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If you're using Google Compute Engine, see the details about limiting traffic to specific sources at [Google Compute Engine firewall documentation][gce-firewall-docs].
[cloud-console]: https://console.developer.google.com
[gce-firewall-docs]: https://cloud.google.com/compute/docs/networking#firewalls
### Step Eight: Cleanup <a id="step-eight"></a>
After you're done playing with the guestbook, you can cleanup by deleting the guestbook service and removing the associated resources that were created, including load balancers, forwarding rules, target pools, and Kubernetes replication controllers and services.
Delete all the resources by running the following `kubectl delete -f` *`filename`* command:
```console
$ kubectl delete -f guestbook-go
guestbook-controller
guestbook
redid-master-controller
redis-master
redis-replica-controller
redis-replica
```
Tip: To turn down your Kubernetes cluster, follow the corresponding instructions in the version of the
[Getting Started Guides](https://kubernetes.io/docs/getting-started-guides/) that you previously used to create your cluster.
<|endoftext|>
# source: k8s_examples/web/guestbook-go/guestbook-service.yaml type: yaml
kind: Service
apiVersion: v1
metadata:
name: guestbook
labels:
app: guestbook
spec:
ports:
- port: 3000
targetPort: http-server
selector:
app: guestbook
type: LoadBalancer
<|endoftext|>
# source: k8s_examples/web/guestbook-go/redis-master-service.yaml type: yaml
kind: Service
apiVersion: v1
metadata:
name: redis-master
labels:
app: redis
role: master
spec:
ports:
- port: 6379
targetPort: redis-server
selector:
app: redis
role: master
<|endoftext|>
# source: k8s_examples/AI/README.md type: docs
# AI/ML Examples on Kubernetes
Welcome to the AI/ML examples section! Our goal is to provide a collection of
community-curated, open-source reference manifests for deploying and managing
AI/ML workloads, MLOps toolchains, and end-to-end platforms on Kubernetes.
This area is under active development as part of a broader initiative to enhance
the `kubernetes/examples` repository. We aim to simplify the developer and operator
experience for AI applications on Kubernetes, promoting best practices and interoperability.
## Vision for AI/ML Examples
We envision this section housing examples such as:
* Setups for distributed training frameworks.
* Configurations for model serving solutions.
* Blueprints for data versioning and experiment tracking integrations.
* End-to-end MLOps platform examples.
* "AI Kits" designed to help AI/ML experts quickly get started on Kubernetes.
## Call for Contributions
The success of this initiative depends on community contributions! If you have expertise
in running AI/ML workloads on Kubernetes or ideas for valuable examples, we strongly
encourage you to contribute.
We are particularly interested in examples that are:
* Educational and provide an easy start for AI/ML practitioners new to Kubernetes.
* Modular and showcase best practices.
* Cover a diverse range of tools and MLOps stages.
## Current Status
_This section is currently being populated. Check back soon for our first set of AI/ML examples!_
<|endoftext|>
# source: k8s_examples/AI/model-serving-tensorflow/service.yaml type: yaml
apiVersion: v1
kind: Service
metadata:
name: tf-serving
spec: