labcas.workflow 0.1.0

Get Planetary Data from the Planetary Data System (PDS)

LabCas Workflow

Run workflows for Labcas

Install

locally

Preferably use a virtual environment with python 3.9

pip install -e '.[dev]'

With Dask on docker

Create certificates:

cd docker/certs
./generate-certs.sh

Build the docker image:

docker build -f docker/Dockerfile . -t labcas/workflow

Start the scheduler:

docker network create dask
docker run --network dask -p 8787:8787 -p 8786:8786 labcas/workflow scheduler

Start one worker

docker run  --network dask -p 8786:8786 labcas/workflow worker 

Start the client, same as in following section

With dask on ECS

Deploy the image created in the previous section on ECR

Have a s3 bucket labcas-infra for the terraform state.

Other pre-requisites are:

• a VPC
• subnets
• a security group allowing incoming request whre the client runs, at JPL, on EC2 or Airflow, to port 8786 and port 8787
• a task role allowing to write on CloudWatch
• a task execution role which pull image from ECR and standard ECS task Excecution role policy "AmazonECSTaskExecutionRolePolicy"

Deploy the ECS cluster with the following terraform command:

cd terraform
terraform init
terraform apply \
    -var consortium="edrn" \
    -var venue="dev" \
    -var aws_fg_image=<uri of the docker image deployed on ECR>
    -var aws_fg_subnets=<private subnets of the AWS account> \
    -var aws_fg_vpc=<vpc of the AWS account> \
    -var aws_fg_security_groups  <security group> \
    -var ecs_task_role <arn of a task role>
    -var ecs_task_execution_role <arn of task execution role>

Run

Set you local AWS credentials to access the data

./aws-login.darwin.amd64

Start the dask cluster

Run the processing

python ./src/labcas/workflow/manager/main.py