What is the Workflow System?

Workflow is an over-arching framework to manage the lifecycle of any action/work performed by the CHIME/FRB Telescope.

Philosophy

The core tenant of workflow is to provide impeccable bookkeeping. It acts as a singular coordination layer for performing any telescope task through a simple and intuitive interface. At the core of the workflow system is the Work object which consists of a generic set of metadata that can be used to describe any action performed by the telescope. The workflow system manages the entire lifecycle of the work object, from its creation to its completion. The user can then query the workflow framework to get the input parameters or results of the work at any point.

Architecture

The workflow system is designed to be modular and extensible. It is composed of five main components:

1. The Work object.
2. The Buckets backend.
3. A user-provided Function to do Work.
4. The Results backend.
5. The Workflow Web UI.

stateDiagram-v2
    [*] --> Work: User creates a work object
    Work --> Buckets: deposit
    Buckets --> Function: withdraw
    Function --> Buckets: success
    Function --> Buckets: failure
    Buckets --> Results: automatic transfer of completed work
    Buckets --> WebUI: realtime
    Results --> WebUI: search and query any work

Usage

Define Work

The Work object at the core of the workflow system is simply a python dictionary object which contains all parameters that can be used to define a task at the telescope. The Work object is defined as follows:

Python

from chime_frb_api.workflow import Work

work = Work(pipeline="sample-pipeline", parameters={"a": 1, "b": 2})

At a minimum, the Work object requires only requires the pipeline parameter. Additional lifecycle are automatically added to the Work object as it progresses through the workflow system. At any time in its lifecycle, you can see all the parameters in the Work object by simply running,

Python

work.payload
{
    "pipeline": "sample-pipeline",
    "parameters": None,
    "results": None,
    "path": ".",
    "event": None,
    "tags": None,
    "group": None,
    "timeout": 3600,
    "retries": 2,
    "priority": 3,
    "products": None,
    "plots": None,
    "site": "local",
    "user": None,
    "archive": True,
    "precursors": None,
    "config": None,
    "id": None,
    "creation": 1673298165.260349,
    "start": None,
    "stop": None,
    "attempt": 1,
    "status": "created",
}

This dictionary is the exact representation of the Work object in the database.

Deposit Work

To deposit the Work object into the workflow system, simply run,

Python

work.deposit()

Perform Work

Work is usually performed by a user provided python function. This function can be run anywhere, but it is recommended to run it in a container. In future releases of the workflow system, we will provide a container orchestration layer to run the function at any scale or any telescope site and also at Compute Canada sites.

To perform a unit of work, the user needs to withdraw the work, perform it, update it with the results and finally deposit it back to the workflow system. This can be done manually by the user, or it can also be done automatically by the workflow system using the workflow run CLI command.

User Provided Function

Lets say we have the following function, that you wish to run,

Python

# Function that performs the work.
# We assume that this function is availaible as an import.
def add(a: int, b: int) -> int:
    result = a + b
    return result

Manual Withdraw and Deposit

Python

from chime_frb_api.workflow import Work

work = Work.withdraw(pipeline="sample-pipeline")
result = add(**work.parameters)
work.results = result
work.status = "success"
work.update()

Automatic Withdraw and Deposit

Bash

workflow run add sample-pipeline

The simplest and preferred way to run a user function is throught the workflow run CLI Command. This command will automatically withdraw the work, perform the work by passing the parameters field to the user function, and then deposit the results returned back into the workflow system.

CLI Command: workflow run

Bash

workflow run {pipeline-name} {module.submodule.function}

where,

• pipeline-name: the name of the pipeline. This defines what work will be withdrawn by the running pipeline.

• module.submodule.function: the full python specficication of the pipeline function that will be ran.

The pipeline CLI assumes that the pipeline function follows the CHIME/FRB pipeline interface specification. That is, it takes keyword arguments as inputs and outputs a results dictionary, a products list of paths to output files, and a plots lists of paths to output plots.

Example for header localization persistent container:

Bash

poetry workflow run header-localization frb_l2l3.utils.header_localization.main

Recommended Fields

It is strongly recommended to use the maximum number of fields in the work object. This will make it easier not only to manage the workflow, but also to manage retrieve the results of the work once completed.

Python

work.event = [12345, 67890]
work.user = "some-user"
work.tag = ["some-tag", "some-other-tag"]
work.group = ["some-working-group", "some-other-working-group"]
work.site = "chime"

Python

work.results = {"some-parameter": "some-value"}
work.plots = ["some-plot.png"]
work.products = ["some-product.fits"]
work.status = "success"

For a full list of parameters that can be set in the Work object see Work Object.