snay2

import uuid
import time
import boto
import json
from boto.sqs.message import RawMessage

AWSKey = "{redacted}"
AWSSecret = "{redacted}"
testQueue = "testqueue"

ExpandOcean

# coding:utf-8
from kivy.app import App
from kivy.uix.image import Image
from kivy.clock import Clock
from kivy.graphics.texture import Texture
import cv2


class KivyCamera(Image):
    def __init__(self, capture, fps, **kwargs):

JaviLorbada

Videos

• Introduction to Functional Programming Johannes Weiß - https://vimeo.com/100786088
• ReactiveCocoa at MobiDevDay Andrew Sardone - https://vimeo.com/65637501
• The Future Of ReactiveCocoa Justin Spahr-Summers - https://www.youtube.com/watch?v=ICNjRS2X8WM
• Enemy of the State Justin Spahr-Summers - https://www.youtube.com/watch?v=7AqXBuJOJkY
• WWDC 2014 Session 229 - Advanced iOS Application Architecture and Patterns Andy Matuschak - https://developer.apple.com/videos/play/wwdc2014/229/
• Functioning as a Functionalist Andy Matuschak - https://www.youtube.com/watch?v=rJosPrqBqrA
• Controlling Complexity in Swift Andy Matuschak - https://realm.io/news/andy-matuschak-controlling-complexity/

lukehedger

$ ffmpeg -i input.mp4 -vcodec h264 -acodec mp2 output.mp4

DarrenN

# Version key/value should be on his own line
PACKAGE_VERSION=$(cat package.json \
  | grep version \
  | head -1 \
  | awk -F: '{ print $2 }' \
  | sed 's/[",]//g')

echo $PACKAGE_VERSION

kylemcdonald

import PIL.Image
from cStringIO import StringIO
import IPython.display
import numpy as np
def showarray(a, fmt='png'):
    a = np.uint8(a)
    f = StringIO()
    PIL.Image.fromarray(a).save(f, fmt)
    IPython.display.display(IPython.display.Image(data=f.getvalue()))

jorgemf

# docker build --pull -t tf/tensorflow-serving --label 1.6 -f Dockerfile .
# export TF_SERVING_PORT=9000
# export TF_SERVING_MODEL_PATH=/tf_models/mymodel
# export CONTAINER_NAME=tf_serving_1_6
# CUDA_VISIBLE_DEVICES=0 docker run --runtime=nvidia -it -p $TF_SERVING_PORT:$TF_SERVING_PORT -v $TF_SERVING_MODEL_PATH:/root/tf_model --name $CONTAINER_NAME tf/tensorflow-serving /usr/local/bin/tensorflow_model_server --port=$TF_SERVING_PORT --enable_batching=true --model_base_path=/root/tf_model/
# docker start -ai $CONTAINER_NAME

FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04

hereismari

Setting up a MSI laptop with GPU (gtx1060)

Installing Ubuntu 18.04, CUDA, CDNN, Pytorch and TensorFlow

Installing Ubuntu 18.04

Get Image

https://tutorials.ubuntu.com/tutorial/tutorial-create-a-usb-stick-on-ubuntu#0

Install (solving issues)

WillKoehrsen

from sklearn.datasets import load_iris
iris = load_iris()

# Model (can also use single decision tree)
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators=10)

# Train
model.fit(iris.data, iris.target)
# Extract single tree

joshfp