service-datamonkey

This is a WIP REST service intended to drive Datamonkey3 server-side. It is designed to conform to the OpenAPI specification defined at api-datamonkey using the go-gin-server generator from OpenAPI Generator.

Development

Handy helpers:

make update to pull down the OpenAPI specification from api-datamonkey and generate a GO Gin server stub from it.
make build to build just the service-datamonkey container
make start to start the entire datamonkey 3 backend for dev/testing using docker compose
make stop to stop the datamonkey 3 backend containers

Hopefully it'll eventually have options like:

make install to manage dependencies. for now, have to manage them yourself if you mean to do anything more than run whats already been developed. The important ones are golang >= 1.20 and npx w openapitools/openapi-generator-cli

NOTE for now you should also check out service-slurm and simply docker compose up -d and then docker compose down before using this repo. I'll fix that eventually, but its just to get built slurm images this docker compose can use.

TLDR

For now that means if its your first time here, starting in the parent directory for this project you should do the following:

git clone [email protected]:d-callan/service-slurm.git
cd service-slurm
docker compose up -d
docker compose down
cd ../service-datamonkey
make build
make start

Testing

Make sure things are healthy

In the root directory of the project, where you started the service, do the following:

export $(docker compose exec c2 scontrol token)
curl -k -vvvv -H X-SLURM-USER-TOKEN:${SLURM_JWT} -X GET 'http://localhost:9300/api/v1/health'

Please note Slurm user token needs updating each time you re-start the service.

Upload input files for jobs

You can upload files like:

curl -X POST -H "Content-type: multipart/form-data" -F meta='{"name":"TEST","type":"TEST TYPE","description":"TEST DESC"}' -F [email protected] http://localhost:9300/api/v1/datasets where test.txt contains anything at all.

Then go to http://localhost:9300/api/v1/datasets and confirm its worked, or find dataset_ids.

Datasets uploaded will persist across re-starts of containers, etc. To clear them: docker volume rm service-datamonkey_uploaded_data.

PLEASE NOTE THAT CURRENTLY THIS WILL REMOVE JOB RESULTS AND LOGS AS WELL

If instead you'd like to remove specific files: docker compose exec c2 rm /data/uploads/[filename]

Starting jobs

For this in particular I'd recommend using Postman, for convenience. Whatever method though, you want to use a url like http://localhost:9300/api/v1/methods/fel-start to start and monitor jobs, and one like http://localhost:9300/api/v1/methods/fel-result to get results. POST body should look something like:

{
  "alignment": "2ddaaa7f2d54e25f81062ab8cda13b38",
  "tree": "31fa9ce04076f0f9dc403278c7c1717c",
  "ci": false,
  "srv": true,
  "resample": 0,
  "multiple_hits": "None",
  "site_multihit": "Estimate",
  "genetic_code": {
    "value": "Universal",
    "display_name": "Universal code"
  },
  "branches": []
}

Here, alignment and tree are references to dataset ids of uploaded data (see below). Starting, monitoring and fetching results for methods also requires the X-SLURM_USER_TOKEN header in the request, similar to the health endpoint (see above).

Clearing the jobs tracker from previous sessions

This is important for the service to be able to meaningfully track Slurm jobs. Slurm job ids restart from 0 on restart, and so for now at least, to make sure we only get jobs from the current session we need to restart our own jobs tracking. I'll figure out what I actually want to do about this in a bit.

In the root directory of the project, where the service was started, do: docker compose exec c2 rm /data/uploads/job_tracker.tab.

Debugging

To see logs for service-datamonkey: docker logs service-datamonkey To see logs for the Slurm head node: docker logs c2 To see logs for the Slurm db: docker logs slurmdbd

# README