arjun-kava

How to create an HTTPS certificate for localhost domains

This focuses on generating the certificates for loading local virtual hosts hosted on your computer, for development only.

Do not use self-signed certificates in production ! For online certificates, use Let's Encrypt instead (tutorial).

arjun-kava

How to: Allow Node to bind to port 80 without sudo

TL;DR

Only do this if you understand the consequences: all node programs will be able to bind on ports < 1024

sudo setcap 'cap_net_bind_service=+ep' /usr/local/bin/node

Important: your node location may vary. Use which node to find it, or use it directly in the command:

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# script used to publish package to npm and push new tag
# step 1 - change version from package json
# step 2 - run ./scripts/publish-npm.sh $version  i.e ./scripts/publish-npm.sh 0.0.3
set -e

if [ $# -eq 0 ]
  then
    echo "Please specify version."
    exit
fi

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mogrify -set comment 'Extraneous bytes removed' *.jpg

arjun-kava

from keras.datasets import mnist
import cv2
import os
from tqdm import tqdm

from config import *

(x_train, y_train), (x_test, y_test) = mnist.load_data()

def extract(x,y,dir_path):

arjun-kava

import { print, isSpecifiedScalarType, isSpecifiedDirective } from 'graphql';

export function printSchemaWithDirectives(schema) {
  const str = Object
    .keys(schema.getTypeMap())
    .filter(k => !k.match(/^__/))
    .reduce((accum, name) => {
      const type = schema.getType(name);
      return !isSpecifiedScalarType(type)
        ? accum += `${print(type.astNode)}\n`

arjun-kava

#### KERAS ####
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.3
set_session(tf.Session(config=config))

#### TF ####
config = tf.ConfigProto(
    gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5),

arjun-kava

from __future__ import print_function

from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense, Embedding
from keras.layers import LSTM
from keras.datasets import imdb

max_features = 20000
maxlen = 80  # cut texts after this number of words (among top max_features most common words)

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# -*- coding: utf-8 -*-
"""ResNet50 model for Keras with fused intermediate layers
# Reference:
    https://arxiv.org/pdf/1604.00133.pdf
Adapted from original resnet
"""
from __future__ import print_function
from __future__ import absolute_import

import warnings

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from keras import backend as K

cfg = K.tf.ConfigProto()
cfg.gpu_options.allow_growth = True
K.set_session(K.tf.Session(config=cfg))

K.clear_session()