Qrackmin container system deployed in rancher through the ThereminQ HELM definitions
- The
:latest
container image is meant to be used on a single node with Nvidia-Docker2 and Linux support
docker run --gpus all --device=/dev/dri:/dev/dri --privileged -d twobombs/qrackmin[:tag] [--memory 24G --memory-swap 250G]
-v /var/log/qrack:/var/log/qrack
for saving of measured results outside container
docker exec -ti [containerID] bash
-
ThereminQ repo with runfiles is checked out on
/root
-
Windows users should install
WSL2, Docker Desktop, docker.io, nvidia-docker2
to run this (CUDA
only)
-
on demand AWS template proposals for x86 and ARM - CUDA, OpenCL and CPU powered
-
:AWS
boilerplate binary runtime code for Qrack as a Service - QFT RND benchmarks output -
:BRAKET
boilerplate python runtime code forPyQrack
as a|BraKET>
container service
:pocl
container image adds the generic OpenCL-ICD and is to be used with high memory & CPU count hosts
- Simulate performance and measured results on CPU
- For validation before GPU cluster deployment
The :vcl
tag contains VCL
binaries that are copyrighted by Amnon Barak to run VCL as a backend and host
sudo mkdir /var/log/vcl /var/log/vcl/etc /var/log/vcl/etc/vcl/ /var/log/vcl/etc/init.d /var/log/vcl/usr /var/log/vcl/usr/bin /var/log/vcl/etc/rc0.d /var/log/vcl/etc/rc1.d /var/log/vcl/etc/rc2.d /var/log/vcl/etc/rc3.d /var/log/vcl/etc/rc4.d /var/log/vcl/etc/rc5.d /var/log/vcl/etc/rc6.d
You will be asked two questions:
- the amount of virtual nodes you want to create
- the NVIDIA devices you want to expose ( often 'all' will suffice, otherwise use the device number )*
- the nodes' IPs will be scraped
- the host container will be started and will initialize
- you'll drop into the
host-containers' bash
- then run workloads through
./vcl-1.25/vclrun [command]
(*) = other OpenCL device types such as an IntelIGP that are also recognised will also be taken into the cluster