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Top 23 C++ Python Projects
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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PaddlePaddle
PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）
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Kodi Home Theater Software
Kodi is an award-winning free and open source home theater/media center software and entertainment hub for digital media. With its beautiful interface and powerful skinning engine, it's available for Android, BSD, Linux, macOS, iOS, tvOS and Windows.
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MMKV
An efficient, small mobile key-value storage framework developed by WeChat. Works on Android, iOS, macOS, Windows, and POSIX.
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LightGBM
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
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SaaSHub
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annoy
Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk
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DearPyGui
Dear PyGui: A fast and powerful Graphical User Interface Toolkit for Python with minimal dependencies
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assimp
The official Open-Asset-Importer-Library Repository. Loads 40+ 3D-file-formats into one unified and clean data structure.
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esphome
ESPHome is a system to control your ESP8266/ESP32 by simple yet powerful configuration files and control them remotely through Home Automation systems.
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perspective
A data visualization and analytics component, especially well-suited for large and/or streaming datasets.
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DALI
A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.
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SaaSHub
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# L2-normalize the encoding tensors image_encoding = tf.math.l2_normalize(image_encoding, axis=1) audio_encoding = tf.math.l2_normalize(audio_encoding, axis=1) # Find euclidean distance between image_encoding and audio_encoding # Essentially trying to detect if the face is saying the audio # Will return nan without the 1e-12 offset due to https://github.com/tensorflow/tensorflow/issues/12071 d = tf.norm((image_encoding - audio_encoding) + 1e-12, ord='euclidean', axis=1, keepdims=True) discriminator = keras.Model(inputs=[image_input, audio_input], outputs=[d], name="discriminator")
Project mention: FlatBuffers – an efficient cross platform serialization library for many langs | news.ycombinator.com | 2023-09-18
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I prefer Kodi: https://kodi.tv/ It is free and open sourced and won't use DRM or phone home on you.
Nothing comes out on DVD anymore, everything is Video Streaming paid per month or year.
react-native-mmkv is a wrapper around MMKV that allows you to easily implement secure storage in your app. It is arguably the fastest key-value storage for React Native apps
Project mention: SIRUS.jl: Interpretable Machine Learning via Rule Extraction | /r/Julia | 2023-06-29SIRUS.jl is a pure Julia implementation of the SIRUS algorithm by Bénard et al. (2021). The algorithm is a rule-based machine learning model meaning that it is fully interpretable. The algorithm does this by firstly fitting a random forests and then converting this forest to rules. Furthermore, the algorithm is stable and achieves a predictive performance that is comparable to LightGBM, a state-of-the-art gradient boosting model created by Microsoft. Interpretability, stability, and predictive performance are described in more detail below.
I'm investigating using C++ to build a REST server, and would love to know of people's experiences with Crow-- or whether they would recommend something else as a "medium-level" abstraction C++ web server. As background, I started off experimenting with Python/FastAPI, which is great, but there is too much friction to translate from pybind11-exported C++ objects to the format that FastAPI expects, and, of course, there are inherent performance limitations using Python, which could impact scaling up if the project were to be successful.
Microsoft also has similar courses on IoT, and Data Science. I found the IoT one really nice [0], and it covers a lot of ground.
[0]: https://github.com/microsoft/IoT-For-Beginners
https://github.com/exaloop/codon/blob/develop/LICENSE
Here are some others: https://github.com/search?q=%22Business+Source+License%22+%2...
Project mention: Modern Image Processing Algorithms Implementation in C | news.ycombinator.com | 2023-06-06
The focus on the top 10 in vector search is a product of wanting to prove value over keyword search. Keyword search is going to miss some conceptual matches. You can try to work around that with tokenization and complex queries with all variations but it's not easy.
Vector search isn't all that new a concept. For example, the annoy library (https://github.com/spotify/annoy) has been around since 2014. It was one of the first open source approximate nearest neighbor libraries. Recommendations have always been a good use case for vector similarity.
Recommendations are a natural extension of search and transformers models made building the vectors for natural language possible. To prove the worth of vector search over keyword search, the focus was always on showing how the top N matches include results not possible with keyword search.
In 2023, there has been a shift towards acknowledging keyword search also has value and that a combination of vector + keyword search (aka hybrid search) operates in the sweet spot. Once again this is validated through the same benchmarks which focus on the top 10.
On top of all this, there is also the reality that the vector database space is very crowded and some want to use their performance benchmarks for marketing.
Disclaimer: I am the author of txtai (https://github.com/neuml/txtai), an open source embeddings database
Project mention: Show HN: A Vulkan-Video-based game streaming tool for Linux | news.ycombinator.com | 2024-04-27> Would the Swift UI also work on an iPad?
Yes, but probably not for the first version.
> Do you have any comparisons with other tools (eg steam streaming, moonlight)
Steam streaming just doesn't really work on linux. Moonlight is somewhat similar in terms of direction, and has an established client base. I know of at least two projects to build servers for the Moonlight protocol[1][2].
The Moonlight protocol is a bit weird, because it's an open-source reverse engineering of a dead NVIDIA project, GeForce now. There are fundamental limitations to the protocol, for example that the cursor must be rendered in-stream or simulated. Using my tool, the cursor is rendered locally, and custom cursor images can actually be pushed to the client, for a seamless experience. This sounds like a minor detail but it matters a lot for subjective latency. I'm also working on employing tricks like hierarchical coding using FEC in the protocol, because I hate VBR encoding for games (it makes text blurry and breaks immersion). Those tricks aren't really possible in Moonlight.
All of the Linux solutions I know about have significantly higher latency compared to Magic Mirror, although I don't have numbers for exactly how much higher. (I have a benchmark to test the latency of my tool, but the others don't.) I'd encourage you to try them out and get a feel for the difference.
Finally, I think Magic Mirror is the easiest to install and get going on the server. It has almost zero runtime library or service dependencies (there's a pesky dynamic link against libxkbcommon which I haven't managed to remove), so you don't need to mess with pipewire or docker or anything - it's completely self-contained.
All that said, the existing tools have the advantage of a larger user and contributor base, whereas Magic Mirror is just me on a mission so far :) So they're likely to be much more stable and usable.
[1]: https://github.com/LizardByte/Sunshine
For native GUI, DearPyGui[0] as modern as you can.
For browser web-based GUI, you can use nicegui[1]
[0] -- https://github.com/hoffstadt/DearPyGui
[1] -- https://github.com/zauberzeug/nicegui
Project mention: Does anyone else agree that the links to the latest development version of Open3D don't work? | /r/cscareerquestions | 2023-07-10I was going to file a bug about another issue, but I have to download the development version. This is why I want this solved quickly. None of the links seem to work: https://github.com/isl-org/Open3D/issues/6259
Project mention: The Asset-Importer-Lib Minor Release Version 5.3.0 is out | /r/GraphicsProgramming | 2023-09-26
For the ESP32, an hero is in the process of adding LVGL to ESPHome. You can try it out now: https://github.com/esphome/esphome/pull/6363
Here's the (very good!) preview documentation: https://deploy-preview-3678--esphome.netlify.app/components/...
This is such a game-changer for me that I'll be using the ESP32 over the ESP8266 for any projects involving displays from now on.
Project mention: Ask HN: How Can I Make My Front End React to Database Changes in Real-Time? | news.ycombinator.com | 2024-04-17
The interesting thing about Polars is that it does not try to be a drop-in replacement to pandas, like Dask, cuDF, or Modin, and instead has its own expressive API. Despite being a young project, it quickly got popular thanks to its easy installation process and its “lightning fast” performance.
Project mention: Wechsel von Windows auf Linux - zu viele Programme Windows-only? | /r/de_EDV | 2023-06-30
Project mention: Minimal implementation of Mamba, the new LLM architecture, in 1 file of PyTorch | news.ycombinator.com | 2023-12-20>"everyone" seems to know Mamba. I never heard of Mamba
Only the "everybody who knows what mamba is" are the ones upvoting and commenting. Think of all the people who ignore it. For me, Mamba is the faster version of Conda [1], and that's why I clicked on the article.
https://github.com/mamba-org/mamba
Yet another TEDIOUS BATTLE: Python vs. C++/C stack.
This project gained popularity due to the HIGH DEMAND for running large models with 1B+ parameters, like `llama`. Python dominates the interface and training ecosystem, but prior to llama.cpp, non-ML professionals showed little interest in a fast C++ interface library. While existing solutions like tensorflow-serving [1] in C++ were sufficiently fast with GPU support, llama.cpp took the initiative to optimize for CPU and trim unnecessary code, essentially code-golfing and sacrificing some algorithm correctness for improved performance, which isn't favored by "ML research".
NOTE: In my opinion, a true pioneer was DarkNet, which implemented the YOLO model series and significantly outperformed others [2]. Same trick basically like llama.cpp
[1] https://github.com/tensorflow/serving
Another option is DALI https://github.com/NVIDIA/DALI For my project while training EfficientNet2, it was a game changer. But it a way harder to implement in code than TorchVision or Kornia.
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
C++ Python related posts
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Side Quest Devblog #1: These Fakes are getting Deep
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How moving from Pandas to Polars made me write better code without writing better code
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Bug in std:shared_mutex on Windows
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Ask HN: How do you find employment opportunities in 2024?
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Lessons from leetcode: 347 Top K Frequent Elements
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RapidFuzz: Rapid fuzzy string matching in Python
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AMD Funded a Drop-In CUDA Implementation Built on ROCm: It's Open-Source
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A note from our sponsor - InfluxDB
www.influxdata.com | 11 May 2024
Index
What are some of the best open-source Python projects in C++? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | tensorflow | 182,693 |
| 2 | FlatBuffers | 22,088 |
| 3 | PaddlePaddle | 21,642 |
| 4 | Kodi Home Theater Software | 17,591 |
| 5 | CNTK | 17,435 |
| 6 | MMKV | 16,905 |
| 7 | LightGBM | 16,076 |
| 8 | pybind11 | 14,835 |
| 9 | IoT-For-Beginners | 14,718 |
| 10 | codon | 13,861 |
| 11 | Dlib | 13,054 |
| 12 | annoy | 12,740 |
| 13 | Sunshine | 12,744 |
| 14 | DearPyGui | 12,347 |
| 15 | Open3D | 10,538 |
| 16 | assimp | 10,304 |
| 17 | esphome | 7,780 |
| 18 | perspective | 7,574 |
| 19 | cudf | 7,311 |
| 20 | albert | 7,092 |
| 21 | mamba | 6,312 |
| 22 | serving | 6,085 |
| 23 | DALI | 4,924 |
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