overlink 0.4.0

Bridge between local PC and cloud GPU processing

Overlink - Cloud GPU Bridge

Overlink is a Python framework that connects your local application to cloud GPU resources (Google Colab, Kaggle, etc.) through ngrok tunnels. With Overlink, you can easily leverage powerful cloud GPUs without complicated setup.

🌟 Key Features

• 🚀 Automatic ngrok tunnel setup - No manual configuration needed
• ⚡ Leverage cloud GPUs - Handle intensive AI/ML tasks seamlessly
• 🔄 Simple API - Easy integration into existing projects
• 🔒 Secure HTTPS connection - Safeguard your data
• 📊 Connection monitoring - Track server status in real time
• 🧩 YOLO model support - Built-in Ultralytics YOLOv5/v8 integration

📦 Installation

Install Overlink via pip:

pip install overlink

System requirements:

• Python 3.6+
• Dependencies are automatically installed with the package

🚀 Quick Start

1. Setup server on Google Colab

!pip install -q overlink
from overlink import OvercloudServer
import getpass

# Enter your ngrok authtoken (get it at: https://dashboard.ngrok.com/get-started/your-authtoken)
ngrok_token = getpass.getpass("🔑 Enter your ngrok authtoken: ")

# Initialize the server
server = OvercloudServer(
    authtoken=ngrok_token,
    model_path="/content/yolov8n.pt",  # Path to your model
    port=5000  # Optional port (default: 3001)
)

# Start the server and get the public URL
public_url = server.start()

# Keep the server running
server.keep_alive()

2. Use client on your local machine

from overlink import OvercloudClient
import cv2

# Initialize the client with the server URL
client = OvercloudClient("https://your-ngrok-url")

# Check connection
if client.ping():
    print("✅ Successfully connected to cloud GPU!")

    # Process image via cloud
    result = client.process_image("input.jpg")

    # Display and save the result
    cv2.imshow("Result", result)
    cv2.waitKey(0)
    cv2.imwrite("output.jpg", result)
else:
    print("❌ Cannot connect to server")

📚 Detailed Guide

Server configuration

Parameter	Default	Description
authtoken	Required	Ngrok authtoken (get from ngrok dashboard)
model_path	None	Path to YOLO model (.pt file)
port	3001	Local port for Flask server
flask_debug	False	Enable Flask debug mode

Advanced Example:

server = OvercloudServer(
    authtoken="2w1Z93Yc23gB6Jl6jncvYfEkQC4_KpA55x6yTnakDb81HXJo",
    model_path="/content/custom_model.pt",
    port=5000
)

# Custom image processing endpoint
@server.app.route('/custom-process', methods=['POST'])
def custom_process():
    # Add your custom processing code here
    pass

public_url = server.start()

Using the client

Main methods

1. ping() - Check server connectivity
   • Returns True if the server is up, False otherwise
2. process_image(image_path) - Process an image via the server
   • image_path: Path to the image file to process
   • Returns the processed image as a numpy array (OpenCV format)

Example: Batch processing

import os
from tqdm import tqdm

input_dir = "input_images"
output_dir = "processed_images"

os.makedirs(output_dir, exist_ok=True)

for filename in tqdm(os.listdir(input_dir)):
    if filename.endswith(('.jpg', '.png', '.jpeg')):
        input_path = os.path.join(input_dir, filename)
        output_path = os.path.join(output_dir, f"processed_{filename}")

        try:
            result = client.process_image(input_path)
            cv2.imwrite(output_path, result)
        except Exception as e:
            print(f"Error processing {filename}: {str(e)}")

🧪 Performance Test

Input Image	Processing Time (Colab T4 GPU)	Processing Time (Local CPU)
640x480	120ms	850ms
1280x720	250ms	2200ms
1920x1080	450ms	4800ms

Test results with YOLOv8n using the same hardware

🔧 Common Troubleshooting

1. Ngrok connection error

   • Make sure your authtoken is correct
   • Check the server's internet connection

2. Timeout while processing image

   • Increase timeout on the client:

     # In client.py, update timeout=60
     response = requests.post(..., timeout=60)
     

   • Reduce the input image size

3. Failed to load model

   • Check the model path on the server
   • Ensure the model is compatible with the Ultralytics version

4. GPU out of memory

   • Reduce batch size
   • Use a smaller model
   • Upgrade your Colab GPU (Pro/Premium)

🌐 System Architecture

graph LR
    A[Local PC] -->|Send image| B[OvercloudClient]
    B -->|HTTPS Request| C[ngrok Tunnel]
    C --> D[Cloud Server]
    D -->|GPU Processing| E[YOLO Model]
    E -->|Result| D
    D -->|Response| C
    C -->|Processed image| B
    B --> A

💡 Example Applications

1. GUI Client (using Tkinter)

import tkinter as tk
from tkinter import filedialog
from PIL import Image, ImageTk
import cv2
import numpy as np
from overlink import OvercloudClient

class OverlinkGUI(tk.Tk):
    def __init__(self):
        super().__init__()
        self.title("Overlink Client")
        self.geometry("1200x600")

        # Build user interface
        self.create_widgets()

        # Initialize client
        self.client = None

    def create_widgets(self):
        # URL input section
        url_frame = tk.Frame(self)
        url_frame.pack(fill=tk.X, padx=10, pady=10)

        tk.Label(url_frame, text="Server URL:").pack(side=tk.LEFT)
        self.url_entry = tk.Entry(url_frame, width=50)
        self.url_entry.pack(side=tk.LEFT, padx=5, fill=tk.X, expand=True)

        self.connect_btn = tk.Button(
            url_frame,
            text="Connect",
            command=self.connect_server
        )
        self.connect_btn.pack(side=tk.LEFT)

        # Image display sections
        img_frame = tk.Frame(self)
        img_frame.pack(fill=tk.BOTH, expand=True, padx=10, pady=10)

        # Original image
        self.orig_frame = tk.LabelFrame(img_frame, text="Original Image")
        self.orig_frame.pack(side=tk.LEFT, fill=tk.BOTH, expand=True, padx=5)
        self.orig_label = tk.Label(self.orig_frame)
        self.orig_label.pack(fill=tk.BOTH, expand=True, padx=5, pady=5)

        # Processed result image
        self.result_frame = tk.LabelFrame(img_frame, text="Result")
        self.result_frame.pack(side=tk.LEFT, fill=tk.BOTH, expand=True, padx=5)
        self.result_label = tk.Label(self.result_frame)
        self.result_label.pack(fill=tk.BOTH, expand=True, padx=5, pady=5)

        # Action buttons
        btn_frame = tk.Frame(self)
        btn_frame.pack(fill=tk.X, pady=10)

        self.select_btn = tk.Button(
            btn_frame,
            text="Choose Image",
            command=self.select_image,
            state=tk.DISABLED
        )
        self.select_btn.pack(side=tk.LEFT, padx=20)

        self.save_btn = tk.Button(
            btn_frame,
            text="Save Result",
            command=self.save_result,
            state=tk.DISABLED
        )
        self.save_btn.pack(side=tk.LEFT, padx=20)

        # Status bar
        self.status_var = tk.StringVar(value="Not connected")
        status_bar = tk.Label(self, textvariable=self.status_var, bd=1, relief=tk.SUNKEN, anchor=tk.W)
        status_bar.pack(side=tk.BOTTOM, fill=tk.X)

    def connect_server(self):
        server_url = self.url_entry.get().strip()
        if not server_url:
            self.status_var.set("❌ Please enter the server URL")
            return

        try:
            self.client = OvercloudClient(server_url)
            if self.client.ping():
                self.status_var.set(f"✅ Connected to: {server_url}")
                self.select_btn.config(state=tk.NORMAL)
            else:
                self.status_var.set(f"❌ Cannot connect to server")
        except Exception as e:
            self.status_var.set(f"Connection error: {str(e)}")

    def select_image(self):
        file_path = filedialog.askopenfilename(
            filetypes=[("Image files", "*.jpg *.jpeg *.png")]
        )
        if file_path:
            # Display original image
            self.display_image(file_path, self.orig_label)

            # Process the image
            self.process_image(file_path)

    def process_image(self, file_path):
        try:
            result = self.client.process_image(file_path)

            # Convert for display
            result_rgb = cv2.cvtColor(result, cv2.COLOR_BGR2RGB)
            self.result_image = Image.fromarray(result_rgb)
            self.display_result(self.result_image)

            self.status_var.set("✅ Successfully processed!")
            self.save_btn.config(state=tk.NORMAL)
        except Exception as e:
            self.status_var.set(f"❌ Processing error: {str(e)}")

    def display_image(self, path, label):
        img = Image.open(path)
        img.thumbnail((500, 500))
        photo = ImageTk.PhotoImage(img)
        label.config(image=photo)
        label.image = photo

    def display_result(self, img):
        img.thumbnail((500, 500))
        photo = ImageTk.PhotoImage(img)
        self.result_label.config(image=photo)
        self.result_label.image = photo

    def save_result(self):
        if hasattr(self, 'result_image'):
            file_path = filedialog.asksaveasfilename(
                defaultextension=".jpg",
                filetypes=[("JPEG files", "*.jpg"), ("All files", "*.*")]
            )
            if file_path:
                self.result_image.save(file_path)
                self.status_var.set(f"✅ Saved at: {file_path}")

if __name__ == "__main__":
    app = OverlinkGUI()
    app.mainloop()

2. Integrate into image processing pipeline

from overlink import OvercloudClient
import cv2
import time

class ImageProcessor:
    def __init__(self, use_cloud=False, cloud_url=None):
        self.use_cloud = use_cloud
        if use_cloud:
            self.client = OvercloudClient(cloud_url)
            assert self.client.ping(), "Cannot connect to cloud server"

    def process(self, image):
        if self.use_cloud:
            # Save a temp image to send to server
            temp_path = "temp_input.jpg"
            cv2.imwrite(temp_path, image)
            return self.client.process_image(temp_path)
        else:
            # Local processing
            return self.local_processing(image)

    def local_processing(self, image):
        # Your CPU local processing code here
        # ...
        return processed_image

# Usage
processor = ImageProcessor(
    use_cloud=True,
    cloud_url="https://your-ngrok-url"
)

cap = cv2.VideoCapture(0)  # Webcam

while True:
    ret, frame = cap.read()
    if not ret:
        break

    start = time.time()
    result = processor.process(frame)
    fps = 1 / (time.time() - start)

    cv2.putText(result, f"FPS: {fps:.1f}", (10, 30),
                cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
    cv2.imshow("Real-time Processing", result)

    if cv2.waitKey(1) == 27:  # ESC
        break

cap.release()
cv2.destroyAllWindows()

🤝 Contributions

Overlink is an open source project and welcomes all contributions! Get involved by:

1. Reporting bugs: Open an issue on GitHub
2. Suggesting features: Share your new ideas
3. Contributing code: Submit a pull request
4. Improving documentation: Help make the docs clearer

Detailed contribution guide:

# 1. Fork the repository
# 2. Clone your fork
git clone https://github.com/username/overlink.git

# 3. Create a new branch
git checkout -b feature/new-feature

# 4. Make changes
# 5. Commit and push
git push origin feature/new-feature

# 6. Open a pull request

📜 License

This project is distributed under the MIT License. See the LICENSE file for more details.

📞 Contact

• Author: KhanhRomVN
• Email: [e-mail]
• GitHub: https://github.com/KhanhRomVN
• Project: https://github.com/KhanhRomVN/overlink

🙏 Credits

Overlink relies on these awesome open source projects:

• Ultralytics YOLO - Object detection framework
• Flask - Web framework
• Pyngrok - Python wrapper for ngrok
• OpenCV - Image processing

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Overlink – The simple bridge between your local application and the power of cloud GPUs!