- SCSI is a block-based set of commands that connects computing devices to networked storage, including spinning up storage media and data reads/writes. The protocol uses initiators to send SCSI commands to storage device targets on remote servers.
- Windows VirtIO Drivers. The source for the Windows drivers is hosted in a repository on GIT hub. Anonymous users can clone the repository git clone git://github.com.
- The motherboard has an nVidia chipset. I also have 2 standard SATA hard drives and a SATA SSD drive attached. When I bring up Device Manager, all of the drives are listed as SCSI drives.
If your Windows device has NIVIDIA graphics and NVIDIA display drivers installed, you will have the NVIDIA Control Panel available. By default, the NVIDIA Control Panel notification tray icon will show on the taskbar. The NVIDIA Control Panel is NVIDIA's hardware control application that unlocks the features of NVIDIA drivers.
You are probably familiar with Nvidia as they have been developing graphics chips for laptops and desktops for many years now. But the company has found a new application for its graphic processing units (GPUs): machine learning. It is called CUDA.
Nvidia says:
“CUDA® is a parallel computing platform and programming model invented by NVIDIA. It enables dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU)…CUDA-capable GPUs have hundreds of cores that can collectively run thousands of computing threads.”
Contrast that number with the typical Intel or AMD chip, which has 4 or 8 cores.
Nvidia Scsi & Raid Devices Driver Downloads
a neural network means to find the weights and bias that yield the lowest cost using an activation function. The algorithm to do that is usually gradient descent. All of this requires multiplying very large matrices of numbers. This can be done in parallel since the order you do that does not matter.To recall, finding the solution to a neural network means to apply different coefficient (weights) to each input variable, make a prediction, then see how accurate that prediction is. Then, using the gradient descent algorithm, we try different coefficients and repeat the process. You keep doing that over and over until you reach the point where the neural network most accurately predicts whatever is is supposed to predict. With a large neural network of many thousands of sigmoids (nodes) that can take days.
To further make this simpler to understand, the neural network is represented as a series of inputs X weights W and a bias B, where X, W, and B are matrices. This yields an output, which is typically a small vector of says [0,1,2,3,4,5,6,7,8,9], as in the case of handwritten digit recognition. So we have:
X * W + B
X * W is the dot-product of 2 n-dimensional matrices. For example, for 2 dimensional matrices ,X and W, that is shown below where X is the first matrix, W is the 2nd, and X * W is the dot product. The dot product is the sum of multiplication of each corresponding row-column combination:
| X = [x11, x12, x13, x21, x22, x23, x31, x32, x33] | W = [w11, w12, w13, w21, w22, w23, w31, w32, w33] | X * W = [ (x11 * w11) + (x12 * w21) + (x13 * w31) … |
Which is more easily visualized in this graphic from Math is Fun:
This can be done in parallel since it does not matter which row-column combination you pick first and you can work on all of those at the same time. That massively parallel operation is what GPUs are designed to do.
Doing this multiplication is simple for small matrices. But for large ones it takes much more memory and computing time, which is why using an additional processor makes sense.
Large matrices will even fill up the memory of the computer, a problem that can be solved by setting up machine learning libraries, like TensorFlow, to run in a cluster.
Where can you Run CUDA?
Not all software can run on GPUs since at a low level they operate differently than CPUs. What NVIDIA has done is provide an API to low level languages, like C++, that lets programs written in C++ use the GPU.
There is a special version of TensorFlow that you can easily install to take advantage of that. But that is made more complex because not all operating systems support CUDA, and virtual machines usually do not.
To install TensorFlow GPU version using virtualenv you follow the rather simple instructions here. For example, you install it using pip:
pip install --upgrade tensorflow-gpu
But first you must follow these instructions to install the Nvidia GPU toolkit.
Like I said, it will not work everywhere. For example, it works on Ubuntu but not Debian. And in general it does not work on virtual machines. The reason for that is the VM Is running on a hypervisor, which is responsible for low-level I/O. The VM does not have access to low level graphics.
To illustrate, here is how you check on Linux to see if you have a Nvidia graphics chip (or card. The cards are popular with gamers.):
This command shows that I have a CUDA-enabled GPU since it is listed in the Nvidia list of supported devices.
But if I run the same command on an Ubuntu server running in the cloud I get:
pcilib: Cannot open /proc/bus/pci
lspci: Cannot find any working access method
And it I leave off the grep filter and run this on a CentOs VM in the cloud I get a list of devices, but none of them are the Nvidia graphics card:
00:00.0 Host bridge: Intel Corporation 440BX/ZX/DX - 82443BX/ZX/DX Host bridge (rev 01)
00:01.0 PCI bridge: Intel Corporation 440BX/ZX/DX - 82443BX/ZX/DX AGP bridge (rev 01)
00:07.0 ISA bridge: Intel Corporation 82371AB/EB/MB PIIX4 ISA (rev 08)
00:07.1 IDE interface: Intel Corporation 82371AB/EB/MB PIIX4 IDE (rev 01)
00:07.3 Bridge: Intel Corporation 82371AB/EB/MB PIIX4 ACPI (rev 08)
00:07.7 System peripheral: VMware Virtual Machine Communication Interface (rev 10)
00:0f.0 VGA compatible controller: VMware SVGA II Adapter
00:10.0 SCSI storage controller: LSI Logic / Symbios Logic 53c1030 PCI-X Fusion-MPT Dual Ultra320 SCSI (rev 01)
00:11.0 PCI bridge: VMware PCI bridge (rev 02)
So if you want to experiment with this you can install CUDA on your notebook and desktop and try to multiply try matrices (in this case vectors and TensorFlow) like this:
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)
The Instructions for doing that are here.
And if you really want to scale up this you can buy multiple Nvidia graphics cards and plug them into a desktop and multiple desktops and run a TensorFlow cluster across that.
- 3Installation
- 3.1Using the ISO
Introduction
VirtIO Drivers are paravirtualized drivers for kvm/Linux (see http://www.linux-kvm.org/page/Virtio). In short, they enable direct (paravirtualized) access to devices and peripherals for virtual machines using them, instead of slower, emulated, ones.
A quite extended explanation about VirtIO drivers can be found here http://www.ibm.com/developerworks/library/l-virtio.
At the moment these kind of devices are supported:
- block (disks drives), see Paravirtualized Block Drivers for Windows
- network (ethernet cards), see Paravirtualized Network Drivers for Windows
- balloon (dynamic memory management), see Dynamic Memory Management
You can maximize performances by using VirtIO drivers. The availability and status of the VirtIO drivers depends on the guest OS and platform.
Windows OS Support
Windows does not have native support for VirtIO devices included.But, there is excellent external support through opensource drivers, which are available compiled and signed for Windows:
Note that this repository provides not only the most recent, but also many older versions.Those older versions can still be useful when a Windows VM shows instability or incompatibility with a newer driver version.
The binary drivers are digitally signed by Red Hat, and will work on 32-bit and 64-bit versions of Windows
Installation
Using the ISO
You can download the latest stable or you can download the most recent build of the ISO.Normally the drivers are pretty stable, so one should try out the most recent release first.
You can access the ISO can in a VM by mounting the ISO with a virtual CD-ROM/DVD drive on that VM.
Wizard Installation
You can use an easy wizard to install all, or a selection, of VirtIO drivers.
- Open the Windows Explorer and navigate to the CD-ROM drive.
- Simply execute (double-click on) virtio-win-gt-x64
- Follow its instructions.
- (Optional) use the virtio-win-guest-tools wizard to install the QEMU Guest Agent and the SPICE agent for an improved remote-viewer experience.
- Reboot VM
Manual Installation
- Open the Windows Explorer and navigate to the CD-ROM drive.
- There you can see that the ISO consists of several directories, each having sub-directories for supported OS version (for example, 2k19, 2k12R2, w7, w8.1, w10, ...).
- Balloon
- guest-agent
- NetKVM
- qxl
- vioscsi
- ...
- Manual Installation
- Navigate to the desired driver directories and respective Windows Version
- Right-click on the file with type 'Setup Information'
- A context menu opens, select 'Install' here.
- Repeat that process for all desired drivers
- Reboot VM.
Downloading the Wizard in the VM
Nvidia Scsi & Raid Devices Driver Download Free
You can also just download the most recent virtio-win-gt-x64.msi or virtio-win-gt-x86.msi from inside the VM, if you have already network access.
Then just execute it and follow the installation process.
Troubleshooting
Try an older version of the drivers first, if that does not helps ask in one of our support channels:https://pve.proxmox.com/wiki/Get_support
Further Reading
The source code of those drivers can be found here: https://github.com/virtio-win/kvm-guest-drivers-windows