Hera

See the Quick Start guide to start using Hera to orchestrate your Argo Workflows!

The Argo was constructed by the shipwright Argus,
and its crew were specially protected by the goddess Hera.

Stats after the rename to Hera

Stats before the rename to Hera

Hera is a Python framework for constructing and submitting Argo Workflows. The main goal of Hera is to make the Argo ecosystem accessible by simplifying workflow construction and submission.

You can watch the introductory Hera presentation at the "Argo Workflows and Events Community Meeting 20 Oct 2021" here!

Table of content

• Hera
• Table of content
• Requirements
• Installation
• Examples
  • Single step script
  • DAG diamond
• Contributing
• Comparison

Requirements

Hera requires an Argo server to be deployed to a Kubernetes cluster. Currently, Hera assumes that the Argo server sits behind an authentication layer that can authenticate workflow submission requests by using the Bearer token on the request. To learn how to deploy Argo to your own Kubernetes cluster you can follow the Argo Workflows guide!

Another option for workflow submission without the authentication layer is using port forwarding to your Argo server deployment and submitting workflows to localhost:2746 (2746 is the default, but you are free to use yours). Please refer to the documentation of Argo Workflows to see the command for port forward!

Note Since the deprecation of tokens being automatically created for ServiceAccounts and Argo using Bearer tokens in place, it is necessary to use --auth=server and/or --auth=client when setting up Argo Workflows on Kubernetes v1.24+ in order for hera to communicate to the Argo Server.

Installation

Note

Hera went through a name change - from hera-workflows to hera. This is reflected in the published Python package. If you'd like to install versions prior to 5.0.0, you have to use hera-workflows. Hera currently publishes releases to both hera and hera-workflows for backwards compatibility purposes.

Optional dependencies

yaml

• Install via hera[yaml]
• PyYAML is required for the yaml output format, which is accessible via
  hera.workflows.Workflow.to_yaml(*args, **kwargs). This enables GitOps practices and easier debugging

Examples

Single step script

from hera.workflows import Steps, Workflow, script


@script()
def echo(message: str):
    print(message)


with Workflow(
    generate_name="single-script-",
    entrypoint="steps",
) as w:
    with Steps(name="steps"):
        echo(arguments={"message": "A"})

w.create()

DAG diamond

from hera.workflows import DAG, Workflow, script


@script()
def echo(message: str):
    print(message)


with Workflow(
    generate_name="dag-diamond-",
    entrypoint="diamond",
) as w:
    with DAG(name="diamond"):
        A = echo(name="A", arguments={"message": "A"})
        B = echo(name="B", arguments={"message": "B"})
        C = echo(name="C", arguments={"message": "C"})
        D = echo(name="D", arguments={"message": "D"})
        A >> [B, C] >> D

w.create()

See the examples directory for a collection of Argo workflow construction and submission via Hera!

Contributing

If you plan to submit contributions to Hera you can install Hera in a virtual environment managed by poetry:

poetry install

Once the dependencies are installed, you can use the various make targets to replicate the CI jobs.

make help
check-codegen                  Check if the code is up to date
ci                             Run all the CI checks
codegen                        Generate all the code
events-models                  Generate the Events models portion of Argo Workflows
events-service                 Generate the events service option of Hera
examples                       Generate all the examples
format                         Format and sort imports for source, tests, examples, etc.
help                           Showcase the help instructions for all the available `make` commands
lint                           Run a `lint` process on Hera and report problems
models                         Generate all the Argo Workflows models
services                       Generate the services of Hera
test                           Run tests for Hera
workflows-models               Generate the Workflows models portion of Argo Workflows
workflows-service              Generate the Workflows service option of Hera

Also, see the contributing guide!

Comparison

There have been other libraries available for structuring and submitting Argo Workflows:

• Couler, which aimed to provide a unified interface for constructing and managing workflows on different workflow engines. It has now been unmaintained since its last commit in April 2022.
• Argo Python DSL, which allows you to programmatically define Argo worfklows using Python. It was archived in October 2021.

While the aforementioned libraries provided amazing functionality for Argo workflow construction and submission, they required an advanced understanding of Argo concepts. When Dyno Therapeutics started using Argo Workflows, it was challenging to construct and submit experimental machine learning workflows. Scientists and engineers at Dyno Therapeutics used a lot of time for workflow definition rather than the implementation of the atomic unit of execution - the Python function - that performed, for instance, model training.

Hera presents an intuitive Python interface to the underlying API of Argo, with custom classes making use of context managers and callables, empowering users to focus on their own executable payloads rather than workflow setup.

from hera.workflows import DAG, Container, Parameter, Workflow

with Workflow(
    generate_name="dag-diamond-",
    entrypoint="diamond",
) as w:
    echo = Container(
        name="echo",
        image="alpine:3.7",
        command=["echo", "{{inputs.parameters.message}}"],
        inputs=[Parameter(name="message")],
    )
    with DAG(name="diamond"):
        A = echo(name="A", arguments={"message": "A"})
        B = echo(name="B", arguments={"message": "B"})
        C = echo(name="C", arguments={"message": "C"})
        D = echo(name="D", arguments={"message": "D"})
        A >> [B, C] >> D

w.create()

import couler.argo as couler
from couler.argo_submitter import ArgoSubmitter


def job(name):
    couler.run_container(
        image="docker/whalesay:latest",
        command=["cowsay"],
        args=[name],
        step_name=name,
    )


def diamond():
    couler.dag(
        [
            [lambda: job(name="A")],
            [lambda: job(name="A"), lambda: job(name="B")],  # A -> B
            [lambda: job(name="A"), lambda: job(name="C")],  # A -> C
            [lambda: job(name="B"), lambda: job(name="D")],  # B -> D
            [lambda: job(name="C"), lambda: job(name="D")],  # C -> D
        ]
    )


diamond()
submitter = ArgoSubmitter()
couler.run(submitter=submitter)

from argo.workflows.dsl import Workflow

from argo.workflows.dsl.tasks import *
from argo.workflows.dsl.templates import *


class DagDiamond(Workflow):

    @task
    @parameter(name="message", value="A")
    def A(self, message: V1alpha1Parameter) -> V1alpha1Template:
        return self.echo(message=message)

    @task
    @parameter(name="message", value="B")
    @dependencies(["A"])
    def B(self, message: V1alpha1Parameter) -> V1alpha1Template:
        return self.echo(message=message)

    @task
    @parameter(name="message", value="C")
    @dependencies(["A"])
    def C(self, message: V1alpha1Parameter) -> V1alpha1Template:
        return self.echo(message=message)

    @task
    @parameter(name="message", value="D")
    @dependencies(["B", "C"])
    def D(self, message: V1alpha1Parameter) -> V1alpha1Template:
        return self.echo(message=message)

    @template
    @inputs.parameter(name="message")
    def echo(self, message: V1alpha1Parameter) -> V1Container:
        container = V1Container(
            image="alpine:3.7",
            name="echo",
            command=["echo", "{{inputs.parameters.message}}"],
        )

        return container