This repository offers a robust solution for interacting with a model inference API using ZeroMQ (ZMQ). With support for popular machine learning frameworks such as PyTorch and TensorFlow, it provides versatility for various project requirements.

• ZeroMQ Integration: Seamlessly communicate with the model inference API using ZeroMQ, ensuring efficient and reliable message passing.

• Framework Support: Choose between PyTorch and TensorFlow for model inference, catering to diverse machine learning workflows.

• Asynchronous Communication: Opt for asynchronous communication to enhance responsiveness and handle multiple requests concurrently.

• Continuous Interaction: Utilize a while loop for continuous interaction with the API, ensuring smooth and uninterrupted data flow between the client and server.

1. Installation: Clone the repository and install the required dependencies using pip.

   git clone https://github.com/jamal-saeedi/zmq-inference-server-client-pytorch-tf
   cd zmq-inference-server-client-pytorch-tf
   pip install -r requirements.txt

2. Usage: Start by initializing the server and connecting the client to begin communicating with the model inference API.

   # Start the server
   python inference_server_torch.py or inference_server_tf.py
   
   # Connect the client
   python client_async.py or client_loop.py

3. Customization: Customize the code to fit your specific use case, such as integrating custom models or extending functionality as needed.

Contributions are welcome! Feel free to open issues for bug fixes, feature requests, or submit pull requests to enhance the repository's functionality.

This project is licensed under the MIT License

Feel free to adjust or expand upon this template based on your specific project requirements!