Intel to Present Neural Compute Stick 2

Neural Compute Stick 2 is an autonomous neural network on a USB drive
15 November 2018   434

At the Beijing conference, Intel introduced Neural Compute Stick 2, a device that facilitates the development of smart software for peripheral devices. These include not only network equipment, but also IoT systems, video cameras, industrial robots, medical systems and drones. The solution is intended primarily for projects that use computer vision.

Neural Compute Stick 2 is an autonomous neural network on a USB drive and should speed up and simplify the development of software for peripheral devices by transferring most of the computation needed for learning to the specialized Intel Movidius Myriad X processor. Neural Compute Engine, responsible for the high-speed neural network of deep learning.

The first Neural Compute Stick was created by Movidius, which was acquired by Intel in 2016. The second version is 8 times faster than the first one and can work on Linux OS. The device is connected via a USB interface to a PC, laptop or peripheral device.

Intel said that Intel NCS 2 allows to quickly create, configure and test prototypes of neural networks with deep learning. Calculations in the cloud and even access to the Internet for this is not needed.

The module with a neural network has already been released for sale at a price of $ 99. Even before the start of sales, some developers got access to Intel NCS 2. With its help, projects such as Clean Water AI, which use machine vision with a microscope to detect harmful bacteria in water, BlueScan AI, scanning the skin for signs of melanoma, and ASL Classification, real-time translates sign language into text.

Over the Movidius Myriad X VPU, Intel worked with Microsoft, which was announced at the Developer Day conference in March 2018. The AI ​​platform is expected to appear in upcoming Windows updates.

Facebook to Release PyTorch 1.0

This release added support for large cloud platforms, a C ++ interface, a set of JIT compilers
10 December 2018   125

Facebook has released a stable version of the library for machine learning PyTorch 1.0. This iteration added support for large cloud platforms, a C ++ interface, a set of JIT compilers, and various improvements.

The stable version received a set of JIT compilers that eliminate the dependence of the code on the Python interpreter. The model code is transformed into Torch Script - a superstructure over Python. Keeping the opportunity to work with the model in the Python environment, the user can download it to other projects not related to this language. So, the PyTorch developers state that the code processed in this way can be used in the C ++ API.

The torch.distributed package and the torch.nn.parallel.DistributedDataParallel module are completely redesigned. torch.distributed now has better performance and works asynchronously with the Gloo, NCCL and MPI libraries.

The developers added a C ++ wrapper to PyTorch 1.0. It contains analogs of Python interface components, such astorch.nn,torch.optim, torch.data. According to the creators, the new interface should provide high performance for C ++ applications. True, the C ++ API is still experimental, but it can be used in projects now.

To improve the efficiency of working with PyTorch 1.0, a Torch Hub repository has been created, which stores pre-trained models of neural networks. You can publish your own development using the hubconf.py file, after which the model will be available for download by any user via the torch.hub.load API.

Support for C extensions and the module torch.utils.trainer were removed from the library.

Facebook released the preliminary version of PyTorch 1.0 at the beginning of October 2018, and in two months the developers brought the framework to a stable state.