https://github.com/ajithvcoder/emlo4-session-17-ajithvcoder-canary-argocd-kserve
Refer https://github.com/ajithvcoder/emlo4-session-17-ajithvcoder for complete deployment guide The School of AI EMLO-V4 course assignment https://theschoolof.ai/#programs
https://github.com/ajithvcoder/emlo4-session-17-ajithvcoder-canary-argocd-kserve
argocd aws-eks canary-deployment image-classification kserve kubernetes
Last synced: about 2 months ago
JSON representation
Refer https://github.com/ajithvcoder/emlo4-session-17-ajithvcoder for complete deployment guide The School of AI EMLO-V4 course assignment https://theschoolof.ai/#programs
- Host: GitHub
- URL: https://github.com/ajithvcoder/emlo4-session-17-ajithvcoder-canary-argocd-kserve
- Owner: ajithvcoder
- Created: 2025-02-04T06:44:27.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-02-08T17:58:35.000Z (4 months ago)
- Last Synced: 2025-02-11T11:54:23.898Z (3 months ago)
- Topics: argocd, aws-eks, canary-deployment, image-classification, kserve, kubernetes
- Homepage:
- Size: 14.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
### EMLOV4-Session-17 Assignment - Canary Deployment
Deploy three imagenet Hugging face modes with AWS EKS service in canary deployment method with Serving tools like KServe, with deployment tools like argoCD and with monitoring tools like prometheus, grafana.
Note: Please refer [Assignment-17 repo](https://github.com/ajithvcoder/emlo4-session-17-ajithvcoder) - `https://github.com/ajithvcoder/emlo4-session-17-ajithvcoder` for instructions to develop and deploy from scratch
- Have s3-secret.yaml file in argo-apps/s3-secret.yaml folder and update it with your AWS credentails i.e `AWS_ACCESS_KEY_ID` and `AWS_SECRET_ACCESS_KEY`
```
apiVersion: v1
kind: Secret
metadata:
name: s3creds
annotations:
serving.kserve.io/s3-endpoint: s3.ap-south-1.amazonaws.com # replace with your s3 endpoint e.g minio-service.kubeflow:9000
serving.kserve.io/s3-usehttps: "1" # by default 1, if testing with minio you can set to 0
serving.kserve.io/s3-region: "ap-south-1"
serving.kserve.io/s3-useanoncredential: "false" # omitting this is the same as false, if true will ignore provided credential and use anonymous credentials
type: Opaque
stringData: # use `stringData` for raw credential string or `data` for base64 encoded string
AWS_ACCESS_KEY_ID: AKXXXXXXXXXXXXXXXXXXXXX
AWS_SECRET_ACCESS_KEY: "RQHBUNBSJNINQONUKNUKXXXXXX+XQIWOW"
---
apiVersion: v1
kind: ServiceAccount
metadata:
name: s3-read-only
secrets:
- name: s3creds
```
- Create the repo before you start and update the repo url in `argo-apps/models.yaml` file
Deployment
```bash
kubectl apply -f argo-apps
```
**Delete argocd deployments**
Verify app name
- `kubectl get app -n argocd`
Delete cascade
- `kubectl patch app model-deployments -p '{"metadata": {"finalizers": ["resources-finalizer.argocd.argoproj.io"]}}' --type merge -n argocd`
- `kubectl delete app model-deployments -n argocd`
Deletion
- `eksctl delete cluster -f eks-cluster.yaml --disable-nodegroup-eviction`
**Wait paitently see all deletion is successfull in aws cloud formation stack page and then close the system because some times
the deletion gets failed so at backend something would be running and it may cost you high**
**If you triggering a spot instance manually with `peresistent` type ensure that both the spot request is cancelled manually
and the AWS instance is terminated finally**