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Graphics: X.Org Server 1.20, NVIDIA 396.18.11, Mesa 18.1

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Graphics/Benchmarks
  • Several DDX Drivers Aren't Yet Ready For X.Org Server 1.20

    If you were hoping to build the newly-released X.Org Server 1.20 on your system(s) this weekend, be forewarned that a number of the DDX drivers haven't yet been updated for supporting the API/ABI changes of this big server update.

    A number of the smaller, obscure drivers like Tseng, SiS, R128, and March64 haven't yet been updated for xorg-server 1.20 support but also the more prominent xf86-video-ati and xf86-video-amdgpu DDX drivers have not yet seen new releases with xorg-server 1.20 support.

  • NVIDIA 396.18.11 Linux Vulkan Driver Released With Fixes

    The NVIDIA 396.18.11 Vulkan beta driver for Linux was released on Friday as pulling in the latest upstream fixes to the Vulkan beta driver branch for Windows and Linux.

    The 396.18.11 Linux driver and 397.76 Windows driver pull in the latest fixes from their general release driver. For the Linux release, it comes just three days after another small beta update (396.18.08) that was released to fix Alt-Tab freezing with the DXVK Direct3D11-over-Vulkan implementation.

  • Mesa 18.1 Expected To Officially Debut Next Week

    While Mesa 18.0 debuted just about one and a half months ago, the fourth and final release candidate of Mesa 18.1 is now available for testing as the next quarterly feature installment to these primarily OpenGL/Vulkan open-source drivers.

    First time Mesa release manager Dylan Baker issued Mesa 18.1.0-RC4 this Friday evening with 25 queued patches. The affected work ranges from core Mesa fixes to Gallium3D, R600, RADV, RadeonSI, i965, and ANV fixes... Pretty much fixes across the board at least as far as the major drivers are concerned sans Nouveau.

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TransmogrifAI From Salesforce

  • Salesforce plans to open-source the technology behind its Einstein machine-learning services
    Salesforce is open-sourcing the method it has developed for using machine-learning techniques at scale — without mixing valuable customer data — in hopes other companies struggling with data science problems can benefit from its work. The company plans to announce Thursday that TransmogrifAI, which is a key part of the Einstein machine-learning services that it believes are the future of its flagship Sales Cloud and related services, will be available for anyone to use in their software-as-a-service applications. Consisting of less than 10 lines of code written on top of the widely used Apache Spark open-source project, it is the result of years of work on training machine-learning models to predict customer behavior without dumping all of that data into a common training ground, said Shubha Nabar, senior director of data science for Salesforce Einstein.
  • Salesforce open-sources TransmogrifAI, the machine learning library that powers Einstein
    Machine learning models — artificial intelligence (AI) that identifies relationships among hundreds, thousands, or even millions of data points — are rarely easy to architect. Data scientists spend weeks and months not only preprocessing the data on which the models are to be trained, but extracting useful features (i.e., the data types) from that data, narrowing down algorithms, and ultimately building (or attempting to build) a system that performs well not just within the confines of a lab, but in the real world.