archenroot

resource "aws_ecs_service" "ignore_changes_task_definition" {
  count                              = var.enabled && var.ignore_changes_task_definition ? 1 : 0
  name                               = module.default_label.id
  task_definition                    = "${join("", aws_ecs_task_definition.default.*.family)}:${join("", aws_ecs_task_definition.default.*.revision)}"
  desired_count                      = var.desired_count
  deployment_maximum_percent         = var.deployment_maximum_percent
  deployment_minimum_healthy_percent = var.deployment_minimum_healthy_percent
  health_check_grace_period_seconds  = var.health_check_grace_period_seconds
  launch_type                        = var.launch_type
  platform_version                   = var.launch_type == "FARGATE" ? var.platform_version : null

archenroot

package org.archenroot.integration.commons.archive_service.app;

import org.archenroot.integration.commons.archive_service.app.security.SecurityConfig;
import org.archenroot.integration.commons.archive_service.backend.domain.entity.GlobalConfiguration;
import org.archenroot.integration.commons.archive_service.backend.service.UserAccountService;
import org.archenroot.integration.commons.archive_service.backend.util.LocalDateJpaConverter;
import org.archenroot.integration.commons.archive_service.ui.AppUI;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;
import org.springframework.boot.autoconfigure.domain.EntityScan;

archenroot

To generate a pom.xml file just run gradle writeNewPom

If you want to generate it as pom.xml in the root of the project, replace writeTo("$buildDir/newpom.xml") with writeTo("pom.xml")

archenroot

In order to clone the TVN application from sourceforge to github I performed the following steps.

rsync -av rsync://tnv.cvs.sourceforge.net/cvsroot/tnv/* .
svn export --username=guest http://cvs2svn.tigris.org/svn/cvs2svn/trunk cvs2svn-trunk
cp ./cvs2svn-trunk/cvs2git-example.options ./cvs2git.options
vim cvs2git.options
cvs2svn-trunk/cvs2git --options=cvs2git.options --fallback-encoding utf-8
git@github.com:binarytemple/tnv.git tnv-github
git clone git@github.com:binarytemple/tnv.git tnv-github

archenroot

# cat /etc/logrotate.d/mysqld

# This logname can be set in /etc/my.cnf
# by setting the variable "err-log"
# in the [safe_mysqld] section as follows:
#
# [safe_mysqld]
# err-log=/var/log/mysqld.log
#
# If the root user has a password you have to create a

archenroot

docker network ls
docker network create --driver bridge isolated_network   ## create ~ create custom network, bridge ~ use a bridge network, isolated_network ~ name of the custom network
docker network isolated_network
docker network inspect isolated_network
docker run -d --net=isolated_network --name nodeapp -p 3000:3000 abhinavkorpal/node  ## net ~ run container in network, mongodb ~ link to this containe by name

archenroot

VisualSFM by Changchang Wu

Probably the most straight forward way to start generating Point Clouds from a set of pictures.

VisualSFM is a GUI application for 3D reconstruction using structure from motion (SFM). The reconstruction system integrates several of my previous projects: SIFT on GPU(SiftGPU), Multicore Bundle Adjustment, and Towards Linear-time Incremental Structure from Motion. VisualSFM runs fast by exploiting multicore parallelism for feature detection, feature matching, and bundle adjustment.

For dense reconstruction, this program supports Yasutaka Furukawa's PMVS/CMVS tool chain, and can prepare data for Michal Jancosek's CMP-MVS. In addition, the output of VisualSFM is natively supported by Mathias Rothermel and Konrad Wenzel's [SURE]

archenroot

VisualSFM by Changchang Wu

Probably the most straight forward way to start generating Point Clouds from a set of pictures.

VisualSFM is a GUI application for 3D reconstruction using structure from motion (SFM). The reconstruction system integrates several of my previous projects: SIFT on GPU(SiftGPU), Multicore Bundle Adjustment, and Towards Linear-time Incremental Structure from Motion. VisualSFM runs fast by exploiting multicore parallelism for feature detection, feature matching, and bundle adjustment.

For dense reconstruction, this program supports Yasutaka Furukawa's PMVS/CMVS tool chain, and can prepare data for Michal Jancosek's CMP-MVS. In addition, the output of VisualSFM is natively supported by Mathias Rothermel and Konrad Wenzel's [SURE]

archenroot

#!/bin/bash
#title           :mkscript.sh
#description     :This script will make a header for a bash script.
#author		 :bgw
#date            :20111101
#version         :0.4    
#usage		 :bash mkscript.sh
#notes           :Install Vim and Emacs to use this script.
#bash_version    :4.1.5(1)-release
#==============================================================================

archenroot

This gist is an implementation of http://sirile.github.io/2015/05/18/using-haproxy-and-consul-for-dynamic-service-discovery-on-docker.html on top of Docker Machine and Docker Swarm.