built a framework for building dialogue systems that include all state-of-the-art (SOTA) NLP components. The framework contains a set of SOTA NLP models including Named-Entity Recognition (NER), Open-Domain Question Answering (ODQA) and more.

Specifically, their goal is to provide AI-application developers and researchers with a set of pre-trained NLP models, pre-defined dialog system components (ML/DL/Rule-based) and pipeline templates and a framework for implementing and testing their own dialog models

5. Disaster Watch: uses classification on tweets to identify natural disasters

Disaster Watch is a disaster mapping platform that collects data from twitter, extracts disaster-related information from tweets, and visualizes the results on a map. It enables users to quickly locate all the information in different geographic areas at a glance, and to find the physical constraints caused by the disaster, such as non-accessible river bridges, and take an informed action. Such information helps public and disaster responders.

Disaster Watch’s overall architecture




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