Quan Hua quanhua92

Nomad generally uses less CPU and RAM than K3s for the same basic cluster functionality. Nomad is widely regarded as one of the most efficient and lightweight orchestrators available.

For your 5-VPS setup, Nomad's single-binary, minimal approach will result in lower resource utilization and operational overhead compared to K3s, which, despite being "lightweight," still runs the full set of Kubernetes control plane components.

⚖️ Nomad vs. K3s Resource Footprint

The difference in resource consumption stems from the architectural complexity of the two systems.

Resource	Nomad Agent (Server/Client)	K3s Agent / Server

	#!/bin/sh
	curl -s https://bunnycdn.com/api/system/edgeserverlist -H "Accept: application/json" \| jq -r .[] > /tmp/bunny_ips
	echo "" >> /tmp/bunny_ips
	curl -s https://bunnycdn.com/api/system/edgeserverlist/ipv6 -H "Accept: application/json" \| jq -r .[] >> /tmp/bunny_ips

	for ip in `cat /tmp/bunny_ips`; do ufw allow proto tcp from $ip comment 'Bunny IP'; done;

	curl -sL yabs.sh \| bash -s -- -r9

	3x vCPU Cores
	75 GB PURE SSD RAID-10 Storage
	4 GB RAM
	10,000GB Monthly Premium Bandwidth
	1Gbps Public Network Port
	Full Root Admin Access
	1 Dedicated IPv4 Address
	KVM / SolusVM Control Panel - Reboot, Reinstall, Manage rDNS, & much more

	Command:
	curl -s wget.racing/nench.sh \| bash; curl -s wget.racing/nench.sh \| bash

	-------------------------------------------------
	nench.sh v2019.07.20 -- https://git.io/nench.sh
	benchmark timestamp: 2021-12-22 13:27:07 UTC
	-------------------------------------------------

	Processor: Intel(R) Xeon(R) CPU E5-2697 v2 @ 2.70GHz
	CPU cores: 3

	import torch
	import numpy as np
	import copy

	from deodr import read_obj
	from deodr.pytorch import Scene3DPytorch, CameraPytorch
	from deodr.pytorch.triangulated_mesh_pytorch import ColoredTriMeshPytorch as ColoredTriMesh

	def get_camera(camera_center, width, height, focal=None):
	if focal is None:

	function addnoise_asl(cleanfile, noisefile, outfile, snr)
	% ----------------------------------------------------------------------
	% This function adds noise to a file at a specified SNR level. It uses
	% the active speech level to compute the speech energy. The
	% active speech level1 is computed as per ITU-T P.56 standard [1].
	%
	% Usage: addnoise_asl(cleanFile.wav, noiseFile.wav, noisyFile.wav, SNR)
	%
	% cleanFile.wav - clean input file in .wav format
	% noiseFile.wav - file containing the noise signal in .wav format


	function scroll_to(pos){
	$('html,body').animate({ scrollTop: pos }, 'fast');
	}

	function scroll_and_grid(current){
	scroll_to(9999999); grid(false);

	if(current > 0){
	scroll_and_grid(current - 1);

	"""
	This is the implementation of AlexNet which is modified from [Jeicaoyu's AlexNet].
	Note:
	- The number of Conv2d filters now matches with the original paper.
	- Use PyTorch's Local Response Normalization layer which is implemented in Jan 2018. [PR #4667]
	- This is for educational purpose only. We don't have pretrained weights for this model.
	References:
	- Jeicaoyu's AlexNet Model: [jiecaoyu](https://github.com/jiecaoyu/pytorch_imagenet/blob/984a2a988ba17b37e1173dd2518fa0f4dc4a1879/networks/model_list/alexnet.py)
	- PR #4667: https://github.com/pytorch/pytorch/pull/4667
	"""

	"""
	This is AlexNet implementation from pytorch/torchvision.
	Note:
	- The number of nn.Conv2d doesn't match with the original paper.
	- This model uses `nn.AdaptiveAvgPool2d` to allow the model to process images with arbitrary image size. [PR #746]
	- This model doesn't use Local Response Normalization as described in the original paper.
	- This model is implemented in Jan 2017 with pretrained model.
	- PyTorch's Local Response Normalization layer is implemented in Jan 2018. [PR #4667]
	References:
	- Model: https://github.com/pytorch/vision/blob/ac2e995a4352267f65e7cc6d354bde683a4fb402/torchvision/models/alexnet.py