soumith

conda uninstall --force pillow -y

# install libjpeg-turbo to $HOME/turbojpeg
git clone https://github.com/libjpeg-turbo/libjpeg-turbo
pushd libjpeg-turbo
mkdir build
cd build
cmake .. -DCMAKE_INSTALL_PREFIX:PATH=$HOME/turbojpeg
make
make install

ctmakro

# if input image is in range 0..1, please first multiply img by 255
# assume image is ndarray of shape [height, width, channels] where channels can be 1, 3 or 4
def imshow(img):
    import cv2
    import IPython
    _,ret = cv2.imencode('.jpg', img) 
    i = IPython.display.Image(data=ret)
    IPython.display.display(i)

Brainiarc7

Setting up Conky on Ubuntu 16.04LTS for the Clevo P751DM2-G

System Information:

We extract this with inxi:

installation:

sudo apt-get install inxi

terabyte

Prologue

I wrote this answer on stackexchange, here: https://stackoverflow.com/posts/12597919/

It was wrongly deleted for containing "proprietary information" years later. I think that's bullshit so I am posting it here. Come at me.

The Question

Amazon is a SOA system with 100s of services (or so says Amazon Chief Technology Officer Werner Vogels). How do they handle build and release?

wassname

import numpy as np
from matplotlib import pyplot as plt

class LivePlotNotebook(object):
    """
    Live plot using %matplotlib notebook in jupyter notebook
    
    Usage:
    ```
    import time

chtzvt

# Charlton Trezevant's Zoomin DNSMasq Config - Version 1.0

# Having a large local cache speeds up subsequent DNS queries significantly (from several hundred msec to around 25-30)
# You may need to adjust this depending on the amount of free space you have
cache-size=10000 
# This ensures local reverse lookup queries are never sent upstream (e.g. dig +noall +answer -x 10.0.1.1)
bogus-priv
# Names without a dot or other domain part will also not be forwarded upstream
domain-needed
# We won't need dnsmasq to overwrite the system's resolv.conf, as we have our own cache.

muety

# Inspired by https://keon.io/deep-q-learning/

import random
import gym
import math
import numpy as np
from collections import deque
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam

kevinzakka

"""
Create train, valid, test iterators for CIFAR-10 [1].
Easily extended to MNIST, CIFAR-100 and Imagenet.

[1]: https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4
"""

import torch
import numpy as np

edz-o

def DepthConversion(PointDepth, f):
    H = PointDepth.shape[0]
    W = PointDepth.shape[1]
    i_c = np.float(H) / 2 - 1
    j_c = np.float(W) / 2 - 1
    columns, rows = np.meshgrid(np.linspace(0, W-1, num=W), np.linspace(0, H-1, num=H))
    DistanceFromCenter = ((rows - i_c)**2 + (columns - j_c)**2)**(0.5)
    PlaneDepth = PointDepth / (1 + (DistanceFromCenter / f)**2)**(0.5)
    return PlaneDepth

joel-wright

#!/bin/sh
path=/sys/class/backlight/intel_backlight

luminance() {
    read -r level < "$path"/actual_brightness
    factor=$((max / 100))
    ret=`printf '%d\n' "$((level / factor))"`
    if [ $ret -gt 100 ]; then
       ret=100
    fi