mrm8488

printenv

ifconfig -a

iptable -L

cat /etc/apache2/site-enabled/*

netstat -punta

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export default function generateSocialImage({
    title,
    tagline,
+   cloudName,
+   imagePublicID,
+   cloudinaryUrlBase = 'https://res.cloudinary.com',
+   version = null,
    titleFont = 'arial',
    titleExtraConfig = '',
    taglineExtraConfig = '',

mrm8488

import pdftotext
from gtts import gTTS
from sys import argv
with open(argv[1], "rb") as f:
    pdf = pdftotext.PDF(f)
    document= "\n\n".join(pdf)
    tts = gTTS(document)
    print("Saving Audio file")
    tts.save(argv[1]+".mp3")

mrm8488

#!/bin/sh
set -x

# == Swarm training (alpha release) ==

# Setup:
#
#   git clone https://github.com/shawwn/gpt-2
#   cd gpt-2
#   git checkout dev-shard

mrm8488

'use strict'

var express = require('express');
var app = express();
var mcache = require('memory-cache');

app.set('view engine', 'jade');

var cache = (duration) => {
  return (req, res, next) => {

mrm8488

# Easy Steps to persist Kaggle profile by @mrm8488 (Manuel Romero)

# Download kaggle.json from Kaggle -- MyAccount -> Create New API Token -> auto downloads as "kaggle.json

# Import json into notebook - run in a cell

from google.colab import files
files.upload()

# Browse to downloaded kaggle.json and upload

mrm8488

while ps auxw | grep '[m]yscript'; do sleep 30; done | stdbuf -o0 uniq | ts 
# Monitor changes in memory usage of myscript and timestamp the lines using ts. stdbuf -o0 turns off output buffering. [m] in the grep expression prevents the grep process line itself from being matched.

mrm8488

var mongoose = require('mongoose');
 
mongoose.connect('mongodb://localhost/test');
 
var db = mongoose.connection;
 
db.on('error', function() {
  return console.error.bind(console, 'connection error: ');
});
 

mrm8488

from torch.utils.data import IterableDataset

class CustomIterableDataset(IterableDataset):

    def __init__(self, filename):

        #Store the filename in object's memory
        self.filename = filename

        #And that's it, we no longer need to store the contents in the memory

mrm8488

#Creating the iterable dataset object
dataset     = CustomIterableDataset('path_to/somefile')
#Creating the dataloader
dataloader  = DataLoader(dataset, batch_size = 64)

for data in dataloader:
    #Data is a list containing 64 (=batch_size) consecutive lines of the file
    print(len(data)) #[64,]
    
    #We still need to separate the text and labels from each other and preprocess the text