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import numpy as np
import pandas as pd
import requests
import bs4
import lxml.etree as xml
import time
from sqlalchemy import create_engine
import pymysql
import pymssql

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• Install R, Rtools (Required), RStudio (Optional).
• Install Git for Windows for retrive the latest version of xgboost.
• Install Visual Studio Community, for c++ development environment.
  • Select Workload: Desktop/Mobile/Linux Development with C++ to obtain C++ CMake tool

• Install xgboost, follow this official installation guide, note that:

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# Generate random matrix with dim = N
N <- 3
A <- matrix(runif(N*N), N, N, byrow=TRUE)
A <- A+t(A)

# Setting up eigenvalues lambda, true eigenvector V and derived eigenvector U
ev <- eigen(A)
(lambda <- ev$values)
(V <- ev$vectors)
U <- matrix(0L, N, N)

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#--------------------------
# USER-SPECIFIED DATA
#--------------------------

# Tune these parameters

num_classes = 2
image_shape = (160, 576)
EPOCHS = 40
BATCH_SIZE = 16

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  CREATE TEMPORARY FUNCTION

    -- In this function, we're going to be working on arrays of values.
    -- we're also going to define a set of functions 'inside' the kMeans.

    -- *heavily borrowing from https://github.com/NathanEpstein/clusters* --

    kMeans(x ARRAY<FLOAT64>,  -- ESR1 gene expression
           y ARRAY<FLOAT64>,  -- EGFR gene expression
           iterations FLOAT64,  -- the number of iterations

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Training MNIST using Kubeflow, Minio, and Argo on Azure

This example guides you through the process of taking an example model, modifying it to run better within Kubeflow, and serving the resulting trained model. We will be using Argo to manage the workflow, Minio for saving model training info, Tensorboard to visualize the training, and Kubeflow to deploy the Tensorflow operator and serve the model.

Prerequisites

Before we get started there a few requirements.

Deploy an AKS Cluster

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# Load data
# ips <- read.csv("https://lookaside.fbsbx.com/file/ipdatas.csv?token=AWyvZbsOT-DxjUjUySQf4vqWu9VSj7WTKytXNHkow4054oYjLFN_7FW0FPNwDtmUbQXXzJyCm99cQoRV6c-xS-Clpc7ss0lw-C04A41hBtFP3Yy2d4sR_W31BNrLip_XcqyuJbOxhjC9xKBq32jQ8zfxnDO3OdbSdq4-SbOnWnMQSLcjWrx6hfqbKo1a3gVttSZLhKqna7TpJhOm8uKdUvde")
ips <- read.csv("ipdatas.csv")

# 如果已安裝則省略
# install.packages("maps")
# install.packages("ggplot2")

library(ggplot2)

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import os, csv
from urllib2 import Request, urlopen
from StringIO import StringIO
from PyPDF2 import PdfFileReader

# Get the moneydj urls into a list.
with open('/money_url_list.csv', 'rb') as money_url_list:
    reader = csv.reader(money_url_list, delimiter = ',')
    moneydj_list = list(reader)
    

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# METEOR CORE:

Anywhere: Meteor.isClient
Anywhere: Meteor.isServer
Anywhere: Meteor.startup(func)
Anywhere: Meteor.absoluteUrl([path], [options])
Anywhere: Meteor.settings
Anywhere: Meteor.release

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##TUNING##

Configuration

System: set file descriptors to 32K or 64K

vim /etc/security/limit.conf