https://github.com/cloudcoil/models-knative-serving
Generated model repository for knative-serving
https://github.com/cloudcoil/models-knative-serving
cloudcoil cloudcoil-models
Last synced: 3 months ago
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Generated model repository for knative-serving
- Host: GitHub
- URL: https://github.com/cloudcoil/models-knative-serving
- Owner: cloudcoil
- License: apache-2.0
- Created: 2025-01-29T22:16:06.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2026-02-09T13:53:08.000Z (4 months ago)
- Last Synced: 2026-03-05T22:35:39.971Z (3 months ago)
- Topics: cloudcoil, cloudcoil-models
- Language: Makefile
- Size: 867 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# cloudcoil-models-knative-serving
Versioned knative-serving models for cloudcoil.
[](https://pypi.python.org/pypi/cloudcoil.models.knative_serving)
[](https://pepy.tech/project/cloudcoil.models.knative_serving)
[](https://opensource.org/license/apache-2-0/)
[](https://github.com/cloudcoil/models-knative-serving/actions/workflows/ci.yml)
> [!WARNING]
> This repository is auto-generated from the [cloudcoil repository](https://github.com/cloudcoil/cloudcoil/tree/main/models/knative-serving). Please do not submit pull requests here. Instead, submit them to the main repository at https://github.com/cloudcoil/cloudcoil.
## 🔧 Installation
> [!NOTE]
> For versioning information and compatibility, see the [Versioning Guide](https://github.com/cloudcoil/cloudcoil/blob/main/VERSIONING.md).
Using [uv](https://github.com/astral-sh/uv) (recommended):
```bash
# Install with Knative Serving support
uv add cloudcoil.models.knative-serving
```
Using pip:
```bash
pip install cloudcoil.models.knative-serving
```
## 💡 Examples
### Using Knative Serving Models
```python
from cloudcoil import apimachinery
import cloudcoil.models.knative_serving.serving.v1 as serving
# Create a Service
service = serving.Service(
metadata=apimachinery.ObjectMeta(name="hello"),
spec=serving.ServiceSpec(
template=serving.Template(
spec=serving.Spec(
containers=[
serving.Container(
image="gcr.io/knative-samples/helloworld-go",
ports=[serving.Port(container_port=8080)],
env=[
serving.Env(name="TARGET", value="World")
]
)
]
)
)
)
).create()
# List Services
for svc in serving.Service.list():
print(f"Found Service: {svc.metadata.name}")
```
### Using the Fluent Builder API
Cloudcoil provides a powerful fluent builder API for Knative Serving resources:
```python
from cloudcoil.models.knative_serving.serving.v1 import Service
# Create a Service using the fluent builder
service = (
Service.builder()
# Metadata configuration
.metadata(lambda metadata: metadata
.name("hello")
.namespace("default")
.labels({"app": "hello"})
)
# Complex nested structures with proper collection handling
.spec(lambda spec: spec
.template(lambda template: template
.metadata(lambda t_metadata: t_metadata
.labels({"app": "hello"})
)
.spec(lambda revision_spec: revision_spec
# Container list with nested collection handling
.containers(lambda containers: containers.add(
lambda container: container
.name("hello")
.image("gcr.io/knative-samples/helloworld-go")
# Collections use add() helper for better type support
.ports(lambda ports: ports
.add(lambda p: p.container_port(8080))
)
.env(lambda env: env
.add(lambda e: e.name("TARGET").value("World"))
.add(lambda e: e.name("PORT").value("8080"))
)
# Resource requirements can be chained
.resources(lambda r: r
.requests({"cpu": "100m", "memory": "128Mi"})
.limits({"cpu": "200m", "memory": "256Mi"})
)
))
)
)
)
.build()
)
```
### Using the Context Manager Builder API
For complex serving configurations, you can use the context manager-based builder:
```python
from cloudcoil.models.knative_serving.serving.v1 import Service
# Create a Service using context managers
with Service.new() as service:
with service.metadata() as metadata:
metadata.name("hello")
metadata.namespace("default")
with service.spec() as spec:
with spec.template() as template:
with template.spec() as revision_spec:
with revision_spec.containers() as container_list:
with container_list.add() as container:
container.name("hello")
container.image("gcr.io/knative-samples/helloworld-go")
with container.ports() as port_list:
with port_list.add() as port:
port.container_port(8080)
with container.env() as env_list:
with env_list.add() as env:
env.name("TARGET")
env.value("World")
final_service = service.build()
```
### Mixing Builder Styles
You can mix different builder styles based on your needs:
```python
from cloudcoil.models.knative_serving.serving.v1 import Service
from cloudcoil import apimachinery
# Create a Service using mixed styles
with Service.new() as service:
# Direct object initialization
service.metadata(apimachinery.ObjectMeta(
name="hello"
))
# Fluent style for spec
service.spec(lambda s: s
.template(lambda t: t
.spec(lambda rs: rs
.containers(lambda containers: containers.add(
lambda container: container
.name("hello")
.image("gcr.io/knative-samples/helloworld-go")
# Collections use add() helper for better type support
.ports(lambda ports: ports
.add(lambda p: p.container_port(8080))
)
))
)
)
)
final_service = service.build()
```
## 📚 Documentation
For complete documentation, visit [cloudcoil.github.io/cloudcoil](https://cloudcoil.github.io/cloudcoil)
## 📜 License
Apache License, Version 2.0 - see [LICENSE](LICENSE)