Arch Linux

Embark on Your Deep Learning Journey with Arch Linux

Think deep learning is tough? Try doing it on Arch Linux. It’s like assembling IKEA furniture with no instructions—challenging but incredibly rewarding. Join me as we tackle this setup together, armed with patience, humor, and a willingness to embrace the chaos!

Photo by Possessed Photography / Unsplash

Setting up deep learning on an Arch-based distro is like choosing to run a marathon uphill... barefoot. But hey, who said we were here for the easy route? Whether you're a die-hard Arch fan or just love the thrill of a challenge, this guide will walk you through installing a full AI development environment. Spoiler: It's not as scary as it sounds—unless, of course, you miss a step. Let's get started

Installation

Install cuda and nvidia packages with pacman

sudo pacman -S cuda cudnn nvidia-dkms nvidia-utils

Reboot your device reboot

Run the deviceQuery script

git clone https://github.com/NVIDIA/cuda-samples.git
cd cuda-samples/Samples/1_Utilities/deviceQuery
make
./deviceQuery

Figure: deviceQuery output (Source)

And just like that, you're all set... well, at least for CUDA.

Install python-tensorflow-opt-cuda

sudo pacman -S python-tensorflow-opt-cuda

Now, let's see if TensorFlow is smart enough to actually use your GPU. Spoiler: it better be.

python

from tensorflow.python.client import device_lib
print(device_lib.list_local_devices())

Expect something along the lines of this... unless, of course, the tech gods have other plans.

I0000 00:00:1729602105.640182    4530 gpu_device.cc:2022] Created device /device:GPU:0 with 4168 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6
[name: "/device:CPU:0"
device_type: "CPU"
memory_limit: 268435456
locality {
}
incarnation: 13700164101737687728
xla_global_id: -1
, name: "/device:GPU:0"
device_type: "GPU"
memory_limit: 4370595840
locality {
  bus_id: 1
  links {
  }
}
incarnation: 4529659632096840357
physical_device_desc: "device: 0, name: NVIDIA GeForce RTX 3060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.6"
xla_global_id: 416903419
]

Install keras here

git clone https://github.com/fchollet/keras
cd keras
sudo python setup.py install

After countless attempts and a few existential crises, NVIDIA and TensorFlow still aren’t playing nice on Arch. Errors everywhere—just another day in paradise.

/usr/include/c++/14.1.1/bits/stl_vector.h:1130: constexpr std::vector<_Tp, _Alloc>::reference std::vector<_Tp, _Alloc>::operator[](size_type) [with _Tp = pybind11::object; _Alloc = std::allocator<pybind11::object>; reference = pybind11::object&; size_type = long unsigned int]: Assertion '__n < this->size()' failed.

Honestly, you'd be better off using platforms like Google Colab, AWS, or Azure for your machine learning and AI work. They're faster, more efficient, and won't make you lose your mind.