Joe Young joeking11829

The fastest remote directory rsync over ssh archival I can muster (40MB/s over 1gb NICs)

This creates an archive that does the following:

rsync (Everyone seems to like -z, but it is much slower for me)

a: archive mode - rescursive, preserves owner, preserves permissions, preserves modification times, preserves group, copies symlinks as symlinks, preserves device files.
H: preserves hard-links
A: preserves ACLs

注意：本文内容适用于 Tmux 2.3 及以上的版本，但是绝大部分的特性低版本也都适用，鼠标支持、VI 模式、插件管理在低版本可能会与本文不兼容。

Tmux 快捷键 & 速查表

启动新会话：

tmux [new -s 会话名 -n 窗口名]

恢复会话：

Convert Binary Data to OpenCV Image Format

在影像處理時，常常會讀取影像資料(binary data)後轉成影像格式(cv::Mat)，在進行影像操作。

那我們如何轉成可用的OpenCV影像格式呢?

首先讀取檔案後得到binary data，在進行解碼轉成cv::Mat。

當然如果單純讀檔後轉成OpenCV影像格式，OpenCV是有提供cv::imread這個API可以使用。

Caffe + Ubuntu 12.04 64bit + CUDA 6.5 配置说明

本步骤能实现用Intel核芯显卡来进行显示，用NVIDIA GPU进行计算。

1. 安装开发所需的依赖包

安装开发所需要的一些基本包

sudo apt-get install build-essential
sudo apt-get install vim cmake git
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev

	#!/bin/bash

	# ssh-cipher-benchmark.sh - Assesses speed of SSH encryption between specific hosts.
	# Usage:
	# ssh-cipher-benchmark.sh <remotehost> [ciphers]
	# Default ciphers: all we can find...
	#
	# Note: In some cases, the first cipher tested runs faster than the others, regardless of order.
	# Cause of this is not known, but changing the order of testing shows it to be true. Run the
	# first one twice if you suspect this. Perhaps it is due to buffering?

	#!/usr/bin/env python
	# -- coding: utf-8 --
	from argparse import ArgumentParser

	import torch
	import torch.distributed as dist
	from torch.nn.parallel import DistributedDataParallel as DDP
	from torch.utils.data import DataLoader, Dataset
	from torch.utils.data.distributed import DistributedSampler
	from transformers import BertForMaskedLM

	create /etc/lightdm/lightdm.conf.d/20-ubuntu.conf as follows:

	Add to the new file:
	[SeatDefaults]
	greeter-setup-script=/usr/bin/synergyc --daemon [server address]
	user-session=Ubuntu

	Set the proper permissions:
	$ sudo chmod 600 20-lubuntu.conf
	$ ls 20-lubuntu.conf

	#!/bin/sh
	# Set CPU to full performance on NVIDIA Jetson TK1 Development Kit
	if [ $(id -u) != 0 ]; then
	echo "This script requires root permissions"
	echo "$ sudo "$0""
	exit
	fi

	# To obtain full performance on the CPU (eg: for performance measurements or benchmarking or when you don't care about power draw), you can disable CPU scaling and force the 4 main CPU cores to always run at max performance until reboot:

	#!/bin/sh
	# Install cuDNN V2 on NVIDIA Jetson TK1
	# Register as a NVIDIA developer and download the cuDNN package
	# Package is named cudnn-6.5-linux-ARMv7-V2.tgz
	# This script and the cuDNN package should be in the same directory, usually ~/Downloads
	# This script places the library and include files for cudnn in the CUDA 6.5 directories
	# Make sure this is executing as root
	if [ $(id -u) != 0 ]; then
	echo "This script requires root permissions"
	echo "$ sudo "$0""

	#!/bin/sh
	# Install and compile Caffe on NVIDIA Jetson TK1 Development Kit
	sudo add-apt-repository universe
	sudo apt-get update
	sudo apt-get install libprotobuf-dev protobuf-compiler gfortran \
	libboost-dev cmake libleveldb-dev libsnappy-dev \
	libboost-thread-dev libboost-system-dev \
	libatlas-base-dev libhdf5-serial-dev libgflags-dev \
	libgoogle-glog-dev liblmdb-dev -y