The data set would be astronomy sub-images that are either bad (edge of chip artifacts, bright star saturation and spikes, internal reflections, chip flaws) or good (populated with fuzzy-dot stars and galaxies and asteroids and stuff). Let's say the typical image is 512x512 but it varies a lot. Because the bad features tend to be big, I'd probably like to bin the images down to say 64x64 for compactness and speed. It has to run fast on tens of thousands of images. I'm sort of tempted by the solution of adopting PlaidML as my back end (if I understand what its role is), because it can compile the problem for many architectures, like CUDA, CPU-only, OpenCL.

This will act as our development branch going forward and will allow us to more rapidly prototype the changes we're making without breaking our existing user base. As a precaution, please note that certain features, tests, and hardware targets may be broken in plaidml-v1. You can continue to use code on the master branch or from our releases on PyPI. For your convenience, the contents of our master branch will be released as version 0.7.0. We are keeping the master branch of PlaidML stable and maintaining it until plaidml-v1 is ready for production.

In acquiring Vertex.AI for its Movidius unit, Intel is envisioning a tomorrow where deep learning will feature in many aspects of business. Chip giant Intel has acquired Vertex.AI, a Seattle start-up that is developing deep learning for every platform. The start-up will join the Movidius group, which is focused on self-learning and artificial intelligence (AI) technology on a myriad of devices. Intel acquired Movidius in 2016 for an undisclosed sum, rumoured to be in the region of $300m. This was part of a $1bn spending spree on AI tech companies, including Mighty AI, DataRobot, Lumiata, AEye and others.

As I discussed in my review of PyTorch, the foundational deep neural network (DNN) frameworks such as TensorFlow (Google) and CNTK (Microsoft) tend to be hard to use for model building. However, TensorFlow now contains three high-level APIs for creating models, one of which, tf.keras, is a bespoke version of Keras. Amazon is currently working on developing a MXNet back end for Keras. It's also possible to use PlaidML (an independent project) as a back end for Keras to take advantage of PlaidML's OpenCL support for all GPUs. As an aside, the name Keras is from the Greek for horn, κέρας, and refers to a passage from the Odyssey. The dream spirits that come through the gate made of horn are the ones that announce a true future; the ones that come through the gate made of ivory, ἐλέφας, deceive men with false visions.

Intel has been on an artificial intelligence (AI) buying spree lately. On the heels of its Nervana, Mobileye, and Movidius acquisitions, it today announced that it's buying Vertex.ai, Vertex.ai will join the chipmaker's Artificial Intelligence Products Group, according to a note on its website, where it'll "support a variety of hardware" and work to integrate PlaidML, its "multi-language acceleration platform" that allows developers to deploy AI models on Linux, macOS, and Windows devices, with Intel's nGraph machine learning backend. It'll continue to develop the PlaidML, which is open source, under the Apache 2.0 license. "Intel has acquired Vertex.ai, a Seattle-based startup focused on deep learning compilation tools and associated technology," Intel said in a statement.

Intel has an ambition to bring more artificial intelligence technology into all aspects of its business, and today is stepping up its game a little in the area with an acquisition. The computer processing giant has acquired Vertex.AI, a startup that had a mission of making it possible to develop "deep learning for every platform", and had built a deep learning engine called PlaidML to do this. Terms of the deal have not been disclosed but Intel has provided us with the following statement, confirming the deal and that the whole team -- including founders Choong Ng and Brian Retford -- will be joining Intel. "Intel has acquired Vertex.AI, a Seattle-based startup focused on deep learning compilation tools and associated technology. The seven-person Vertex.AI team joined the Movidius team in Intel's Artificial Intelligence Products Group. With this acquisition, Intel gained an experienced team and IP to further enable flexible deep learning at the edge. Additional details and terms are not being disclosed."

For the 0.3.3 release of PlaidML, support for running deep learning networks on macOS has improved with the ability to use Apple's native Metal API. Metal offers "near-direct access to the graphics processing unit (GPU)", allowing machine learning tasks to run faster on any Mac where Metal is supported. As previously announced, Mac users could accelerate their PlaidML workloads by using the OpenCL backend. In our internal testing, in some cases, we see an up to 5x speed up by using Metal over OpenCL. Next, run plaidml-setup to select the desired Metal-based device.

We're pleased to announce the next step towards deep learning for every device and platform. Today Vertex.AI is releasing PlaidML, our open source portable deep learning engine. Our mission is to make deep learning accessible to every person on every device, and we're building PlaidML to help make that a reality. The initial version of PlaidML runs on most existing PC hardware with OpenCL-capable GPUs from NVIDIA, AMD, or Intel. Additionally, we're including support for running the widely popular Keras framework on top of Plaid to allow existing code and tutorials to run unchanged. Our company uses PlaidML at the core of our deep learning vision systems for embedded devices, and to date we've focused on support for image processing neural networks like ResNet-50, Xception, and MobileNet.