timercrack

{//服务器使用的方案：VLESS+TLS+WS+NGINX+CDN，看情况替换成自己的
  "log": {
    "access": "/dev/null",
    "error": "/var/log/xray_error.log",
    "loglevel": "warning"
  },
  "inbounds": [
    {
      "tag":"transparent",
      "port": 12345,

carlthome

keithito

# Copyright (c) 2017 Keith Ito
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in

mjdietzx

#!/bin/bash

# install CUDA Toolkit v8.0
# instructions from https://developer.nvidia.com/cuda-downloads (linux -> x86_64 -> Ubuntu -> 16.04 -> deb (network))
CUDA_REPO_PKG="cuda-repo-ubuntu1604_8.0.61-1_amd64.deb"
wget http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1604/x86_64/${CUDA_REPO_PKG}
sudo dpkg -i ${CUDA_REPO_PKG}
sudo apt-get update
sudo apt-get -y install cuda

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.

kastnerkyle

from __future__ import print_function
import os
import shutil
import stat
import subprocess
import time
import numpy as np
from scipy.io import wavfile
import re
import glob

kastnerkyle

from __future__ import print_function
import subprocess
import shutil
import os
import stat
import time

# This script looks extremely defensive, but *should* let you rerun at
# any stage along the way. Also a lot of code repetition due to eventual support
# for "non-blob" install from something besides the magic kk_all_deps.tar.gz

kastnerkyle

% Assumptions: vctk is in ~/vctk/VCTK-Corpus.tar.gz
% htk is in ~/htk/HTK-3.4.1.tar.gz
% festival is in ~/festival/festival-2.4-release.tar.gz
% speech_tools is in ~/speech_tools/speech_tools-2.4-release.tar.gz
% sptk is in ~/sptk
% extrafiles from http://festvox.org/packed/festival/2.4/
% festlex_CMU.tar.gz
% festlex_POSTLEX.tar.gz
% festlex_OALD.tar.gz
% festvox_cmu_us_slt_cg.tar.gz

dsaiztc

# -*- coding: utf-8 -*-
"""Example Google style docstrings.

This module demonstrates documentation as specified by the `Google Python
Style Guide`_. Docstrings may extend over multiple lines. Sections are created
with a section header and a colon followed by a block of indented text.

Example:
    Examples can be given using either the ``Example`` or ``Examples``
    sections. Sections support any reStructuredText formatting, including

lefticus

THIS DOCUMENT

IS OUT OF

DATE

See https://github.com/lefticus/cppbestpractices/blob/master/00-Table_of_Contents.md Instead!

C++ Coding Standards Part 0: Automated Code Analysis

Automated analysis is the main advantage to working with a modern statically typed compiled language like C++. Code analysis tools can inform us when we have implemented an operator overload with a non-canonical form, when we should have made a method const, or when the scope of a variable can be reduced.