joseph-zhong

#!/usr/bin/python
"""Replacement for htpasswd"""
# Original author: Eli Carter

import os
import sys
import random
from optparse import OptionParser

# We need a crypt module, but Windows doesn't have one by default.  Try to find

joseph-zhong

"""
Gets the name of the active Git branch as a string.
Depends on GitPython

pip install GitPython
"""
from git import Repo

repo = Repo('/path/to/your/repo')
branch = repo.active_branch

joseph-zhong

Metadata

...

"time_range_start": 
"time_range_end": 
"time_range_start": 

joseph-zhong

These instructions will explain how to install tensorflow on mac with cuda enabled GPU suport. I assume you know what tensorflow is and why you would want to have a deep learning framework running on your computer.

Prerequisites

Make sure to update your homebrew formulas

brew update

joseph-zhong

To answer that question, I'd like to describe member's accessors first in my own words. If you already know this, skip to the heading "next:".

There are three accessors that I'm aware of: public, protected and private.

Let:

class Base {
    public:
        int publicMember;
    protected:

joseph-zhong

public class BackgroundVideoRecorder extends Service implements SurfaceHolder.Callback {

    private WindowManager windowManager;
    private SurfaceView surfaceView;
    private Camera camera = null;
    private MediaRecorder mediaRecorder = null;

    @Override
    public void onCreate() {

joseph-zhong

Power save
Min: 300 MHz
Max: 1036 MHz
Governor: INTERACTIVE
I/O scheduler: ROW
Cores: 0 and 3 only

Efficiency
Min: 300 MHz

joseph-zhong

To build OpenCV3.0 for AWS Lambda Python

Summary

Because AWS Lambda runs in a Amazon Linux environment, to run external modules you must

Create the OpenCV build environment

sudo yum update -y

joseph-zhong

Permissions

As of API level 23, the following permissions are classified as normal and dangerous

PROTECTION_NORMAL:

ACCESS_LOCATION_EXTRA_COMMANDS
ACCESS_NETWORK_STATE
ACCESS_NOTIFICATION_POLICY

joseph-zhong

total_bytes = os.stat('path/to/file').st_size
PROGRESS_BYTES = 0
def upload_callback(bytes, progress_bytes=PROGRESS_BYTES):
  progress_bytes += bytes
  sys.stdout.write('\r')
  sys.stdout.write('part uploaded: %d of %d bytes' % (progress_bytes, total_bytes))
  sys.stdout.flush()

with open(output_file, 'rb') as ouput_data: