wermarter

#!/bin/bash

SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd )
PARENT_DIR=$( dirname $SCRIPT_DIR )

wermarter

const nReadlines = require("n-readlines");
const fs = require("fs");

const events = require('events');

main().then(() => {
  const used = process.memoryUsage().heapUsed / 1024 / 1024;
  console.log(
    `The script uses approximately ${Math.round(used * 100) / 100} MB`
  );

wermarter

C:\Windows\system32\wsl.exe ssh %*

wermarter

export class BaseWorkerService<TaskInput, TaskOutput> {
  private queue: QueueObject<TaskInput>;
  private logger: Logger;
  private workCounter = 0;

  constructor(
    workerName: string,
    concurrency: number,
    batchSize: number,
    batchProcessor: (input: TaskInput[]) => Promise<TaskOutput[]>,

wermarter

const timeout_function = async (func, timeLimit) => {
  return new Promise((resolve, reject) => {
    const timer = setTimeout(() => {
      console.log("Timeout reached");
      reject(null);
    }, timeLimit);
    
    const result = func();
    
    resolve(result);

wermarter

import os
from TheFetchers.TheCommons import filename_check

with open(input('> '), 'r') as f:
    files = [i.strip() for i in f.readlines()]
for file in files:
    fn = os.path.splitext(file)[0]+'.mp4'
    output_file = filename_check(fn, True)
    cmd = "ffmpeg -i \"%s\" -vf scale=-2:480 -c:v libx264 \"%s\"" % (file, output_file)
    os.system(cmd)

wermarter

import origami

APP_TOKEN = "nongh::2113789:5001:8000:54.158.186.33"

app = origami.register(APP_TOKEN)

@origami.crossdomain
@app.listen()
def concat():
    allText = origami.getTextArray()

wermarter

import tensorflow as tf
import tflearn
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
import cv2

import tflearn.datasets.mnist as mnist
trainX, trainY, testX, testY = mnist.load_data(one_hot=True)

wermarter

import tensorflow as tf
import tflearn
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm

import tflearn.datasets.mnist as mnist
trainX, trainY, testX, testY = mnist.load_data(one_hot=True)

TENSORBOARD_DIR='./tmp/tflearn/vae'

wermarter

import tensorflow as tf
import tflearn
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm

import tflearn.datasets.mnist as mnist
trainX, trainY, testX, testY = mnist.load_data(one_hot=True)

TENSORBOARD_DIR='./logs/vae'