TensorRT-Specific LLM Optimizations for Jetson (NVIDIA Edge AI)

 

🚀 TensorRT-Specific LLM Optimizations for Jetson (NVIDIA Edge AI)

TensorRT is NVIDIA’s deep learning optimizer that dramatically improves inference speed for LLMs on Jetson devices. It enables:
✅ Faster inference (2-4x speedup) with lower latency.
✅ Lower power consumption on edge devices.
✅ Optimized memory usage for LLMs.

---

1️⃣ Install TensorRT & Dependencies

First, install TensorRT on your Jetson Orin/Nano:

sudo apt update
sudo apt install -y nvidia-cuda-toolkit tensorrt python3-libnvinfer

Confirm installation:

dpkg -l | grep TensorRT

---

2️⃣ Convert LLM to TensorRT Engine

TensorRT requires models in ONNX format before optimization.

Convert GGUF/Quantized Model → ONNX

First, convert your LLaMA/Mistral model to ONNX format:

python convert_to_onnx.py --model model.gguf --output model.onnx

(Use onnx_exporter.py from Hugging Face if needed.)

---

3️⃣ Optimize ONNX with TensorRT

Use trtexec to compile the ONNX model into a TensorRT engine:

trtexec --onnx=model.onnx --saveEngine=model.trt --fp16

🔹 --fp16: Uses 16-bit floating point for speed boost.
🔹 --saveEngine: Saves the optimized model as model.trt.

---

4️⃣ Run Inference Using TensorRT-Optimized LLM

Now, run the optimized .trt model with TensorRT:

import tensorrt as trt
import numpy as np

# Load TensorRT model
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
runtime = trt.Runtime(TRT_LOGGER)
with open("model.trt", "rb") as f:
    engine = runtime.deserialize_cuda_engine(f.read())

def infer_tensorrt(input_text):
    # Preprocess input, run inference, and return response
    return "AI Response from TensorRT model"

print(infer_tensorrt("What is Edge AI?"))

---

5️⃣ Deploy as a FastAPI Edge AI Agent

Run a FastAPI-based chatbot on Jetson:

from fastapi import FastAPI
import subprocess

app = FastAPI()

@app.get("/ask")
def ask(question: str):
    cmd = f'./main --engine model.trt -p "{question}" -n 100'
    response = subprocess.check_output(cmd, shell=True).decode()
    return {"response": response}

# Run API: uvicorn app:app --host 0.0.0.0 --port 8000

---

🔥 Benchmark TensorRT vs. CPU/GPU Performance

Compare TensorRT vs. CPU vs. GPU inference speed:

trtexec --loadEngine=model.trt --benchmark

💡 Expected Speedup:
🚀 TensorRT (2-4x faster) > CUDA (cuBLAS) > CPU (Slowest)

---

📌 Conclusion

✅ TensorRT accelerates LLM inference on Jetson Edge AI.
✅ Use ONNX + TensorRT Engine to optimize LLaMA/Mistral models.
✅ Deploy as a FastAPI agent for real-time inference.

🚀 Docker Setup for TensorRT-Optimized LLM on Jetson

This guide provides a fully containerized solution to run an LLM-optimized TensorRT agent on Jetson Orin/Nano.

---

📦 1️⃣ Create Dockerfile for TensorRT LLM

Create a Dockerfile to set up TensorRT, FastAPI, and LLM inference:

# Base image with CUDA and TensorRT (JetPack version should match your Jetson)
FROM nvcr.io/nvidia/l4t-tensorrt:r8.5.2-runtime

# Set environment variables for CUDA and TensorRT
ENV DEBIAN_FRONTEND=noninteractive
ENV PATH="/usr/local/bin:${PATH}"

# Install necessary dependencies
RUN apt update && apt install -y \
    python3 python3-pip wget git \
    && rm -rf /var/lib/apt/lists/*

# Install Python dependencies
RUN pip3 install --upgrade pip
RUN pip3 install fastapi uvicorn numpy onnxruntime-gpu tensorrt

# Copy LLM model and scripts
WORKDIR /app
COPY model.trt /app/
COPY server.py /app/

# Expose API port
EXPOSE 8000

# Start FastAPI server
CMD ["uvicorn", "server:app", "--host", "0.0.0.0", "--port", "8000"]

---

📝 2️⃣ Create FastAPI Server (server.py)

This script loads TensorRT-optimized LLM and serves responses via FastAPI.

from fastapi import FastAPI
import tensorrt as trt
import numpy as np

app = FastAPI()

# Load TensorRT engine
TRT_LOGGER = trt.Logger(trt.Logger.WARNING)
runtime = trt.Runtime(TRT_LOGGER)

with open("model.trt", "rb") as f:
    engine = runtime.deserialize_cuda_engine(f.read())

def infer_tensorrt(input_text):
    """ Run LLM inference using TensorRT """
    # Preprocess text input and run inference here
    return f"Response from TensorRT model: {input_text}"

@app.get("/ask")
def ask(question: str):
    return {"response": infer_tensorrt(question)}

---

🐳 3️⃣ Build & Run Docker Container

Build the Docker Image

docker build -t jetson-trt-llm .

Run the Container

docker run --runtime nvidia --network host --rm -it jetson-trt-llm

---

🔥 4️⃣ Test the Edge AI LLM API

Once the container is running, test the API:

curl "http://localhost:8000/ask?question=What is Edge AI?"

🔹 Expected Output:

{"response": "Response from TensorRT model: What is Edge AI?"}

---

📌 Conclusion

✅ Dockerized FastAPI agent running a TensorRT-optimized LLM on Jetson.
✅ Real-time, low-latency inference with NVIDIA TensorRT acceleration.
✅ Scalable Edge AI solution for private, offline GenAI models.

Convert Docker Compose to Kubernetes Orchestration

If you already have a Docker Compose based application. And you may want to orchestrate the containers with Kubernetes. If you are new to Kubernetes then you can search various articles in this blog or Kubernetes website.

Here's a step-by-step plan to migrate your Docker Compose application to Kubernetes:

Step 1: Create Kubernetes Configuration Files

Create a directory for your Kubernetes configuration files (e.g., k8s-config).

Create separate YAML files for each service (e.g., api.yaml, pgsql.yaml, mongodb.yaml, rabbitmq.yaml).

Define Kubernetes resources (Deployments, Services, Persistent Volumes) for each service.

Step 2: Define Kubernetes Resources

Deployment YAML Example (api.yaml)

YAML

apiVersion: apps/v1

kind: Deployment

metadata:

  name: api-deployment

spec:

  replicas: 1

  selector:

    matchLabels:

      app: api

  template:

    metadata:

      labels:

        app: api

    spec:

      containers:

      - name: api

        image: <your-docker-image-name>

        ports:

        - containerPort: 8000

Service YAML Example (api.yaml)

YAML

apiVersion: v1

kind: Service

metadata:

  name: api-service

spec:

  selector:

    app: api

  ports:

  - name: http

    port: 8000

    targetPort: 8000

  type: ClusterIP

Repeat this process for other services (pgsql, mongodb, rabbitmq).

Step 3: Configure Persistent Storage

Create Persistent Volume Claims (PVCs) for databases.

Define StorageClass for dynamic provisioning.

Persistent Volume Claim YAML Example (pgsql-pvc.yaml)

YAML

apiVersion: v1

kind: PersistentVolumeClaim

metadata:

  name: pgsql-pvc

spec:

  accessModes:

    - ReadWriteOnce

  resources:

    requests:

      storage: 5Gi

StorageClass YAML Example (storage-class.yaml)

YAML

apiVersion: storage.k8s.io/v1

kind: StorageClass

metadata:

  name: local-storage

provisioner: kubernetes.io/no-provisioner

volumeBindingMode: WaitForFirstConsumer

Step 4: Deploy to Kubernetes

Create namespace: kubectl create namespace <namespace-name>

Apply configuration files: kubectl apply -f <config-file>.yaml

Verify deployments: kubectl get deployments -n <namespace-name>

Verify pods: kubectl get pods -n <namespace-name>

Verify services: kubectl get svc -n <namespace-name>

Step 5: Expose Services

Create Ingress resources for external access.

Configure Ingress Controller (e.g., NGINX).

Ingress YAML Example (ingress.yaml)

YAML

apiVersion: networking.k8s.io/v1

kind: Ingress

metadata:

  name: api-ingress

spec:

  rules:

  - host: <your-domain>

    http:

      paths:

      - path: /

        backend:

          serviceName: api-service

          servicePort: 8000

Step 6: Monitor and Maintain

Set up monitoring tools (e.g., Prometheus, Grafana).

Implement logging solutions (e.g., Fluentd, Elasticsearch).

Regularly update images and configurations.

Implementation Tools

kubectl: Kubernetes command-line tool.

kustomize: Configuration management tool.

Helm: Package manager for Kubernetes.

Additional Tips

Test locally with Minikube or Kind before deploying to production.

Use environment variables for configuration.

Implement rolling updates for zero-downtime deployments.

This migration plan provides a solid foundation. Adjust configurations according to your specific requirements.

Say I have a Docker Compose yaml file eg.

# version: '3.8'

services:

  pgsql:

    container_name: pgsql

    image: postgres:15-alpine

    volumes:

      - postgres-data-volume:/var/lib/postgresql/data/

      - ./app/init.sql:/docker-entrypoint-initdb.d/init.sql

    ports:

      - 5433:5432

    environment:

      - POSTGRES_USER=postgres

      - POSTGRES_PASSWORD=password123

      - POSTGRES_DB=lcnc_db_dev

    networks:

      - app-network

    healthcheck:

      test: ["CMD-SHELL", "pg_isready -U postgres"]

      interval: 5s

      retries: 5

  # adminer:

  #   image: adminer

  #   container_name: pgsql-adminer

  #   ports:

  #     - 8080:8080

  #   depends_on:

  #     - pgsql

  #   networks:

  #     - app-network

  app:

    build:

      context: ./app  # Change context to the app folder

      dockerfile: Dockerfile  # Dockerfile name remains the same

    container_name: app

    env_file:

      - ./.env.dev

    environment:

      - PYTHONPATH=/app

    volumes:

      - ./app:/app

      - ./app/generated_files:/app/generated_files  # Mount for generated backend files 

      - ./app/dart_files:/app/dart_files

    ports:

      - "8001:8001"

    depends_on:

      pgsql:

        condition: service_healthy

      redis:

        condition: service_healthy

    networks:

      - app-network

    deploy:

      resources:

        limits:

          cpus: '2.00'

          memory: 4G

  redis:

    image: redis:alpine

    container_name: redis

    ports:

      - "6379:6379"

    networks:

      - app-network

    healthcheck:

      test: ["CMD", "redis-cli", "ping"]

      interval: 5s

      timeout: 3s

      retries: 3

    deploy:

      resources:

        limits:

          cpus: '1.00'

          memory: 2G

    restart: always

  redis_insight:

    image: redis/redisinsight:latest

    container_name: redis_insight

    ports:

      - "5540:5540"

    networks:

      - app-network

    depends_on:

      redis:

        condition: service_healthy

  celery_worker:

    build:

      context: ./app

      dockerfile: Dockerfile.celery-worker

    container_name: celery_worker

    environment:

      - PYTHONPATH=/app

      - BROKER_URL=redis://redis:6379/0

      - RESULT_BACKEND=redis://redis:6379/0

      - PREFETCH_MULTIPLIER=1

      - MAX_MEMORY_PER_CHILD=512

    depends_on:

      - app

      - redis

      - pgsql

    volumes:

      - ./app:/app

      - ./app/generated_files:/app/generated_files

      - ./app/dart_files:/app/dart_files

    networks:

      - app-network

    restart: unless-stopped

volumes:

  postgres-data-volume:  # Named volume for PostgreSQL data

networks:

  app-network:

    driver: bridge

Here's the equivalent Kubernetes configuration for the provided Docker Compose file:

Step 1: Create Namespace

Bash

kubectl create namespace dev-namespace

Step 2: PostgreSQL Deployment and Service

YAML

# pgsql-deployment.yaml

apiVersion: apps/v1

kind: Deployment

metadata:

  name: pgsql-deployment

  namespace: dev-namespace

spec:

  replicas: 1

  selector:

    matchLabels:

      app: pgsql

  template:

    metadata:

      labels:

        app: pgsql

    spec:

      containers:

      - name: pgsql

        image: postgres:15-alpine

        ports:

        - containerPort: 5432

        volumeMounts:

        - name: postgres-data-volume

          mountPath: /var/lib/postgresql/data/

        - name: init-sql-volume

          mountPath: /docker-entrypoint-initdb.d/

        env:

        - name: POSTGRES_USER

          value: "postgres"

        - name: POSTGRES_PASSWORD

          value: "password123"

        - name: POSTGRES_DB

          value: "lcnc_db_dev"

      volumes:

      - name: postgres-data-volume

        persistentVolumeClaim:

          claimName: postgres-pvc

      - name: init-sql-volume

        configMap:

          name: init-sql-cm

  strategy:

    type: Recreate

# pgsql-service.yaml

apiVersion: v1

kind: Service

metadata:

  name: pgsql-service

  namespace: dev-namespace

spec:

  selector:

    app: pgsql

  ports:

  - name: pgsql

    port: 5432

    targetPort: 5432

  type: ClusterIP

# Create ConfigMap for init.sql

kubectl create configmap init-sql-cm --from-file=init.sql -n dev-namespace

# Create Persistent Volume Claim

# postgres-pvc.yaml

apiVersion: v1

kind: PersistentVolumeClaim

metadata:

  name: postgres-pvc

  namespace: dev-namespace

spec:

  accessModes:

    - ReadWriteOnce

  resources:

    requests:

      storage: 5Gi

Step 3: Redis Deployment and Service

YAML

# redis-deployment.yaml

apiVersion: apps/v1

kind: Deployment

metadata:

  name: redis-deployment

  namespace: dev-namespace

spec:

  replicas: 1

  selector:

    matchLabels:

      app: redis

  template:

    metadata:

      labels:

        app: redis

    spec:

      containers:

      - name: redis

        image: redis:alpine

        ports:

        - containerPort: 6379

  strategy:

    type: Recreate

# redis-service.yaml

apiVersion: v1

kind: Service

metadata:

  name: redis-service

  namespace: dev-namespace

spec:

  selector:

    app: redis

  ports:

  - name: redis

    port: 6379

    targetPort: 6379

  type: ClusterIP

Step 4: App Deployment and Service

YAML

# app-deployment.yaml

apiVersion: apps/v1

kind: Deployment

metadata:

  name: app-deployment

  namespace: dev-namespace

spec:

  replicas: 1

  selector:

    matchLabels:

      app: app

  template:

    metadata:

      labels:

        app: app

    spec:

      containers:

      - name: app

        image: <your-docker-image-name>

        ports:

        - containerPort: 8001

        volumeMounts:

        - name: app-volume

          mountPath: /app

        - name: generated-files-volume

          mountPath: /app/generated_files

        - name: dart-files-volume

          mountPath: /app/dart_files

        env:

        - name: PYTHONPATH

          value: "/app"

  strategy:

    type: Recreate

  volumes:

  - name: app-volume

    persistentVolumeClaim:

      claimName: app-pvc

  - name: generated-files-volume

    emptyDir: {}

  - name: dart-files-volume

    emptyDir: {}

# app-service.yaml

apiVersion: v1

kind: Service

metadata:

  name: app-service

  namespace: dev-namespace

spec:

  selector:

    app: app

  ports:

  - name: app

    port: 8001

    targetPort: 8001

  type: ClusterIP

# Create Persistent Volume Claim

# app-pvc.yaml

apiVersion: v1

kind: PersistentVolumeClaim

metadata:

  name: app-pvc

  namespace: dev-namespace

spec:

  accessModes:

    - ReadWriteOnce

  resources:

    requests:

      storage: 2Gi

Step 5: Celery Worker Deployment

YAML

# celery-worker-deployment.yaml

apiVersion: apps/v1

kind: Deployment

metadata:

  name: celery-worker-deployment

  namespace: dev-namespace

spec:

  replicas: 1

  selector:

    matchLabels:

      app: celery-worker

  template:

    metadata:

      labels:

        app: celery-worker

    spec:

      containers:

      - name: celery-worker

        image: <your-docker-image-name>

        volumeMounts:

        - name: app-volume

          mountPath: /app

        - name: generated-files-volume

          mountPath: /app/generated_files

        - name: dart-files-volume

          mountPath: /app/dart_files

        env:

        - name: PYTHONPATH

          value: "/app"

        - name: BROKER_URL

          value: "redis://redis-service:6379/0"

        - name: RESULT_BACKEND

          value: "redis://redis-service:6379/0"

  strategy:

    type: Recreate

  volumes:

  - name: app-volume

    persistentVolumeClaim:

      claimName: app-pvc

  - name: generated-files-volume

    emptyDir: {}

  - name: dart-files-volume

    emptyDir: {}

Step 6: Apply Kubernetes Configuration

Bash

kubectl apply -f pgsql-deployment.yaml

kubectl apply -f pgsql-service.yaml

kubectl apply -f redis-deployment.yaml

kubectl apply -f redis-service.yaml

kubectl apply -f app-deployment.yaml

kubectl apply -f app-service.yaml

kubectl apply -f celery-worker-deployment.yaml

Step 7: Verify Deployments

Bash

kubectl get deployments -n dev-namespace

kubectl get pods -n dev-namespace

kubectl get svc -n dev-namespace

This Kubernetes configuration mirrors the Docker Compose file. Adjust image names, resource requests and other parameters as necessary.

Additional Tips

Use Secret resources for sensitive data (e.g., passwords).

Implement Horizontal Pod Autoscaling (HPA) for dynamic scaling.

Monitor cluster performance with Prometheus and Grafana.

Here are examples of applying Kubernetes in Google Cloud and Azure:

Google Cloud (GKE)

Step 1: Create a GKE Cluster

Create a new project: gcloud projects create <project-name>

Enable Kubernetes Engine API: gcloud services enable container.googleapis.com

Create a cluster: gcloud container clusters create <cluster-name> --zone <zone> --num-nodes 3

Step 2: Deploy Application

Create Deployment YAML file (e.g., deployment.yaml)

Apply Deployment: kubectl apply -f deployment.yaml

Expose Service: kubectl expose deployment <deployment-name> --type LoadBalancer --port 80

Step 3: Verify Deployment

Get Cluster credentials: gcloud container clusters get-credentials <cluster-name> --zone <zone>

Verify pods: kubectl get pods

Verify services: kubectl get svc

GKE Example Commands

Bash

# Create project and enable API

gcloud projects create my-project

gcloud services enable container.googleapis.com

# Create GKE cluster

gcloud container clusters create my-cluster --zone us-central1-a --num-nodes 3

# Deploy application

kubectl apply -f deployment.yaml

# Expose service

kubectl expose deployment my-app --type LoadBalancer --port 80

# Verify deployment

gcloud container clusters get-credentials my-cluster --zone us-central1-a

kubectl get pods

kubectl get svc

Azure (AKS)

Step 1: Create AKS Cluster

Create resource group: az group create --name <resource-group> --location <location>

Create AKS cluster: az aks create --resource-group <resource-group> --name <cluster-name> --node-count 3

Step 2: Deploy Application

Create Deployment YAML file (e.g., deployment.yaml)

Apply Deployment: kubectl apply -f deployment.yaml

Expose Service: kubectl expose deployment <deployment-name> --type LoadBalancer --port 80

Step 3: Verify Deployment

Get Cluster credentials: az aks get-credentials --resource-group <resource-group> --name <cluster-name>

Verify pods: kubectl get pods

Verify services: kubectl get svc

AKS Example Commands

Bash

# Create resource group and AKS cluster

az group create --name my-resource-group --location eastus

az aks create --resource-group my-resource-group --name my-aks-cluster --node-count 3

# Deploy application

kubectl apply -f deployment.yaml

# Expose service

kubectl expose deployment my-app --type LoadBalancer --port 80

# Verify deployment

az aks get-credentials --resource-group my-resource-group --name my-aks-cluster

kubectl get pods

kubectl get svc

Additional Tips

Use managed identities for authentication.

Implement network policies for security.

Monitor cluster performance with Azure Monitor or Google Cloud Monitoring.

Kubernetes Deployment YAML Example

YAML

apiVersion: apps/v1

kind: Deployment

metadata:

  name: my-app

spec:

  replicas: 3

  selector:

    matchLabels:

      app: my-app

  template:

    metadata:

      labels:

        app: my-app

    spec:

      containers:

      - name: my-app

        image: <your-docker-image-name>

        ports:

        - containerPort: 80

Microservices Application with Flutter Flask MongoDB RabbitMQ

A complete microservice application setup with a Flutter app, MongoDB, and RabbitMQ, along with all the necessary files and folder structure. The setup uses Docker Compose to orchestrate the services.

Folder Structure

```

microservice-app/

│

├── backend/

│   ├── Dockerfile

│   ├── requirements.txt

│   ├── main.py

│   └── config.py

│

├── frontend/

│   ├── Dockerfile

│   ├── pubspec.yaml

│   └── lib/

│       └── main.dart

│

├── docker-compose.yml

└── README.md

```

1. `docker-compose.yml`

```yaml

version: '3.8'

services:

  backend:

    build: ./backend

    container_name: backend

    ports:

      - "8000:8000"

    depends_on:

      - mongodb

      - rabbitmq

    environment:

      - MONGO_URI=mongodb://mongodb:27017/flutterdb

      - RABBITMQ_URI=amqp://guest:guest@rabbitmq:5672/

    networks:

      - microservice-network

  mongodb:

    image: mongo:latest

    container_name: mongodb

    ports:

      - "27017:27017"

    networks:

      - microservice-network

  rabbitmq:

    image: rabbitmq:3-management

    container_name: rabbitmq

    ports:

      - "5672:5672"

      - "15672:15672"

    networks:

      - microservice-network

  frontend:

    build: ./frontend

    container_name: frontend

    ports:

      - "8080:8080"

    depends_on:

      - backend

    networks:

      - microservice-network

networks:

  microservice-network:

    driver: bridge

```

2. Backend Service

2.1 `backend/Dockerfile`

```dockerfile

FROM python:3.9-slim

WORKDIR /app

COPY requirements.txt requirements.txt

RUN pip install -r requirements.txt

COPY . .

CMD ["python", "main.py"]

```

2.2 `backend/requirements.txt`

```txt

fastapi

pymongo

pika

uvicorn

```

2.3 `backend/config.py`

```python

import os

MONGO_URI = os.getenv('MONGO_URI')

RABBITMQ_URI = os.getenv('RABBITMQ_URI')

```

2.4 `backend/main.py`

```python

from fastapi import FastAPI

from pymongo import MongoClient

import pika

import config

app = FastAPI()

client = MongoClient(config.MONGO_URI)

db = client.flutterdb

# RabbitMQ Connection

params = pika.URLParameters(config.RABBITMQ_URI)

connection = pika.BlockingConnection(params)

channel = connection.channel()

@app.get("/")

async def read_root():

    return {"message": "Backend service running"}

@app.post("/data")

async def create_data(data: dict):

    db.collection.insert_one(data)

    channel.basic_publish(exchange='', routing_key='flutter_queue', body=str(data))

    return {"message": "Data inserted and sent to RabbitMQ"}

```

3. Frontend Service

3.1 `frontend/Dockerfile`

```dockerfile

FROM cirrusci/flutter:stable

WORKDIR /app

COPY . .

RUN flutter build web

CMD ["flutter", "run", "-d", "chrome"]

```

3.2 `frontend/pubspec.yaml`

```yaml

name: flutter_app

description: A new Flutter project.

version: 1.0.0+1

environment:

  sdk: ">=2.7.0 <3.0.0"

dependencies:

  flutter:

    sdk: flutter

  http: ^0.13.3

dev_dependencies:

  flutter_test:

    sdk: flutter

```

#### 3.3 `frontend/lib/main.dart`

```dart

import 'package:flutter/material.dart';

import 'package:http/http.dart' as http;

void main() {

  runApp(MyApp());

}

class MyApp extends StatelessWidget {

  @override

  Widget build(BuildContext context) {

    return MaterialApp(

      title: 'Flutter Demo',

      theme: ThemeData(

        primarySwatch: Colors.blue,

      ),

      home: MyHomePage(),

    );

  }

}

class MyHomePage extends StatefulWidget {

  @override

  _MyHomePageState createState() => _MyHomePageState();

}

class _MyHomePageState extends State<MyHomePage> {

  Future<void> sendData() async {

    final response = await http.post(

      Uri.parse('http://backend:8000/data'),

      body: {'key': 'value'},

    );

    print('Response status: ${response.statusCode}');

    print('Response body: ${response.body}');

  }

  @override

  Widget build(BuildContext context) {

    return Scaffold(

      appBar: AppBar(

        title: Text('Flutter Microservice App'),

      ),

      body: Center(

        child: ElevatedButton(

          onPressed: sendData,

          child: Text('Send Data to Backend'),

        ),

      ),

    );

  }

}

```

4. `README.md`

```markdown

# Microservice Application

## Overview

This is a microservice application setup consisting of a Flutter app (frontend), a FastAPI service (backend), MongoDB, and RabbitMQ. All services are orchestrated using Docker Compose.

## How to Run

1. Clone the repository:

   ```bash

   git clone https://github.com/your-repo/microservice-app.git

   cd microservice-app

   ```

2. Build and run the containers:

   ```bash

   docker-compose up --build

   ```

3. Access the services:

   - Frontend: `http://localhost:8080`

   - Backend: `http://localhost:8000`

   - RabbitMQ Management: `http://localhost:15672`

   - MongoDB: `mongodb://localhost:27017`

```

### Instructions to Run the Application

1. Ensure Docker and Docker Compose are installed on your machine.

2. Place the folder structure and files as described above.

3. Navigate to the root of the `microservice-app` folder.

4. Run `docker-compose up --build` to build and start the application.

5. Access the frontend on `http://localhost:8080`, backend on `http://localhost:8000`, and RabbitMQ Management UI on `http://localhost:15672`.

This setup provides a working microservice application with a Flutter frontend, FastAPI backend, MongoDB for storage, and RabbitMQ for messaging.

Code Generation Engine Concept

Architecture Details for Code Generation Engine (Low-code)

1. Backend Framework:

- Python Framework:

  - FastAPI: A modern, fast (high-performance) web framework for building APIs with Python 3.6+ based on standard Python type hints.

  - SQLAlchemy: SQL toolkit and Object-Relational Mapping (ORM) library for database management.

  - Jinja2: A templating engine for rendering dynamic content.

  - Pydantic: Data validation and settings management using Python type annotations.

2. Application Structure:

- Project Root:

  - `app/`

    - `main.py` (Entry point of the application)

    - `models/`

      - `models.py` (Database models)

    - `schemas/`

      - `schemas.py` (Data validation schemas)

    - `api/`

      - `endpoints/`

        - `code_generation.py` (Endpoints related to code generation)

    - `core/`

      - `config.py` (Configuration settings)

      - `dependencies.py` (Common dependencies)

    - `services/`

      - `code_generator.py` (Logic for code generation)

    - `templates/` (Directory for Jinja2 templates)

  - `Dockerfile`

  - `docker-compose.yml`

  - `requirements.txt`

3. Docker-based Application:

#Dockerfile:

```dockerfile

# Use an official Python runtime as a parent image

FROM python:3.9-slim

# Set the working directory in the container

WORKDIR /app

# Copy the current directory contents into the container at /app

COPY . /app

# Install any needed packages specified in requirements.txt

RUN pip install --no-cache-dir -r requirements.txt

# Make port 80 available to the world outside this container

EXPOSE 80

# Define environment variable

ENV NAME CodeGenEngine

# Run app.py when the container launches

CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "80"]

```

#docker-compose.yml:

```yaml

version: '3.8'

services:

  web:

    build: .

    ports:

      - "80:80"

    environment:

      - DATABASE_URL=postgresql://user:password@db/codegen

    depends_on:

      - db

  db:

    image: postgres:12

    environment:

      POSTGRES_USER: user

      POSTGRES_PASSWORD: password

      POSTGRES_DB: codegen

    volumes:

      - postgres_data:/var/lib/postgresql/data

volumes:

  postgres_data:

```

4. Code Generation Engine:

- Template Engine:

  - Jinja2: Use templates to define the structure of the generated code.

  

- Model-Driven Development:

  - Pydantic Models: Define the models for data validation and generation logic.

  

- Code Generation Logic:

  - Implement logic in `services/code_generator.py` to translate user configurations into functional code using templates.

5. API Endpoints:

- Define API endpoints in `api/endpoints/code_generation.py` to handle user requests and trigger the code generation process.

6. Sample Endpoint for Code Generation:

```python

from fastapi import APIRouter, Depends

from app.schemas import CodeGenRequest, CodeGenResponse

from app.services.code_generator import generate_code

router = APIRouter()

@router.post("/generate", response_model=CodeGenResponse)

def generate_code_endpoint(request: CodeGenRequest):

    code = generate_code(request)

    return {"code": code}

```

7. Sample Code Generation Logic:

```python

from jinja2 import Environment, FileSystemLoader

from app.schemas import CodeGenRequest

def generate_code(request: CodeGenRequest) -> str:

    env = Environment(loader=FileSystemLoader('app/templates'))

    template = env.get_template('template.py.j2')

    code = template.render(model=request.model)

    return code

```

8. Sample Template (`template.py.j2`):

```jinja

class {{ model.name }}:

    def __init__(self{% for field in model.fields %}, {{ field.name }}: {{ field.type }}{% endfor %}):

        {% for field in model.fields %}self.{{ field.name }} = {{ field.name }}

        {% endfor %}

```

Compare Ububtu and MacOS

 Features #Ubuntu Desktop #macOS Overall developer experience:

Ubuntu Offers a seamless, powerful platform that mirrors production environments on cloud, server, and IoT deployments. A top choice for AI and machine learning developers.

macOS Provides a user-friendly and intuitive interface with seamless integration across other Apple devices. Its well-documented resources and developer tools make it attractive for developers within the Apple ecosystem.

#Cloud development:

Ubuntu Aligns with Ubuntu Server, the most popular OS on public clouds, for simplified cloud-native development. Supports cloud-based developer tools like #Docker, LXD, MicroK8s, and #Kubernetes. Ensures portability and cost optimisation since it can run on any private or public cloud platform.

macOSRelies on Docker and other #virtualisation technologies for cloud development. Has seamless integration with iCloud services and native support for cloudbased application development.

#Server operations:

Ubuntu Offers wide support for server-side #development, including a range of supported applications and services, automation and debugging tools, and scripting languages. Offers robust security features.

macOS Provides robust support for server-side development with strong security features and a user-friendly approach, but the range of application and service support may not be as extensive as Ubuntu.

#IoT innovation:

Ubuntu Core is designed specifically for IoT and embedded devices, offering a smooth development process. The snap packaging system simplifies the creation of highly confined, self-contained applications.

macOS Does not offer a comparable IoT-focused operating system.

#AI and #machinelearning:

Ubuntu With native support for #Python, $R, and other popular AI/ML languages, developers can easily create their preferred environment. Ubuntu is the reference platform for #NVIDIA’s #CUDA, optimal for #GPU accelerated ML tasks. Popular ML libraries run efficiently on Ubuntu.

macOS Provides native support for popular AI/ ML languages such as Python and R, but doesn’t have the same level of integration with GPU-accelerated tasks. Offers robust support for ML frameworks and tools.

collected from Ubuntu.

Reproducibility of Python

Ensuring the reproducibility of Python statistical analysis is crucial in research and scientific computing. Here are some ways to achieve reproducibility:

1. Version Control

Use version control systems like Git to track changes in your code and data.

2. Documentation

Document your code, methods, and results thoroughly.

3. Virtual Environments

Use virtual environments like conda or virtualenv to manage dependencies and ensure consistent package versions.

4. Seed Values

Set seed values for random number generators to ensure reproducibility of simulations and modeling results.

5. Data Management

Use data management tools like Pandas and NumPy to ensure data consistency and integrity.

6. Testing

Write unit tests and integration tests to ensure code correctness and reproducibility.

7. Containerization

Use containerization tools like Docker to package your code, data, and dependencies into a reproducible environment.

8. Reproducibility Tools

Utilize tools like Jupyter Notebook, Jupyter Lab, and Reproducible Research Tools to facilitate reproducibility.

Details these steps:

1. Use a Fixed Random Seed:

    ```python

    import numpy as np

    import random

    np.random.seed(42)

    random.seed(42)

    ```

2. Document the Environment:

    - List all packages and their versions.

    ```python

    import sys

    print(sys.version)

    

    !pip freeze > requirements.txt

    ```

3. Organize Code in Scripts or Notebooks:

    - Keep the analysis in well-documented scripts or Jupyter Notebooks.

4. Version Control:

    - Use version control systems like Git to track changes.

    ```bash

    git init

    git add .

    git commit -m "Initial commit"

    ```

5. Data Management:

    - Ensure data used in analysis is stored and accessed consistently.

    - Use data versioning tools like DVC (Data Version Control).

6. Environment Management:

    - Use virtual environments or containerization (e.g., `virtualenv`, `conda`, Docker).

    ```bash

    python -m venv env

    source env/bin/activate

    ```

7. Automated Tests:

    - Write tests to check the integrity of your analysis.

    ```python

    def test_mean():

        assert np.mean([1, 2, 3]) == 2

    ```

8. Detailed Documentation:

    - Provide clear and detailed documentation of your workflow.

By following these steps, you can ensure that your Python statistical analysis is reproducible.

MySql with Docker

Running a MySQL database in a Docker container is straightforward. Here are the steps:

Pull the Official MySQL Image:

The official MySQL image is available on Docker Hub. You can choose the version you want (e.g., MySQL 8.0): docker pull mysql:8.0

Create a Docker Volume (Optional):

To persist your database data, create a Docker volume or bind mount. Otherwise, data will be lost when the container restarts.

Example using a volume: docker volume create mysql-data

Run the MySQL Container:

Use the following command to start a MySQL container: docker run --name my-mysql -e MYSQL_ROOT_PASSWORD=secret -v mysql-data:/var/lib/mysql -d mysql:8.0

Replace secret with your desired root password.

The MySQL first-run routine will take a few seconds to complete.

Check if the database is up by running: docker logs my-mysql

Look for a line that says “ready for connections.”

Access MySQL Shell:

To interact with MySQL, attach to the container and run the mysql command: docker exec -it my-mysql mysql -p

Enter the root password when prompted.

To import an SQL file from your filesystem: docker exec -it my-mysql mysql -psecret database_name < path-to-file.sql

Access MySQL from Host:

If you want to access MySQL from your host machine, set up a port binding:

Add the following to your docker-compose.yml file (if using Docker Compose):

services:

mysql:

ports:

- 33060:3306

If not using Docker Compose, pass -p 33060:3306 to docker run.

That’s it! You now have a MySQL database running in a Docker container.