TensorRT
onnx-simplifier
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| TensorRT | onnx-simplifier | |
|---|---|---|
| 5 | 3 | |
| 2,328 | 3,546 | |
| 3.2% | - | |
| 9.6 | 7.1 | |
| 5 days ago | 16 days ago | |
| Python | C++ | |
| BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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TensorRT
- Learn TensorRT optimization
- I made TensorRT example. I hope this will help beginners. And I also have a question about TensorRT best practice.
- [P] [D] I made TensorRT example. I hope this will help beginners. And I also have a question about TensorRT best practice.
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[P] 4.5 times faster Hugging Face transformer inference by modifying some Python AST
Have you tried the new Torch-TensorRT compiler from NVIDIA?
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PyTorch 1.10
You can quantize your model to FP16 or Int8 using PTQ as well and it should give you an additional speed up inference wise.
Here is a tutorial[2] to leverage TRTorch.
[1] https://github.com/NVIDIA/TRTorch/tree/master/core
onnx-simplifier
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Show: Cross-platform Image segmentation on video using eGUI, onnxruntime and ffmpeg
onnx-simplifier can shed some of incompatibilities in widespread use, but is itself bug ridden and lagging behind the standard. For any serious model, or when you don't get lucky simplifying the model upstream, you'd generally want good support of opset 11.
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[Technical Article] OCR Upgrade
ONNX Simplifier：https://github.com/daquexian/onnx-simplifier
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PyTorch 1.10
As far as I know, the ONNX format won't give you a performance boost on its own. However, there are ONNX optimizers for the ONNX runtime which will speed up your inference.
But if you are using Nvidia Hardware, then TensorRT should give you the best performance possible, especially if you change the precision level. Don't forget to simplify your ONNX model before you converting it to TensorRT though: https://github.com/daquexian/onnx-simplifier
What are some alternatives?
torch2trt - An easy to use PyTorch to TensorRT converter
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
cutlass - CUDA Templates for Linear Algebra Subroutines
PaddleOCR - Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
functorch - functorch is JAX-like composable function transforms for PyTorch.
transformer-deploy - Efficient, scalable and enterprise-grade CPU/GPU inference server for 🤗 Hugging Face transformer models 🚀
nn - 🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
jetson - Self-driving AI toy car 🤖🚗.
ncnn - ncnn is a high-performance neural network inference framework optimized for the mobile platform