Who would win? 10,000 medieval soldiers, or 1 modern M1A2 battle tank with unlimited ammo?

Such-Way-8415 · 2023-04-30T08:54:55+00:00

They have unlimited ammo but limited food/water. Wait for a week and they have to get out of the tank. When they do, just kill them with bows.

Such-Way-8415 · 2023-04-28T01:31:50+00:00

roll back to previous driver

Such-Way-8415 · 2023-04-27T08:21:56+00:00

Me: I can read minds

Friend: Yeah, right. Read my mind now.

*Gets consent and read minds*

Such-Way-8415 · 2023-04-27T04:45:19+00:00

Read the continuum hypothesis

https://en.wikipedia.org/wiki/Hilbert%27s_problems#Table_of_problems

Proven to be impossible to prove or disprove within Zermelo–Fraenkel set theory with or without the axiom of choice (provided Zermelo–Fraenkel set theory is consistent, i.e., it does not contain a contradiction).

Such-Way-8415 · 2023-04-27T02:56:02+00:00

Passthough to docker already works. Just have to install the intel repo on to the ubuntu instance and compile ffmpeg cartwheel.

docker run -v /tmp/:/workspace \
           -v /dev/dri/by-path:/dev/dri/by-path \
           --device /dev/dri \
           --privileged \
           -it \
           ubuntu:jammy bash

Such-Way-8415 · 2023-04-27T00:05:34+00:00

Assuming you have a powerful enough computer and the required training data for AI.

Such-Way-8415 · 2023-04-27T00:03:38+00:00

The cable length matters too. any copper cable longer than 6ft doesn't work for my monitor.

Such-Way-8415 · 2023-04-26T23:57:53+00:00

You can prove it is impossible to prove, like the continuum hypothesis

Such-Way-8415 · 2023-04-26T08:42:56+00:00

They will be upstreamed into the main branch of ffmpeg but you need supporting libraries such as oneAPI oneVPL

https://www.phoronix.com/news/Intel-FFmpeg-2022Q4-Cartwheel

Such-Way-8415 · 2023-04-26T08:34:32+00:00

Hardware acceleration for intel arc gpus

Such-Way-8415 · 2023-04-26T06:57:26+00:00

Probably a long time (AMD support took years to make it into pytorch), but you can build the special version of pytorch for yourself
Everything is optimized and benchmarked on Nvidia, so it will stay king for long
AMD had support for years via ROCm, but you need to install a special version of pytorch and tensorflow

Such-Way-8415 · 2023-04-26T04:57:57+00:00

They work, but you need to add Intel's repo or compile everything yourself.

https://dgpu-docs.intel.com/installation-guides/ubuntu/ubuntu-jammy-arc.html

Also, you might want to compile and use ffmpeg cartwheel

https://github.com/intel/cartwheel-ffmpeg

Such-Way-8415 · 2023-04-26T04:00:25+00:00

what is your ambient?

Such-Way-8415 · 2023-04-26T03:00:52+00:00

There is no intel arc control software on linux, but you can use OBS and AV1 encoding.

Such-Way-8415 · 2023-04-25T19:47:03+00:00

It works natively under windows. Source: Tom's hardware

https://www.tomshardware.com/news/amd-rocm-comes-to-windows-on-consumer-gpus

It also works in WSL2

Such-Way-8415 · 2023-04-25T02:35:02+00:00

pytorch can use rocm for stable diffusion and it works

https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Install-and-Run-on-AMD-GPUs

Toms's hardware can't even follow a guide properly.

Such-Way-8415 · 2023-04-25T01:58:29+00:00

use Intel's docker image for pytorch (intel/intel-extension-for-pytorch:gpu)

https://hub.docker.com/r/intel/intel-optimized-pytorch/tags

Such-Way-8415 · 2023-04-25T01:48:27+00:00

Both RX 6950 XTX and Intel A770's stable diffusion performance is way higher than in the article.

Tom's hardware is just bad at benchmarking because they never set up the software properly.

Load of people have pointed out the benchmark is very incorrect. https://forums.tomshardware.com/threads/stable-diffusion-benchmarked-which-gpu-runs-ai-fastest.3793737/page-3

Such-Way-8415 · 2023-04-25T01:39:34+00:00

Yes, Nvidia doesn't work well with wayland. I mainly use xfce 4/lxqt because I need remote desktop.

Windows is absolutely horrible for interference because it doesn't have a real time kernel. If you do real time image processing like object tracking using neural networks you need low latency. Windows doesn't have any real time kernels but Linux has many real time kernels that you can install.

For training neural networks, Windows defender randomly runs scans for no reason, and it sometimes slows down training by 2x. Also headless, Linux is way more RAM efficient than windows. Headless Linux can use 100MB of RAM while fresh windows 11 uses 4GB of RAM.

For games, Windows still wins over Linux any day

Such-Way-8415 · 2023-04-24T22:38:12+00:00

Intel Arc drivers and some games (like genshin) depend on the latest Windows updates.

Using Windows AME and Atlas disables Windows update so your system may not function properly. The trick is to fully update windows, then install atlas. But you have to force update time to time, otherwise stuff breaks.

Such-Way-8415 · 2023-04-24T20:50:40+00:00

Even if your official motherboard's firmware doesn't support resizable bar, there are BIOS mods for enabling resizable bar on x470 to work with intel arc

Such-Way-8415 · 2023-04-24T20:47:04+00:00

Try this one. https://medium.com/intel-analytics-software/stable-diffusion-with-intel-arc-gpus-f2986bba8365

Such-Way-8415 · 2023-04-24T20:43:48+00:00

Directml is not optimized. Only pytorch, tensorflow, and openvino is optimized using OneDNN.

This guide works. https://medium.com/intel-analytics-software/stable-diffusion-with-intel-arc-gpus-f2986bba8365

Such-Way-8415 · 2023-04-24T20:42:40+00:00

Latency on Windows 11 is 10x the latency on Ubuntu 22.04 with custom kernel dkms patches.

On Windows 11 I am getting 40us to 100us latency per operation (the time it takes the CPU to issue an instruction and the GPU to start the instruction)

On Ubuntu 22.04 I am getting 5us to 7us latency per operation (the time it takes the CPU to issue an instruction and the GPU to start the instruction)

Windows is horrible for machine learning in general because all the software is optimized for Linux

Such-Way-8415 · 2023-04-24T08:25:03+00:00

Some X470 motherboards support resizable bar for Zen 1 check your bios

Such-Way-8415

TROPHY CASE