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from ctypes import *
import math
import random

def sample(probs):
    s = sum(probs)
    probs = [a/s for a in probs]
    r = random.uniform(0, 1)
    for i in range(len(probs)):
        r = r - probs[i]

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Section "InputClass"
        Identifier "system-keyboard"
        MatchIsKeyboard "on"
        Option "XkbLayout" "us,np"
        Option "XkbModel" "pc104"
        Option "XkbVariant" ",dvorak"
        Option "XkbOptions" "grp:lwin_toggle"
EndSection

# put this file in /etc/X11/xorg.conf.d/

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Method 1

First of all, we have tf.device thing that tensorflow has to offer for us to choose which gpu we want. Like:

with tf.device('/gpu:0'):
    a = tf.constant(3.0)

This allocates for gpu 0.

Method 2

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First get the app id

• Go to the pat h/home/paradox/.local/share/applications/
• When you open each file, you will see the texts in following format:

#!/usr/bin/env xdg-open
[Desktop Entry]
Version=1.0
Terminal=false
Type=Application
Name=ScreenMeter Time Tracker

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#wget --load-cookies /tmp/cookies.txt "https://drive.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://drive.google.com/uc?export=download&id=<FILEID>' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=<FILEID>" -O <FILENAME> && rm -rf /tmp/cookies.txt

#/usr/bin/env bash

echo "i am paradox"

FILEID="1hJKCb4eF1AJih_dhZOJSF5VR-ZtRNaap"
FILENAME="model.zip"

link="https://drive.google.com/uc?export=download&id=$FILEID"

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# POST simple text
curl -XPOST localhost:8000/test --data "i am paradox" -H "Content-Type: text/plain"

# POST JSON


# GET
curl -XGET localhost:8000/test

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build docker image

docker build -t <imagename> .

example

docker build -t testapi

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def euclidean_distance(vects):
    x, y = vects
    sum_square = K.sum(K.square(x - y), axis=1, keepdims=True)
    return K.sqrt(K.maximum(sum_square, K.epsilon()))

def eucl_dist_output_shape(shapes):
    shape1, shape2 = shapes
    return (shape1[0], 1)

def cosine_distance(vests):

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""" Python implementation of the OASIS algorithm.
Graham Taylor

Based on Matlab implementation of:
Chechik, Gal, et al.
"Large scale online learning of image similarity through ranking."
The Journal of Machine Learning Research 11 (2010): 1109-1135.
"""

from __future__ import division

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Convert sequence of image files into video.
The image filepaths are inside "files.txt" with each line representing.

mencoder -nosound -noskip -oac copy -ovc copy -o output.avi -mf fps=6 'mf://@files.txt'