emlearn
gazpacho
| emlearn | gazpacho | |
|---|---|---|
| 5 | 1 | |
| 378 | 730 | |
| 4.8% | - | |
| 9.2 | 3.2 | |
| 27 days ago | 5 months ago | |
| Python | 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.
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
gazpacho
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Ask HN: What are some tools / libraries you built yourself?
I've been working on gazpacho [1] for last two years.
It's a general purpose web scraping library for Python that replaces BeautifulSoup + requests for most projects.
Just surpassed ~2K downloads every week!
[1] https://github.com/maxhumber/gazpacho
What are some alternatives?
miceforest - Multiple Imputation with LightGBM in Python
selectolax - Python binding to Modest and Lexbor engines (fast HTML5 parser with CSS selectors).
cppflow - Run TensorFlow models in C++ without installation and without Bazel
lxml - The lxml XML toolkit for Python
fselect - Find files with SQL-like queries
html5lib - Standards-compliant library for parsing and serializing HTML documents and fragments in Python
pico-wake-word - MicroSpeech Wake Word example on the Raspberry Pi Pico. This is a port of the example on the TensorFlow repository.
xmltodict - Python module that makes working with XML feel like you are working with JSON
sklearn-project-template - Machine learning template for projects based on sklearn library.
xhtml2pdf - A library for converting HTML into PDFs using ReportLab
experta - Expert Systems for Python
untangle - Converts XML to Python objects