sparseml
pytorch2keras
sparseml | pytorch2keras | |
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12 | 2 | |
1,988 | 846 | |
1.6% | - | |
9.6 | 0.0 | |
6 days ago | over 1 year ago | |
Python | Python | |
Apache License 2.0 | MIT License |
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.
sparseml
- Can You Achieve GPU Performance When Running CNNs on a CPU?
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[D] DeepSparse: 1,000X CPU Performance Boost & 92% Power Reduction with Sparsified Models in MLPerf™ Inference v3.0
SparseML is opensource https://github.com/neuralmagic/sparseml
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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)
All models and code are open-sourced, try it out with the walk-through in SparseML.
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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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Tutorial: Prune and quantize YOLOv5 for 12x smaller size and 10x better performance on CPUs
Hi mikedotonline, we haven't focused on any datasets specifically for natural/forest environments. If you have any in mind, we could do some quick transfer learning runs to see how these models perform on them! Also if you wanted to try them out, we have a tutorial pushed up that walks through transfer learning the sparse architectures to new data: https://github.com/neuralmagic/sparseml/blob/main/integrations/ultralytics-yolov5/tutorials/yolov5_sparse_transfer_learning.md
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Tutorial: Real-time YOLOv3 on a Laptop Using Sparse Quantization
Apply the sparse-quantized results to your dataset by following the YOLOv3 tutorial. All software is open source or freely available.
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Pruning and Quantizing Ultralytics YOLOv3
We’ve noticed YOLOv3 runs pretty slowly on CPUs restricting its use for real-time requests. Given that, we looked into combining pruning and quantization using the Ultralytics YOLOv3 model, and the results turned out well, over 5X faster over a dense FP32 baseline! We open sourced the integration and models on GitHub for anyone to play around with; if you’re interested, please check it out and give us feedback.
pytorch2keras
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Help Needed: Converting PlantNet-300k Pretrained Model Weights from Tar to h5 Format Help
It's almost certainly a pickled pytorch model so you will first need to load it using pytorch and then write it out to h5 (legacy keras format) with https://github.com/gmalivenko/pytorch2keras.
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Is Pytorch good deployment wise?
But of course, you can always train a model with one framework and run inference with another. For instance, pytorch2keras does exactly this.
What are some alternatives?
deepsparse - Sparsity-aware deep learning inference runtime for CPUs
NudeNet - Neural Nets for Nudity Detection and Censoring
model-optimization - A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.
InvoiceNet - Deep neural network to extract intelligent information from invoice documents.
sparsify - ML model optimization product to accelerate inference.
facenet-pytorch - Pretrained Pytorch face detection (MTCNN) and facial recognition (InceptionResnet) models
LAVIS - LAVIS - A One-stop Library for Language-Vision Intelligence
efficientnet-lite-keras - Keras reimplementation of EfficientNet Lite.
tflite-micro - Infrastructure to enable deployment of ML models to low-power resource-constrained embedded targets (including microcontrollers and digital signal processors).
onnx-tensorflow - Tensorflow Backend for ONNX
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
cnn-watermark-removal - Fully convolutional deep neural network to remove transparent overlays from images