Cube in a Box

The Cube in a Box is a simple way to run the OpenDataCube.

How to use:

If you have make installed you can use it to save some typing using the instructions a little further down.

All you need to know:

Set environment variables for ODC_ACCESS_KEY and ODC_SECRET_KEY to something valid with your AWS account credentials.
Start a local environment: docker-compose up
Set up your local postgres database (after the above has finished) using:
- docker-compose exec jupyter datacube -v system init
- docker-compose exec jupyter datacube product add /opt/odc/docs/config_samples/dataset_types/ls_usgs.yaml
Before indexing Landsat 8, you need to grab the pathrows index. Download the file from here and save the zip file to data/wrs2_descending.zip
Index a default region with:
- docker-compose exec jupyter bash -c "cd /opt/odc/scripts && python3 ./autoIndex.py -p '/opt/odc/data/wrs2_descending.zip' -e '146.30,146.83,-43.54,-43.20'"
View the Jupyter notebook at http://localhost using the password secretpassword
Shutdown your local environment:
- docker-compose down

If you have make:

Set environment variables for ODC_ACCESS_KEY and ODC_SECRET_KEY to something valid with your AWS account credentials.
Start a local environment using make up
Set up your local postgres database (after the above has finished) using make initdb
Before indexing Landsat 8, you need to grab the pathrows index using make download-pathrows-file
Index a default region with make index
- Edit the Makefile to change the region of interest
View the Jupyter notebook at http://localhost using the password secretpassword

Todo:

Set up notebooks that work on indexed data

Deploying to AWS:

To deploy to AWS, you cam either do it on the command line, with the AWS command line installed or the magic URL below and the AWS console.

Once deployed, if you navigate to the IP of the deployed instance, you can access Jupyter with the password you set in the parameters.json file or in the AWS UI if you used the magic URL.

Magic URL

Launch a Cube in a Box

You need to be logged in to the AWS Console deploy using this URL. Once logged in, click the link, and follow the prompts including settings a bounding box region of interest, EC2 instance type and password for Jupyter.

Command line

Alter the parameters in the parameters.json file
Run make create-infra
If you want to change the stack, you can do make update-infra (although it may be cleaner to delete and re-create the stack)

IMPORTANT NOTES

In your local environment, in order to be able to get data from S3, you need to ensure that the environment variables ODC_ACCESS_KEY and ODC_SECRET_KEY are set to something valid.