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README.md

Global Azure Bootcamp 2019 - Science Lab

Global Azure Bootcamp 2019 - Science Lab

This project contains instructions to deploy the Global Azure Bootcamp 2019 Science Lab

Quickstart

To quickly deploy the science lab using Azure Container Instances, click on the button below. If it's your first deployment, we strongly recommend to read the instructions below.

Introduction

This project contains all the source code for the Global Azure Bootcamp 2019 Science Lab. Created by David Rodriguez (@davidjrh), Martin Abbott (@martinabbott) and Santiago Porras (@saintwukong) for the Global Azure Bootcamp 2019 Science Lab running Enric Pallé, Diego Hidalgo and Sebastian Hidalgo's Machine Learning algorithms for exoplanet hunting at the Instituto de Astrofisica de Canarias using TESS mission data from NASA.

See more at https://global.azurebootcamp.net/global-azure-science-lab-2019/

Getting Started

Requirements

In order to participate on the GAB Science Lab you will need:

  • An active Azure subscription. The easiest way to deploy the lab is by using Azure Container Instances. You will need an active Azure subscription to deploy the containers on Azure. You can signup for a free subscription here or use the Azure Passes shared on the Global Azure Bootcamp event.
  • You can deploy the client on any other Docker powered environment (see deployment instructions at the end of this document):

Deploying the lab using Azure Container Instances (ACI)

The easiest way to deploy the Science Lab is by using Azure Container Instances. We have prepared a resource manager template that simplifies this step, by asking you some parameters that are used in the container that will be used later on the Global Dashboards for statistics and for fun.

  1. Click on the deployment button below to start the process:
  1. Fill the form. You can get info about each field if you hold the cursor over the info icon.

    • Choose the subscription and resource group you where you want to deploy the container instances
    • Location: IMPORTANT: the Azure Container Instances service is not available in all the regions. At time of writing, you must choose between one of these locations:
      • "Central US"
      • "East US"
      • "East US2"
      • "North Central US"
      • "South Central US"
      • "West US"
      • "West US2"
      • "North Europe"
      • "West Europe"
      • "East Asia"
      • "Southeast Asia"
      • "Japan East"
      • "Australia East"
      • "Central India"
      • "South India"
      • "Canada Central"
    • Email, FullName, TeamName, CompanyName: fill with your personal info. It be displayed on the global dashboards (e-mail will not)
    • CountryCode: the 2 character ISO2 country code. Find your code at Wikipedia
    • LabKeyCode: Is a predefined string with your location LAB Key. Ask admin staff at your location for the code. If you don't know any, just use THE-GAB-ORG as key.
    • InstanceCount: Number of container instance groups (60 or less, there is a limit of 60 ACIs per Azure susbcription). Check the available instances/quotas in your subscription before setting up a big number. TIP: You can start with 1 or 2 container instance groups and repeat this process later to deploy more instances

    Deployment parameters

Click on the Accept the Terms and Conditions checkbox, and relax waiting for the green check. Will take around 5 minutes to complete.

Deployment completed

Verifying the lab is working properly

Once the lab has been deployed, you will see a set of resources under the resource group, one per container instance group. Each group will container just one container instance.

Resource group

Click on one of the container instances, and get the public DNS name from the General Settings area.

Deployment URL

Browse the URL and you will be able to see if the lab is working properly. There are three areas:

  • Inputs Downloaded: a green light indicates that is working properly. Every 10 seconds a background process checks if there are no inputs to process, and then downloads a new batch of inputs;
  • Processing: a green light indicates that is working properly. A background process starts processing the inputs as soon as they are available locally. The inputs are processed one by one and results are saved into an internal output queue;
  • Ouputs Uploaded: a green light indicates that is working properly. Every 10 seconds a background process checks if there are outputs ready to be uploaded to the GAB server.

There is also a log area where you can check what is happening inside the GAB client.

Deployment details

Each input takes around 5 minutes to be processed by a container (pipeline 1 + pipeline 2 execution times). After the input is processed, it goes to the upload queue, and once uploaded, you start appearing on the Global Azure Bootcamp Science lab Dashboards, available at https://gablabdashboard.azurewebsites.net.

Decomissioning the Science Lab

Your lab deployment will continue working processing inputs until you delete the deployment resources. Note that this year our intention is to continue processing information after the GAB day.

In order to delete your deployment:

  1. Select the Resource Group containing your science lab deployment
  2. Click on Delete and confirm by typing your resource group name

Thanks for your support on Global Azure Bootcamp 2019 Science Lab. Live Long and Prosper!

Frequently Asked Questions

  1. How much will cost?

The lab uses an Azure Container Instances. The cost of each ACI is around $1 for a full day (consumption 1vCPU and 1GB RAM over 24h).
So for example, if you deploy the science lab with 4 container instances during 12 hours, the costs will be under $2. For more information about pricing:

  1. How many instances can I deploy?

If you are deploying the lab using Azure Container Instances, there is a limit of 60 ACIs per Azure Subscription. You can deploy more than 60 if you use more subscriptions, but please, do the maths following FAQ #1. Remember you can also deploy the science lab on your own laptop or on any other Docker powered environment.

  1. Can I start crunching data before April 27th?

You can deploy the lab before April 27th just for testing purposes, but note that we will reset all the data, stats and dashboards on April 27th.

  1. Can I continue processing data after April 27th?

Yes, this year we want to continue hosting the Science Lab after the Global Azure Bootcamp day. Our intention is to continue processing data until the end of the TESS mission.

Running the lab outside Azure

You can also run the science lab client on Windows, Linux or Mac, just because is implemented as a Docker container. The container image is available at Docker Hub https://hub.docker.com/r/globalazurebootcamp/sciencelab2019

You can deploy the client on any other Docker powered environment:

Deploying a local container with the science lab client

Once you have Docker installed locally, follow this steps:

  1. Create a text file called variables.env with the following data (replace the values with your own data):
BatchClient__Email=johndoe@foo.com
BatchClient__Fullname=John Doe
BatchClient__TeamName=Global Azure Team
BatchClient__CompanyName=Global Azure Bootcamp Org.
BatchClient__CountryCode=XX
BatchClient__LabKeyCode=THE-GAB-ORG
  1. Run the following Docker command:
docker run -d -p 8080:80 --env-file variables.env --restart always globalazurebootcamp/sciencelab2019:latest

This downloads the science lab client image and runs it on a container instance. If you browse http://localhost:8080, you will notice how the science lab is progressing.

Deleting the science lab client on a local environment

Once you want to stop running the science lab, run the following commands:

  1. Search the container id by executing the following command and writing down the container id with the name "globalazurebootcamp/sciencelab2019:latest"
docker ps -l
  1. Run the command to delete the container instance:
docker rm <containerid> -f

Troubleshooting

I'm seeing "Minimum client version must be GAB.Client/1.x.x.x. Please, upgrade your container instance to the latest version" on the logs

This is because we needed to break the backwards compatibility with the previous client, and you need to redeploy your container. When running on Azure Container instances, this can be easily accomplished by clicking on the "Restart" button available on the Overview section of your container when using the Azure Portal.

You can’t perform that action at this time.