hmldd

# coding:utf-8

from elasticsearch import Elasticsearch
import json

# Define config
host = "127.0.0.1"
port = 9200
timeout = 1000
index = "index"

jychstar

import argparse
import sys

from tensorflow.examples.tutorials.mnist import input_data
from time import time
t0 = time()

import tensorflow as tf

tf.summary.FileWriterCache.clear()

guilhermepontes

// original gist
const shuffleArray = arr => arr.sort(() => Math.random() - 0.5);

// fully random by @BetonMAN
const shuffleArray = arr => arr
  .map(a => [Math.random(), a])
  .sort((a, b) => a[0] - b[0])
  .map(a => a[1]);

shuffleArray([1, 2, 3]) //[3, 1, 2]

avloss

TensorFlow Serving in 10 minutes!

TensorFlow SERVING is Googles' recommended way to deploy TensorFlow models. Without proper computer engineering background, it can be quite intimidating, even for people who feel comfortable with TensorFlow itself. Few things that I've found particularly hard were:

• Tutorial examples have C++ code (which I don't know)
• Tutorials have Kubernetes, gRPG, Bezel (some of which I saw for the first time)
• It needs to be compiled. That process takes forever!

After all, it worked just fine. Here I present an easiest possible way to deploy your models with TensorFlow Serving. You will have your self-built model running inside TF-Serving by the end of this tutorial. It will be scalable, and you will be able to query it via REST.

vgaidarji

pre.highlight,
.highlight pre { background-color: #272822; }
.highlight .hll { background-color: #22282A }
.highlight .c { color: #99AA8A } /* Comment */
.highlight .err { color: #960050; background-color: #1e0010 } /* Error */
.highlight .k { color: #93C763 } /* Keyword */
.highlight .l { color: #ae81ff } /* Literal */
.highlight .n { color: #F1F2F3 } /* Name */
.highlight .o { color: #E8E2B7 } /* Operator */
.highlight .p { color: #F1F2F3 } /* Punctuation */

luiscape

#
#  Original solution via StackOverflow:
#    http://stackoverflow.com/questions/35802939/install-only-available-packages-using-conda-install-yes-file-requirements-t
#

#
#  Install via `conda` directly.
#  This will fail to install all
#  dependencies. If one fails,
#  all dependencies will fail to install.

wangruohui

In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install NVIDIA Graphics Driver via runfile
  • Remove Previous Installations (Important)
  • Download the Driver
  • Install Dependencies
  • Creat Blacklist for Nouveau Driver

• Stop lightdm/gdm/kdm

gunjanpatel

Revert the full commit

Sometimes you may want to undo a whole commit with all changes. Instead of going through all the changes manually, you can simply tell git to revert a commit, which does not even have to be the last one. Reverting a commit means to create a new commit that undoes all changes that were made in the bad commit. Just like above, the bad commit remains there, but it no longer affects the the current master and any future commits on top of it.

git revert {commit_id}

About History Rewriting

Delete the last commit

Deleting the last commit is the easiest case. Let's say we have a remote origin with branch master that currently points to commit dd61ab32. We want to remove the top commit. Translated to git terminology, we want to force the master branch of the origin remote repository to the parent of dd61ab32:

hugobowne

class MyStreamListener(tweepy.StreamListener):
    def __init__(self, api=None):
        super(MyStreamListener, self).__init__()
        self.num_tweets = 0
        self.file = open("tweets.txt", "w")

    def on_status(self, status):
        tweet = status._json
        self.file.write( json.dumps(tweet) + '\n' )
        self.num_tweets += 1

olih

Processing JSON using jq

jq is useful to slice, filter, map and transform structured json data.

Installing jq

On Mac OS

brew install jq