Imagine your Edge AI application running on a heterogeneous compute device with just a few clicks. It requires a platform that abstracts the complexity of the hardware and seamlessly ports a Python ML pipeline into hardware-optimized code.
This is the rationale behind the creation of the Intelligent Pipeline Generator (IPG)—a GenAI-based workflow that takes a complex ML Python implementation as input and translates it into a hardware-optimized GStreamer pipeline. Whether you're working with computer vision, object detection, or classification models, our tool ensures a smooth experience from development to deployment.
Feature Highlights
Available as Debian Package. The Intelligent Pipeline Generator is now available as a .deb package installation. This ensures a secure, reliable setup process to get you started in minutes. The Debian package installation is a mark of quality, indicating our tool has been thoroughly tested for Linux systems, providing users with a stable and efficient development environment.
Intuitive Project Management. Once logged in, create and manage projects tailored to your deployment needs. Our sleek interface organizes your models, trace data, and generated pipelines in an intuitive workspace where progress is automatically saved. Whether you're debugging a complex model deployment or validating tensor operations, you can seamlessly pick up where you left off.
Intelligent Code Tracing. The heart of our system lies in its sophisticated tracing technology. The Tracer automatically captures your Python code execution, understanding every transformation and operation your model performs. This detailed insight eliminates the guesswork from pipeline generation, ensuring that the hardware-optimized implementation of the pipeline matches the input Python implementation without requiring the user to manually understand the underlying code.
Visual Pipeline Architecture. Watch your Python implementation transform into a structured pipeline architecture. Our tool provides a detailed, auto-generated pipeline architecture document with a visual representation and a concise description of your model’s data flow, from preprocessing through inference to output handling. This visual representation enables the user to understand the tool’s interpretation of the task, identify potential bottlenecks, and explore opportunities for further optimization using manual input before generating the optimized code.
One-Click Pipeline Generation. Transform your validated architecture into a production-ready NNStreamer pipeline with a single click. Our tool handles the complexity of pipeline creation while maintaining the exact specifications of your model's requirements. The generated pipeline is ready for immediate testing and deployment.
PipeFix Debugger. Deploy with confidence using our advanced debugger. PipeFix automates two types of debugging:
- Functional Debugging: Ensures that each component in the pipeline is configured correctly. This includes proper parameter configuration, compatibility across software plugins, and the validity of the end-to-end pipeline.
- Accuracy Debugging: Enables users to compare output tensors between the Python implementation and the generated pipeline at each plugin level, allowing for quick identification of plugins that cause degradation in output accuracy.
Built-in Terminal Integration. Access a built-in terminal for easy and direct interaction with your code and pipeline within the IPG user interface, eliminating the need to switch between multiple windows during development. This seamless integration allows for quick testing and validation without switching between different tools or environments.
A Glimpse into the Future
Extended Framework Support. While currently optimized for NNStreamer, we expect that devices natively supporting NNStreamer, such as NXP i.MX processors, will be able to run the generated pipelines directly. We are expanding support for many more devices, including NVIDIA DeepStream and Xilinx VITIS-AI/VVAS frameworks. This expansion will extend automation in deployment to a wide range of heterogeneous edge compute devices, enabling users to rapidly switch between devices and test the same pipeline.
Enhanced Validation Features. Future releases will include advanced tensor analysis tools, automated performance optimization suggestions, and expanded hardware compatibility validation.
Comprehensive Pipeline Management. Upcoming features will include pipeline version control, allowing you to track changes and maintain multiple pipeline configurations for different deployment scenarios.
Why Choose the Intelligent Pipeline Generator?
Building the future of Edge AI development requires tools that understand the complexities of hardware, software frameworks, and production environments. The Intelligent Pipeline Generator bridges this gap by using our patented technologies at the development stage, combined with our decades of experience in developing solutions; enabling developers to develop and deploy their ML pipelines onto state-of-the-art Machine Learning accelerators and systems. Every feature, from intelligent tracing to pipefix, is designed to make your deployment process more reliable and efficient.
Ready to transform your Edge AI deployment workflow? Join our Early Access Program today and be among the first to experience new features, influence our development roadmap, and become part of our growing developer community. Don't miss this opportunity to shape the future of Edge AI deployment debugging.
Sign up for Early Access Program and get:
- Exclusive access to beta features
- Direct input into our product roadmap
- Priority support from our development team
- Access to our developer community
- Early insights into upcoming capabilities
Secure your spot in our Early Access Program today!
