transformer-deploy
Efficient, scalable and enterprise-grade CPU/GPU inference server for 🤗 Hugging Face transformer models 🚀 (by ELS-RD)
mmrazor
OpenMMLab Model Compression Toolbox and Benchmark. (by open-mmlab)
Our great sponsors
| transformer-deploy | mmrazor | |
|---|---|---|
| 8 | 4 | |
| 1,614 | 1,352 | |
| 0.7% | 2.9% | |
| 6.8 | 2.8 | |
| 6 months ago | 6 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
transformer-deploy
Posts with mentions or reviews of transformer-deploy.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-10-28.
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[D] How to get the fastest PyTorch inference and what is the "best" model serving framework?
For 2), I am aware of a few options. Triton inference server is an obvious one as is the ‘transformer-deploy’ version from LDS. My only reservation here is that they require the model compilation or are architecture specific. I am aware of others like Bento, Ray serving and TorchServe. Ideally I would have something that allows any (PyTorch model) to be used without the extra compilation effort (or at least optionally) and has some convenience things like ease of use, easy to deploy, easy to host multiple models and can perform some dynamic batching. Anyway, I am really interested to hear people's experience here as I know there are now quite a few options! Any help is appreciated! Disclaimer - I have no affiliation or are connected in any way with the libraries or companies listed here. These are just the ones I know of. Thanks in advance.
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[P] Up to 12X faster GPU inference on Bert, T5 and other transformers with OpenAI Triton kernels
We work for Lefebvre Sarrut, a leading European legal publisher. Several of our products include transformer models in latency sensitive scenarios (search, content recommendation). So far, ONNX Runtime and TensorRT served us well, and we learned interesting patterns along the way that we shared with the community through an open-source library called transformer-deploy. However, recent changes in our environment made our needs evolve:
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Convert Pegasus model to ONNX [Discussion]
here you will find a notebook for T5 on GPU with some tricks to make it fast: https://github.com/ELS-RD/transformer-deploy/blob/main/demo/generative-model/t5.ipynb
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[P] What we learned by benchmarking TorchDynamo (PyTorch team), ONNX Runtime and TensorRT on transformers model (inference)
Check the notebook https://github.com/ELS-RD/transformer-deploy/blob/main/demo/TorchDynamo/benchmark.ipynb for detailed results, but what we will keep in mind:
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[P] What we learned by making T5-large 2X faster than Pytorch (and any autoregressive transformer)
notebook: https://github.com/ELS-RD/transformer-deploy/blob/main/demo/generative-model/t5.ipynb (Onnx Runtime only)
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[P] 4.5 times faster Hugging Face transformer inference by modifying some Python AST
Regarding CPU inference, quantization is very easy, and supported by Transformer-deploy , however performance on transformer are very low outside corner cases (like no batch, very short sequence and distilled model), and last Intel generation CPU based instance like C6 or M6 on AWS are quite expensive compared to a cheap GPU like Nvidia T4, to say it otherwise, on transformer, until you are ok with slow inference and takes a small instance (for a PoC for instance), CPU inference is probably not a good idea.
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[P] First ever tuto to perform *GPU* quantization on 🤗 Hugging Face transformer models -> 2X faster inference
The end to end tutorial: https://github.com/ELS-RD/transformer-deploy/blob/main/demo/quantization_end_to_end.ipynb
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[P] Python library to optimize Hugging Face transformer for inference: < 0.5 ms latency / 2850 infer/sec
Want to try it 👉 https://github.com/ELS-RD/transformer-deploy
mmrazor
Posts with mentions or reviews of mmrazor.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-11-21.
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MMDeploy: Deploy All the Algorithms of OpenMMLab
MMRazor: OpenMMLab model compression toolbox and benchmark.
- Still worrying about model compression? MMRazor may work for you.
- Still worrying about model compression? MMRazor is all you need
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[P] 4.5 times faster Hugging Face transformer inference by modifying some Python AST
https://github.com/open-mmlab/mmrazor ,it may work for you~
What are some alternatives?
When comparing transformer-deploy and mmrazor you can also consider the following projects:
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
neural-compressor - SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime
FasterTransformer - Transformer related optimization, including BERT, GPT
mmaction2 - OpenMMLab's Next Generation Video Understanding Toolbox and Benchmark
torch2trt - An easy to use PyTorch to TensorRT converter
Pointnet_Pointnet2_pytorch - PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
TensorRT - PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT
PaddleViT - :robot: PaddleViT: State-of-the-art Visual Transformer and MLP Models for PaddlePaddle 2.0+
OpenSeeFace - Robust realtime face and facial landmark tracking on CPU with Unity integration
sparsednn - Fast sparse deep learning on CPUs
mmrotate - OpenMMLab Rotated Object Detection Toolbox and Benchmark
transformer-deploy vs TensorRT
mmrazor vs neural-compressor
transformer-deploy vs FasterTransformer
mmrazor vs mmaction2
transformer-deploy vs torch2trt
mmrazor vs Pointnet_Pointnet2_pytorch
transformer-deploy vs TensorRT
mmrazor vs PaddleViT
transformer-deploy vs OpenSeeFace
mmrazor vs sparsednn
transformer-deploy vs sparsednn
mmrazor vs mmrotate