| #!/bin/bash | |
| # Taken from http://gregmac.net/git/2013/06/13/locating-large-objects.html | |
| GIT_CMD=/usr/local/bin/git | |
| RANGE=$1 # like '1.month.ago' | |
| SIZE_THRESHOLD=$2 # size in bytes | |
| # Iterate over a list of commits | |
| for commit in $(${GIT_CMD} rev-list --all --since={${RANGE}} --pretty=oneline | cut -d' ' -f1); do |
| import mock | |
| import sys | |
| class Finder(object): | |
| def find_module(self, name, path=None): | |
| print name, path | |
| raise ImportError | |
| import threading |
| #!/bin/bash | |
| echo Processing $1 | |
| SIZE=`convert -identify -ping $1 /dev/null | cut -d ' ' -f 3` | |
| X=`echo $SIZE | cut -d 'x' -f 1` | |
| Y=`echo $SIZE | cut -d 'x' -f 2` | |
| X=`expr $X - 1500` | |
| Y=`expr $Y - 100` | |
| mv $1 _$1 | |
| set -o verbose | |
| convert _$1 -font ~/Downloads/digital_7/digital-7-mono.ttf -pointsize 300 -fill red -annotate +$X+$Y %[exif:DateTimeOriginal] $1 |
| import sys | |
| import os | |
| import json | |
| import virtualenv | |
| SOURCE = 'src.py' | |
| RUNSH = 'run.sh' | |
| REQUIREMENTS = 'requirements.txt' | |
| BOOTSTRAPENV = 'bootstrap.env' |
| - (NSManagedObjectContext *) managedObjectContext { | |
| if (_managedObjectContext != nil) { | |
| return _managedObjectContext; | |
| } | |
| NSPersistentStoreCoordinator *coordinator = [self persistentStoreCoordinator]; | |
| if (coordinator != nil) { | |
| _managedObjectContext = [[NSManagedObjectContext alloc] init]; | |
| [_managedObjectContext setPersistentStoreCoordinator: coordinator]; | |
| } |
| // this script copies a youtube playlist to the "watch later" list, so you can easily clone the | |
| // list by creating a new one off the watch later list. You must run this on a playlist overview | |
| // page like http://www.youtube.com/playlist?list=xxxxxxxxx | |
| // this script is meant to be converted into a bookmarklet, using something like http://mrcoles.com/bookmarklet/ | |
| // after conversion, it becomes this: | |
| // javascript:(function()%7Bvar%20buttons%20%3D%20document.querySelectorAll(%22button.addto-watch-later-button%22)%3Bvar%20videos%20%3D%20document.querySelectorAll(%22.playlist-video-item%22)%3Bif%20(videos.length%20!%3D%20buttons.length)%20%7Balert(%22Some%20videos%20are%20already%20in%20watch%20later!%20Found%20%22%20%2B%20videos.length%20%2B%20%22%20videos%20but%20%22%20%2B%20buttons.length%20%2B%20%22%20buttons.%20Abort%20now.%22)%3B%7D%20else%20%7Bif%20(confirm(%22Add%20%22%20%2B%20buttons.length%20%2B%20%22%20videos%3F%22))%20%7Bvar%20dialog%20%3D%20document.createElement(%22h1%22)%3Bdialog.innerHTML%20%3D |
Run sudo ubuntu-drivers devices to find what drivers are available, and then use apt to install the driver. In my case, it is apt install nvidia-driver-390. Reboot after installation completes, and use nvidia-smi command to ensure the driver is loaded.
Use Anaconda to simplify the process of installing tensorflow and related packages. Install it with wget https://repo.anaconda.com/archive/Anaconda3-5.3.0-Linux-x86_64.sh and then execute it.
Forked from @William-Zhang's gist