emlearn
yadm
emlearn | yadm | |
---|---|---|
5 | 82 | |
424 | 4,803 | |
15.1% | - | |
9.2 | 2.4 | |
9 days ago | 3 months ago | |
Python | Python | |
MIT License | GNU General Public License v3.0 only |
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
yadm
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Dotfiles: Unofficial Guide to Dotfiles on GitHub
I'm using yadm for some years now, which works really well:
https://github.com/TheLocehiliosan/yadm
- Yadm: Yet Another Dotfiles Manager
- YADM: Yet Another Dotfiles Manager
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Ask HN: What Underrated Open Source Project Deserves More Recognition?
Everyone hand-rolls their own dotfile management system, but YADM already does everything you need:
https://yadm.io/
- Yet Another Dotfiles Manager
- Tell HN: My Favorite Tools
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Dotfiles Matter
I've been working around this using tools built on top of git like [yadm](https://github.com/TheLocehiliosan/yadm) and relying on `ls-files` to list all my tracked dotfiles and their paths.
Still having everything in one place would make things much simpler. Great idea!
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System settings that aren’t in System Settings
I wonder if the program i use to manage my dotfiles could help manage your scripts and extend your setup to all your desktops? Its called yadm (https://yadm.io/) it makes it so easy to have a laptop and a desktop or two.
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The right way to keep config files synced across devices?
I really like that one but still prefer yadm because you can just edit your files as usual and then yadm add them wherever you are.
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Just got a new M2 Pro after my 2016 became outdated. What are your first steps to setting up a new computer?
If you haven’t already, this is the time to install a tool like yadm and get your computer configuration into version control. Your command-line tools can be managed by yadm directly, your system settings can mostly be managed with a yadm bootstrap script that runs things like defaults write, and the software you install can be managed with a Brewfile that the yadm bootstrap script uses to install software with Homebrew. Don’t manually download Xcode, use xcodes to do it.
What are some alternatives?
miceforest - Multiple Imputation with LightGBM in Python
GNU Stow - GNU Stow - mirror of savannah git repository occasionally with more bleeding-edge branches
cppflow - Run TensorFlow models in C++ without installation and without Bazel
chezmoi - Manage your dotfiles across multiple diverse machines, securely.
fselect - Find files with SQL-like queries
Home Manager using Nix - Manage a user environment using Nix [maintainer=@rycee]
pico-wake-word - MicroSpeech Wake Word example on the Raspberry Pi Pico. This is a port of the example on the TensorFlow repository.
dotbot - A tool that bootstraps your dotfiles ⚡️
sklearn-project-template - Machine learning template for projects based on sklearn library.
homesick - Your home directory is your castle. Don't leave your dotfiles behind.
experta - Expert Systems for Python
Ansible - Ansible is a radically simple IT automation platform that makes your applications and systems easier to deploy and maintain. Automate everything from code deployment to network configuration to cloud management, in a language that approaches plain English, using SSH, with no agents to install on remote systems. https://docs.ansible.com.