sentinel2-cloud-detector
torch2trt
| sentinel2-cloud-detector | torch2trt | |
|---|---|---|
| 3 | 5 | |
| 400 | 4,403 | |
| 1.5% | 1.2% | |
| 5.9 | 7.6 | |
| 4 months ago | 7 days ago | |
| Python | Python | |
| Creative Commons Attribution Share Alike 4.0 | MIT License |
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sentinel2-cloud-detector
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Earth 2020 in 3 seconds, extracted from over 3 petabytes of satellite data | [OC]
You can find more info in this Jupyter notebook example, the first part is downloading the data (for this you need the account), but if you start later on with the assumption that you bring the data yourself. You can open the ticket if you run into any issues :)
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.
What are some alternatives?
labelme - Image Polygonal Annotation with Python (polygon, rectangle, circle, line, point and image-level flag annotation).
TensorRT - PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT
biodivMapR - biodivMapR: an R package for α- and β-diversity mapping using remotely-sensed images
onnx-simplifier - Simplify your onnx model
orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
eoreader - Remote-sensing opensource python library reading optical and SAR sensors, loading and stacking bands, clouds, DEM and spectral indices in a sensor-agnostic way.
transformer-deploy - Efficient, scalable and enterprise-grade CPU/GPU inference server for 🤗 Hugging Face transformer models 🚀
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
tensorrt_demos - TensorRT MODNet, YOLOv4, YOLOv3, SSD, MTCNN, and GoogLeNet
trt_pose - Real-time pose estimation accelerated with NVIDIA TensorRT
vision - Datasets, Transforms and Models specific to Computer Vision