Optional - Set format on save and any global prettier options
npm i -D eslint prettier eslint-plugin-prettier eslint-config-prettier eslint-plugin-node eslint-config-node
#!/bin/bash | |
## This gist contains instructions about cuda v11.2 and cudnn 8.1 installation in Ubuntu 18.04 for PyTorch | |
############################################################################################# | |
##### forked by : https://gist.github.com/Mahedi-61/2a2f1579d4271717d421065168ce6a73 ######## | |
############################################################################################# | |
### steps #### | |
# verify the system has a cuda-capable gpu |
import 'package:flutter/material.dart'; | |
import 'dart:ui' as ui; | |
import 'package:flutter/services.dart' show rootBundle; | |
import 'dart:async'; | |
import 'dart:typed_data'; | |
void main() => runApp(new MyApp()); | |
class MyApp extends StatelessWidget { | |
// This widget is the root of your application. |
# This is an example of using | |
# https://github.com/tensorflow/models/blob/master/research/object_detection/dataset_tools/create_pascal_tf_record.py | |
# The structure should be like PASCAL VOC format dataset | |
# +Dataset | |
# +Annotations | |
# +JPEGImages | |
# python create_tfrecords_from_xml.py --image_dir=dataset/JPEGImages | |
# --annotations_dir=dataset/Annotations | |
# --label_map_path=object-detection.pbtxt | |
# --output_path=data.record |
{ | |
"categories": [ | |
{ | |
"name": "Movies", | |
"videos": [ | |
{ | |
"description": "Big Buck Bunny tells the story of a giant rabbit with a heart bigger than himself. When one sunny day three rodents rudely harass him, something snaps... and the rabbit ain't no bunny anymore! In the typical cartoon tradition he prepares the nasty rodents a comical revenge.\n\nLicensed under the Creative Commons Attribution license\nhttp://www.bigbuckbunny.org", | |
"sources": [ | |
"http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/BigBuckBunny.mp4" | |
], |
#!/usr/bin/env bash | |
git branch -r | grep -v '\->' | while read remote; do git branch --track "${remote#origin/}" "$remote"; done | |
git fetch --all | |
git pull --all |
from __future__ import print_function | |
import requests | |
import json | |
import cv2 | |
addr = 'http://localhost:5000' | |
test_url = addr + '/api/test' | |
# prepare headers for http request | |
content_type = 'image/jpeg' |
First of all, we are going to store all the data in a Docker shared volume, called openvpn_data
.
To initialise the OpenVPN configuration and CA:
$ docker run -v openvpn_data:/etc/openvpn --rm evolvedm/openvpn-rpi ovpn_genconfig -u udp://your-vpn.address.com
$ docker run -v openvpn_data:/etc/openvpn --rm -it evolvedm/openvpn-rpi ovpn_initpki
To start daemon (and auto-restart it):
import requests | |
import logging | |
import httplib | |
# Debug logging | |
httplib.HTTPConnection.debuglevel = 1 | |
logging.basicConfig() | |
logging.getLogger().setLevel(logging.DEBUG) | |
req_log = logging.getLogger('requests.packages.urllib3') | |
req_log.setLevel(logging.DEBUG) |