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I will deploy and accelerate your pytorch ai models in cpp for edge devices
Yagiz Cem K.
About this gig
Python is for training. C++ is for production.
Are your PyTorch models running too slow in the real world? Python's overhead is fine for the lab, but edge devices and real-time applications demand bare-metal performance.
I convert your heavy PyTorch models into blazingly fast C++ inference engines. Ideal for autonomous systems, robotics, and real-time video processing where every millisecond matters. I eliminate Python bottlenecks by keeping the entire pipeline native.
Services:
- Model Conversion: Exporting PyTorch to ONNX, TorchScript, or TensorRT for optimized deployment.
- C++ Inference Engines: Building lightweight inference pipelines using LibTorch or ONNX Runtime.
- Vision Pipelines: Writing custom, memory-efficient OpenCV pre/post-processing in native C++.
- Edge Optimization: Maximizing hardware utilization for edge devices and embedded systems.
Please contact me before placing an order to discuss your project specifics and get a precise quote.
Looking forward to working with you and bringing your ideas to life!
Get to know Yagiz Cem K.
Yagiz Cem K.
Computer Vision, 3D Graphics, HPC Engineer
- FromTurkey
- Member sinceJun 2023
- Avg. response time1 hour
- Last delivery5 months
Languages
English, Turkish
Hi, I'm Yagiz. With a strong background in Mathematics and Computer Science, I specialize in Computer Vision, Graphics Programming, and High-Performance Computing (HPC). I engineer mathematically grounded, highly optimized systems.
Using C++, Python, OpenGL, and Vulkan, I build everything from custom rendering engines to robust Deep Learning workflows. I also optimize PyTorch models for blazing-fast C++ inference.
If your project requires low-level memory management, custom AI deployment, or complex algorithmic optimization, I provide strict technical solutions. Let's talk.
Other AI Development Services I Offer
FAQ
Can you integrate the C++ inference engine into my existing project?
Yes. I deliver the engine as a standalone executable, or as a dynamic library (.dll/.so) with a clean C++ API that you can plug directly into your existing codebase.
How do you handle image pre-processing (like resizing or normalization)?
I replicate your Python transformations (e.g., torchvision transforms) exactly in C++ using OpenCV or custom array operations. This ensures the C++ engine feeds the same tensor format to the model as your training script did, avoiding accuracy drops.
