Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.
$ python -m SimpleHTTPServer 8000| #!/bin/bash | |
| # docker exec -it sharelatex bash | |
| apt-get update -y | |
| echo "listing ttf directory" | |
| ls /usr/share/fonts/ttf | |
| echo "listing truetype directory" | |
| ls /usr/share/fonts/truetype |
| IP="167.71.219.140" | |
| PORT="442" | |
| SERVER_SECRET="xdh4-Q2MP8Mdegf9sC-5Th" # from the apiURL prior to the 'certsha' | |
| curl --insecure -i -X PUT \ | |
| https://$IP:$PORT/$SERVER_SECRET/server/port-for-new-access-keys \ | |
| -H 'Content-Type: application/json' \ | |
| -d '{ | |
| "port": 444 | |
| }' |
| version: "2.2" | |
| services: | |
| sharelatex: | |
| restart: always | |
| image: dennis1f/sharelatex-texlive2018 # sharelatex/sharelatex:latest | |
| container_name: sharelatex | |
| depends_on: | |
| mongo: | |
| condition: service_healthy | |
| redis: |
Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.
$ python -m SimpleHTTPServer 8000| if which fasd >/dev/null; then | |
| # install fasd hooks and basic aliases in the shell | |
| eval "$(fasd --init auto)" | |
| # if there is fzf available use it to search fasd results | |
| if which fzf >/dev/null; then | |
| alias v >/dev/null && unalias v | |
| alias vd >/dev/null && unalias vd | |
| alias z >/dev/null && unalias z |
| function preview_pdf () { | |
| if [ $1 = "" ]; then | |
| echo "Need filename." | |
| else | |
| convert $1 /var/TMP/output.jpg | |
| imgcat /var/TMP/output*.jpg; | |
| echo "cleaning up" | |
| rm -f /var/TMP/output*.jpg; | |
| # ls /var/TMP/output*.jpg; | |
| fi |
| #!/bin/bash | |
| ABS_PATH=.\ | |
| # Colourise the output | |
| RED='\033[0;31m' # Red | |
| GRE='\033[0;32m' # Green | |
| YEL='\033[1;33m' # Yellow | |
| NCL='\033[0m' # No Color |
Forked from @William-Zhang's gist
Largely based on the Tensorflow 1.6 gist, and Tensorflow 1.7 gist for xcode, this should hopefully simplify things a bit.