Windows 10: WTF is Nividia container and why it consumes 20% of GPU !!

/u/SpecterX95 · Nov 26, 2020

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:)

Brink · Nov 26, 2020

Bringing GPU acceleration to Windows containers

At the release of Windows Server 2019 last year, we announced support for a set of hardware devices in Windows containers. One popular type of device missing support at the time: GPUs. We’ve heard frequent feedback that you want hardware acceleration for your Windows container workloads, so today, we’re pleased to announce the first step on that journey: starting in Windows Server 2019, we now support GPU acceleration for DirectX-based apps and frameworks in Windows containers.

The best part is, you can use the Windows Server 2019 build you have today—no new OS patches or configuration is necessary. All you need is a new build of Docker and the latest display drivers. Read on for detailed requirements and to learn how you can get started with GPU accelerated DirectX in Windows containers today.

Background: Why GPU acceleration?

Containers are an excellent tool for packaging and deploying many kinds of workloads. For many of these, traditional CPU compute resources are sufficient. However, for a certain class of workload, the massively parallel compute power offered by GPUs (graphics processing units) can speed up operations by orders of magnitude, bringing down cost and improving throughput immensely.

GPUs are already a common tool for many popular workloads, from traditional rendering and simulation to machine learning training and inference. With today’s announcement, we’re unlocking new app scenarios for Windows containers and enabling more applications to be successfully shifted into Windows containers.

GPU-accelerated DirectX, Windows ML, and more

For some users, DirectX conjures associations with gaming. But DirectX is about more than games—it also powers a large ecosystem of multimedia, design, computation, and simulation frameworks and applications.

As we looked at adding GPU support to Windows containers, it was clear that starting with the DirectX APIs—the foundation of accelerated graphics, compute, and AI on Windows—was a natural first step.

By enabling GPU acceleration for DirectX, we’ve also enabled GPU acceleration for the frameworks built on top of it. One such framework is Windows ML, a set of APIs providing fast and efficient AI inferencing capabilities. With GPU acceleration in Windows containers, developers now have access to a first-class inferencing runtime that can be accelerated across a broad set of capable GPU acceleration hardware.

Usage

On a system meeting the requirements (see below), start a container with hardware-accelerated DirectX support by specifying the --device option at container runtime, as follows:

docker run --isolation process --device class/5B45201D-F2F2-4F3B-85BB-30FF1F953599 <your Docker image>

Note that this does not assign GPU resources exclusively to the container, nor does it prevent GPU access on the host. Rather, GPU resources are scheduled dynamically across the host and containers in much the same way as they are scheduled among apps running on your personal device today. You can have several Windows containers running on a host, each with hardware-accelerated DirectX capabilities.

Requirements

For this feature to work, your environment must meet the following requirements:

The container host must be running Windows Server 2019 or Windows 10, version 1809 or newer.

The container base image must be microsoft.com/windows:1809 or newer. Windows Server Core and Nano Server container images are not currently supported.

The container must be run in process isolation mode. Hyper-V isolation mode is not currently supported.

The container host must be running Docker Engine 19.03 or newer.

The container host must have a GPU running display drivers version WDDM 2.5 or newer.

To check the WDDM version of your display drivers, run the DirectX Diagnostic Tool (dxdiag.exe) on your container host. In the tool’s “Display” tab, look in the “Drivers” section as indicated below.

Getting started

Operating system support for this feature is already complete and broadly available as part of Windows Server 2019 and Windows 10, version 1809. Formal Docker support is scheduled for the upcoming Docker EE Engine 19.03 release. Until then, if you’re eager to try out the feature early, you can check out our sample on GitHub and follow the README instructions to get started. We’ll show you how to acquire a nightly build of Docker and use it to run a containerized Windows ML inferencing app with GPU acceleration.

Going forward

We look forward to getting your feedback on this experience. Please leave a comment below or tweet us with your thoughts. What are the next things you’d like to be able to do with GPU acceleration in containers on Windows?

Cheers,
Rick Manning, Graphics PM
@CraigWilhite, Windows Container PM
Click to expand...

Source: Bringing GPU acceleration to Windows containers - Microsoft Tech Community - 393939

Brink · Nov 26, 2020

Bringing GPU acceleration to Windows containers

At the release of Windows Server 2019 last year, we announced support for a set of hardware devices in Windows containers. One popular type of device missing support at the time: GPUs. We’ve heard frequent feedback that you want hardware acceleration for your Windows container workloads, so today, we’re pleased to announce the first step on that journey: starting in Windows Server 2019, we now support GPU acceleration for DirectX-based apps and frameworks in Windows containers.

The best part is, you can use the Windows Server 2019 build you have today—no new OS patches or configuration is necessary. All you need is a new build of Docker and the latest display drivers. Read on for detailed requirements and to learn how you can get started with GPU accelerated DirectX in Windows containers today.

Background: Why GPU acceleration?

Containers are an excellent tool for packaging and deploying many kinds of workloads. For many of these, traditional CPU compute resources are sufficient. However, for a certain class of workload, the massively parallel compute power offered by GPUs (graphics processing units) can speed up operations by orders of magnitude, bringing down cost and improving throughput immensely.

GPUs are already a common tool for many popular workloads, from traditional rendering and simulation to machine learning training and inference. With today’s announcement, we’re unlocking new app scenarios for Windows containers and enabling more applications to be successfully shifted into Windows containers.

GPU-accelerated DirectX, Windows ML, and more

For some users, DirectX conjures associations with gaming. But DirectX is about more than games—it also powers a large ecosystem of multimedia, design, computation, and simulation frameworks and applications.

As we looked at adding GPU support to Windows containers, it was clear that starting with the DirectX APIs—the foundation of accelerated graphics, compute, and AI on Windows—was a natural first step.

By enabling GPU acceleration for DirectX, we’ve also enabled GPU acceleration for the frameworks built on top of it. One such framework is Windows ML, a set of APIs providing fast and efficient AI inferencing capabilities. With GPU acceleration in Windows containers, developers now have access to a first-class inferencing runtime that can be accelerated across a broad set of capable GPU acceleration hardware.

Usage

On a system meeting the requirements (see below), start a container with hardware-accelerated DirectX support by specifying the --device option at container runtime, as follows:

docker run --isolation process --device class/5B45201D-F2F2-4F3B-85BB-30FF1F953599 <your Docker image>

Note that this does not assign GPU resources exclusively to the container, nor does it prevent GPU access on the host. Rather, GPU resources are scheduled dynamically across the host and containers in much the same way as they are scheduled among apps running on your personal device today. You can have several Windows containers running on a host, each with hardware-accelerated DirectX capabilities.

Requirements

For this feature to work, your environment must meet the following requirements:

The container host must be running Windows Server 2019 or Windows 10, version 1809 or newer.

The container base image must be microsoft.com/windows:1809 or newer. Windows Server Core and Nano Server container images are not currently supported.

The container must be run in process isolation mode. Hyper-V isolation mode is not currently supported.

The container host must be running Docker Engine 19.03 or newer.

The container host must have a GPU running display drivers version WDDM 2.5 or newer.

To check the WDDM version of your display drivers, run the DirectX Diagnostic Tool (dxdiag.exe) on your container host. In the tool’s “Display” tab, look in the “Drivers” section as indicated below.

Getting started

Operating system support for this feature is already complete and broadly available as part of Windows Server 2019 and Windows 10, version 1809. Formal Docker support is scheduled for the upcoming Docker EE Engine 19.03 release. Until then, if you’re eager to try out the feature early, you can check out our sample on GitHub and follow the README instructions to get started. We’ll show you how to acquire a nightly build of Docker and use it to run a containerized Windows ML inferencing app with GPU acceleration.

Going forward

We look forward to getting your feedback on this experience. Please leave a comment below or tweet us with your thoughts. What are the next things you’d like to be able to do with GPU acceleration in containers on Windows?

Cheers,
Rick Manning, Graphics PM
@CraigWilhite, Windows Container PM
Click to expand...

Source: Bringing GPU acceleration to Windows containers - Microsoft Tech Community - 393939

TheFreeOne · Nov 26, 2020

Nividia Context Menu Issue

I am running Windows 10 1809 with latest updates installed with latest Nividia drivers 418 version on a Dell G3 now my issue is the following :

As attached when i click on the option to "Change default graphics processor" nothing happens it does not open nividia control panel or anything

now my issue is not with changing the graphics driver i can do that manually by opening nividia control panel my issue is why the option in the context menu does nothing

Steps i tried to fix the issue :

1- un-installed nividia drivers and re-installed them from website of nividia

2- downgrade nividia drivers version to lower version which is 398 did not help

3- un-installed nividia drivers from device manager restarted pc so windows can install drivers by itself

Now all above failed and issue still exists so any idea what could be reason that option does not work

Choosing to run with integrated graphics or nividia from context menu works fine so it is only the "Change default graphics processor "

that does not work.

Photo attached is for reference it is not taken from my pc

Windows 10: WTF is Nividia container and why it consumes 20% of GPU !!

WTF is Nividia container and why it consumes 20% of GPU !!

WTF is Nividia container and why it consumes 20% of GPU !!

WTF is Nividia container and why it consumes 20% of GPU !!

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