TensorRT
onnx-tensorrt
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| TensorRT | onnx-tensorrt | |
|---|---|---|
| 22 | 4 | |
| 9,031 | 2,745 | |
| 3.6% | 2.0% | |
| 5.0 | 4.1 | |
| 13 days ago | 12 days ago | |
| C++ | C++ | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
TensorRT
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Show HN: Ollama for Linux – Run LLMs on Linux with GPU Acceleration
- https://github.com/NVIDIA/TensorRT
TVM and other compiler-based approaches seem to really perform really well and make supporting different backends really easy. A good friend who's been in this space for a while told me llama.cpp is sort of a "hand crafted" version of what these compilers could output, which I think speaks to the craftmanship Georgi and the ggml team have put into llama.cpp, but also the opportunity to "compile" versions of llama.cpp for other model architectures or platforms.
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Nvidia Introduces TensorRT-LLM for Accelerating LLM Inference on H100/A100 GPUs
https://github.com/NVIDIA/TensorRT/issues/982
Maybe? Looks like tensorRT does work, but I couldn't find much.
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Train Your AI Model Once and Deploy on Any Cloud
highly optimized transformer-based encoder and decoder component, supported on pytorch, tensorflow and triton
TensorRT, custom ml framework/ inference runtime from nvidia, https://developer.nvidia.com/tensorrt, but you have to port your models
- A1111 just added support for TensorRT for webui as an extension!
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WIP - TensorRT accelerated stable diffusion img2img from mobile camera over webrtc + whisper speech to text. Interdimensional cable is here! Code: https://github.com/venetanji/videosd
It uses the nvidia demo code from: https://github.com/NVIDIA/TensorRT/tree/main/demo/Diffusion
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[P] Get 2x Faster Transcriptions with OpenAI Whisper Large on Kernl
The traditional way to deploy a model is to export it to Onnx, then to TensorRT plan format. Each step requires its own tooling, its own mental model, and may raise some issues. The most annoying thing is that you need Microsoft or Nvidia support to get the best performances, and sometimes model support takes time. For instance, T5, a model released in 2019, is not yet correctly supported on TensorRT, in particular K/V cache is missing (soon it will be according to TensorRT maintainers, but I wrote the very same thing almost 1 year ago and then 4 months ago so… I don’t know).
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Speeding up T5
I've tried to speed it up with TensorRT and followed this example: https://github.com/NVIDIA/TensorRT/blob/main/demo/HuggingFace/notebooks/t5.ipynb - it does give considerable speedup for batch-size=1 but it does not work with bigger batch sizes, which is useless as I can simply increase the batch-size of HuggingFace model.
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An open-source library for optimizing deep learning inference. (1) You select the target optimization, (2) nebullvm searches for the best optimization techniques for your model-hardware configuration, and then (3) serves an optimized model that runs much faster in inference
Open-source projects leveraged by nebullvm include OpenVINO, TensorRT, Intel Neural Compressor, SparseML and DeepSparse, Apache TVM, ONNX Runtime, TFlite and XLA. A huge thank you to the open-source community for developing and maintaining these amazing projects.
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I was looking for some great quantization open-source libraries that could actually be applied in production (both edge or cloud CPU/GPU). Do you know if I am missing any good libraries?
Nvidia Quantization | Quantization with TensorRT
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Can you run a quantized model om GPU?
You might want to try Nvidia's quantization toolkit for pytorch: https://github.com/NVIDIA/TensorRT/tree/main/tools/pytorch-quantization
onnx-tensorrt
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[P] [D]How to get TensorFlow model to run on Jetson Nano?
Conversion was done from Keras Tensorflow using to ONNX https://github.com/onnx/keras-onnx followed by ONNX to TensorRT using https://github.com/onnx/onnx-tensorrt The Python code used for inference using TensorRT can be found at https://github.com/jonnor/modeld/blob/tensorrt/tensorrtutils.py
What are some alternatives?
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
FasterTransformer - Transformer related optimization, including BERT, GPT
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
vllm - A high-throughput and memory-efficient inference and serving engine for LLMs
openvino - OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
stable-diffusion-webui - Stable Diffusion web UI
flash-attention - Fast and memory-efficient exact attention
jetson-inference - Hello AI World guide to deploying deep-learning inference networks and deep vision primitives with TensorRT and NVIDIA Jetson.
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
tensorrtx - Implementation of popular deep learning networks with TensorRT network definition API
llama.cpp - LLM inference in C/C++
whisper - Robust Speech Recognition via Large-Scale Weak Supervision