ruslanmustafin’s gists

ruslanmustafin / setup kube worker

Created September 17, 2022 15:11

	# disable swap
	sudo swapoff -a
	sed -ri '/\sswap\s/s/^#?/#/' /etc/fstab

	# allow UFW ports
	sudo ufw allow 10250
	sudo ufw allow 30000:32767\tcp

	# setup kubeadm, kubelet
	sudo apt-get update && sudo apt-get install -y apt-transport-https ca-certificates curl

ruslanmustafin / remove_old_docker_images.sh

Created March 4, 2021 14:12

https://stackoverflow.com/a/35721111

	docker images --no-trunc --format '{{.ID}} {{.CreatedSince}}' \
	\| grep ' months' \| awk '{ print $1 }' \
	\| xargs --no-run-if-empty docker rmi

ruslanmustafin / get-pip.py

Created February 1, 2021 00:23

This file has been truncated, but you can view the full file.

	#!/usr/bin/env python
	#
	# Hi There!
	# You may be wondering what this giant blob of binary data here is, you might
	# even be worried that we're up to something nefarious (good for you for being
	# paranoid!). This is a base85 encoding of a zip file, this zip file contains
	# an entire copy of pip (version 20.2.4).
	#
	# Pip is a thing that installs packages, pip itself is a package that someone
	# might want to install, especially if they're looking to run this get-pip.py

ruslanmustafin / infinite_dataloader.py

Created April 20, 2019 15:53 — forked from MFreidank/infinite_dataloader.py

A pytorch DataLoader that generates an unbounded/infinite number of minibatches from the dataset.

	from torch.utils.data import DataLoader


	class InfiniteDataLoader(DataLoader):
	def __init__(self, args, *kwargs):
	super().__init__(args, *kwargs)
	# Initialize an iterator over the dataset.
	self.dataset_iterator = super().__iter__()

	def __iter__(self):

ruslanmustafin / pytorch_conv2dsame.py

Last active July 16, 2019 13:53

As seen in https://github.com/pytorch/pytorch/issues/3867#issuecomment-407663012

	import torch
	import torch.nn as nn

	class Conv2dSame(nn.Module):
	def __init__(self, in_channels, out_channels, kernel_size, stride=1, bias=True, padding_layer=nn.ReflectionPad2d):
	super().__init__()
	ka = kernel_size // 2
	kb = ka - 1 if kernel_size % 2 == 0 else ka

	self.net = nn.Sequential(

ruslanmustafin / enable tqdm widgets in jupyter lab

Last active April 22, 2019 09:29

	#usual installation
	pip install ipywidgets
	jupyter nbextension enable --py widgetsnbextension
	#you are my saver!
	jupyter labextension install @jupyter-widgets/jupyterlab-manager

ruslanmustafin / scramble_input.py

Created February 22, 2019 19:14

	import argparse
	import random

	parser = argparse.ArgumentParser()
	parser.add_argument('--input', required=True, help='Input sentence to scramble')

	args = parser.parse_args()


	def scramble_word(w):

ruslanmustafin / configure_cuda_p70.md

Created February 6, 2019 11:08 — forked from alexlee-gk/configure_cuda_p70.md

Use integrated graphics for display and NVIDIA GPU for CUDA on Ubuntu 14.04

This was tested on a ThinkPad P70 laptop with an Intel integrated graphics and an NVIDIA GPU:

lspci | egrep 'VGA|3D'
00:02.0 VGA compatible controller: Intel Corporation Device 191b (rev 06)
01:00.0 VGA compatible controller: NVIDIA Corporation GM204GLM [Quadro M3000M] (rev a1)

A reason to use the integrated graphics for display is if installing the NVIDIA drivers causes the display to stop working properly. In my case, Ubuntu would get stuck in a login loop after installing the NVIDIA drivers. This happened regardless if I installed the drivers from the "Additional Drivers" tab in "System Settings" or the ppa:graphics-drivers/ppa in the command-line.

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