alkavan

################################
gource commands
################################
# basic command for big and long projects 
gource  --max-user-speed 500 --seconds-per-day 0.05 --file-idle-time 10 -e 0.005 -f --max-files 300 --hide-files

# some easy to understand commands

# for output file
--output-ppm-stream ~/ppm/ppm-kohana

oldcai

import time
import requests
import zlib
#!pip install lz4 pylzma zstd
import lz4.block
import pylzma as lzma
import zstd

def measure_time_and_compress_decompress(compress_func, decompress_func, data, *args):
    # Measure compression time

lolo32

CLang optimizations on Mac OSX

Version:

Apple LLVM version 6.0 (clang-600.0.57) (based on LLVM 3.5svn)
Target: x86_64-apple-darwin14.1.0
Thread model: posix

This was made with commands:

elvinio

void poller(int socketIn, std::string srcIP, int port){
 struct pollfd fds[2];
 // Initialize the fds to 0
 memset(fds, 0, sizeof(fds));

 // Wait in ms before poll times out. -1 for infinite
 int timeout = 15000;

 fds[0].fd = socketIn;

xerpi

#
# Automatically generated file; DO NOT EDIT.
# Buildroot 2020.11-312-gfd5eeabac0 Configuration
#
BR2_HAVE_DOT_CONFIG=y
BR2_HOST_GCC_AT_LEAST_4_9=y
BR2_HOST_GCC_AT_LEAST_5=y
BR2_HOST_GCC_AT_LEAST_6=y
BR2_HOST_GCC_AT_LEAST_7=y
BR2_HOST_GCC_AT_LEAST_8=y

dreikanter

Symmetic encryption

For symmetic encryption, you can use the following:

To encrypt:

openssl aes-256-cbc -salt -a -e -in plaintext.txt -out encrypted.txt

To decrypt:

xerpi

#!/bin/sh

TOOLCHAIN=/opt/armv6-eabihf--glibc--bleeding-edge-2020.02-2/bin/arm-buildroot-linux-gnueabihf-

cp arch/arm/configs/nintendo3ds_defconfig .config

make ARCH=arm CROSS_COMPILE=$TOOLCHAIN -j8
make ARCH=arm CROSS_COMPILE=$TOOLCHAIN nintendo3ds_ctr.dtb

echo "Output file: ./arch/arm/boot/zImage"

mikoim

Parameters

Container: MP4

Parameter	YouTube recommends setting
-movflags faststart	moov atom at the front of the file (Fast Start)

Video codec: H.264

mlorant

REGIONS = {
    'Auvergne-Rhône-Alpes': ['01', '03', '07', '15', '26', '38', '42', '43', '63', '69', '73', '74'],
    'Bourgogne-Franche-Comté': ['21', '25', '39', '58', '70', '71', '89', '90'],
    'Bretagne': ['35', '22', '56', '29'],
    'Centre-Val de Loire': ['18', '28', '36', '37', '41', '45'],
    'Corse': ['2A', '2B'],
    'Grand Est': ['08', '10', '51', '52', '54', '55', '57', '67', '68', '88'],
    'Guadeloupe': ['971'],
    'Guyane': ['973'],
    'Hauts-de-France': ['02', '59', '60', '62', '80'],

kmhofmann

Building TensorFlow from source (TF 2.3.0, Ubuntu 20.04)

Why build from source?

The official instructions on installing TensorFlow are here: https://www.tensorflow.org/install. If you want to install TensorFlow just using pip, you are running a supported Ubuntu LTS distribution, and you're happy to install the respective tested CUDA versions (which often are outdated), by all means go ahead. A good alternative may be to run a Docker image.

I am usually unhappy with installing what in effect are pre-built binaries. These binaries are often not compatible with the Ubuntu version I am running, the CUDA version that I have installed, and so on. Furthermore, they may be slower than binaries optimized for the target architecture, since certain instructions are not being used (e.g. AVX2, FMA).

So installing TensorFlow from source becomes a necessity. The official instructions on building TensorFlow from source are here: ht