agoose77

When implementing development environments inside a container, usually I want to:

1. Run as non-root inside the container (many tools do not like being run as root).
2. Behave as the non-root user executing the container when reading/writing from mounted volumes

There are some additional (small) security benefits from running as non-root inside a container.

The solution to these problems is complicated by the way that different container runtimes behave:

keep-id

Across the different container runtimes, there is often a flag such as userns=keep-id that maps the UID:GID of the host to the container. Linux creates users by default with UID 1000, so default non-root users will often map directly to host UID.

agoose77

#!/usr/bin/python3

# Originally from from Mark Slater 29/11/16
# Modified for per-user name mapping by Mark Colclough, 28 July 2017
# Modified to suppress SystemExit exception by Angus Hollands, 3 Dec 2019

# Install as /usr/lib/cups/backend/safeq 
# Configure a safeq backend in CUPS (lpinfo -v should show it)
# URL/Connection:  safeq://localhost   (direct)
# PPD: Generic PostScript Printer Foomatic/Postscript 

agoose77

# Use F1 as modifier
unbind C-b
set -g prefix F1
bind F1 send-prefix

# split panes using h and b
bind h split-window -h
bind v split-window -v
unbind '"'

agoose77

agoose77

import numpy as np

def pearson_r(x: np.ndarray, y: np.ndarray) -> np.ndarray:
    n = len(x)
    return (n * x @ y - x.sum() * y.sum()) / np.sqrt(
        (n * (x @ x) - x.sum() ** 2) * (n * (y @ y) - y.sum() ** 2)
    )


def lstsq_1d(

agoose77

import operator
import inspect
import pytest


def getattribute(obj, name):
    return object.__getattribute__(obj, name)

def setattribute(obj, name, value):
    object.__setattr__(obj, name, value)

pervognsen

# Reverse-mode automatic differentiation

import math

# d(-x) = -dx
def func_neg(x):
    return -x, [-1]

# d(x + y) = dx + dy
def func_add(x, y):

nadavrot

High-Performance Matrix Multiplication

This is a short post that explains how to write a high-performance matrix multiplication program on modern processors. In this tutorial I will use a single core of the Skylake-client CPU with AVX2, but the principles in this post also apply to other processors with different instruction sets (such as AVX512).

Intro

Matrix multiplication is a mathematical operation that defines the product of

danizen

from urllib.parse import urlsplit

from tornado import gen
from tornado.escape import url_escape
from tornado.httputil import url_concat

from traitlets import (
    Unicode, Integer, Dict, TraitError, List, Bool, Any,
    Type, Set, Instance, Bytes, Float,
    observe, default,

Mnkai

TDP and turbo parameter modification with MSR on non-overclockable CPU

Disclaimer

• MSR modification may void your CPU's (or system board's) warranty. Proceed with care. I'm not responsible for any destruction caused by this article.
• MSR address (greatly) differs from CPU to CPU. Check your own CPU's MSR address using Intel's documentation.
• Only tested on Intel i7-8550U (Kaby Lake R).
• This article is translation of this article. If you can understand Korean, I recommend reading that article, not this.

Start