nn
onnx-simplifier
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| nn | onnx-simplifier | |
|---|---|---|
| 26 | 3 | |
| 48,004 | 3,546 | |
| 8.5% | - | |
| 7.7 | 7.1 | |
| about 1 month ago | 16 days ago | |
| Jupyter Notebook | C++ | |
| MIT License | Apache License 2.0 |
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nn
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Can't remember name of website that has explanations side-by-side with code
Hey are you talking about https://nn.labml.ai/ ?
- [D] Recent ML papers to implement from scratch
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[P] GPT-NeoX inference with LLM.int8() on 24GB GPU
Implementation & LM Eval Harness Results
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[P] Fine-tuned the GPT-Neox Model to Generate Quotes
Github: https://github.com/labmlai/annotated_deep_learning_paper_implementations/tree/master/labml_nn/neox
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Best resources to learn recent transformer papers and stay updated [D]
Regarding implementations this helps me: https://nn.labml.ai/
- Introductory papers to implement
- How to convert research papers to code?
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[D] How to convert papers to code?
Dunno if this is directly helpful, but this website has implementation with the math side by side https://nn.labml.ai/
- [D] Looking for open source projects to contribute
- Resource for papers explanation
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?
GFPGAN-for-Video-SR - A colab notebook for video super resolution using GFPGAN
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
labml - 🔎 Monitor deep learning model training and hardware usage from your mobile phone 📱
torch2trt - An easy to use PyTorch to TensorRT converter
functorch - functorch is JAX-like composable function transforms for PyTorch.
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)
ZoeDepth - Metric depth estimation from a single image
Basic-UI-for-GPT-J-6B-with-low-vram - A repository to run gpt-j-6b on low vram machines (4.2 gb minimum vram for 2000 token context, 3.5 gb for 1000 token context). Model loading takes 12gb free ram.
TensorRT - PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT
Behavior-Sequence-Transformer-Pytorch - This is a pytorch implementation for the BST model from Alibaba https://arxiv.org/pdf/1905.06874.pdf
ncnn - ncnn is a high-performance neural network inference framework optimized for the mobile platform