cppflow
emlearn
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cppflow | emlearn | |
---|---|---|
9 | 5 | |
761 | 374 | |
- | 7.2% | |
0.0 | 9.2 | |
11 months ago | 23 days ago | |
C++ | Python | |
MIT License | MIT License |
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.
cppflow
- Easily run TensorFlow models from C++
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[P] libtensorflow_cc: Pre-built TensorFlow C++ API
It’s been awhile since I’ve looked at it, so not sure how hard it would be to get to work. I only commented since you mentioned that you would support other operating systems. For others interested in cross platform support there is also cppflow.
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Deep learning classification with C++
what about start with keras and convert model to c++ ? https://github.com/pplonski/keras2cpp https://github.com/serizba/cppflow
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Using embedding model in C++ app
My solution so far: I am using a compiled Tensorflow C DLL in combination with cppflow (https://github.com/serizba/cppflow). However, I get problems when I take models which use operations from the tensorflow_text python module since I don’t know how to get their C++ API.
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What is the most used library for AI in C++ ?
I use cppflow to run compiled tensorflow models natively in C++. It works like a charm :)
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[Python] Importing a TensorFlow AI?
I toyed around with this idea a while back but I never got around to finishing the implementation. If all you need is inference with no training and you are relatively familiar with c++ you could look into creating a module for Godot that interfaces with the Tensorflow C API. Something like cppflow would provide an even easier API to work with. Looking into that project could also explain how they interface with the Tensorflow C API if you'd rather cut out the middle man. A module like this would let you train your model in Python and then load it and perform inference in Godot natively.
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Simplest way to deploy Keras NN model into C++?
If your re using keras with TensorFlow you can save it as a saved model format and then you can easily use cppflow to perform inference with it.
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I trained a Neural Network to understand my commands when playing my game
The whole game is written in C++ using SFML for the graphics, entt as Entity-Component-System and tensorflow for the Neural Network. Tensorflow itself is written in C, so I use cppflow to integrate it into my C++ framework.
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TF-agent with C/C++ environment
Found this which seems more recent (uses TF 2, updated 4 days ago): https://github.com/serizba/cppflow
emlearn
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EleutherAI announces it has become a non-profit
> My big gripe, and for obvious reasons, is that we need to step away from cloud-based inference, and it doesn't seem like anyone's working on that.
I think there are steps being taken in this direction (check out [1] and [2] for interesting lightweight transpile / ad-hoc training projects) but there is a lack of centralized community for these constrained problems.
[1] https://github.com/emlearn/emlearn
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Simple and embedded friendly C code for Machine Learning inference algorithms
Examples: Gaussian Mixture Models (GMM) for anomaly detection or clustering Mahalanobis distance (EllipticEnvelope) for anomaly detection Decision trees and tree ensembles (Random Forest, ExtraTrees) Feed-forward Neural Networks (Multilayer Perceptron, MLP) for classification Gaussian Naive Bayes for classification
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[D] Drop your best open source Deep learning related Project
https://github.com/emlearn/emlearn is a ML inference engine for microcontrollers and embedded systems, allowing to deploy models to any platform with a C99 compiler. Has also been used for network traffic analysis as a Linux kernel module, and embedded in Android apps.
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Regression with the C64
The C64 has 64 kB of RAM. That is more than many contemporary microcontrollers. Using something like https://github.com/emlearn/emlearn allows to generate portable C code of ML models for such targets. Should be able to classify digits (MNIST) no problem on such hardware. Assuming there is a workable C compiler available.
Disclosure: Maintainer of emlearn project.
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Ask HN: What are some tools / libraries you built yourself?
I built emlearn, a Machine Learning inference engine for microcontrollers and embedded systems. It allows converting traditional ML models to simple and portable C99, following best practices in embedded software (no dynamic allocations etc). https://github.com/emlearn/emlearn
What are some alternatives?
examples - TensorFlow examples
miceforest - Multiple Imputation with LightGBM in Python
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
fselect - Find files with SQL-like queries
qt-tf-lite-example - Qt TensorFlow Lite example
pico-wake-word - MicroSpeech Wake Word example on the Raspberry Pi Pico. This is a port of the example on the TensorFlow repository.
keras2cpp - This is a bunch of code to port Keras neural network model into pure C++.
sklearn-project-template - Machine learning template for projects based on sklearn library.
ssd_keras - A Keras port of Single Shot MultiBox Detector
experta - Expert Systems for Python
DeepSpeech - DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers.
gutenberg - A fast static site generator in a single binary with everything built-in. https://www.getzola.org