victorhcm

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# Authors: Keiller Nogueira, Victor de Melo
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# nvidia-docker seems more stable now and several images are starting to adopt it (including 
# [kaixhin](https://github.com/NVIDIA/nvidia-docker/issues/85).
# hence, we suggest to use it instead, as it solves a few issues with gpu passthrough.
# usage: it is only required when creating a container, i.e, with `run` and related arguments
# you're not required to provide $DOCKER_NVIDIA_DEVICES as it will find the devices itself

victorhcm

# coding: utf-8

import matplotlib.pyplot as plt
import Image

img = Image.open('truman-stairs.jpg')
fig, ax = plt.subplots(figsize=(12,12))
ax.imshow(img, aspect='equal')

ax.add_patch(

victorhcm

#!/usr/bin/env python
# encoding: utf-8


"""Minimal python commad line."""

import sys
import argparse
import logging

victorhcm

# The network is used for the video description experiments of S2VT [1].
# Please consider citing S2VT [1] if you use this example in your work.
#
# [1] S. Venugopalan, M. Rohrbach, J. Donahue, R. Mooney, T. Darrell,
#     K. Saenko. "Sequence to Sequence - Video to Text." ICCV 2015.

# The data is prepared using framefc7_stream_text_to_hdf5.py
# It is in (32)  parallel streams.
name: "s2vt"
layer {

victorhcm

# A yaml constructor is for loading from a yaml node.
# This is taken from @misha 's answer: http://stackoverflow.com/a/15942429
def opencv_matrix_constructor(loader, node):
    mapping = loader.construct_mapping(node, deep=True)
    mat = np.array(mapping["data"])
    mat.resize(mapping["rows"], mapping["cols"])
    return mat
yaml.add_constructor(u"tag:yaml.org,2002:opencv-matrix", opencv_matrix_constructor)

# A yaml representer is for dumping structs into a yaml node.

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name: "VGG_FACE_16_layer"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 224
input_dim: 224
layer {
  name: "data"
  type: "Data"
  top: "data"

victorhcm

Latency numbers every programmer should know

L1 cache reference ......................... 0.5 ns
Branch mispredict ............................ 5 ns
L2 cache reference ........................... 7 ns
Mutex lock/unlock ........................... 25 ns
Main memory reference ...................... 100 ns             
Compress 1K bytes with Zippy ............. 3,000 ns  =   3 µs
Send 2K bytes over 1 Gbps network ....... 20,000 ns  =  20 µs
SSD random read ........................ 150,000 ns  = 150 µs

Read 1 MB sequentially from memory ..... 250,000 ns = 250 µs

victorhcm

victorhcm

#!/usr/bin/env python3

import sys
from hashlib import md5
from base64 import b64encode
from binascii import hexlify, unhexlify
from time import time


def reduction_function(hash, max_password_length):

victorhcm

import av
import numpy as np


def write_packets(filename, packets, video_stream):
    container = av.open(filename, mode='w')
    stream = container.add_stream(template=video_stream)
    stream.options = {}
    for p in packets:
        if p.dts is not None: