Hera is a Python framework for constructing and submitting Argo Workflows. The main goal of Hera is to make Argo Workflows more accessible by abstracting away some setup that is typically necessary for constructing Argo workflows.
Project description
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
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=serverand/or--auth=clientwhen 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.
| Source | Command |
|---|---|
| PyPi | pip install hera |
| PyPi | pip install hera-workflows |
| Conda | conda install -c conda-forge hera-workflows |
| GitHub repo | python -m pip install git+https://github.com/argoproj-labs/hera --ignore-installed/pip install . |
Optional dependencies
yaml
- Install via
hera[yaml] - PyYAML is required for the
yamloutput 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.
Here's a side by side comparison of Hera, Couler, and Argo Python DSL
You will see how Hera has focused on reducing the complexity of Argo concepts while also reducing the total lines of
code required to construct the diamond example, which can
be
found in the upstream Argo repository.
| Hera | Couler | Argo Python DSL |
|---|---|---|
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
|
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