monklof

/**
 * just for demo purpose, bugs maybe found
 * g++ -shared -fPIC pyio.cc -o pyio.so -std=c++11 -I/usr/include/python2.7/
 * equilavent python code:

def write(filename, string):
    global error
    if not string: error = “empty string"
    with open(filename, ‘w’) as f:
        f.write(string)

monklof

# coding: utf-8
import math
from PIL import Image
from pdf2image import convert_from_path, convert_from_bytes
import sys,os
print(sys.argv)
inputpath = sys.argv[1]
outputpath = sys.argv[2]
filename = '.'.join(inputpath.split('/')[-1].split('.')[:-1])

monklof

import tensorflow as tf
from tensorflow.python.client import timeline

run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()

predictions = use_sess.run(use_out, {'DecodeJpeg/contents:0': image_file.file.getvalue()}, options=run_options, run_metadata=run_metadata)

# Create the Timeline object, and write it to a json
tl = timeline.Timeline(run_metadata.step_stats)

monklof

# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

monklof

# -*- coding: utf-8 -*-

import os.path

# This is a placeholder for a Google-internal import.

import tensorflow as tf
import tensorflow.contrib.slim as slim
from nets import resnet_v2
from preprocessing import  vgg_preprocessing as vgg

monklof

def show_graph_details(graph, mark):
    import tensorflow as tf
    import pprint
    if isinstance(graph, tf.GraphDef):
        with tf.Session(graph=tf.Graph()) as sess:
            tf.import_graph_def(graph)
        graph = sess.graph
    elif isinstance(graph, tf.MetaGraphDef):
        with tf.Session(graph=tf.Graph()) as sess:
            tf.import_graph_def(graph.graph_def)

monklof

from tensorflow.python.client import device_lib

def get_available_gpus():
    local_device_protos = device_lib.list_local_devices()
    return [x.name for x in local_device_protos if x.device_type == 'GPU']

monklof

Latency Comparison Numbers
--------------------------
L1 cache reference                           0.5 ns
Branch mispredict                            5   ns
L2 cache reference                           7   ns                      14x L1 cache
Mutex lock/unlock                           25   ns
Main memory reference                      100   ns                      20x L2 cache, 200x L1 cache
Compress 1K bytes with Zippy             3,000   ns        3 us
Send 1K bytes over 1 Gbps network       10,000   ns       10 us
Read 4K randomly from SSD*             150,000   ns      150 us          ~1GB/sec SSD

monklof

#! /bin/sh -
# Build one or more packages in parallel on one or more build hosts.
#
# Usage:
#	build-all [ --? ]
#		  [ --all "..." ]
#		  [ --cd "..." ]
#		  [ --check "..." ]
#		  [ --configure "..." ]
#		  [ --environment "..." ]

monklof

import os, sys
from flask import jsonify
import datetime

def convert_datatype(val):
    if type(val) == dict:
        converted = {}
        for k in val:
            if type(k) != unicode and type(k) != str:
                raise TypeError('the key of dict must be strings.')