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README.md

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# `odc_box`
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This repository hosts all you need to install an *Open Data Cube* instance as a docker container that serves a Jupyter notebook environment running a Python kernel and an R kernel. It is based on the [Cube-in-a-box](https://github.com/opendatacube/cube-in-a-box) project. The changes and additions include (i) a revised `Dockerfile`, adding instructions to install R, its upstream dependencies and connect it with the Jupyter environment, (ii) helper scripts for quick re-deploys of the container environment, (iii) a revised `README.md` and (iv) additional Python dependencies. See the commit history for all changes to the original repository.
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This repository hosts all you need to install an *Open Data Cube* instance as a docker container that serves a Jupyter notebook environment running a Python kernel and an R kernel. It is based on the [Cube-in-a-box](https://github.com/opendatacube/cube-in-a-box) project.
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## Additional features
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* an R kernel and its upstream dependencies, initialized to connect with the Jupyter environment,
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* a pre-installed suite of R packages for spatial analysis as well as there system requirements
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* helper scripts for quick starting, stoping and re-deploying of the container environment (see below),
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* a revised `README.md` with installation instructions
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See the commit history for all changes to the original repository.
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## Installation
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Make sure to have [`docker`](https://docs.docker.com/engine/install/ubuntu/#install-using-the-repository) and [`docker-compose`](https://docs.docker.com/compose/install/#install-compose-on-linux-systems) installed on your host system.
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Make sure to have [`docker`](https://docs.docker.com/engine/install/ubuntu/#install-using-the-repository) and [`docker-compose`](https://docs.docker.com/compose/install/#install-compose-on-linux-systems) installed on your host system. Depending on your host system and your `docker`/`docker_compose` installation, you might need `sudo` rights for the following steps.
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Clone this repository to a directory of your choice, e.g. using
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and `cd` into its main directory. To start the container (and build it the first time), run:
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```
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sudo ./docker_start
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./docker_start
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```
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To initialize the jupyter environment and pull Sentinel-2A example data, open a new shell and run:
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```
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sudo ./docker_init
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./docker_init
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```
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Wait for its completion. You may then now access your local Jupyter environment in a browser on your host machine under [http://localhost](http://localhost). Use the password `secretpassword` to authenticate.
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To stop the container, from a shell other then the one the docker container is running in, run:
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```
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sudo ./docker_stop
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./docker_stop
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```
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To fully clean your docker environment from images pulled for `odc_box` and to allow a fresh re-deploy, run
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```
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sudo ./docker_clean
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./docker_clean
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```
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before starting over. Note that with each re-deploy, a new docker volume is created containing your indexed data. You may want to prune your docker volumes from time to time, e.g. using `sudo docker volume rm $(sudo docker volume ls -q -f 'dangling=true')`. Note that this will remove **all** docker volumes, also those from other docker instances that might be running on your host system.
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before starting over. Note that with each re-deploy, a new docker volume is created containing your indexed data. You may want to prune your docker volumes from time to time, e.g. using `docker volume rm $(docker volume ls -q -f 'dangling=true')`. Note that this will remove **all** docker volumes, also those from other docker instances that might be running on your host system.
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## Troubleshooting
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Error message:
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```
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sudo ./docker_start
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./docker_start
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#> ERROR: for postgres Cannot start service postgres: driver failed programming external connectivity on endpoint odc_box_postgres_1 (...): Error starting userland proxy: listen tcp4 0.0.0.0:5432: bind: address already in use
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```
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Reason: The default `postgres` port `5432` seems to be used by some service (maybe `postgres`?) running on your host system.
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Solution: Check whether this is true by running `sudo lsof -i :5432`. You may want to kill the processes that are displayed using their associated PIDs with `kill <PID>`.
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Solution: Check whether this is true by running `lsof -i :5432`. You may want to kill the processes that are displayed using their associated PIDs with `kill <PID>`.
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docker-compose-prod.yml

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docker-compose.yml

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docker_clean

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#!/bin/bash
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docker volume rm $(docker volume ls -q -f 'dangling=true')
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docker-compose down -v --rmi all --remove-orphans

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