update the cv modal inference proxy server with optimization

workspace/sprint_1_2/CODEBASE/infra/implementation/cicd/jenkin_run.sh

@@ -1,31 +0,0 @@
-# Install Docker image 
-docker network create jenkins
-
-# install docker-integratable image
-docker run --name jenkins-docker --rm --detach \
-  -p 8080:8080  -p 50000:50000 \
---restart=on-failure \
-  --privileged --network jenkins --network-alias docker \
-  --env DOCKER_TLS_CERTDIR=/certs \
-  --volume jenkins-docker-certs:/certs/client \
-  --volume jenkins-data:/var/jenkins_home \
-  --publish 2376:2376 \
-  docker:dind --storage-driver overlay2 \
--v jenkins_home:/var/jenkins_home jenkins/jenkins:lts 
-
-# install the docker images
-docker run \
-  --name jenkins-blueocean \
-  --restart=on-failure \
-  --detach \
-  --network jenkins \
-  --env DOCKER_HOST=tcp://docker:2376 \
-  --env DOCKER_CERT_PATH=/certs/client \
-  --env DOCKER_TLS_VERIFY=1 \
-  --publish 8080:8080 \
-  --publish 50000:50000 \
-  --volume jenkins-data:/var/jenkins_home \
-  --volume jenkins-docker-certs:/certs/client:ro \
-  jenkins/jenkins:lts
-
-

workspace/sprint_1_2/CODEBASE/infra/implementation/cicd/suggested_cicd_pipeline.md

@@ -0,0 +1,42 @@
+
+### 1. The Core Application Stack (Replacing the streaming engine)
+
+* **Database:** **PostgreSQL** or **SQLite**
+* *Why:* Extremely lightweight, universally supported, and standard for medical/relational data (like patient exercises, joint ranges of motion, and logs).
+
+
+* **Backend:** **Node.js (Express)**, **Python (FastAPI)**, or **Go**
+* *Why:* Fast to build for a PoC, easily containerized via Docker, and highly efficient. FastAPI is excellent if you plan to incorporate any musculoskeletal data analysis or ML later.
+
+
+
+### 2. CI/CD & Infrastructure Components
+
+* **Codebase:** **Gitea**
+* *Why:* A lightweight, self-hosted Git platform that runs in a simple local Docker container using very little memory.
+
+
+* **CI/CD Orchestrator & Build Server:** **Woodpecker CI** or **GitHub Actions** (if you're okay using GitHub until migrating to a private cloud).
+* *Why:* Woodpecker is an open-source, container-first CI engine that runs locally with almost zero overhead.
+
+
+* **Artifact Repository:** **Gitea Packages** or **Docker Registry**
+* *Why:* You can store your built application images right inside Gitea or a tiny local Docker registry container.
+
+
+
+### 3. Environments & Deployment
+
+* **Deployment-Manager:** **Docker Compose** or **Portainer**
+* *Why:* To deploy your musculoskeletal app, you just need Docker Compose to orchestrate your backend and database containers. Portainer gives you a simple web GUI to manage them locally.
+
+
+* **Staging & Production Env:** Isolated local containers or separate virtual machines.
+
+### 4. Feedback & Monitoring Loop
+
+* **User Feedback Collector:** A custom-built form widget inside your app or an open-source tool like **Feedback Fish** / **Air Table**
+* **Feedback-Resolve:** **Focalboard** or **Leantime** (Self-hosted, lightweight project boards to track bugs and user feature requests).
+* **Monitoring & Logging:** **Prometheus + Grafana**
+* *Why:* Perfect for tracking application uptime, API response times, and server health.
+

workspace/sprint_1_2/CODEBASE/infra/implementation/cicd/the_pipeline.md

@@ -0,0 +1,49 @@
+
+```planUML
+@startuml C4_Elements
+!include https://raw.githubusercontent.com/plantuml-stdlib/C4-PlantUML/master/C4_Component.puml
+
+
+title Component Diagram for CI/CD Pipeline Architecture
+
+Person(developer, "Developer(s)", "Writes code and pushes changes.")
+Person(enduser, "End-User(s)", "Interacts with the production application.")
+
+Container_Boundary(pipeline_boundary, "CI/CD Pipeline System") {
+    Component(codebase, "Codebase", "Git Repository", "Stores the source code and tracks version history.")
+    Component(orchestrator, "CI/CD Orchestrator", "Workflow Engine", "Triggers actions based on repository events.")
+    Component(build_server, "Build Server", "Compiler/Packager", "Compiles code and builds release packages.")
+    Component(artifact_repo, "Artifact Repository", "Storage", "Stores compiled binaries or container images.")
+    Component(test_pipeline, "Test-Pipeline", "Automation Suite", "Runs unit, integration, and security tests.")
+    Component(deploy_manager, "Deployment-Manager", "CD Engine", "Orchestrates deployment to various environments.")
+    Component(user_feedback, "User Feedback Collector", "In-App/Portal Widget", "Collects explicit feature requests and bug reports from users.")
+    Component(feedback_resolve, "Feedback-Resolve", "Tracking System", "Manages issues, bugs, and deployment logs.")
+    Component(monitoring, "Monitoring & Logging", "Observability", "Monitors application health and metrics.")
+
+    Container_Boundary(deploy_env, "Deployment Environments") {
+        Component(staging_env, "Staging/QA Env", "Environment", "Pre-production environment for testing.")
+        Component(prod_env, "Production Env", "Environment", "Live environment hosting the customer application.")
+    }
+}
+
+' Directional Flow Links
+Rel(developer, codebase, "1. Pushes code")
+Rel(codebase, orchestrator, "2. Triggers event webhook")
+Rel(orchestrator, build_server, "3. Triggers build job")
+Rel(build_server, artifact_repo, "4. Stores compiled artifact")
+Rel(orchestrator, test_pipeline, "5. Runs automated tests")
+Rel(orchestrator, deploy_manager, "6. Signals ready for deployment")
+
+Rel(deploy_manager, artifact_repo, "7. Pulls latest artifact")
+Rel(deploy_manager, staging_env, "8. Deploys to")
+Rel(deploy_manager, prod_env, "9. Promotes approved changes to")
+
+Rel(enduser, prod_env, "10. Interacts with application")
+Rel(enduser, user_feedback, "11. Submits requests & feedback")
+Rel(user_feedback, feedback_resolve, "12. Forwards user tickets")
+Rel(monitoring, prod_env, "13. Reads metrics and errors")
+Rel(monitoring, feedback_resolve, "14. Feeds performance/error logs")
+Rel(feedback_resolve, developer, "15. Alerts for loop closure")
+
+@enduml
+```

workspace/sprint_1_2/CODEBASE/infra/implementation/triton_run/modal_triton.py

@@ -205,6 +205,9 @@ async def forward_list_models():
     min_containers=1, # for keeping warm and prevention,
     buffer_containers=2, # Number of additional idle containers to maintain under active load.
     scaledown_window=30, # Max time (in seconds) a container can remain idle while scaling down.
+    volumes= {
+        '/mnt/vkist-ml-model' : modal.CloudBucketMount(bucket_name="vkist-ml-model", secret=modal.Secret.from_name("aws-secrets"))
+    },
     secrets=[modal.Secret.from_name("aws-secrets")]
 )
 @modal.asgi_app()
@@ -213,7 +216,11 @@ def unified_triton_server():
     
     # Spawns Triton in the background. It will automatically read 
     # your "aws-secrets" environment keys to mount s3://vkist-ml-model/
-    cmd = ["tritonserver", "--model-repository=s3://vkist-ml-model/"]
+    # cmd = ["tritonserver", "--model-repository=s3://vkist-ml-model/"] # bad pattern causing latency
+    cmd = ["tritonserver", "--model-repository=/mnt/vkist-ml-model/"]
+    
+    # triton issue network connection -> AWS -> adding cold-off latency
+    # idea mounting the model to Modal Volume
     subprocess.Popen(cmd)
     
     print("📋 Triton background process delegated. Handing routing control over to FastAPI.")

workspace/sprint_1_2/CODEBASE/infra/implementation/triton_run/run.sh

@@ -1,2 +1,2 @@
 cd ../PILOT_PROJECT/workspace/sprint_1_2/CODEBASE/infra/implementation/triton_run
-MODAL_PROFILE=dtj-tran modal deploy PILOT_PROJECT/workspace/sprint_1_2/codebase/infra/implementation/triton_run/modal_triton.py
+MODAL_PROFILE=dtj-tran modal deploy modal_triton.py