eavidan

import ray
import pandas as pd
import numpy as np

ray.init(local_mode=True)


def ge(a, b, by):
    gt = a > b
    eq = a == b

eavidan

import tensorflow as tf
from tensorflow_serving.apis import predict_pb2
import time
import base64
import requests
import pickle
import numpy as np
import random

eavidan

FROM ubuntu:16.04

MAINTAINER Eran Avidan

# Install general packages
RUN apt-get update && apt-get install -y \
        curl \
        libcurl3-dev \
        unzip \
        wget \

eavidan

import pickle
import numpy as np
import requests

host = 'localhost'
port = '8501'
batch_size = 1
image_path = "./mnist_image.pkl"
model_name = 'mnist'
signature_name = 'predict_images'

eavidan

import pickle
import numpy as np
import time
import requests
import subprocess
import re
from grpc.beta import implementations
import tensorflow as tf
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2

eavidan

def a_friendly_professor():

    return "".join(
    [chr        (int       (x**i+i*0.01          ))for i, x          in enumerate(
    [0,          68,         10+                0.677   ,4.8058       ,3.2+
    0.06+      0.0075        ,2,               2.0348   ,1.7279       ,1.54
    ,1.6032    ,1.5864      ,1.527             ,1.486   ,1.4261        ,1.3
    +0.0971,1.372,          1.3422               ,1.318505429          ])])

eavidan

import org.apache.spark.ml.Pipeline
import org.apache.spark.ml.classification.RandomForestClassifier
import org.apache.spark.ml.evaluation.{BinaryClassificationEvaluator, MulticlassClassificationEvaluator}
import org.apache.spark.ml.feature._
import org.apache.spark.sql.{Column, DataFrame, Row, SparkSession}
import org.apache.spark.sql.functions._
import org.apache.spark.ml.tuning.{CrossValidator, ParamGridBuilder}


/**

eavidan

def predictResponse_into_nparray(response, output_tensor_name):
    dims = response.outputs[output_tensor_name].tensor_shape.dim
    shape = tuple(d.size for d in dims)
    print(shape)
    return np.reshape(response.outputs[output_tensor_name].float_val, shape)

eavidan

val names = df.columns.toList
println(names)
df.foreachPartition(rows => {
  var cols = scala.collection.mutable.Map[String, List[Any]]()
  names.foreach(col => cols(col) = List())
  rows.foreach(row => names.zip(row.toSeq).map(x => {
    cols(x._1) = cols(x._1) :+ x._2 
  }))
  println(cols)
})

eavidan

import subprocess

# simply running a command and getting the output
cmd = ['ps', '-ax']
s = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
(stdoutdata, stderrdata) = s.communicate()
#print(stdoutdata)

# you can chain commands
# the following runs 'ls /etc | grep ntp' by sending the 'ls etc' output to the input of the 'grep ntp'