torch2trt
tensorrt_demos
| torch2trt | tensorrt_demos | |
|---|---|---|
| 5 | 5 | |
| 4,395 | 1,720 | |
| 1.0% | - | |
| 3.1 | 3.1 | |
| 5 days ago | about 1 year ago | |
| Python | Python | |
| MIT License | MIT License |
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torch2trt
- [D] How you deploy your ML model?
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PyTorch 1.10
Main thing you want for server inference is auto batching. It's a feature that's included in onnxruntime, torchserve, nvidia triton inference server and ray serve.
If you have a lot of preprocessing and post logic in your model it can be hard to export it for onnxruntime or triton so I usually recommend starting with Ray Serve (https://docs.ray.io/en/latest/serve/index.html) and using an actor that runs inference with a quantized model or optimized with tensorrt (https://github.com/NVIDIA-AI-IOT/torch2trt)
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Jetson Nano: TensorFlow model. Possibly I should use PyTorch instead?
https://github.com/NVIDIA-AI-IOT/torch2trt <- pretty straightforward https://github.com/jkjung-avt/tensorrt_demos <- this helped me a lot
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How to get TensorFlow model to run on Jetson Nano?
I find Pytorch easier to work with generally. Nvidia has a Pytorch --> TensorRT converter which yields some significant speedups and has a simple Python API. Convert the Pytorch model on the Nano.
tensorrt_demos
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lowering size of YOLOV4 detection model
tensorrt_demo github repository
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Jetson Nano: TensorFlow model. Possibly I should use PyTorch instead?
https://github.com/NVIDIA-AI-IOT/torch2trt <- pretty straightforward https://github.com/jkjung-avt/tensorrt_demos <- this helped me a lot
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PyTorch 1.8 release with AMD ROCm support
> I'll also add a caveat that toolage for Jetson boards is extremely incomplete.
A hundred times this. I was about to write another rant here but I already did that[0] a while ago, so I'll save my breath this time. :)
Another fun fact regarding toolage: Today I discovered that many USB cameras work poorly on Jetsons (at least when using OpenCV), probably due to different drivers and/or the fact that OpenCV doesn't support ARM64 as well as it does x86_64. :(
> They supply you with a bunch of sorely outdated models for TensorRT like Inceptionv3 and SSD-MobileNetv2 and VGG-16.
They supply you with such models? That's news to me. AFAIK converting something like SSD-MobileNetv2 from TensorFlow to TensorRT still requires substantial manual work and magic, as this code[1] attests to. There are countless (countless!) posts on the Nvidia forums by people complaining that they're not able to convert their models.
[0]: https://news.ycombinator.com/item?id=26004235
[1]: https://github.com/jkjung-avt/tensorrt_demos/blob/master/ssd... (In fact, this is the only piece of code I've found on the entire internet that managed to successfully convert my SSD-MobileNetV2.)
- I'm tired of this anti-Wayland horseshit
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H.264 hardware acceleration for surveillance station performance
It was some work getting compiled on nano but I used this guy's work to get started. https://jkjung-avt.github.io/tensorrt-yolov4/ and https://github.com/jkjung-avt/tensorrt_demos
What are some alternatives?
TensorRT - PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT
YOLOX - YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. Documentation: https://yolox.readthedocs.io/
onnx-simplifier - Simplify your onnx model
yolov4-custom-functions - A Wide Range of Custom Functions for YOLOv4, YOLOv4-tiny, YOLOv3, and YOLOv3-tiny Implemented in TensorFlow, TFLite, and TensorRT.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
tensorflow-yolov4-tflite - YOLOv4, YOLOv4-tiny, YOLOv3, YOLOv3-tiny Implemented in Tensorflow 2.3.1, Android. Convert YOLO v4 .weights tensorflow, tensorrt and tflite
transformer-deploy - Efficient, scalable and enterprise-grade CPU/GPU inference server for 🤗 Hugging Face transformer models 🚀
jetson-inference - Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
wayvnc - A VNC server for wlroots based Wayland compositors
trt_pose - Real-time pose estimation accelerated with NVIDIA TensorRT