If you are deploying to CPUs (and let's be honest, 90% of inference still happens on CPUs), you are leaving performance on the table by not using DLDT.
Take your slowest production model, run it through the Model Optimizer, and benchmark the result. You will be shocked. Have you used OpenVINO or the Intel DLDT in production? Let me know your latency improvements in the comments below! intel deep learning deployment toolkit
What if I told you that your existing Intel Xeon CPUs (or even your Core i5 laptop) are hiding a massive amount of untapped performance? The secret isn't buying new hardware; it's using the . If you are deploying to CPUs (and let's
Stop wrestling with framework dependencies. Start deploying optimized models at the edge. If you have ever trained a beautiful model in PyTorch or TensorFlow only to watch it crawl across the finish line on a production CPU, you know the pain. We’ve all been there: high latency, bloated memory usage, and the sinking feeling that you need to buy expensive GPUs just to serve inference. Have you used OpenVINO or the Intel DLDT in production
pip install openvino Assume you have an ONNX export of your PyTorch model:
mo --input_model my_model.onnx --output_dir ./optimized_model Here is a Python snippet to run your newly minted IR model:
If you are deploying to CPUs (and let's be honest, 90% of inference still happens on CPUs), you are leaving performance on the table by not using DLDT.
Take your slowest production model, run it through the Model Optimizer, and benchmark the result. You will be shocked. Have you used OpenVINO or the Intel DLDT in production? Let me know your latency improvements in the comments below!
What if I told you that your existing Intel Xeon CPUs (or even your Core i5 laptop) are hiding a massive amount of untapped performance? The secret isn't buying new hardware; it's using the .
Stop wrestling with framework dependencies. Start deploying optimized models at the edge. If you have ever trained a beautiful model in PyTorch or TensorFlow only to watch it crawl across the finish line on a production CPU, you know the pain. We’ve all been there: high latency, bloated memory usage, and the sinking feeling that you need to buy expensive GPUs just to serve inference.
pip install openvino Assume you have an ONNX export of your PyTorch model:
mo --input_model my_model.onnx --output_dir ./optimized_model Here is a Python snippet to run your newly minted IR model: