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Journey to Deep Learning: Cuda GPU passthrough to a LXC container

Journey to Deep Learning: Cuda GPU passthrough to a LXC container

So here we go.

Step 0 – Some hardware considerations

First of all, if you’re considering a headless server, I strongly recommend you to get an IPMI compatible motherboard. Supermicro is probably the best or maybe check AsRock Rack.
IPMI allows you to connect remotely to the server, even at BIOS stage, view the actual display, use your mouse/keyboard and USB devices (including portable HDD), load ISOs, and even record everything you did.
That’s also called KVM-over-IP (KVM for Keyboard Video Mouse not Kernel Virtual Machine).

I will also assume that you have an nvidia GPU, at least until Vega and the ROCm Compute platform supports Theano, Tensorflow, Caffe and Torch, Radeons just aren’t an option.
And actually it’s the proprietary driver that is problematic with the special devices it creates in /dev/*

My screenshots will use IPMI but you can reproduce the initial steps that if you have an actual monitor + keyboard connected to your server.

Step 1 – Installing Proxmox

Why Proxmox ?

Proxmox is a popular distribution build to manage multiple virtual machine and containers. It’s based on Debian Linux.
I choose it because I wanted:
– Linux container virtualization for speed
– the possibility to load VM appliances (Storage like Rockstor, Xpenology or OpenMediaVault for example)
– GPU and PCI passthrough
– the possibility to load a Windows VM, passthrough the GPU to it and use it like a regular PC
– Bonus point if there was a browser-based ZFS or BTRFS management GUI for my storage need

Other similar distribution include:
– SmartOS (Solaris based), GPU passthrough seemed impossible
– CoreOS, specialized in Docker management, AFAIK cannot run regular VMs
– Xen server, no containers
– VMWare vSphere, no containers
Unfortunately though smartOS and Proxmox support ZFS, none had a nice GUI to manage my disks.

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