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input {
  stdin { }
  file {
    type => "postfix"
    path => ["/var/log/mail.*"]
  }
}

filter {
  if [type] == "postfix" {

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# Syslog stuff
COMPONENT ([\w._\/%-]+)
COMPID postfix\/%{COMPONENT:component}(?:\[%{POSINT:pid}\])?
POSTFIX %{SYSLOGTIMESTAMP:timestamp} %{SYSLOGHOST:hostname} %{COMPID}: %{QUEUEID:queueid}?

# Milter
HELO (?:\[%{IP:helo}\]|%{HOST:helo}|%{DATA:helo})

MILTERCONNECT %{QUEUEID:qid}: milter-reject: CONNECT from %{RELAY:relay}: %{GREEDYDATA:milter_reason}; proto=%{WORD:proto}
MILTERUNKNOWN %{QUEUEID:qid}: milter-reject: UNKNOWN from %{RELAY:relay}: %{GREEDYDATA:milter_reason}; proto=%{WORD:proto}

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#encoding=UTF-8
# Configuration files must begin with a line specifying the encoding
#  of the the file.

# Instance such as prod01
set.INSTANCE=Shipper
# client abreviation such as kp for Kaiser
set.CLIENT=Logstash

set.RUBYLIB=C:\logstash-1.4.0\lib

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AWS_ACCESS_KEY = 'XXXXXXXXXXX'
AWS_SECRET_KEY  = 'XXXXXXXXXXXXXXXXXXXXXXXXXXXX'
 
 
import os
import random, string
import boto
from boto.s3.connection import S3Connection
import hashlib
from subprocess import call

kobrinartem

# Windows AMIs don't have WinRM enabled by default -- this script will enable WinRM
# AND install 7-zip, curl and .NET 4 if its missing.
# Then use the EC2 tools to create a new AMI from the result, and you have a system 
# that will execute user-data as a PowerShell script after the instance fires up!
# This has been tested on Windows 2008 SP2 64bits AMIs provided by Amazon
# 
# Inject this as user-data of a Windows 2008 AMI, like this (edit the adminPassword to your needs):
#
# <powershell>
# Set-ExecutionPolicy Unrestricted

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# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: MIT-0

AWSTemplateFormatVersion: '2010-09-09'

Description: Creates 1-, 2- or 3-tier network environment with 1, 2, or 3 Availability Zones (AZs)

Metadata:
  cfn-lint:
    config:

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client.create_model(
    ModelName=“Your model name”,
    PrimaryContainer={
    “Image”: <AWS_DEEP_LEARNING_CONTAINER_URI>,
    “ModelDataUrl”: <MODEL_S3_LOCATION>,
    “Environment”: {
        “SAGEMAKER_PROGRAM”: “inference.py”,
        “SAGEMAKER_SUBMIT_DIRECTORY”: “<INFERENCE_SCRIPT_S3_LOCATION>”,
        “SAGEMAKER_CONTAINER_LOG_LEVEL”: “20”,
        “SAGEMAKER_REGION”: <REGION>,