Earlier this month Google announced that the TF Object Detection API (OD API) officially supports TensorFlow 2. This comes as the tech giant has been working on making the TF ecosystem more compatible with frequently used models and libraries. The company has been migrating TF Object Detection API models to be TensorFlow 2 compatible since a year, which was evident in the Object Detection API GitHub repository, since the last few months. The company highlighted that this move aims at exposing users to the benefits of TF2 and Keras while being able to use TF1. "We believe that there might be many teams out there grappling with similar migration projects, so we thought that a few words about our thought process and approach here might be useful even for non-object-detection TensorFlow users," it said. With this, there are three kinds of users in the codebase -- new users, existing TF1 users who want to migrate to TF2 and existing TF1 users who do not want to migrate just yet.

In this week's Friday Faves, we have Tensorflow 2 dropping and a beautiful bonus, next level physics-based ML, and a problem with a Harvard deep learning paper. Tensorflow 2.0 dropped this week and it has Eager Execution (read "normal behaviour") per default and the Keras API per default. If you're familiar with PyTorch, you'll wonder "that's news?" but you can now use one of three, which for PyTorch users will be another "that's news?" Depending on how reproducible and complicated your model gets, you'll need to make that choice. I have personally yet to use Subclassing, but I hear good things.

Inspur has announced the open-source release of TF2, an FPGA-based efficient AI computing framework. The inference engine of this framework employs the world's first DNN shift computing technology, combined with a number of the latest optimization techniques, to achieve FPGA-based high-performance low-latency deployment of universal deep learning models. This is also the world's first open-sourced FPGA-based AI framework that contains comprehensive solutions ranging from model pruning, compression, quantization, and a general DNN inference computing architecture based on FPGA. The open source project can be found at https://github.com/TF2-Engine/TF2. Many companies and research institutions, such as Kuaishou, Shanghai University, and MGI, are said to have joined the TF2 open source community, which will jointly promote open-source cooperation and the development of AI technology based on customizable FPGAs, reducing the barriers to high-performance AI computing technology, and shortening development cycles for AI users and developers.