sundisee

## VMware Workstation 16 Pro and Player keys
VMware Workstation Pro 16.x Serials

YA7RA-F6Y46-H889Z-LZMXZ-WF8UA
ZV7HR-4YX17-M80EP-JDMQG-PF0RF
UC3XK-8DD1J-089NP-MYPXT-QGU80
GV100-84W16-M85JP-WXZ7E-ZP2R6
YF5X2-8MW91-4888Y-DNWGC-W68TF
AY1XK-0NG5P-0855Y-K6ZXG-YK0T4

VMware Workstation Player 16.x Serials

## Dockerfile_TFserving_1_5
# docker build --pull -t $USER/tensorflow-serving-devel -f Dockerfile .
# export TF_SERVING_PORT=9000
# export TF_SERVING_MODEL_PATH=/tf_models/mymodel
# export CONTAINER_NAME=my_container
# CUDA_VISIBLE_DEVICES=0 docker run --runtime=nvidia -it -p $TF_SERVING_PORT:$TF_SERVING_PORT -v $TF_SERVING_MODEL_PATH:/root/tf_model --name $CONTAINER_NAME $USER/tensorflow-serving-devel  /root/serving/bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server --port=$TF_SERVING_PORT --enable_batching=true --model_base_path=/root/tf_model/
# docker start -ai $CONTAINER_NAME

FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04

ENV TF_CUDA_VERSION=9.0 \

## compile_tensorflow_serving.sh
#!/bin/bash

TENSORFLOW_COMMIT=9e76bf324f6bac63137a02bb6e6ec9120703ea9b # August 16, 2017
TENSORFLOW_SERVING_COMMIT=267d682bf43df1c8e87332d3712c411baf162fe9 # August 18, 2017
MODELS_COMMIT=78007443138108abf5170b296b4d703b49454487 # July 25, 2017

if [ -z $TENSORFLOW_SERVING_REPO_PATH ]; then
	TENSORFLOW_SERVING_REPO_PATH="serving"
fi
INITIAL_PATH=$(pwd)

## ntfs_mount.py
#! /usr/bin/env python
# -*- coding: utf-8 -*-

import subprocess
import re

ntfs_pattern = re.compile(r'File System Personality:  NTFS')
ntfs_device_node = re.compile(r'.*Device Node:.*')
device_dict = {}

## capture_rect.js
var base = 'http://dev.kaid.me/c';

var ids = [1, 2, 3, 41, 42, 43, 51, 52, 53];

ids.forEach(function(id) {
    var page   = require('webpage').create(),
        url    = base + id,
        output = 'c' + id + '.png';

    page.clipRect = {top:122, left:65, width:360, height:600};

## sci_classifier.py
# coding: utf-8

import sys
import jieba
import numpy
from sklearn import metrics
from sklearn.feature_extraction.text import HashingVectorizer
from sklearn.naive_bayes import MultinomialNB

## .curlrc
proxy=http://username:password@host:port
	VMware Workstation Pro 16.x Serials

	YA7RA-F6Y46-H889Z-LZMXZ-WF8UA
	ZV7HR-4YX17-M80EP-JDMQG-PF0RF
	UC3XK-8DD1J-089NP-MYPXT-QGU80
	GV100-84W16-M85JP-WXZ7E-ZP2R6
	YF5X2-8MW91-4888Y-DNWGC-W68TF
	AY1XK-0NG5P-0855Y-K6ZXG-YK0T4

	VMware Workstation Player 16.x Serials
	# docker build --pull -t $USER/tensorflow-serving-devel -f Dockerfile .
	# export TF_SERVING_PORT=9000
	# export TF_SERVING_MODEL_PATH=/tf_models/mymodel
	# export CONTAINER_NAME=my_container
	# CUDA_VISIBLE_DEVICES=0 docker run --runtime=nvidia -it -p $TF_SERVING_PORT:$TF_SERVING_PORT -v $TF_SERVING_MODEL_PATH:/root/tf_model --name $CONTAINER_NAME $USER/tensorflow-serving-devel /root/serving/bazel-bin/tensorflow_serving/model_servers/tensorflow_model_server --port=$TF_SERVING_PORT --enable_batching=true --model_base_path=/root/tf_model/
	# docker start -ai $CONTAINER_NAME

	FROM nvidia/cuda:9.0-cudnn7-devel-ubuntu16.04

	ENV TF_CUDA_VERSION=9.0 \
	#!/bin/bash

	TENSORFLOW_COMMIT=9e76bf324f6bac63137a02bb6e6ec9120703ea9b # August 16, 2017
	TENSORFLOW_SERVING_COMMIT=267d682bf43df1c8e87332d3712c411baf162fe9 # August 18, 2017
	MODELS_COMMIT=78007443138108abf5170b296b4d703b49454487 # July 25, 2017

	if [ -z $TENSORFLOW_SERVING_REPO_PATH ]; then
	TENSORFLOW_SERVING_REPO_PATH="serving"
	fi
	INITIAL_PATH=$(pwd)
	#! /usr/bin/env python
	# -- coding: utf-8 --

	import subprocess
	import re

	ntfs_pattern = re.compile(r'File System Personality: NTFS')
	ntfs_device_node = re.compile(r'.Device Node:.')
	device_dict = {}
	var base = 'http://dev.kaid.me/c';

	var ids = [1, 2, 3, 41, 42, 43, 51, 52, 53];

	ids.forEach(function(id) {
	var page = require('webpage').create(),
	url = base + id,
	output = 'c' + id + '.png';

	page.clipRect = {top:122, left:65, width:360, height:600};
	# coding: utf-8

	import sys
	import jieba
	import numpy
	from sklearn import metrics
	from sklearn.feature_extraction.text import HashingVectorizer
	from sklearn.naive_bayes import MultinomialNB