Docker container that runs the s2-ard-processor workflow.
The mapped input folder contains a set of S2 granules that will be processed as a swath. The processing can take place sequentially or in parallel using MPI on the JASMIN cluster.
Build to image:
cd workflow
docker build -t s2-ard-processor .
Use --no-cache
to build from scratch
Run Interactively:
docker run -i --entrypoint /bin/bash
-v /<hostPath>/input:/input
-v /<hostPath>/output:/output
-v /<hostPath>/state:/state
-v /<hostPath>/static:/static
-v /<hostPath>/working:/working
-v /<hostPath>/report:/report
-v /<hostPath>/database:/database
-t s2-ard-processor
Where <hostpath> is the path on the host to the mounted folder
Build an apptainer image using your Docker image
sudo apptainer build s2-ard-processor.sif docker-daemon://s2-ard-processor:latest
Run:
apptainer exec
--bind /<hostPath>/input:/input
--bind /<hostPath>/output:/output
--bind /<hostPath>/state:/state
--bind /<hostPath>/static:/static
--bind /<hostPath>/working:/working
--bind /<hostPath>/report:/report
--bind /<hostPath>/database:/database
s2-ard-processor.sif /app/exec.sh
GenerateReport
--dem=dem.kea
--outWkt=outwkt.txt
--projAbbv=osgb
--metadataConfigFile=metadata.config.json
--metadataTemplate=metadataTemplate.xml
--reportFileName=reportfile.csv
--dbFileName=s2ardProducts.db
--local-scheduler
For the full list of parameters and more details on the folder setups, see the workflow readme at workflow/app/workflows/README.md
.
The code in this repo will be jointly maintained by JNCC and DEFRA/CGI. Use the steps below as a guideline for making new changes:
- Create a new
feature
branch frommain
and commit your changes there until you're ready to merge - Open a pull request to merge back into
main
and add a reviewer from both JNCC and CGI to notify them - JNCC then uses the
feature
branch to build a jncc/s2-ard-processor-dev docker image which both parties can use for testing and QA - Once it passes QA, JNCC will approve the PR, merge it into
main
, and build a live jncc/s2-ard-processor docker image which can be deployed to production