Update docker-deploy.yml

.github/workflows/docker-deploy.yml

@@ -3,17 +3,22 @@
 # Pipeline CI/CD — conforme au cours ENSAE "Mise en production"
 #
 # Reproduit exactement le schéma du cours :
-#   push sur une branche → build image Docker → push sur Docker Hub
+# Build image Docker puis push sur le registre
 #
 # Secrets GitHub à configurer :
 #   DOCKERHUB_USERNAME : votre identifiant Docker Hub
 #   DOCKERHUB_TOKEN    : Access Token Docker Hub
 #                        (hub.docker.com → Account Settings → Security)
+# KUBECONFIG_SSP : kubeconfig SSP Cloud encodé en base64
 # ─────────────────────────────────────────────────────────────────
 name: Build & Deploy FastCrowdVision
 
 on:
   push:
+branches:
+  - main
+tags:
+  - "v*"
 
 jobs:
   docker:
@@ -51,4 +56,3 @@ jobs:
           push: true
           tags: ${{ steps.meta.outputs.tags }}
           labels: ${{ steps.meta.outputs.labels }}
-

README.md

@@ -3,7 +3,7 @@ Implementing vision algorithms for human detection on mobile devices.
 
 
 
-for inference uvicorn server:app --reload
+for inference uvicorn server:app --reload 
 
 ## Setup
 ```

argocd/application.yaml

@@ -0,0 +1,18 @@
+apiVersion: argoproj.io/v1alpha1
+kind: Application
+metadata:
+  name: fastcrowdvision
+  namespace: argocd
+spec:
+  project: default
+  source:
+    repoURL: https://github.com/aayrapet/FastCrowdVision.git
+    targetRevision: feat/argocd-migration
+    path: kubernetes
+  destination:
+    server: https://kubernetes.default.svc
+    namespace: user-pmakamwe
+  syncPolicy:
+    automated:
+      prune: true
+      selfHeal: true

kubernetes/deployment.yaml

@@ -82,4 +82,4 @@ spec:
             claimName: fastcrowdvision-hf-cache
         - name: tmp-dir
           emptyDir:
-            sizeLimit: 2Gi
+            sizeLimit: 2Gi