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provider "aws" { | |
region = "us-east-1" | |
} | |
# Main VPC | |
# https://registry.terraform.io/providers/hashicorp/aws/latest/docs/resources/vpc | |
resource "aws_vpc" "main" { | |
cidr_block = "10.0.0.0/18" | |
tags = { |
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Requirement | Description | Priority | |
---|---|---|---|
Automation & Scheduling | Data science code should run at the end on the cloud Thus deploying and scheduling this code ideally should be done easily by a data scientist without the need for a data engineer to do this. | Must have |
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firstname | lastname | age | |
---|---|---|---|
X | Y | 27 | |
Y | Z | 30 |
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#!/bin/python | |
import sys | |
import os | |
import json | |
class Stream(object): | |
def __init__(self, content): |
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from metaflow import FlowSpec, step, batch | |
class LinearFlow(FlowSpec): | |
@step | |
def start(self): | |
print('This step is running on local machine') | |
self.next(self.a) | |
@batch(memory=60000, cpu=1). # using batch to execute step on AWS |
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from metaflow import Flow | |
flow = Flow('ModelCreationFlow'). # which flow you want to inspect | |
for run in flow: # iterate over all the historical runs | |
if run.successful: | |
print(run.finished_at) # print when was the run | |
run_id = run.id # get the run id | |
df_predection = Flow("ModelCreationFlow")[run_id].data.df_predection |
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from metaflow import FlowSpec, step | |
class LinearFlow(FlowSpec): | |
@step | |
def start(self): | |
self.my_var = 'hello world' | |
self.next(self.a) | |
@step |
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var page = require('webpage').create(); | |
//viewportSize being the actual size of the headless browser | |
page.viewportSize = { width: 1027, height: 763 }; | |
//the clipRect is the portion of the page you are taking a screenshot of | |
//page.clipRect = { top: 0, left: 0, width: 1024, height: 768 }; | |
//the rest of the code is the same as the previous example | |
page.open('http://www.kooora.com/', function() { | |
page.render('github.png'); | |
phantom.exit(); | |
}); |
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