tflite-micro
Infrastructure to enable deployment of ML models to low-power resource-constrained embedded targets (including microcontrollers and digital signal processors). (by tensorflow)
sparseml
Libraries for applying sparsification recipes to neural networks with a few lines of code, enabling faster and smaller models (by neuralmagic)
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| tflite-micro | sparseml | |
|---|---|---|
| 2 | 12 | |
| 1,631 | 1,974 | |
| 5.0% | 2.5% | |
| 9.4 | 9.6 | |
| 6 days ago | 4 days ago | |
| C++ | 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.
tflite-micro
Posts with mentions or reviews of tflite-micro.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-07-26.
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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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Recognizing Speech with a Raspberry Pi
Love that project! On the microcontroller side, I also work on TensorFlow Lite Micro, which has some (very basic) examples of speech detection for tiny devices: https://github.com/tensorflow/tflite-micro/tree/main/tensorflow/lite/micro/examples/micro_speech
sparseml
Posts with mentions or reviews of sparseml.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-09-10.
- 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.
What are some alternatives?
When comparing tflite-micro and sparseml you can also consider the following projects:
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
deepsparse - Sparsity-aware deep learning inference runtime for CPUs
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
model-optimization - A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
sparsify - ML model optimization product to accelerate inference.
openvino - OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
LAVIS - LAVIS - A One-stop Library for Language-Vision Intelligence
nebuly - The user analytics platform for LLMs
pytorch2keras - PyTorch to Keras model convertor
neural-compressor - SOTA low-bit LLM quantization (INT8/FP8/INT4/FP4/NF4) & sparsity; leading model compression techniques on TensorFlow, PyTorch, and ONNX Runtime