DEPRECATION NOTICE - 2022-02-08

The geobase image is being deprecated and is no longer maintained. Whilst is has been useful to provide a consistent geospatial library build there are now other sources which are more actively maintained that are suitable. As an example of the changes necessary to utilise an alternate base image (e.g. osgeo/gdal) please see datacube-docker#59

opendatacube/geobase

Set of docker images to build recent versions of geospatial libraries and python environments that use them.

GEOS 3.8.0 (from apt)
PROJ 7.2.1 (compiled)
KEA 1.4.14 (compiled)
LERC 2.2.1 (compiled)
GDAL 3.3.0 (compiled)

Quick Start

Create requirements.txt and constraints.txt (could be empty)

rasterio[s3]
pyproj

Create Dockerfile

FROM opendatacube/geobase-builder as env_builder
COPY requirements.txt /
COPY constraints.txt /
RUN env-build-tool new /requirements.txt /constraints.txt /env


FROM opendatacube/geobase-runner
COPY --from=env_builder /env /env
ENV LC_ALL=C.UTF-8
ENV PATH="/env/bin:${PATH}"

See sample directory for more information.

Overview

Basic idea is to use multi-stage builds to minimize output docker image size and to reduce surface area for security/deployment concerns.

Each step is described in more detail below. Overall structure is as following

Build C/C++ libs for PROJ,LERC,KEA,GDAL in base/builder, package those in .deb
Include run-time libs needed by libs built in stages 1 and 2 in base/runner
Use multi-stage building technique to construct docker image with customized python environment that suits your needs:
- Base builder stage on opendatacube/geobase-builder:${V_BASE}
- Install any extra missing dev libs you need via apt-get
- Construct python environment taking care to use pre-compiled wheels where possible
- Base runner stage on opendatacube/geobase-runner:${V_BASE}
- Install any extra C/C++ run time libs via apt-get
- Copy the entire python environment across from builder stage

base/builder

Based on buildack-deps:focal (Ubuntu 20.04)
Builds LERC, KEA and GDAL from source
Contains lots of dev libs to enable compilation of common python modules that call out to C/C++
Least changing layer

Folder structure:

base/builder/Dockerfile base builder image: docker pull opendatacube/geobase-builder
- /dl/ contains downloaded sources
- /opt/ contains built .deb for geos/gdal/proj6
base/builder/gdal.opts feature selection for compiled GDAL, removing features should be easy, adding might require installing extra build dependencies with apt-get, might also need to add those extra libs to base/runner/Dockerfile.

base/runner

Derives from ubuntu:20.04, has all the necessary C/C++/Fortran libs installed in non-dev mode to run python wheels compiled in base/builder.

It is used as base for "runner dockers" that use multi-stage building technique: builder stage constructs a python environment using pre-compiled wheels + whatever extra downloaded from pypi.

Building Test Environment for your library

You need to supply 3 files

requirements.txt contains top level requirements you need for testing your library (avoid pinning versions in there)
constraints.txt often it's output of pip freeze from a known to work environment (also have a look at pip-compile tool)
nobinary.txt can be empty, contains libraries you prefer to compile locally rather than using manylinux wheels from pypi. Note that common geospatial libraries will be compiled to ensure consistent C library usage across those packages regardless of the content of your nobinary.txt file. For a full list see /conf/nobinary-geo.txt inside the docker.

Put all three files in one folder. Then you can run something like the script below to generate wheels and build python environment from that. Wheels and sources will be stored in ./wheels directory, and python environment is ./env, there will also be .cache/pip directory.

#!/bin/bash

# points to your code on dev/test machine
# must be an absolute path, hence `readlink -f ...`
CODE=$(readlink -f "$HOME/src/your_lib")

dkr() {
  local img=${IMG:-"opendatacube/geobase-builder:latest"}
  docker run --rm -ti \
    -v "${CODE}":/code \
    -v $(pwd):/wk \
    ${img} \
    $@
}

# Download wheels and sources
dkr env-build-tool download requirements.txt constraints.txt ./wheels

# Compile sources
dkr env-build-tool compile ./wheels

# Assemble environment
dkr env-build-tool new_no_index requirements.txt constraints.txt ./env ./wheels

# Install your code in edit mode
dkr /wk/env/bin/python -m pip install -e /code

# Verify tests can run
dkr /wk/env/bin/pytest /code