Brainiarc7

Building Tensorflow from source on Ubuntu 16.04LTS for maximum performance:

TensorFlow is now distributed under an Apache v2 open source license on GitHub.

On Ubuntu 16.04LTS+:

Step 1. Install NVIDIA CUDA:

To use TensorFlow with NVIDIA GPUs, the first step is to install the CUDA Toolkit as shown:

tzmartin

1. Copy m3u8 link

2. Run command

echo "Enter m3u8 link:";read link;echo "Enter output filename:";read filename;ffmpeg -i "$link" -bsf:a aac_adtstoasc -vcodec copy -c copy -crf 50 $filename.mp4

protrolium

ffmpeg

Converting Audio into Different Formats / Sample Rates

Minimal example: transcode from MP3 to WMA:
ffmpeg -i input.mp3 output.wma

You can get the list of supported formats with:
ffmpeg -formats

You can get the list of installed codecs with:

sergray

"""Asynchronous requests in Flask with gevent"""

from time import time

from flask import Flask, Response

from gevent.pywsgi import WSGIServer
from gevent import monkey

import requests

kaka19ace

#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
#  @file     gevent_zmq_redis_demo.py
#  @author   kaka_ace <[email protected]>
#  @date     Tue Oct 14 2014

"""
    fetures: there are two concurrent tasks
    1. From Redis server, Getting the notify msg with BLPOP operation, 

Zulko

#
# This Python script makes a summary of a football game by cutting
# the video around the 10 % loudest moments, which generally 
# include the goals and other important events.
# For more details, see this blog post:
# http://zulko.github.io/blog/2014/07/04/automatic-soccer-highlights-compilations-with-python/
#
# LICENCE: Creative Commons 0 - Public Domain
# I, the author of this script, wave any rights and place this work in the public domain.
#

ctokheim

Cython

Cython has two major benefits:

1. Making python code faster, particularly things that can't be done in scipy/numpy
2. Wrapping/interfacing with C/C++ code

Cython gains most of it's benefit from statically typing arguments. However, statically typing is not required, in fact, regular python code is valid cython (but don't expect much of a speed up). By incrementally adding more type information, the code can speed up by several factors. This gist just provides a very basic usage of cython.

bistaumanga

'''Implementation and of K Means Clustering
Requires : python 2.7.x, Numpy 1.7.1+'''
import numpy as np

def kMeans(X, K, maxIters = 10, plot_progress = None):

    centroids = X[np.random.choice(np.arange(len(X)), K), :]
    for i in range(maxIters):
        # Cluster Assignment step
        C = np.array([np.argmin([np.dot(x_i-y_k, x_i-y_k) for y_k in centroids]) for x_i in X])

commandodev

from eventlet.pools import Pool
from eventlet.timeout import Timeout

class SocketPool(Pool):
    """A pool of sockets connected to a component

    If a socket times out in use, simply close if before handing it back to the
    pool and it will be discarded and a replacement inserted into the pool."""

    def __init__(self, address, **kwargs):