Can anyone tell me if one can directly select the DNN AI core for neural network evaluation on HoloLens 2.
I have read about the HPU, which includes and DNN AI core in the GitHub repo here. But in the doc for the devices that can be used only CPU and GPU are listed.
Currently Windows AI only supports inference on CPUs or GPUs.
Unfortunately there isn't a way to perform inference on the HoloLens2 HPU DNN AI Core at the moment.
Related
Question speaks for itself. It is required for testing purposes. Currently owe GeForce MX330 and would like to simulate different nvidia gpu architecture.. Is that even possible with some tool or to trick the software that requires tensore cores?
I am aware of the cloud services..
I have a Windows desktop app using Direct2D. On systems with both integrated and discrete GPUs it's possible for the user to configure the app to use the more powerful discrete GPU via Display Settings, as described here. https://www.amd.com/en/support/kb/faq/gpu-110
Is there a way of adjusting this setting programmatically, so that the application is automatically configured to use the "high performance" GPU without user fettling? Possibly thought a manifest or version resource?
I am currently running out of memory and RAM on my 2013 Macbook Pro (8gb 1600 MHz DDR3 memory, and 2 GHz Intel Core i7 processor) while running different scikit-learn (Random Search on MLPRegressor and GradientBoostingRegressor) models on a 50,000 sample data set with ~70 features, most of which are categorical. I have setup a VM on Google Cloud Platform, but have not seen much of an improvement in execution time. Here are the specs of the VM: Machine type: n1-standard-8 (8 vCPUs, 30 GB memory), Source image: ubuntu-1604-xenial-v20180126. I'm wondering if anyone has any recommendations on tweaking vm specs for learning data science. I'm not looking to add any GPUs due to cost. Thank you
As the volume and nature of the data you plan to process is known to you, simply experimenting with different machines on Google Cloud Platform is the best way to choose the most effective option.
General information on subject is to be found in the "Google Cloud Platform for Data Scientists" document.
In case you may still consider this option, "Graphics Processing Unit (GPU)" provides an overview of this component in the context of data science.
Constraints:
My application requires SSE4.2 instruction set.
I am using packer.io to provision my Windows Azure VM (OpenLogic 6.5 OS.)
Windows Azure returns an AMD-processor-backed-VM about 15% of the time. The rest of the time - they are Intel-processor-based. AMD processors do not support SSE4.2, but they do support SSE4a. So, my application is terminated with SIGILL on AMD processors.
Questions:
Can I request specific architecture (Intel CPU) when Packer
provisions a VM? I know that instance types >= A8 come only with Intel processors, but they are more expensive, and I would not want to use them for development.
If Packer cannot do it, what are the other options
(Powershell, ect...) that would give me this functionality?
Thank you.
Answering my own question. Azure does not provide a way to request processor type. The only way to ensure Intel processor is to not use A-series machines (as confirmed by a MSFT representative.) Thus, no tool can do it.
I am thinking about running some graphics intensive programs on Windows Azure virtual machine, but not sure what kind of hardware do they have. Does all the VM have the same GPU? What is your experience of it?
The GPUs in Azure Virtual Machine are likely to be very basic and will most probably not have anywhere near the processing power you will need for carrying out intensive graphics manipulation. To my knowledge MS don't publish the details of the graphics hardware behind their Virtual Machines (If they actually use them at all?).
There's a question here on running WPF in an Azure cloud service which may be helpful.
Can Azure run WPF?
The N series Azure VMs support beefy GPUs. The NC series VM sports Tesla K80, with DDA (discreet device assignment) it supposed to provide close to bare metal performance. NV series VMs offer Tesla M60 with nVidia GRID.
More:
https://www.hpcwire.com/2015/09/29/microsoft-puts-gpu-boosters-on-azure-cloud/
https://blogs.technet.microsoft.com/hybridcloudbp/2016/12/13/n-series-azure-vms-with-gpu/
It's fascinating that there are FPGAs in Azure machines too (although not publicly accessible):
https://www.microsoft.com/en-us/research/project/project-catapult/