ncurses-rs
tract
Our great sponsors
| ncurses-rs | tract | |
|---|---|---|
| 4 | 20 | |
| 663 | 2,053 | |
| - | 2.9% | |
| 0.0 | 9.8 | |
| almost 2 years ago | about 20 hours ago | |
| Rust | Rust | |
| GNU General Public License v3.0 or later | Apache 2.0/MIT |
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.
ncurses-rs
-
Is there any way to host an executable/application REMOTELY that anyone with a terminal can run/interact with without any installation? Currently, I'm limited to CURLable web scripts, but those are tedious and don't have interactable menus or animations or anything.
Which language do you use? Here is a Rust implementation of an SSH server. It handles opening the port to listen, creating the client connection, and retrieving data from the client (using getch). Then you can funnel that to an NCurses libraryand send the output back to the user
-
Hey Rustaceans! Got an easy question? Ask here (39/2021)!
Maybe this https://crates.io/crates/ncurses can help.
- Terminal application development
-
Hey Rustaceans! Got an easy question? Ask here (13/2021)!
There is a Dependents tab on each crate. Dependents of ncurses: https://crates.io/crates/ncurses/reverse_dependencies
tract
-
Are there any ML crates that would compile to WASM?
Tract is the most well known ML crate in Rust, which I believe can compile to WASM - https://github.com/sonos/tract/. Burn may also be useful - https://github.com/burn-rs/burn.
-
[Discussion] What crates would you like to see?
tract!!
-
tract VS burn - a user suggested alternative
2 projects | 25 Mar 2023
-
Machine Learning Inference Server in Rust?
we use tract for inference, integrated into our runtime and services.
- onnxruntime
- Rust Native ML Frameworks?
-
Neural networks - what crates to use?
Not for training, but for inference this looks nice: https://github.com/sonos/tract
-
Brain.js: GPU Accelerated Neural Networks in JavaScript
There's also tract, from sonos[0]. 100% rust.
I'm currently trying to use it to do speech recognition with a variant of the Conformer architecture (exported to ONNX).
The final goal is to do it in WASM client-side.
[0] https://github.com/sonos/tract
-
Serving ML at the Speed of Rust
As the article notes, there isn't any official Rust-native support for any common frameworks.
tract (https://github.com/sonos/tract) seems like the most mature for ONNX (for which TF/PT export is good nowadays), and recently it successfully implemented BERT.
-
Run deep neural network models from scratch
There are some DL libraries written in Rust: https://github.com/sonos/tract , https://docs.rs/neuronika/latest/neuronika/index.html . The second one could be used for training, I think.
What are some alternatives?
Termion - Mirror of https://gitlab.redox-os.org/redox-os/termion
onnxruntime-rs - Rust wrapper for Microsoft's ONNX Runtime (version 1.8)
Cursive - A Text User Interface library for the Rust programming language
MTuner - MTuner is a C/C++ memory profiler and memory leak finder for Windows, PlayStation 4 and 3, Android and other platforms
Native Windows GUI - A light windows GUI toolkit for rust
wonnx - A WebGPU-accelerated ONNX inference run-time written 100% in Rust, ready for native and the web
QML-rust - QML (Qt Quick) bindings for Rust language
linfa - A Rust machine learning framework.
imgui-rs - Rust bindings for Dear ImGui
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
nfd-rs - OS-native file dialogs on Linux, OS X and Windows
tangram - Tangram makes it easy for programmers to train, deploy, and monitor machine learning models.