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Nextflow Cloud

!!!
NF-Cloud is renamed to Massive Accessible Workflow Platform (MaCWorP), moved to https://github.com/cubimedrub/macworp and is now developed as part of CUBiMed.RUB.
!!!

Nextflow Cloud or NF-Cloud is a web interface to run nextflow workflows in a cloud environment.
NF-Cloud has three compontents

Backend

A web API written in Flask for managing/scheduling workflows.

Frontend

A web interface written in NuxtJS. This is basically a GUI for the web interface.

Worker

A worker witten in python which runs the scheduled workflows.

NF-Cloud structure

Developing

Make sure the dependencies in

are installed.

Prepare development environment

# Create environment
conda env create -f environment.yml

# Already creates the environment and need updates???
conda env update -f environment.yml --prune

# Upgrade pip and setuptools
conda activate nf_cloud

# Install node requirements
yarn --cwd ./frontend install

Start

# Shell 1
docker-compose up
# Shell 2
python -m nf_cloud_backend database migrate
python -m nf_cloud_backend utility rabbitmq prepare
honcho -e dev.env start
Component Access User Password
Frontend http://localhost:5001
API http://localhost:3001
Fusionauth http://localhost:9011 [email protected] developer

For development, Flask is configured to add CORS-Headers by default.

Database migrations

To keep track of database changes, this project uses peewee_migrate. Migrations are located in nf_cloud_backend/migrations/. For now this needs to be added manually when using pw_migrate.

Create new migration

pw_migrate create --directory nf_cloud_backend/migrations --database postgresql://postgres:[email protected]:5434/nf_cloud "<description>"

Run migrations

pw_migrate migrate --database 'postgresql://postgres:[email protected]:5434/nf_cloud' --directory nf_cloud_backend/migrations

Accessing the database

psql postgresql://postgres:[email protected]:5434/nf_cloud

Testing deployment

In production NF-Cloud is designed to run as Gunicorn service behind a NginX reverse proxy. This setup can be tested with make production-test
This will build and start back- & frontend containers, start multiple instances and put them behind a NginX reverse proxy, running on the port 16160 (NF-Cloud) and 16161 (Fusionauth). Both applications are served under HTTPS with a self signed certificate for testing and the hosts name as domain.
A worker can be started with make production-worker-test

Test data

Use python -m nf_cloud_backend database seed --drop to insert some test records (e.g. workflows) into the database.

Production

External services

RabbitMQ

Set the consumer_timeout to a high value. Otherwise scheduled workflows can't be acknowledged after finishing and getting rescheduled

Preparation

The following part contains CLI usage of nf_cloud_backend, which assume you use the nativ installation. If you run nf_cloud_backend in docker just replace python -m nf_cloud_backend with docker run mpc/nf_cloud_backend.

Docker image

Creation (backend)

Create the docker image with

docker build -t mpc/nf-cloud-backend:latest -f docker/backend.dockerfile .

You can use the build arguments (--build-arg) NEW_MAMBA_USER_ID and NEW_MAMBA_USER_GID to change the user and group ID of the container user. Useful for harmonizing the ownership of the uploaded files with a local user.

Creation (frontend)
docker build -t mpc/nf-cloud-frontend:latest -f docker/frontend.dockerfile .
Usage

The images entrypoint is the nf_cloud_backend command line interface, so users can start using command line arguments (e.g. --help). If the option serve --gunicorn is given, the image will start NF-Cloud as Gunicorn application, unlike the native installation which prints the Gunicorn parameters.
NF-Cloud is running in the folder /home/app. So please mount a local configuration accordingly when running the images, e.g.: docke run -v <some-local-config>:/home/app/nf_cloud.local.config.yaml. To persist the uploaded files a persistent volume or bind-mount should be used.

Attention: The worker needs to be deployed natively to use Docker containers in workflows.

Native

Clone the repository and use Conda or Miniconda to create an environment with all necessary dependencies: conda env create -f environment.yml (the environment name will be nf_cloud)

Usage

Make sure to activate the environment and go into the root folder of the repository before using nf_cloud_backend or use conda run -n nf_cloud python -m nf_cloud_worker

Worker

Run conda run -n nf_cloud python

Create a new configuration file

python -m nf_cloud_backend utility config create . this will create a new config named nf_cloud.local.config.yaml in the current directory. You can also print the configuration with python -m nf_cloud_backend utility config print (useful for piping the results from a Docker container).

ToDos

  • Try to move Nextflow intermediate result folders into a subfolder.
  • Add some more inputs
    • Radio
    • Select
  • Add description to workflows
  • Improve logging
    • Save Nextflow stdout/stderr
    • Enable report and show resulting HTML in web interface
  • Rework docker deployment
    • Investigate if the worker is able to run Nextflow in Docker containers when running in container itself