yolo-tf2
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deepsparse
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yolo-tf2 | deepsparse | |
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1 | 21 | |
747 | 2,829 | |
- | 2.9% | |
7.6 | 9.6 | |
over 1 year ago | about 12 hours ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 or later |
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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.
yolo-tf2
We haven't tracked posts mentioning yolo-tf2 yet.
Tracking mentions began in Dec 2020.
deepsparse
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[D] How to get the fastest PyTorch inference and what is the "best" model serving framework?
For 1), what is the easiest way to speed up inference (assume only PyTorch and primarily GPU but also some CPU)? I have been using ONNX and Torchscript but there is a bit of a learning curve and sometimes it can be tricky to get the model to actually work. Is there anything else worth trying? I am enthused by things like TorchDynamo (although I have not tested it extensively) due to its apparent ease of use. I also saw the post yesterday about Kernl using (OpenAI) Triton kernels to speed up transformer models which also looks interesting. Are things like SageMaker Neo or NeuralMagic worth trying? My only reservation with some of these is they still seem to be pretty model/architecture specific. I am a little reluctant to put much time into these unless I know others have had some success first.
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[R] New sparsity research (oBERT) enabled 175X increase in CPU performance for MLPerf submission
Utilizing the oBERT research we published at Neural Magic and some further iteration, we’ve enabled an increase in NLP performance of 175X while retaining 99% accuracy on the question-answering task in MLPerf. A combination of distillation, layer dropping, quantization, and unstructured pruning with oBERT enabled these large performance gains through the DeepSparse Engine. All of our contributions and research are open-sourced or free to use. Read through the oBERT paper on arxiv, try out the research in SparseML, and dive into the writeup to learn more about how we achieved these impressive results and utilize them for your own use cases!
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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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[R] BERT-Large: Prune Once for DistilBERT Inference Performance
BERT-Large (345 million parameters) is now faster than the much smaller DistilBERT (66 million parameters) all while retaining the accuracy of the much larger BERT-Large model! We made this possible with Intel Labs by applying cutting-edge sparsification and quantization research from their Prune Once For All paper and utilizing it in the DeepSparse engine. It makes BERT-Large 12x smaller while delivering 8x latency speedup on commodity CPUs. We open-sourced the research in SparseML; run through the overview here and give it a try!
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[R] How well do sparse ImageNet models transfer? Prune once and deploy anywhere for inference performance speedups! (arxiv link in comments)
And benchmark/deploy with 8X better performance in DeepSparse!
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[P] SparseServer.UI : A UI to test performance of Sparse Transformers
💻code: https://github.com/neuralmagic/deepsparse/tree/main/examples/sparseserver-ui
Hi _Arsenie, this runs the deepsparse.server command for multiple models. and btw, we recently updated the READMEs for the Deepsparse Engine https://github.com/neuralmagic/deepsparse
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what is the easiest way to deploy a nlp model?
HTTP Server: you can try out the DeepSparse Server to serve ONNX models. https://github.com/neuralmagic/deepsparse/tree/main/src/deepsparse/server
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[P] Compound sparsification: using pruning, quantization, and layer dropping to improve BERT performance
Hi u/_Arsenie_Boca_, definitely. Our recipes and sparse models along with the SparseZoo Python API to download them are open-sourced and the SparseZoo UI that can be used to explore them is free to use. The SparseML codebase to apply recipes enabling the creation of the sparse models is open sourced. The Sparsify codebase to create recipes through a UI is as well. And finally, the DeepSparse Engine's backend is closed sourced but free to use.
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YOLOv5 on CPUs: Sparsifying to Achieve GPU-Level Performance
Disclosure: I work for Neural Magic.
Hi carbocation, we'd love to see what you think of the performance using the DeepSparse engine for CPU inference: https://github.com/neuralmagic/deepsparse
Take a look through our getting started pages that walk through performance benchmarking, training, and deployment for our featured models: https://sparsezoo.neuralmagic.com/getting-started
What are some alternatives?
NudeNet - Neural Nets for Nudity Detection and Censoring
yolov5 - YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
openvino - OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
model-optimization - A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.
Deep-SORT-YOLOv4 - People detection and optional tracking with Tensorflow backend.
sparseml - Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models
Real-time-Object-Detection-for-Autonomous-Driving-using-Deep-Learning - My Computer Vision project from my Computer Vision Course (Fall 2020) at Goethe University Frankfurt, Germany. Performance comparison between state-of-the-art Object Detection algorithms YOLO and Faster R-CNN based on the Berkeley DeepDrive (BDD100K) Dataset.
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
PINTO_model_zoo - A repository for storing models that have been inter-converted between various frameworks. Supported frameworks are TensorFlow, PyTorch, ONNX, OpenVINO, TFJS, TFTRT, TensorFlowLite (Float32/16/INT8), EdgeTPU, CoreML.
Beginner-Traffic-Light-Detection-OpenCV-YOLOv3 - This is a python program using YOLO and OpenCV to detect traffic lights. Works in The Netherlands, possibly other countries
sparsify - ML model optimization product to accelerate inference.
BERT-NER - Pytorch-Named-Entity-Recognition-with-BERT