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https://github.com/cermakm/argo-python-dsl
Python DSL for Argo Workflows | Mirrored to https://github.com/argoproj-labs/argo-python-dsl
https://github.com/cermakm/argo-python-dsl
argo argo-workflows kubernetes python
Last synced: 29 days ago
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Python DSL for Argo Workflows | Mirrored to https://github.com/argoproj-labs/argo-python-dsl
- Host: GitHub
- URL: https://github.com/cermakm/argo-python-dsl
- Owner: CermakM
- License: apache-2.0
- Created: 2019-12-11T12:23:26.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2020-03-19T14:10:33.000Z (almost 5 years ago)
- Last Synced: 2024-11-01T05:34:25.162Z (about 2 months ago)
- Topics: argo, argo-workflows, kubernetes, python
- Language: Python
- Homepage: https://github.com/argoproj/argo
- Size: 209 KB
- Stars: 55
- Watchers: 5
- Forks: 6
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.rst
- License: LICENSE
- Codeowners: .github/CODEOWNERS
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README
# argo-python-dsl [](https://github.com/argoproj-labs/argo-python-dsl/releases/latest)
[](https://github.com/argoproj-labs/argo-python-dsl/blob/master/LICENSE) [](https://github.com/argoproj-labs/argo-python-dsl/actions)
### Python DSL for [Argo Workflows](https://github.com/argoproj/argo)
If you're new to Argo, we recommend checking out the examples in pure YAML. The language is descriptive and the Argo [examples](https://github.com/argoproj/argo/tree/master/examples) provide an exhaustive explanation.
For a more experienced audience, this DSL grants you the ability to programatically define Argo Workflows in Python which is then translated to the Argo YAML specification.
The DSL makes use of the Argo models defined in the [Argo Python client](https://github.com/argoproj-labs/argo-client-python) repository. Combining the two approaches we are given the whole low-level control over Argo Workflows.
## Getting started
#### Hello World
This example demonstrates the simplest functionality. Defining a `Workflow` by subclassing the `Workflow` class and a single template with the `@template` decorator.
The entrypoint to the workflow is defined as an `entrypoint` class property.
Argo YAMLArgo Python
```yaml
# @file: hello-world.yaml
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
name: hello-world
generateName: hello-world-
spec:
entrypoint: whalesay
templates:
- name: whalesay
container:
name: whalesay
image: docker/whalesay:latest
command: [cowsay]
args: ["hello world"]
```
```python
from argo.workflows.dsl import Workflow
from argo.workflows.dsl import template
from argo.workflows.dsl.templates import V1Container
class HelloWorld(Workflow):
entrypoint = "whalesay"
@template
def whalesay(self) -> V1Container:
container = V1Container(
image="docker/whalesay:latest",
name="whalesay",
command=["cowsay"],
args=["hello world"]
)
return container
```
#### DAG: Tasks
This example demonstrates tasks defined via dependencies forming a *diamond* structure. Tasks are defined using the `@task` decorator and they **must return a valid template**.
The entrypoint is automatically created as `main` for the top-level tasks of the `Workflow`.
Argo YAMLArgo Python
```yaml
# @file: dag-diamond.yaml
# The following workflow executes a diamond workflow
#
# A
# / \
# B C
# \ /
# D
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
name: dag-diamond
generateName: dag-diamond-
spec:
entrypoint: main
templates:
- name: main
dag:
tasks:
- name: A
template: echo
arguments:
parameters: [{name: message, value: A}]
- name: B
dependencies: [A]
template: echo
arguments:
parameters: [{name: message, value: B}]
- name: C
dependencies: [A]
template: echo
arguments:
parameters: [{name: message, value: C}]
- name: D
dependencies: [B, C]
template: echo
arguments:
parameters: [{name: message, value: D}]
# @task: [A, B, C, D]
- name: echo
inputs:
parameters:
- name: message
container:
name: echo
image: alpine:3.7
command: [echo, "{{inputs.parameters.message}}"]
```
```python
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
```
#### Artifacts
`Artifacts` can be passed similarly to `parameters` in three forms: `arguments`, `inputs` and `outputs`, where `arguments` is the default one (simply `@artifact` or `@parameter`).
I.e.: `inputs.artifact(...)`
Both artifacts and parameters are passed **one by one**, which means that for multiple artifacts (parameters), one should call:
```python
@inputs.artifact(name="artifact", ...)
@inputs.parameter(name="parameter_a", ...)
@inputs.parameter(...)
def foo(self, artifact: V1alpha1Artifact, prameter_b: V1alpha1Parameter, ...): pass
```
A complete example:
Argo YAMLArgo Python
```yaml
# @file: artifacts.yaml
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
name: artifact-passing
generateName: artifact-passing-
spec:
entrypoint: main
templates:
- name: main
dag:
tasks:
- name: generate-artifact
template: whalesay
- name: consume-artifact
template: print-message
arguments:
artifacts:
# bind message to the hello-art artifact
# generated by the generate-artifact step
- name: message
from: "{{tasks.generate-artifact.outputs.artifacts.hello-art}}"
- name: whalesay
container:
name: "whalesay"
image: docker/whalesay:latest
command: [sh, -c]
args: ["cowsay hello world | tee /tmp/hello_world.txt"]
outputs:
artifacts:
# generate hello-art artifact from /tmp/hello_world.txt
# artifacts can be directories as well as files
- name: hello-art
path: /tmp/hello_world.txt
- name: print-message
inputs:
artifacts:
# unpack the message input artifact
# and put it at /tmp/message
- name: message
path: /tmp/message
container:
name: "print-message"
image: alpine:latest
command: [sh, -c]
args: ["cat", "/tmp/message"]
```
```python
from argo.workflows.dsl import Workflow
from argo.workflows.dsl.tasks import *
from argo.workflows.dsl.templates import *
class ArtifactPassing(Workflow):
@task
def generate_artifact(self) -> V1alpha1Template:
return self.whalesay()
@task
@artifact(
name="message",
_from="{{tasks.generate-artifact.outputs.artifacts.hello-art}}"
)
def consume_artifact(self, message: V1alpha1Artifact) -> V1alpha1Template:
return self.print_message(message=message)
@template
@outputs.artifact(name="hello-art", path="/tmp/hello_world.txt")
def whalesay(self) -> V1Container:
container = V1Container(
name="whalesay",
image="docker/whalesay:latest",
command=["sh", "-c"],
args=["cowsay hello world | tee /tmp/hello_world.txt"]
)
return container
@template
@inputs.artifact(name="message", path="/tmp/message")
def print_message(self, message: V1alpha1Artifact) -> V1Container:
container = V1Container(
name="print-message",
image="alpine:latest",
command=["sh", "-c"],
args=["cat", "/tmp/message"],
)
return container
```
## Going further: `closure` and `scope`
This is where it gets quite interesting. So far, we've only scratched the benefits that the Python implementation provides.
What if we want to use native Python code and execute it as a step in the Workflow. What are our options?
**Option A)** is to reuse the existing mindset, dump the code in a string, pass it as the source to the `V1ScriptTemplate` model and wrap it with the `template` decorator.
This is illustrated in the following code block:
```python
import textwrap
class ScriptsPython(Workflow):
...
@template
def gen_random_int(self) -> V1alpha1ScriptTemplate:
source = textwrap.dedent("""\
import random
i = random.randint(1, 100)
print(i)
""")
template = V1alpha1ScriptTemplate(
image="python:alpine3.6",
name="gen-random-int",
command=["python"],
source=source
)
return template
```
Which results in:
```yaml
api_version: argoproj.io/v1alpha1
kind: Workflow
metadata:
generate_name: scripts-python-
name: scripts-python
spec:
entrypoint: main
...
templates:
- name: gen-random-int
script:
command:
- python
image: python:alpine3.6
name: gen-random-int
source: 'import random\ni = random.randint(1, 100)\nprint(i)\n'
```
Not bad, but also not living up to the full potential. Since we're already writing Python, why would we wrap the code in a string? This is where we introduce `closure`s.
#### `closure`s
The logic of `closure`s is quite simple. Just wrap the function you want to execute in a container in the `@closure` decorator. The `closure` then takes care of the rest and returns a `template` (just as the `@template` decorator).
The only thing we need to take care of is to provide it an image which has the necessary Python dependencies installed and is present in the cluster.
> There is a plan to eliminate even this step in the future, but currently it is inavoidable.
Following the previous example:
```python
class ScriptsPython(Workflow):
...
@closure(
image="python:alpine3.6"
)
def gen_random_int() -> V1alpha1ScriptTemplate:
import random
i = random.randint(1, 100)
print(i)
```
The closure implements the `V1alpha1ScriptTemplate`, which means that you can pass in things like `resources`, `env`, etc...
Also, make sure that you `import` whatever library you are using, the context is not preserved --- `closure` behaves as a staticmethod and is *sandboxed* from the module scope.
#### `scope`s
Now, what if we had a function (or a whole script) which is quite big. Wrapping it in a single Python function is not very Pythonic and it gets tedious. This is where we can make use of `scope`s.
Say that we, for example, wanted to initialize logging before running our `gen_random_int` function.
```python
...
@closure(
scope="main",
image="python:alpine3.6"
)
def gen_random_int(main) -> V1alpha1ScriptTemplate:
import random
main.init_logging()
i = random.randint(1, 100)
print(i)
@scope(name="main")
def init_logging(level="DEBUG"):
import logging
logging_level = getattr(logging, level, "INFO")
logging.getLogger("__main__").setLevel(logging_level)
```
Notice the 3 changes that we've made:
```python
@closure(
scope="main", # <--- provide the closure a scope
image="python:alpine3.6"
)
def gen_random_int(main): # <--- use the scope name
```
```python
@scope(name="main") # <--- add function to a scope
def init_logging(level="DEBUG"):
```
Each function in the given scope is then namespaced by the scope name and injected to the closure.
I.e. the resulting YAML looks like this:
```yaml
...
spec:
...
templates:
- name: gen-random-int
script:
command:
- python
image: python:alpine3.6
name: gen-random-int
source: |-
import logging
import random
class main:
"""Scoped objects injected from scope 'main'."""
@staticmethod
def init_logging(level="DEBUG"):
logging_level = getattr(logging, level, "INFO")
logging.getLogger("__main__").setLevel(logging_level)
main.init_logging()
i = random.randint(1, 100)
print(i)
```
The compilation also takes all imports to the front and remove duplicates for convenience and more natural look so that you don't feel like poking your eyes when you look at the resulting YAML.
For more examples see the [examples](https://github.com/argoproj-labs/argo-python-dsl/tree/master/examples) folder.
---
Authors:
- [ Maintainer ] Marek Cermak ,