Supercharging Visualization with Apache Arrow

[unable to retrieve full-text content]Interactive visualization of large datasets on the web has traditionally been impractical. Apache Arrow provides a new way to exchange and visualize data at unprecedented speed and scale.
Original Post: Supercharging Visualization with Apache Arrow

Choosing an Open Source Machine Learning Library: TensorFlow, Theano, Torch, scikit-learn, Caffe

[unable to retrieve full-text content]Open Source is the heart of innovation and rapid evolution of technologies, these days. Here we discuss how to choose open source machine learning tools for different use cases.
Original Post: Choosing an Open Source Machine Learning Library: TensorFlow, Theano, Torch, scikit-learn, Caffe

Latest Innovations in TensorFlow Serving

Posted by Posted by Chris Olston, Research Scientist, and Noah Fiedel, Software Engineer, TensorFlow ServingSince initially open-sourcing TensorFlow Serving in February 2016, we’ve made some major enhancements. Let’s take a look back at where we started, review our progress, and share where we are headed next.Before TensorFlow Serving, users of TensorFlow inside Google had to create their own serving system from scratch. Although serving might appear easy at first, one-off serving solutions quickly grow in complexity. Machine Learning (ML) serving systems need to support model versioning (for model updates with a rollback option) and multiple models (for experimentation via A/B testing), while ensuring that concurrent models achieve high throughput on hardware accelerators (GPUs and TPUs) with low latency. So we set out to create a single, general TensorFlow Serving software stack.We decided to make it open-sourceable from the get-go, and…
Original Post: Latest Innovations in TensorFlow Serving

Latest Innovations in TensorFlow Serving

Posted by Chris Olston, Research Scientist, and Noah Fiedel, Software Engineer, TensorFlow ServingSince initially open-sourcing TensorFlow Serving in February 2016, we’ve made some major enhancements. Let’s take a look back at where we started, review our progress, and share where we are headed next.Before TensorFlow Serving, users of TensorFlow inside Google had to create their own serving system from scratch. Although serving might appear easy at first, one-off serving solutions quickly grow in complexity. Machine Learning (ML) serving systems need to support model versioning (for model updates with a rollback option) and multiple models (for experimentation via A/B testing), while ensuring that concurrent models achieve high throughput on hardware accelerators (GPUs and TPUs) with low latency. So we set out to create a single, general TensorFlow Serving software stack.We decided to make it open-sourceable from the get-go, and development started…
Original Post: Latest Innovations in TensorFlow Serving

Announcing OpenFermion: The Open Source Chemistry Package for Quantum Computers

Posted by Ryan Babbush and Jarrod McClean, Quantum Software Engineers, Quantum AI Team“The underlying physical laws necessary for the mathematical theory of a large part of physics and the whole of chemistry are thus completely known, and the difficulty is only that the exact application of these laws leads to equations much too complicated to be soluble.”-Paul Dirac, Quantum Mechanics of Many-Electron Systems (1929)In this passage, physicist Paul Dirac laments that while quantum mechanics accurately models all of chemistry, exactly simulating the associated equations appears intractably complicated. Not until 1982 would Richard Feynman suggest that instead of surrendering to the complexity of quantum mechanics, we might harness it as a computational resource. Hence, the original motivation for quantum computing: by operating a computer according to the laws of quantum mechanics, one could efficiently unravel exact simulations of nature. Such simulations…
Original Post: Announcing OpenFermion: The Open Source Chemistry Package for Quantum Computers