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I have a gaming system (Ryzen 1950X w/ GTX 1080Ti). Is there an easy way to create a bootable external media with most of the stuff preconfigured already? I r

I have a gaming system (Ryzen 1950X w/ GTX 1080Ti). Is there an easy way to create a bootable external media with most of the stuff preconfigured already? I remember it being a pain to get all the drivers working and there was a lot of version incompatibility in the Python ecosystem. I'd rather not mess with the bootloader and just have an external drive I can select to boot to in the BIOS. For some reason Windows wiped out GRUB and I can't boot my old Ubuntu distro anymore.

jph00 8y ago
The story on python is much better nowadays. You can install cuda and drivers from nvidia, and then install anaconda, then just run: conda install -c pytorch -c fastai fastai pytorch-nightly cuda92 If you don't use conda, things can get more complicated, depending on what libs you have installed.
maykr 8y ago
You have a couple of options to consider: 1. Install a modern Linux distribution on the bare metal, get https://github.com/NVIDIA/nvidia-docker https://github.com/NVIDIA/nvidia-docker and run the GPU-accelerated container https://github.com/Paperspace/fastai-docker https://github.com/Paperspace/fastai-docker 2. Try running the Ubuntu Hyper-V image provided by Canonical/Microsoft P.S. I'm considering building a DL box based on Ryzen 2600 w/ GTX 1070Ti, so could you please share your experience with Ryzen and Linux.
- JimmyAustin 8y ago
  My DL box has a Ryzen 1800x and a GTX 1080ti running Ubuntu 18.04. The Ryzen has given me very few issues, Nvidia gave me some setup issues, but now that it's all configured its rock solid.
- bcheung 8y ago
  A Hyper-V image would be great if it can take advantage of the GPU and not cause driver issues. Didn't know they had one for DL. Or is it just pure Ubuntu you mean?
spiantino 8y ago
Shameless plug, but another option for running your experiments on a GPU is Spell (https://spell.run https://spell.run). Very simple to use and will allow you to use as many GPUs as you want at once and only pay for the compute time. You can also run GPU-enabled kernels for Jupiter. Fast.ai instructions here: https://learn.spell.run/fast_ai https://learn.spell.run/fast_ai

jph00 8y ago

The story on python is much better nowadays. You can install cuda and drivers from nvidia, and then install anaconda, then just run: conda install -c pytorch -c fastai fastai pytorch-nightly cuda92 If you don't use conda, things can get more complicated, depending on what libs you have installed.

maykr 8y ago

You have a couple of options to consider: 1. Install a modern Linux distribution on the bare metal, get https://github.com/NVIDIA/nvidia-docker https://github.com/NVIDIA/nvidia-docker and run the GPU-accelerated container https://github.com/Paperspace/fastai-docker https://github.com/Paperspace/fastai-docker 2. Try running the Ubuntu Hyper-V image provided by Canonical/Microsoft P.S. I'm considering building a DL box based on Ryzen 2600 w/ GTX 1070Ti, so could you please share your experience with Ryzen and Linux.

JimmyAustin 8y ago
My DL box has a Ryzen 1800x and a GTX 1080ti running Ubuntu 18.04. The Ryzen has given me very few issues, Nvidia gave me some setup issues, but now that it's all configured its rock solid.
bcheung 8y ago
A Hyper-V image would be great if it can take advantage of the GPU and not cause driver issues. Didn't know they had one for DL. Or is it just pure Ubuntu you mean?

spiantino 8y ago

Shameless plug, but another option for running your experiments on a GPU is Spell (https://spell.run https://spell.run). Very simple to use and will allow you to use as many GPUs as you want at once and only pay for the compute time. You can also run GPU-enabled kernels for Jupiter. Fast.ai instructions here: https://learn.spell.run/fast_ai https://learn.spell.run/fast_ai