Download repo from Appsilon
git clone https://github.com/Appsilon/r-lambda-workflow
cd r-lambda-workflow
Setup a python virtual env (may not be necessary)
| FROM nginx:alpine | |
| RUN echo "My app!" > /usr/share/nginx/html/index.html |
Download repo from Appsilon
git clone https://github.com/Appsilon/r-lambda-workflow
cd r-lambda-workflow
Setup a python virtual env (may not be necessary)
| import time | |
| import cv2 | |
| import boto3 | |
| # Get the Client | |
| session = boto3.Session() | |
| rekog_client = session.client("rekognition", region_name='us-east-1') | |
| width = 1280 | |
| height = 720 | |
| scale_factor = 0.1 |
| { | |
| "AWSTemplateFormatVersion": "2010-09-09", | |
| "Description": "PA16 2018-12-13 - @akirmak - RevHist: PA16: sagemaker notebook role type fixed. PA15:-(parameters added for AcctId and S3 bucket's name initials)", | |
| "Parameters": { | |
| "yourInitials": { | |
| "Description": "Your Initials to be used in the s3-bucket created. All in small letters pls. e.g. It shall be 'fs' for Frank Sinatra", | |
| "Type": "String", | |
| "MinLength": "2", | |
| "MaxLength": "5" | |
| } |
| { | |
| "productName" : "{{commerce.productName}}", | |
| "color" : "{{commerce.color}}", | |
| "department" : "{{commerce.department}}", | |
| "product" : "{{commerce.product}}", | |
| "imageUrl": "{{image.imageUrl}}", | |
| "dateSoldSince": "{{date.past}}", | |
| "dateSoldUntil": "{{date.future}}", | |
| "price": {{random.number( | |
| { |
| { | |
| "AWSTemplateFormatVersion":"2010-09-09", | |
| "Description":"Creates resources necessary to replicate SQLServer database using AWS Database Migration Service to S3 Data lake.", | |
| "Parameters":{ | |
| "KeyName":{ | |
| "Description":"", | |
| "Type":"AWS::EC2::KeyPair::KeyName" | |
| } | |
| }, | |
| "Mappings" : { |
| import boto3 | |
| import json | |
| def lambda_handler(event, context): | |
| payload = '1,13000 \n 1,20000 \n 2,3500 \n 2,5000 \n 3,3000 \n 3,3300 \n 4,2 \n 4,10' | |
| endpoint_name = 'YourInitials-kmeans-anomalydetection' | |
| runtime = boto3.client('runtime.sagemaker') | |
| response = runtime.invoke_endpoint(EndpointName=endpoint_name, |
| import sys | |
| from awsglue.transforms import * | |
| from awsglue.utils import getResolvedOptions | |
| from pyspark.context import SparkContext | |
| from awsglue.context import GlueContext | |
| from awsglue.job import Job | |
| ## @params: [JOB_NAME] | |
| args = getResolvedOptions(sys.argv, ['JOB_NAME']) |
| select sensorname, sensorvalue, anomalyscore from YourInitial_bigdata.analytic_csv2parquet where anomalyscore > 2 limit 10; |