gleeb

sudo docker -H tcp://0.0.0.0:2376 -H unix:///var/run/docker.sock -d &

gleeb

apt-get update
apt-get install -y python-setuptools
apt-get install -y python-pip
apt-get install -y python-dev
apt-get install -y libffi-dev
apt-get install -y libssl-dev
apt-get install -y build-essential
pip install requests[security]
mkdir aws-cfn-bootstrap-latest
curl https://s3.amazonaws.com/cloudformation-examples/aws-cfn-bootstrap-latest.tar.gz | tar xz -C aws-cfn-bootstrap-latest --strip-components 1

gleeb

lazy val slickGenerate = taskKey[Seq[File]]("slick code generation from an existing database")

slickGenerate := {
  import java.io.File
  val dbName = ""
  val userName = ""
  val password = ""
  val url = s"jdbc:mysql://localhost:3306/$dbName" // adapt as necessary to your system
  val jdbcDriver = "com.mysql.jdbc.Driver" // replace if not MySQL
  val slickDriver = "slick.driver.MySQLDriver" // replace if not MySQL

gleeb

if [ $# -ne 4 ]; then
        echo "usage: circle-build.sh <repository> <branch> <commit> <config-yml>"
        exit 1;
fi

ORG=VATBox
API_TOKEN="42d8a7e3cd1ba3a4ae45eb38ec2e59bce74fb278"

REPO="$1"
BRANCH="$2"

gleeb

import argparse
import os
import numpy as np
import pandas as pd
import boto3
from io import StringIO

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

gleeb

gleeb

import os
import sagemaker
import boto3

iam_client = boto3.client('iam')
role = iam_client.get_role(RoleName='AmazonSageMaker-ExecutionRole-xyz')['Role']['Arn']
sm_client = boto3.client('sagemaker', region_name="us-east-1")

model_package_group_name = "Titanic-survival"
model_package_group_input_dict = {

gleeb

import boto3


sm_client = boto3.client('sagemaker', region_name='us-east-1')

model_url = "s3://sagemaker-us-east-1-.../sagemaker-scikit-learn-.../output/model.tar.gz"

modelpackage_inference_specification =  {
    "InferenceSpecification": {
      "Containers": [

gleeb

import boto3
from datetime import datetime


sm_client = boto3.client('sagemaker', region_name='us-east-1')

model_name = 'titanic-survival-v1'
print("Model name : {}".format(model_name))
container_list = [{'ModelPackageName': 'arn:aws:sagemaker:us-east-1:...:model-package/titanic-survival/3'}]

gleeb

import boto3
sm_client = boto3.client('sagemaker', region_name='us-east-1')

endpoint_config_name = 'titanic-survival-endpoint-config'
print(endpoint_config_name)
create_endpoint_config_response = sm_client.create_endpoint_config(
    EndpointConfigName = endpoint_config_name,
    ProductionVariants=[{
        'InstanceType':'ml.m4.xlarge',
        'InitialVariantWeight':1,