GitHub C++ Daily Trending

LadybirdBrowser/ladybird

Truly independent web browser

Ladybird

Ladybird is a truly independent web browser, using a novel engine based on web standards.

Important

Ladybird is in a pre-alpha state, and only suitable for use by developers

Features

We aim to build a complete, usable browser for the modern web.

Ladybird uses a multi-process architecture with a main UI process, several WebContent renderer processes, an ImageDecoder process, and a RequestServer process.

Image decoding and network connections are done out of process to be more robust against malicious content. Each tab has its own renderer process, which is sandboxed from the rest of the system.

At the moment, many core library support components are inherited from SerenityOS:

LibWeb: Web rendering engine
LibJS: JavaScript engine
LibWasm: WebAssembly implementation
LibCrypto/LibTLS: Cryptography primitives and Transport Layer Security
LibHTTP: HTTP/1.1 client
LibGfx: 2D Graphics Library, Image Decoding and Rendering
LibUnicode: Unicode and locale support
LibMedia: Audio and video playback
LibCore: Event loop, OS abstraction layer
LibIPC: Inter-process communication

How do I build and run this?

See build instructions for information on how to build Ladybird.

Ladybird runs on Linux, macOS, Windows (with WSL2), and many other *Nixes.

How do I read the documentation?

Code-related documentation can be found in the documentation folder.

Get in touch and participate!

Join our Discord server to participate in issue and development discussions.

Please read Getting involved with Ladybird if you're new to Ladybird and want to help.

Before opening an issue, please see the issue policy and the detailed issue-reporting guidelines.

The project participation guidelines can be found in CONTRIBUTING.md.

License

Ladybird is licensed under a 2-clause BSD license.

bitcoin/bitcoin

Bitcoin Core integration/staging tree

https://bitcoincore.org

For an immediately usable, binary version of the Bitcoin Core software, see https://bitcoincore.org/en/download/.

What is Bitcoin Core?

Bitcoin Core connects to the Bitcoin peer-to-peer network to download and fully validate blocks and transactions. It also includes a wallet and graphical user interface, which can be optionally built.

Further information about Bitcoin Core is available in the doc folder.

License

Bitcoin Core is released under the terms of the MIT license. See COPYING for more information or see https://opensource.org/license/MIT.

Development Process

The master branch is regularly built (see doc/build-*.md for instructions) and tested, but it is not guaranteed to be completely stable. Tags are created regularly from release branches to indicate new official, stable release versions of Bitcoin Core.

The https://github.com/bitcoin-core/gui repository is used exclusively for the development of the GUI. Its master branch is identical in all monotree repositories. Release branches and tags do not exist, so please do not fork that repository unless it is for development reasons.

The contribution workflow is described in CONTRIBUTING.md and useful hints for developers can be found in doc/developer-notes.md.

Testing

Testing and code review is the bottleneck for development; we get more pull requests than we can review and test on short notice. Please be patient and help out by testing other people's pull requests, and remember this is a security-critical project where any mistake might cost people lots of money.

Automated Testing

Developers are strongly encouraged to write unit tests for new code, and to submit new unit tests for old code. Unit tests can be compiled and run (assuming they weren't disabled during the generation of the build system) with: ctest. Further details on running and extending unit tests can be found in /src/test/README.md.

There are also regression and integration tests, written in Python. These tests can be run (if the test dependencies are installed) with: build/test/functional/test_runner.py (assuming build is your build directory).

The CI (Continuous Integration) systems make sure that every pull request is tested on Windows, Linux, and macOS. The CI must pass on all commits before merge to avoid unrelated CI failures on new pull requests.

Manual Quality Assurance (QA) Testing

Changes should be tested by somebody other than the developer who wrote the code. This is especially important for large or high-risk changes. It is useful to add a test plan to the pull request description if testing the changes is not straightforward.

Translations

Changes to translations as well as new translations can be submitted to Bitcoin Core's Transifex page.

Translations are periodically pulled from Transifex and merged into the git repository. See the translation process for details on how this works.

Important: We do not accept translation changes as GitHub pull requests because the next pull from Transifex would automatically overwrite them again.

gabime/spdlog

Fast C++ logging library.

spdlog

Fast C++ logging library

Install

Header-only version

Copy the include folder to your build tree and use a C++11 compiler.

Compiled version (recommended - much faster compile times)

$ git clone https://github.com/gabime/spdlog.git
$ cd spdlog && mkdir build && cd build
$ cmake .. && cmake --build .

see example CMakeLists.txt on how to use.

Platforms

Linux, FreeBSD, OpenBSD, Solaris, AIX
Windows (msvc 2013+, cygwin)
macOS (clang 3.5+)
Android

Package managers:

Debian: sudo apt install libspdlog-dev
Homebrew: brew install spdlog
MacPorts: sudo port install spdlog
FreeBSD: pkg install spdlog
Fedora: dnf install spdlog
Gentoo: emerge dev-libs/spdlog
Arch Linux: pacman -S spdlog
openSUSE: sudo zypper in spdlog-devel
ALT Linux: apt-get install libspdlog-devel
vcpkg: vcpkg install spdlog
conan: conan install --requires=spdlog/[*]
conda: conda install -c conda-forge spdlog
build2: depends: spdlog ^1.8.2

Features

Very fast (see benchmarks below).
Headers only or compiled
Feature-rich formatting, using the excellent fmt library.
Asynchronous mode (optional)
Custom formatting.
Multi/Single threaded loggers.
Various log targets:
- Rotating log files.
- Daily log files.
- Console logging (colors supported).
- syslog.
- Windows event log.
- Windows debugger (OutputDebugString(..)).
- Log to Qt widgets (example).
- Easily extendable with custom log targets.
Log filtering - log levels can be modified at runtime as well as compile time.
Support for loading log levels from argv or environment var.
Backtrace support - store debug messages in a ring buffer and display them later on demand.

Usage samples

Basic usage

#include "spdlog/spdlog.h"

int main() 
{
    spdlog::info("Welcome to spdlog!");
    spdlog::error("Some error message with arg: {}", 1);
    
    spdlog::warn("Easy padding in numbers like {:08d}", 12);
    spdlog::critical("Support for int: {0:d};  hex: {0:x};  oct: {0:o}; bin: {0:b}", 42);
    spdlog::info("Support for floats {:03.2f}", 1.23456);
    spdlog::info("Positional args are {1} {0}..", "too", "supported");
    spdlog::info("{:<30}", "left aligned");
    
    spdlog::set_level(spdlog::level::debug); // Set *global* log level to debug
    spdlog::debug("This message should be displayed..");    
    
    // change log pattern
    spdlog::set_pattern("[%H:%M:%S %z] [%n] [%^---%L---%$] [thread %t] %v");
    
    // Compile time log levels
    // Note that this does not change the current log level, it will only
    // remove (depending on SPDLOG_ACTIVE_LEVEL) the call on the release code.
    SPDLOG_TRACE("Some trace message with param {}", 42);
    SPDLOG_DEBUG("Some debug message");
}

Create stdout/stderr logger object

#include "spdlog/spdlog.h"
#include "spdlog/sinks/stdout_color_sinks.h"
void stdout_example()
{
    // create a color multi-threaded logger
    auto console = spdlog::stdout_color_mt("console");    
    auto err_logger = spdlog::stderr_color_mt("stderr");    
    spdlog::get("console")->info("loggers can be retrieved from a global registry using the spdlog::get(logger_name)");
}

Basic file logger

#include "spdlog/sinks/basic_file_sink.h"
void basic_logfile_example()
{
    try 
    {
        auto logger = spdlog::basic_logger_mt("basic_logger", "logs/basic-log.txt");
    }
    catch (const spdlog::spdlog_ex &ex)
    {
        std::cout << "Log init failed: " << ex.what() << std::endl;
    }
}

Rotating files

#include "spdlog/sinks/rotating_file_sink.h"
void rotating_example()
{
    // Create a file rotating logger with 5 MB size max and 3 rotated files
    auto max_size = 1048576 * 5;
    auto max_files = 3;
    auto logger = spdlog::rotating_logger_mt("some_logger_name", "logs/rotating.txt", max_size, max_files);
}

Daily files


#include "spdlog/sinks/daily_file_sink.h"
void daily_example()
{
    // Create a daily logger - a new file is created every day at 2:30 am
    auto logger = spdlog::daily_logger_mt("daily_logger", "logs/daily.txt", 2, 30);
}

Backtrace support

// Debug messages can be stored in a ring buffer instead of being logged immediately.
// This is useful to display debug logs only when needed (e.g. when an error happens).
// When needed, call dump_backtrace() to dump them to your log.

spdlog::enable_backtrace(32); // Store the latest 32 messages in a buffer. 
// or my_logger->enable_backtrace(32)..
for(int i = 0; i < 100; i++)
{
  spdlog::debug("Backtrace message {}", i); // not logged yet..
}
// e.g. if some error happened:
spdlog::dump_backtrace(); // log them now! show the last 32 messages
// or my_logger->dump_backtrace(32)..

Periodic flush

// periodically flush all *registered* loggers every 3 seconds:
// warning: only use if all your loggers are thread-safe ("_mt" loggers)
spdlog::flush_every(std::chrono::seconds(3));

Stopwatch

// Stopwatch support for spdlog
#include "spdlog/stopwatch.h"
void stopwatch_example()
{
    spdlog::stopwatch sw;    
    spdlog::debug("Elapsed {}", sw);
    spdlog::debug("Elapsed {:.3}", sw);       
}

Log binary data in hex

// many types of std::container<char> types can be used.
// ranges are supported too.
// format flags:
// {:X} - print in uppercase.
// {:s} - don't separate each byte with space.
// {:p} - don't print the position on each line start.
// {:n} - don't split the output into lines.
// {:a} - show ASCII if :n is not set.

#include "spdlog/fmt/bin_to_hex.h"

void binary_example()
{
    auto console = spdlog::get("console");
    std::array<char, 80> buf;
    console->info("Binary example: {}", spdlog::to_hex(buf));
    console->info("Another binary example:{:n}", spdlog::to_hex(std::begin(buf), std::begin(buf) + 10));
    // more examples:
    // logger->info("uppercase: {:X}", spdlog::to_hex(buf));
    // logger->info("uppercase, no delimiters: {:Xs}", spdlog::to_hex(buf));
    // logger->info("uppercase, no delimiters, no position info: {:Xsp}", spdlog::to_hex(buf));
}

Logger with multi sinks - each with a different format and log level


// create a logger with 2 targets, with different log levels and formats.
// The console will show only warnings or errors, while the file will log all. 
void multi_sink_example()
{
    auto console_sink = std::make_shared<spdlog::sinks::stdout_color_sink_mt>();
    console_sink->set_level(spdlog::level::warn);
    console_sink->set_pattern("[multi_sink_example] [%^%l%$] %v");

    auto file_sink = std::make_shared<spdlog::sinks::basic_file_sink_mt>("logs/multisink.txt", true);
    file_sink->set_level(spdlog::level::trace);

    spdlog::logger logger("multi_sink", {console_sink, file_sink});
    logger.set_level(spdlog::level::debug);
    logger.warn("this should appear in both console and file");
    logger.info("this message should not appear in the console, only in the file");
}

Register several loggers - change global level


// Creation of loggers. Set levels to all registered loggers. 
void set_level_example()
{
    auto logger1 = spdlog::basic_logger_mt("logger1", "logs/logger1.txt");
    auto logger2 = spdlog::basic_logger_mt("logger2", "logs/logger2.txt");

    spdlog::set_default_logger(logger2);
    spdlog::default_logger()->set_level(spdlog::level::trace); // set level for the default logger (logger2) to trace

    spdlog::trace("trace message to the logger2 (specified as default)");

    spdlog::set_level(spdlog::level::off) // (sic!) set level for *all* registered loggers to off (disable)
  
    logger1.warn("warn message will not appear because the level set to off");
    logger2.warn("warn message will not appear because the level set to off");
    spdlog::warn("warn message will not appear because the level set to off");
}

User-defined callbacks about log events


// create a logger with a lambda function callback, the callback will be called
// each time something is logged to the logger
void callback_example()
{
    auto callback_sink = std::make_shared<spdlog::sinks::callback_sink_mt>([](const spdlog::details::log_msg &msg) {
         // for example you can be notified by sending an email to yourself
    });
    callback_sink->set_level(spdlog::level::err);

    auto console_sink = std::make_shared<spdlog::sinks::stdout_color_sink_mt>();
    spdlog::logger logger("custom_callback_logger", {console_sink, callback_sink});

    logger.info("some info log");
    logger.error("critical issue"); // will notify you
}

Asynchronous logging

#include "spdlog/async.h"
#include "spdlog/sinks/basic_file_sink.h"
void async_example()
{
    // default thread pool settings can be modified *before* creating the async logger:
    // spdlog::init_thread_pool(8192, 1); // queue with 8k items and 1 backing thread.
    auto async_file = spdlog::basic_logger_mt<spdlog::async_factory>("async_file_logger", "logs/async_log.txt");
    // alternatively:
    // auto async_file = spdlog::create_async<spdlog::sinks::basic_file_sink_mt>("async_file_logger", "logs/async_log.txt");   
}

Asynchronous logger with multi sinks

#include "spdlog/async.h"
#include "spdlog/sinks/stdout_color_sinks.h"
#include "spdlog/sinks/rotating_file_sink.h"

void multi_sink_example2()
{
    spdlog::init_thread_pool(8192, 1);
    auto stdout_sink = std::make_shared<spdlog::sinks::stdout_color_sink_mt >();
    auto rotating_sink = std::make_shared<spdlog::sinks::rotating_file_sink_mt>("mylog.txt", 1024*1024*10, 3);
    std::vector<spdlog::sink_ptr> sinks {stdout_sink, rotating_sink};
    auto logger = std::make_shared<spdlog::async_logger>("loggername", sinks.begin(), sinks.end(), spdlog::thread_pool(), spdlog::async_overflow_policy::block);
    spdlog::register_logger(logger);
}

User-defined types

template<>
struct fmt::formatter<my_type> : fmt::formatter<std::string>
{
    auto format(my_type my, format_context &ctx) const -> decltype(ctx.out())
    {
        return fmt::format_to(ctx.out(), "[my_type i={}]", my.i);
    }
};

void user_defined_example()
{
    spdlog::info("user defined type: {}", my_type(14));
}

User-defined flags in the log pattern

// Log patterns can contain custom flags.
// the following example will add new flag '%*' - which will be bound to a <my_formatter_flag> instance.
#include "spdlog/pattern_formatter.h"
class my_formatter_flag : public spdlog::custom_flag_formatter
{
public:
    void format(const spdlog::details::log_msg &, const std::tm &, spdlog::memory_buf_t &dest) override
    {
        std::string some_txt = "custom-flag";
        dest.append(some_txt.data(), some_txt.data() + some_txt.size());
    }

    std::unique_ptr<custom_flag_formatter> clone() const override
    {
        return spdlog::details::make_unique<my_formatter_flag>();
    }
};

void custom_flags_example()
{    
    auto formatter = std::make_unique<spdlog::pattern_formatter>();
    formatter->add_flag<my_formatter_flag>('*').set_pattern("[%n] [%*] [%^%l%$] %v");
    spdlog::set_formatter(std::move(formatter));
}

Custom error handler

void err_handler_example()
{
    // can be set globally or per logger(logger->set_error_handler(..))
    spdlog::set_error_handler([](const std::string &msg) { spdlog::get("console")->error("*** LOGGER ERROR ***: {}", msg); });
    spdlog::get("console")->info("some invalid message to trigger an error {}{}{}{}", 3);
}

syslog

#include "spdlog/sinks/syslog_sink.h"
void syslog_example()
{
    std::string ident = "spdlog-example";
    auto syslog_logger = spdlog::syslog_logger_mt("syslog", ident, LOG_PID);
    syslog_logger->warn("This is warning that will end up in syslog.");
}

Android example

#include "spdlog/sinks/android_sink.h"
void android_example()
{
    std::string tag = "spdlog-android";
    auto android_logger = spdlog::android_logger_mt("android", tag);
    android_logger->critical("Use \"adb shell logcat\" to view this message.");
}

Load log levels from the env variable or argv

#include "spdlog/cfg/env.h"
int main (int argc, char *argv[])
{
    spdlog::cfg::load_env_levels();
    // or specify the env variable name:
    // MYAPP_LEVEL=info,mylogger=trace && ./example
    // spdlog::cfg::load_env_levels("MYAPP_LEVEL");
    // or from the command line:
    // ./example SPDLOG_LEVEL=info,mylogger=trace
    // #include "spdlog/cfg/argv.h" // for loading levels from argv
    // spdlog::cfg::load_argv_levels(argc, argv);
}

So then you can:

$ export SPDLOG_LEVEL=info,mylogger=trace
$ ./example

Log file open/close event handlers

// You can get callbacks from spdlog before/after a log file has been opened or closed. 
// This is useful for cleanup procedures or for adding something to the start/end of the log file.
void file_events_example()
{
    // pass the spdlog::file_event_handlers to file sinks for open/close log file notifications
    spdlog::file_event_handlers handlers;
    handlers.before_open = [](spdlog::filename_t filename) { spdlog::info("Before opening {}", filename); };
    handlers.after_open = [](spdlog::filename_t filename, std::FILE *fstream) { fputs("After opening\n", fstream); };
    handlers.before_close = [](spdlog::filename_t filename, std::FILE *fstream) { fputs("Before closing\n", fstream); };
    handlers.after_close = [](spdlog::filename_t filename) { spdlog::info("After closing {}", filename); };
    auto my_logger = spdlog::basic_logger_st("some_logger", "logs/events-sample.txt", true, handlers);        
}

Replace the Default Logger

void replace_default_logger_example()
{
    auto new_logger = spdlog::basic_logger_mt("new_default_logger", "logs/new-default-log.txt", true);
    spdlog::set_default_logger(new_logger);
    spdlog::info("new logger log message");
}

Log to Qt with nice colors

#include "spdlog/spdlog.h"
#include "spdlog/sinks/qt_sinks.h"
MainWindow::MainWindow(QWidget *parent) : QMainWindow(parent)
{
    setMinimumSize(640, 480);
    auto log_widget = new QTextEdit(this);
    setCentralWidget(log_widget);
    int max_lines = 500; // keep the text widget to max 500 lines. remove old lines if needed.
    auto logger = spdlog::qt_color_logger_mt("qt_logger", log_widget, max_lines);
    logger->info("Some info message");
}

Mapped Diagnostic Context

// Mapped Diagnostic Context (MDC) is a map that stores key-value pairs (string values) in thread local storage.
// Each thread maintains its own MDC, which loggers use to append diagnostic information to log outputs.
// Note: it is not supported in asynchronous mode due to its reliance on thread-local storage.
#include "spdlog/mdc.h"
void mdc_example()
{
    spdlog::mdc::put("key1", "value1");
    spdlog::mdc::put("key2", "value2");
    // if not using the default format, use the %& formatter to print mdc data
    // spdlog::set_pattern("[%H:%M:%S %z] [%^%L%$] [%&] %v");
}

Benchmarks

Below are some benchmarks done in Ubuntu 64 bit, Intel i7-4770 CPU @ 3.40GHz

Synchronous mode

[info] **************************************************************
[info] Single thread, 1,000,000 iterations
[info] **************************************************************
[info] basic_st         Elapsed: 0.17 secs        5,777,626/sec
[info] rotating_st      Elapsed: 0.18 secs        5,475,894/sec
[info] daily_st         Elapsed: 0.20 secs        5,062,659/sec
[info] empty_logger     Elapsed: 0.07 secs       14,127,300/sec
[info] **************************************************************
[info] C-string (400 bytes). Single thread, 1,000,000 iterations
[info] **************************************************************
[info] basic_st         Elapsed: 0.41 secs        2,412,483/sec
[info] rotating_st      Elapsed: 0.72 secs        1,389,196/sec
[info] daily_st         Elapsed: 0.42 secs        2,393,298/sec
[info] null_st          Elapsed: 0.04 secs       27,446,957/sec
[info] **************************************************************
[info] 10 threads, competing over the same logger object, 1,000,000 iterations
[info] **************************************************************
[info] basic_mt         Elapsed: 0.60 secs        1,659,613/sec
[info] rotating_mt      Elapsed: 0.62 secs        1,612,493/sec
[info] daily_mt         Elapsed: 0.61 secs        1,638,305/sec
[info] null_mt          Elapsed: 0.16 secs        6,272,758/sec

Asynchronous mode

[info] -------------------------------------------------
[info] Messages     : 1,000,000
[info] Threads      : 10
[info] Queue        : 8,192 slots
[info] Queue memory : 8,192 x 272 = 2,176 KB 
[info] -------------------------------------------------
[info] 
[info] *********************************
[info] Queue Overflow Policy: block
[info] *********************************
[info] Elapsed: 1.70784 secs     585,535/sec
[info] Elapsed: 1.69805 secs     588,910/sec
[info] Elapsed: 1.7026 secs      587,337/sec
[info] 
[info] *********************************
[info] Queue Overflow Policy: overrun
[info] *********************************
[info] Elapsed: 0.372816 secs    2,682,285/sec
[info] Elapsed: 0.379758 secs    2,633,255/sec
[info] Elapsed: 0.373532 secs    2,677,147/sec

Documentation

Documentation can be found in the wiki pages.

Powered by

MAZHARMIK/Interview_DS_Algo

Super Repository for Coding Interview Preperation

Repository Name - Interview_DS_Algo

My YouTube Channel Name - codestorywithMIK

🦸🏻‍♂️On a mission to make a Super Repository for Interview Prep for everyone. 🏁 One stop Solution.

Help me by joining my Youtube Channel for this Repository as well + System Design - Youtube Channel codestorywithMIK (**Subscribers** : 90K+ ♥)
Official Facebook Page - codestorywithMIK
This repo contains solutions to questions asked in various coding interviews topic wise.
Every topic will have sub-topics as well to categorize different types/techniques.
At the top of the solutions, you will find the link to the question and also list of the Companies who have asked the question in interviews.
So, Look for COMPANY TAGS above every solution. 😵
🎉LATEST UPDATE🎉 : Frequency also added from now onwards. 🎉
🎉LATEST UPDATE🎉 : Video Explanation in HINGLISH by me also added from now onwards. 🎉
Why I am doing this ? Because knowledge should be free. You don't need to pay for courses. Learn free, share free. PEACE 💓
You can join this group on LinkedIn - LinkedIn Page : codestorywithMIK

Latest Update⏰ - It helped me to crack interviews of "Microsoft", "Tower Research" and "Tokopedia". I hope to add more to this list later 🙂

Contents : 📝

Contributors

MuShibo/Micro-Wheeled_leg-Robot

全球最小的桌面级双轮腿机器人！

Micro-Wheeled_leg-Robot

The smallest two-wheeled-leg robot!

Video：[自制]首款桌面级双轮腿机器人

Real Robot	3D Design

中文文档

QQ group (unofficial): 991727288

Mechanical Structure Documentation

The file "OriginalRobotModel.stp" is the robot model file.
The "Parts-Manufactured" folder contains parts that need to be processed and produced by yourself, including 3D printing, CNC, and panel cutting.
The "Parts-Purchased" folder contains parts that need to be purchased.

PCB Documentation

There are four PCBs that need to be fabricated; the circuit boards provide both the schematic and PCB source files, and the IDE used is LCEDA.
The main control board is based on the ESP32, with the brushless motor driver chip being the L6234PD013TR.
The encoder chip used is the AS5600, communicating with the main control board via the I2C interface.
The IMU used is the MPU6050 module, which shares the same I2C interface with the right-side encoder.
The servo debugging board unifies the two serial lines into one signal line, completing the task through time-division multiplexing to send and receive data.
In addition, you will need three GH1.25 4PIN double-ended cables, with a recommended length of 15cm. These need to be purchased separately.

Wire Connection

Source Code Usage

Based on Arduino IDE, it is very easy to learn and use.
The brushless motor drive for the wheels is based on simpleFOC.
The left bus servo ID is 1, and the right is 2; the calibration for the legs to fully squat to the mechanical limit position is 2048; configuration is done using FEETECH Debug Software.
The ESP32 itself has WiFi capabilities, with the webpage code stored in flash, transmitting JSON data via the WebSocket communication protocol.
Please use the WebSocket library located in 3.Software/libraries. The recommended esp32 version is 2.0.3.
There are two WiFi models, AP mode and STA mode. AP mode uses the device as a wireless hotspot, and STA mode uses the device as a client to connect to an existing wireless network.

Usage Instructions

1. Connect the battery's XH2.54 plug to the rear interface of the main control board and turn on the switch to power the robot.
1. After turning on the switch, a red light on the main board will indicate that the power is on.
1. Next, the wheels will start FOC motor initialization, with the wheels moving slightly and the legs starting to move.
1. If the battery is sufficient, the blue LED on the main control board will light up. If it does not light up, charging is needed.
1. After this process is complete, press the EN button on the main control board to restart, and you can then connect to the robot's WiFi network starting with WL. The password is the lowercase WiFi name.
1. Open a browser and go to the URL 192.168.1.11. The remote control interface is compatible with Android, iOS, Windows, Linux, macOS, etc., and it is recommended to use Chrome or Firefox.
1. Manually stabilize the robot, with the wheels slightly touching the ground. Click the "Robot go!" button on the web page, and the robot will stand up. You can then control the robot's movement using the joystick.

Open Source Contributors

Mu Shibo
Li Yufeng

opencv/opencv

Open Source Computer Vision Library

OpenCV: Open Source Computer Vision Library

Resources

Homepage: https://opencv.org
- Courses: https://opencv.org/courses
Docs: https://docs.opencv.org/4.x/
Q&A forum: https://forum.opencv.org
- previous forum (read only): http://answers.opencv.org
Issue tracking: https://github.com/opencv/opencv/issues
Additional OpenCV functionality: https://github.com/opencv/opencv_contrib
Donate to OpenCV: https://opencv.org/support/

Contributing

Please read the contribution guidelines before starting work on a pull request.

Summary of the guidelines:

One pull request per issue;
Choose the right base branch;
Include tests and documentation;
Clean up "oops" commits before submitting;
Follow the coding style guide.

Additional Resources

Submit your OpenCV-based project for inclusion in Community Friday on opencv.org
Subscribe to the OpenCV YouTube Channel featuring OpenCV Live, an hour-long streaming show
Follow OpenCV on LinkedIn for daily posts showing the state-of-the-art in computer vision & AI
Apply to be an OpenCV Volunteer to help organize events and online campaigns as well as amplify them
Follow OpenCV on Mastodon in the Fediverse
Follow OpenCV on Twitter
OpenCV.ai: Computer Vision and AI development services from the OpenCV team.

praydog/REFramework

Mod loader, scripting platform, and VR support for all RE Engine games

REFramework

A mod framework, scripting platform, and modding tool for RE Engine games. Inspired by and uses code from Kanan

Installation

The last stable build can be downloaded from the Releases page.

For newer builds, check out the Nightly Developer Builds

Non-VR

Extract only the dinput8.dll from the zip file into your game folder.

VR

Install SteamVR (unless you're using OpenXR on a supported headset)
Extract the whole zip file into your corresponding game folder.

VR Troubleshooting/FAQ

Proton/Linux

Add the launch option WINEDLLOVERRIDES="dinput8.dll=n,b" %command% to your game through Steam's properties after extraction.

Example game folder: G:\SteamLibrary\steamapps\common\RESIDENT EVIL 2 BIOHAZARD RE2

Supports both DirectX 11 and DirectX 12.

Included Mods

Lua Scripting API & Plugin System (All games, check out the Wiki)
VR
- Generic 6DOF VR support for all games
- Motion controls for RE2/RE3/RE7/RE8
First Person (RE2, RE3)
Manual Flashlight (RE2, RE3, RE8)
Free Camera (All games)
Scene Timescale (All games)
FOV Slider (All games)
Vignette Disabler (All games)
Ultrawide/Aspect Ratio fixes (All games)
GUI Hider/Disabler (All games)

Included Fixes

RE8 Startup Crash
RE8 Stutters (killing enemies, taking damage, etc...)
MHRise/RE8 crashes related to third party DLLs

Included Tools (Developer Mode)

Game Objects Display
Object Explorer

Supported Games

Resident Evil 2
Resident Evil 3
Resident Evil 4
Resident Evil 7
Resident Evil Village
Resident Evil Requiem
Devil May Cry 5
Street Fighter 6
Monster Hunter Rise
Monster Hunter Wilds
Monster Hunter Stories 3
Dragon's Dogma 2
Ghosts 'n Goblins Resurrection (Using RE8 build)
Apollo Justice: Ace Attorney Trilogy (Using DD2 build)
Kunitsu-Gami: Path of the Goddess (Using DD2 build)
Onimusha 2: Samurai's Destiny (Using MHWILDS build)

Thanks

SkacikPL for originally creating the Manual Flashlight mod.

cursey for helping develop the VR component and the scripting system.

The Hitchhiker and alphaZomega for the great help stress testing, creating scripts for the scripting system, and helpful suggestions.

duckdb/duckdb

DuckDB is an analytical in-process SQL database management system

DuckDB

DuckDB is a high-performance analytical database system. It is designed to be fast, reliable, portable, and easy to use. DuckDB provides a rich SQL dialect with support far beyond basic SQL. DuckDB supports arbitrary and nested correlated subqueries, window functions, collations, complex types (arrays, structs, maps), and several extensions designed to make SQL easier to use.

DuckDB is available as a standalone CLI application and has clients for Python, R, Java, Wasm, etc., with deep integrations with packages such as pandas and dplyr.

For more information on using DuckDB, please refer to the DuckDB documentation.

Installation

If you want to install DuckDB, please see our installation page for instructions.

Data Import

For CSV files and Parquet files, data import is as simple as referencing the file in the FROM clause:

SELECT * FROM 'myfile.csv';
SELECT * FROM 'myfile.parquet';

Refer to our Data Import section for more information.

SQL Reference

The documentation contains a SQL introduction and reference.

Development

For development, DuckDB requires CMake, Python 3 and a C++17 compliant compiler. In the root directory, run make to compile the sources. For development, use make debug to build a non-optimized debug version. You should run make unit and make allunit to verify that your version works properly after making changes. To test performance, you can run BUILD_BENCHMARK=1 BUILD_TPCH=1 make and then perform several standard benchmarks from the root directory by executing ./build/release/benchmark/benchmark_runner. The details of benchmarks are in our Benchmark Guide.

Please also refer to our Build Guide and Contribution Guide.

Support

See the Support Options page and the dedicated endoflife.date page.

notepad-plus-plus/notepad-plus-plus

Notepad++ official repository

What is Notepad++ ?

Notepad++ is a free (free as in both "free speech" and "free beer") source code editor and Notepad replacement that supports several programming languages and natural languages. Running in the MS Windows environment, its use is governed by GPL License.

See the Notepad++ official site for more information.

Notepad++ GPG Release Key

Since the release of version 7.6.5 Notepad++ is signed using GPG with the following key:

Signer: Notepad++
E-mail: don.h@free.fr
Key ID: 0x8D84F46E
Key fingerprint: 14BC E436 2749 B2B5 1F8C 7122 6C42 9F1D 8D84 F46E
Key type: RSA 4096/4096
Created: 2019-03-11
Expires: 2027-03-13

https://github.com/notepad-plus-plus/notepad-plus-plus/blob/master/nppGpgPub.asc

Supported OS

All the Windows systems still supported by Microsoft are supported by Notepad++. However, not all Notepad++ users can or want to use the newest system. Here is the Supported systems information you may need in case you are one of them.

Build Notepad++

Please follow build guide to build Notepad++ from source.

Contribution

Contributions are welcome. Be mindful of our Contribution Rules to increase the likelihood of your contribution getting accepted.

Notepad++ Contributors

google-ai-edge/LiteRT-LM

LiteRT-LM is Google's production-ready, high-performance, open-source inference framework for deploying Large Language Models on edge devices.

LiteRT-LM

LiteRT-LM is Google's production-ready, high-performance, open-source inference framework for deploying Large Language Models on edge devices.

🔗 Product Website

🔥 What's New: `v0.13`

Gemma4 12B Support: LiteRT-LM now supports Gemma4 12B (blog).
LiteRT LM CLI update: Support OpenAI API Compatible server (doc)
Swift package for MacOS: Swift package supports both macOS and iOS now.
🚀 Agent skill support: Support creating a standalone LiteRT-LM Android demo app with backend selection and multi-modality support. See README.md for the example prompt.

👉 Try Gemma4-E4B with MTP on Linux, macOS, Windows or Raspberry Pi with the LiteRT-LM CLI:

litert-lm run  \
   --from-huggingface-repo=litert-community/gemma-4-E2B-it-litert-lm \
   gemma-4-E4B-it.litertlm \
   --backend=gpu \
   --enable-speculative-decoding=true \
   --prompt="What is the capital of France?"

🌟 Key Features

📱 Cross-Platform Support: Android, iOS, Web, Desktop, and IoT (e.g. Raspberry Pi).
🚀 Hardware Acceleration: Peak performance via GPU and NPU accelerators.
👁️ Multi-Modality: Support for vision and audio inputs.
🔧 Tool Use: Function calling support for agentic workflows.
📚 Broad Model Support: Gemma, Llama, Phi-4, Qwen, and more.

🚀 Production-Ready for Google's Products

LiteRT-LM powers on-device GenAI experiences in Chrome, Chromebook Plus, Pixel Watch, and more.

You can also try the Google AI Edge Gallery app to run models immediately on your device.

Install the app today from Google Play	Install the app today from App Store

📰 Blogs & Announcements

Link	Description
Bringing Gemma 4 12B to your Laptop: Unlocking Local, Agentic Workflows with Google AI Edge	Bring agentic, multimodal AI capabilities to everyday laptops, enabling local data processing and visual insight generation.
Blazing-fast on-device GenAI with LiteRT-LM	Unlock Gemma 4's full potential with blazing speed and incredible efficiency using newly added Swift, JavaScript, and Flutter APIs.
Accelerating Gemma 4: faster inference with multi-token prediction drafters	An overview of how Multi-Token Prediction (MTP) drafters are making Gemma 4 models up to 3x faster at inference.
Bring state-of-the-art agentic skills to the edge with Gemma 4	Deploy Gemma 4 in-app and across a broader range of devices with stellar performance and broad reach using LiteRT-LM.
On-device GenAI in Chrome, Chromebook Plus and Pixel Watch	Deploy language models on wearables and browser-based platforms using LiteRT-LM at scale.
On-device Function Calling in Google AI Edge Gallery	Explore how to fine-tune FunctionGemma and enable function calling capabilities powered by LiteRT-LM Tool Use APIs.
Google AI Edge small language models, multimodality, and function calling	Latest insights on RAG, multimodality, and function calling for edge language models.

🏃 Quick Start

🔗 Key Links

👉 Technical Overview including performance benchmarks, model support, and more.
👉 LiteRT-LM CLI Guide including installation, getting started, and advanced usage.

⚡ Quick Try (No Code)

Try LiteRT-LM immediately from your terminal without writing a single line of code using uv:

uv tool install litert-lm

litert-lm run \
  --from-huggingface-repo=google/gemma-3n-E2B-it-litert-lm \
  gemma-3n-E2B-it-int4 \
  --prompt="What is the capital of France?"

📚 Supported Language APIs

Ready to get started? Explore our language-specific guides and setup instructions.

Language	Status	Best For...	Documentation
Python	✅ Stable	Prototyping & Scripting	Python Guide
Kotlin	✅ Stable	Android apps & JVM	Kotlin Guide
Swift	🚀 Early Preview	Native iOS & macOS	Swift Guide
JavaScript (web)	🚀 Early Preview	Browser environments	JavaScript Guide
Flutter	🚀 Community	Cross-platform mobile	Flutter Guide
C++	✅ Stable	High-performance native	C++ Guide

🏗️ Build From Source

This guide shows how you can compile LiteRT-LM from source. If you want to build the program from source, you should checkout the stable tag.

📦 Releases

v0.13: Support Gemma4 12B. Added Agent skill support for Android demo app, OpenAI API compatible server in CLI, and MacOS support in Swift package.
v0.12: Added early preview of Swift and Web JavaScript APIs, and community Flutter support. Updated LiteRT-LM CLI to have full CPU and GPU backend support across Linux, macOS, and Windows.
v0.11: Support Single Position Multi-token Prediction (MTP) for Gemma 4. Expand LiteRT-LM CLI to run natively on Windows with CPU and GPU backends.
v0.10: Deploy Gemma 4 with stellar performance (blog) and introduce LiteRT-LM CLI.
v0.9: Improvements to function calling capabilities, better app performance stability.
v0.8: Desktop GPU support and Multi-Modality.
v0.7: NPU acceleration for Gemma models.

For a full list of releases, see GitHub Releases.

carbon-language/carbon-lang

Carbon Language's main repository: documents, design, implementation, and related tools. (NOTE: Carbon Language is experimental; see README)

Carbon Language:
An experimental successor to C++

Why? | Goals | Status | Getting started | Join us

See our announcement video from CppNorth. Note that Carbon is not ready for use.

Fast and works with C++

Performance matching C++ using LLVM, with low-level access to bits and addresses
Interoperate with your existing C++ code, from inheritance to templates
Fast and scalable builds that work with your existing C++ build systems

Modern and evolving

Solid language foundations that are easy to learn, especially if you have used C++
Easy, tool-based upgrades between Carbon versions
Safer fundamentals, and an incremental path towards a memory-safe subset

Welcoming open-source community

Clear goals and priorities with robust governance
Community that works to be welcoming, inclusive, and friendly
Batteries-included approach: compiler, libraries, docs, tools, package manager, and more

Why build Carbon?

C++ remains the dominant programming language for performance-critical software, with massive and growing codebases and investments. However, it is struggling to improve and meet developers' needs, as outlined above, in no small part due to accumulating decades of technical debt. Incrementally improving C++ is extremely difficult, both due to the technical debt itself and challenges with its evolution process. The best way to address these problems is to avoid inheriting the legacy of C or C++ directly, and instead start with solid language foundations like modern generics system, modular code organization, and consistent, simple syntax.

Existing modern languages already provide an excellent developer experience: Go, Swift, Kotlin, Rust, and many more. Developers that can use one of these existing languages should. Unfortunately, the designs of these languages present significant barriers to adoption and migration from C++. These barriers range from changes in the idiomatic design of software to performance overhead.

Carbon is fundamentally a successor language approach, rather than an attempt to incrementally evolve C++. It is designed around interoperability with C++ as well as large-scale adoption and migration for existing C++ codebases and developers. A successor language for C++ requires:

Performance matching C++, an essential property for our developers.
Seamless, bidirectional interoperability with C++, such that a library anywhere in an existing C++ stack can adopt Carbon without porting the rest.
A gentle learning curve with reasonable familiarity for C++ developers.
Comparable expressivity and support for existing software's design and architecture.
Scalable migration, with some level of source-to-source translation for idiomatic C++ code.

With this approach, we can build on top of C++'s existing ecosystem, and bring along existing investments, codebases, and developer populations. There are a few languages that have followed this model for other ecosystems, and Carbon aims to fill an analogous role for C++:

JavaScript → TypeScript
Java → Kotlin
C++ → Carbon

Language Goals

We are designing Carbon to support:

Performance-critical software
Software and language evolution
Code that is easy to read, understand, and write
Practical safety and testing mechanisms
Fast and scalable development
Modern OS platforms, hardware architectures, and environments
Interoperability with and migration from existing C++ code

While many languages share subsets of these goals, what distinguishes Carbon is their combination.

We also have explicit non-goals for Carbon, notably including:

A stable application binary interface (ABI) for the entire language and library
Perfect backwards or forwards compatibility

Our detailed goals document fleshes out these ideas and provides a deeper view into our goals for the Carbon project and language.

Project status

Carbon Language is currently an experimental project. We are hard at work on a toolchain implementation with compiler and linker. You can try out the current state at compiler-explorer.com.

We want to better understand whether we can build a language that meets our successor language criteria, and whether the resulting language can gather a critical mass of interest within the larger C++ industry and community.

Currently, we have fleshed out several core aspects of both Carbon the project and the language:

The strategy of the Carbon Language and project.
An open-source project structure, governance model, and evolution process.
Critical and foundational aspects of the language design informed by our experience with C++ and the most difficult challenges we anticipate. This includes designs for:
- Generics
- Class types
- Inheritance
- Operator overloading
- Lexical and syntactic structure
- Code organization and modular structure
An under-development compiler and toolchain that will compile Carbon (and eventually C++ code as well) into standard executable code. This is where most of our current implementation efforts are directed.
- Historically, there was also a prototype explorer interpreter that implemented an older version of the Carbon language design, but is no longer under development and has been archived.

If you're interested in contributing, we're currently focused on developing the Carbon toolchain until it can support Carbon ↔ C++ interop. Beyond that, we plan to continue developing the design and toolchain until we can ship the 0.1 language and support evaluating Carbon in more detail.

You can see our full roadmap for more details.

Carbon and C++

If you're already a C++ developer, Carbon should have a gentle learning curve. It is built out of a consistent set of language constructs that should feel familiar and be easy to read and understand.

The Carbon code here is hypothetical and meant to show the look and feel of the language.

C++ code like this:

corresponds to this Carbon code:

You can call Carbon from C++ without overhead and the other way around. This means you migrate a single C++ library to Carbon within an application, or write new Carbon on top of your existing C++ investment. For example:

Generics

Carbon provides a modern generics system with checked definitions, while still supporting opt-in templates for seamless C++ interop. Checked generics provide several advantages compared to C++ templates:

Generic definitions are fully type-checked, removing the need to instantiate to check for errors and giving greater confidence in code.
- Avoids the compile-time cost of re-checking the definition for every instantiation.
- When using a definition-checked generic, usage error messages are clearer, directly showing which requirements are not met.
Enables automatic, opt-in type erasure and dynamic dispatch without a separate implementation. This can reduce the binary size and enables constructs like heterogeneous containers.
Strong, checked interfaces mean fewer accidental dependencies on implementation details and a clearer contract for consumers.

Without sacrificing these advantages, Carbon generics support specialization, ensuring it can fully address performance-critical use cases of C++ templates. For more details about Carbon's generics, see their design.

In addition to easy and powerful interop with C++, Carbon templates can be constrained and incrementally migrated to checked generics at a fine granularity and with a smooth evolutionary path.

Memory safety

Safety, and especially memory safety, remains a key challenge for C++ and something a successor language needs to address.

We plan to support a two step migration process:

Highly automated, minimal supervision migration from C++ to a dialect of Carbon designed for C++ interop and migration.
Incremental refactoring of the Carbon code to adopt memory-safe designs, patterns, and APIs.

We also want to address important, low-hanging fruit in the safety space immediately when migrating into Carbon:

Tracking uninitialized states better, increased enforcement of initialization, and hardening against initialization bugs when needed.
Designing fundamental APIs and idioms to support dynamic bounds checking.
Switching from undefined behavior to erroneous behavior wherever possible, and marking the remaining undefined behavior with visible unsafe syntax.
Having a default debug build mode that has less runtime overhead while being more comprehensive than existing C++ debug build modes combined with Address Sanitizer.

For more details, see our safety design.

Getting started

To try out Carbon immediately in your browser, you can use the toolchain at: carbon.compiler-explorer.com.

We are developing a traditional toolchain for Carbon that can compile and link programs. However, Carbon is still an early, experimental project, and so we only have very experimental nightly releases of the Carbon toolchain available to download, and only on limited platforms. If you are using a recent Ubuntu Linux or similar (Debian, WSL, etc.), you can try these out by going to our releases page and download the latest nightly toolchain tar file: carbon_toolchain-0.0.0-0.nightly.YYYY.MM.DD.tar.gz. Then you can try it out:

# A variable with the nightly version from yesterday:
VERSION="$(date -d yesterday +0.0.0-0.nightly.%Y.%m.%d)"

# Get the release
wget https://github.com/carbon-language/carbon-lang/releases/download/v${VERSION}/carbon_toolchain-${VERSION}.tar.gz

# Unpack the toolchain:
tar -xvf carbon_toolchain-${VERSION}.tar.gz

# Create a simple Carbon source file:
echo "import Core library \"io\"; fn Run() { Core.Print(42); }" > forty_two.carbon

# Compile to an object file:
./carbon_toolchain-${VERSION}/bin/carbon compile \
  --output=forty_two.o forty_two.carbon

# Install minimal system libraries used for linking. Note that installing `gcc`
# or `g++` for compiling C/C++ code with GCC will also be sufficient, these are
# just the specific system libraries Carbon linking still uses.
sudo apt install libgcc-11-dev

# Link to an executable:
./carbon_toolchain-${VERSION}/bin/carbon link \
  --output=forty_two forty_two.o

# Run it:
./forty_two

As a reminder, the toolchain is still very early and many things don't yet work. Please hold off on filing lots of bugs: we know many parts of this don't work yet or may not work on all systems. We expect to have releases that are much more robust and reliable that you can try out when we reach our 0.1 milestone.

If you want to build Carbon's toolchain yourself or are thinking about contributing fixes or improvements to Carbon, you'll need to install our build dependencies (Clang, LLD, libc++) and check out the Carbon repository. For example, on Debian or Ubuntu:

# Update apt.
sudo apt update

# Install tools.
sudo apt install \
  clang \
  libc++-dev \
  libc++abi-dev \
  lld

# Download Carbon's code.
$ git clone https://github.com/carbon-language/carbon-lang
$ cd carbon-lang

Then you can try out our toolchain which has a very early-stage compiler for Carbon:

# Build and run the toolchain's help to get documentation on the command line.
$ ./scripts/run_bazelisk.py run //toolchain -- help

For complete instructions, including installing dependencies on various different platforms, see our contribution tools documentation.

Learn more about the Carbon project:

Conference talks

Carbon focused talks from the community:

2026

Benchmarking and optimizing the Carbon compiler, NDC {Toronto} (May 5-8)
Carbon: graduating from the experiment, NDC {Toronto} (May 5-8)

2025

Carbon: from C++ to Memory Safety, REBASE - ICFP/SPLASH (slides)
Memory safety everywhere with both Carbon and Rust, RustConf (video, slides)

2024

Generic implementation strategies in Carbon and Clang, LLVM Developers' Meeting (video, slides)
The Carbon Language: Road to 0.1, NDC {TechTown} (video, slides)
How designing Carbon with C++ interop taught me about C++ variadics and overloads, CppNorth (video, slides)
Generic Arity: Definition-Checked Variadics in Carbon, C++Now (video, slides)
Carbon: An experiment in different tradeoffs, panel session, EuroLLVM (video, slides)
- Alex Bradbury's notes
Carbon's high-level semantic IR lightning talk, EuroLLVM (video)

2023

Carbon’s Successor Strategy: From C++ interop to memory safety, C++Now (video, slides)
Definition-Checked Generics, C++Now
- Part 1 (video, slides)
- Part 2 (video, slides)
Modernizing Compiler Design for Carbon’s Toolchain, C++Now (video, slides)

2022

Carbon Language: Syntax and trade-offs, Core C++ (video, slides)
Carbon Language: An experimental successor to C++, CppNorth (video, slides)

Join us

We'd love to have folks join us and contribute to the project. Carbon is committed to a welcoming and inclusive environment where everyone can contribute.

Most of Carbon's design discussions occur on Discord.
To watch for major release announcements, subscribe to our Carbon release post on GitHub and star carbon-lang.
See our code of conduct and contributing guidelines for information about the Carbon development community.

Contributing

You can also directly:

Contribute to the language design: feedback on design, new design proposal
Contribute to the language implementation
- Carbon Toolchain, and project infrastructure

You can check out some "good first issues", or join the #contributing-help channel on Discord. See our full CONTRIBUTING documentation for more details.

microsoft/Foundry-Local

Foundry Local

Download | Documentation | Discord

Ship on-device AI inside your app

Foundry Local is an end-to-end local AI solution for building applications that run entirely on the user's device. It provides native SDKs (C#, JavaScript, Python, and Rust), a curated catalog of optimized models, and automatic hardware acceleration — all in a lightweight package (~20 MB). The compact size makes it easy to integrate into your application and distribute to end users.

User data never leaves the device, responses start immediately with zero network latency, and your app works offline. No per-token costs, no API keys, no backend infrastructure to maintain, and no Azure subscription required.

Key Features

Lightweight runtime — The runtime handles model acquisition, hardware acceleration, model management, and inference (via ONNX Runtime).
Curated model catalog — A catalog of high-quality models optimized for on-device use across a wide range of consumer hardware. The catalog covers chat completions (for example, GPT OSS, Qwen, DeepSeek, Mistral and Phi) and audio transcription (for example, Whisper). Every model goes through extensive quantization and compression to deliver the best balance of quality and performance. Models are versioned, so your application can pin to a specific version or automatically receive updates.
Automatic hardware acceleration — Foundry Local detects the available hardware on the user's device and selects the best execution provider and device (NPU, GPU or CPU).
Smart model management — Foundry Local handles the full lifecycle of models on end-user devices. Models download automatically on first use, are cached locally for instant subsequent launches, and the best-performing variant is selected for the user's specific hardware.
OpenAI-compatible API — Supports OpenAI request and response formats including the OpenAI Responses API format. If your application already uses the OpenAI SDK, point it to a Foundry Local endpoint with minimal code changes.
Optional local server — An OpenAI-compatible web server for serving models to multiple processes, integrating with tools like LangChain, or experimenting through REST calls. For most embedded application scenarios, use the SDK directly — it runs inference in-process without the overhead of a separate server.

🚀 Quickstart

Tip

The following shows a quickstart for Python and JavaScript. C# and Rust language bindings are also available. Take a look at the samples for more details.

JavaScript

Install the SDK:

# Windows (recommended for hardware acceleration)
npm install foundry-local-sdk-winml

# macOS/linux
npm install foundry-local-sdk

Run your first chat completion:

import { FoundryLocalManager } from 'foundry-local-sdk';

const manager = FoundryLocalManager.create({ appName: 'my-app' });

// Download and load a model (auto-selects best variant for user's hardware)
const model = await manager.catalog.getModel('qwen2.5-0.5b');
await model.download((progress) => {
    process.stdout.write(`\rDownloading... ${progress.toFixed(2)}%`);
});
await model.load();

// Create a chat client and get a completion
const chatClient = model.createChatClient();
const response = await chatClient.completeChat([
    { role: 'user', content: 'What is the golden ratio?' }
]);

console.log(response.choices[0]?.message?.content);

// Unload the model when done
await model.unload();

Python

Install the SDK:

# Windows (recommended for hardware acceleration)
pip install foundry-local-sdk-winml

# macOS/Linux
pip install foundry-local-sdk

Run your first chat completion:

from foundry_local_sdk import Configuration, FoundryLocalManager

config = Configuration(app_name="foundry_local_samples")
FoundryLocalManager.initialize(config)
manager = FoundryLocalManager.instance

# Select and load a model from the catalog
model = manager.catalog.get_model("qwen2.5-0.5b")
model.download()
model.load()

# Get a chat client
client = model.get_chat_client()

# Create and send message
messages = [
    {"role": "user", "content": "What is the golden ratio?"}
]
response = client.complete_chat(messages)
print(f"Response: {response.choices[0].message.content}")

model.unload()

💬 Audio Transcription (Speech-to-Text)

The SDK also supports audio transcription via Whisper models (available in JavaScript, C#, Python and Rust):

import { FoundryLocalManager } from 'foundry-local-sdk';

const manager = FoundryLocalManager.create({ appName: 'my-app' });

const whisperModel = await manager.catalog.getModel('whisper-tiny');
await whisperModel.download();
await whisperModel.load();

const audioClient = whisperModel.createAudioClient();
audioClient.settings.language = 'en';

// Transcribe an audio file
const result = await audioClient.transcribe('recording.wav');
console.log('Transcription:', result.text);

// Or stream in real-time
for await (const chunk of audioClient.transcribeStreaming('recording.wav')) {
    process.stdout.write(chunk.text);
}

await whisperModel.unload();

Tip

A single FoundryLocalManager can manage both chat and audio models simultaneously. See the chat-and-audio sample for a complete example.

📦 Samples

Explore complete working examples in the samples/ folder:

Language	Samples	Highlights
C#	12	Native chat, audio transcription, tool calling, model management, web server, tutorials
JavaScript	12	Native chat, audio, Electron app, Copilot SDK, LangChain, tool calling, tutorials
Python	9	Chat completions, audio transcription, LangChain, tool calling, tutorials
Rust	8	Native chat, audio transcription, tool calling, web server, tutorials

🖥️ CLI

The Foundry Local CLI lets you explore models and experiment interactively.

Install:

# Windows
winget install Microsoft.FoundryLocal

# macOS
brew install microsoft/foundrylocal/foundrylocal

Run a model:

foundry model run qwen2.5-0.5b

List available models:

foundry model ls

For the full CLI reference and advanced usage, see the CLI documentation on Microsoft Learn.

Reporting Issues

Please report issues or suggest improvements in the GitHub Issues section.

🎓 Learn More

Foundry Local Documentation on Microsoft Learn
Foundry Local Lab — Hands-on exercises and step-by-step instructions

❔ Frequently asked questions

Is Foundry Local a web server and CLI tool?

No. Foundry Local is an end-to-end local AI solution that your application ships with. It handles model acquisition, hardware acceleration, and inference inside your app process through the SDK. The optional web server and CLI are available for development workflows, but the core product is the local AI runtime and SDK that you integrate directly into your application.

Why doesn't Foundry Local support every available model?

Foundry Local is designed for shipping production applications, not for general-purpose model experimentation. The model catalog is intentionally curated to include models that are optimized for specific application scenarios, tested across a range of consumer hardware, and small enough to distribute to end users. This approach ensures that every model in the catalog delivers reliable performance when embedded in your application — rather than offering a broad selection of models with unpredictable on-device behavior.

Can Foundry Local run on a server?

Foundry Local is optimized for hardware-constrained devices where a single user accesses the model at a time. While you can technically install and run it on server hardware, it isn't designed as a server inference stack.

Server-oriented runtimes like vLLM or Triton Inference Server are built for multi-user scenarios — they handle concurrent request queuing, continuous batching, and efficient GPU sharing across many simultaneous clients. Foundry Local doesn't provide these capabilities. Instead, it focuses on lightweight, single-user inference with automatic hardware detection, KV-cache management, and model lifecycle handling that make sense for client applications.

If you need to serve models to multiple concurrent users, use a dedicated server inference framework. Use Foundry Local when the model runs on the end user's own device.

What platforms are supported?

Foundry Local supports Windows, macOS (Apple silicon), and Linux.

⚖️ License

Foundry Local SDK is licensed under the MIT license. For more details, see the LICENSE file. Foundry Local CLI is licensed under the Microsoft Software License Terms. For more details, read the LICENSE file.

Individual models made available for use with Foundry Local are subject to the each model's license terms, notices, and use restrictions. Refer to the model's documentation or download/listing page for the applicable terms before using or redistributing a model.

NVIDIA/cutlass

CUDA Templates and Python DSLs for High-Performance Linear Algebra

Overview

CUTLASS 4.5.2

CUTLASS 4.5.2 - May 2026

CUTLASS is a collection of abstractions for implementing high-performance matrix-matrix multiplication (GEMM) and related computations at all levels and scales within CUDA. It incorporates strategies for hierarchical decomposition and data movement. CUTLASS decomposes these "moving parts" into reusable, modular software components and abstractions.

Primitives for different levels of a conceptual parallelization hierarchy can be specialized and tuned via custom tiling sizes, data types, and other algorithmic policy. The resulting flexibility simplifies their use as building blocks within custom kernels and applications.

CUTLASS has been providing CUDA C++ template abstractions for high-performance linear algebra since 2017 and these abstractions provide extensive support for a wide range of computations including mixed-precision computations, specialized data-movement (async copy) and multiply-accumulate abstractions for FP64, FP32, TF32, FP16, BF16, FP32 emulation via tensor core instruction, 8b floating point types (e5m2 and e4m3), block scaled data types (NVIDIA NVFP4 and OCP standard MXFP4, MXFP6, MXFP8), narrow integer types (4 and 8b signed and unsigned integers), and binary 1b data types (where architectures allow for the native support of such data types) across NVIDIA's Volta, Turing, Ampere, Ada, Hopper, and Blackwell architectures.

To this rich ecosystem of C++ based kernel programming abstractions, CUTLASS 4 adds CUTLASS DSLs. These are Python native interfaces for writing high-performance CUDA kernels based on core CUTLASS and CuTe concepts without any performance compromises. This allows for a much smoother learning curve, orders of magnitude faster compile times, native integration with DL frameworks without writing glue code, and much more intuitive metaprogramming that does not require deep C++ expertise.

Overall we envision CUTLASS DSLs as a family of domain-specific languages (DSLs). With the release of 4.0, we are releasing the first of these in CuTe DSL. This is a low level programming model that is fully consistent with CuTe C++ abstractions — exposing core concepts such as layouts, tensors, hardware atoms, and full control over the hardware thread and data hierarchy.

CuTe DSL demonstrates optimal matrix multiply and other linear algebra operations targeting the programmable, high-throughput Tensor Cores implemented by NVIDIA's Ampere, Hopper, and Blackwell architectures.

We believe it will become an indispensable tool for students, researchers, and performance engineers alike — flattening the learning curve of GPU programming, rapidly prototyping kernel designs, and bringing optimized solutions into production.

CuTe DSL is currently in public beta and will graduate out of beta by end of summer 2025.

To get started quickly - please refer :

What's New in CUTLASS 4.5

CuTe DSL

New features
- New Block API block_copy() to simplify TMA and S2T copy. Users can ignore detail about multicast and 2CTA partition for TMA by block_copy() and need not to invoke tma_partition(). And users can remove bulk of S2T initialization to simplify S2T copy.
- MXF8F6F4 mixed precision support
  - BlockScaled MMA now supports MXF8MXF4 or MXF8MXF6
- Block Scaled MMA for SM120 now works on Spark
- EFC broadcast semantics support
  - EFC epilogue functions can now broadcast and remap tensor modes via C.remap_modes[:, 0, 1] subscript syntax (where : marks a broadcast dimension and integers select source mode indices). Covers scalar broadcast, row/column broadcast, and arbitrary mode permutations (e.g. transpose). The PyTorch reference evaluator mirrors the same transformations.
- Initial linter support: Improved type hints on CuTe DSL APIs to support static type checkers like MyPy
- dataclasses.dataclass is now supported for JIT compilaton and cute.compile for both plain and tvm-ffi path
- cute.copy now supports user specified loop unrolling
- Python 3.14t is now supported with GIL enabled
Bug fixing and improvements
- Improved source code correlation for profiling/debugging
- Fixed an aarch64 segfault issue with tvm-ffi
- Re-organization for CuTe DSL examples/tutorials for better discoverability
- Fixed following issues: https://github.com/NVIDIA/cutlass/issues/3219 https://github.com/NVIDIA/cutlass/issues/3218 https://github.com/NVIDIA/cutlass/issues/3212 https://github.com/NVIDIA/cutlass/issues/3210 https://github.com/NVIDIA/cutlass/issues/3208 https://github.com/NVIDIA/cutlass/issues/3201 https://github.com/NVIDIA/cutlass/issues/3227 https://github.com/NVIDIA/cutlass/issues/3240 https://github.com/NVIDIA/cutlass/issues/3241
- Fixed Jax int64 stride divisibility issue
- Fixed issues for SM120 blockscaled MMAs
  - added missing MXFP8MMAOP and MXF8F6F4MMAOP for sm120.
More examples of authorizing peak-performance kernels
- MOE examles
  - A new style of grouped-gemm that aligns to torch's grouped_mm and scaled_groued_mm interface.
  - Expert-wise tensormap descriptor setup by a cheap helper kernel (~2us) to avoid long latency in tile switching, kernel structure is much more closer to a normal GEMM.
  - Compared to torch_210_cu13, very few problem has worse perf in B200.
    - mxfp8_2dx3d: avg 1.29 speedup;
    - mxfp8_2dx2d: avg 1.41 speedup;
      - nvfp4_2dx3d: avg 1.11 speedup;
    - nvfp4_2dx2d: avg 1.12 speedup (worst case 0.98)
    - bf16_2dx3d: avg 1.15 speedup (worst case 0.98)
    - bf16_2dx2d: avg 1.17 speedup (worst case 0.96)
    - Note: The perf is measured from torch profiler, this impl includes the helper kernel + main kernel, while torch's includes its setup kernel and cutlass_cpp main kernel.
API changes
- ab_dtype is deprecated in make_trivial_tiled_mma and make_blockscaled_trivial_tiled_mma from blackwell_helpers.py. Please specify a_dtype and b_dtype separately instead.

CUTLASS C++

Add 2SM MMA instruction support to mixed TMA+CpAsync SM100 vanilla GEMM kernels.
- Mixed TMA+CpAsync can now accept static, but non trivial cluster shapes.
- Uses TMA multicast for A tile when using non-trivial cluster size along N mode.
- Uses an additional barrier (mma_trampoline_barrier) to track cp.async arrivals in both CTAs.
- Changes included in example 92.
Add support for 128x32xK and 128x64xK tile sizes for SM120 blockscaled MMA collective builders, yielding up to 30% performance improvement on Blackwell SM121 related kernels.
Add static load to tensor memory support, included in example 77.
Use 64-bit adds for SM100 MMA descriptor offsets and reduce move instructions for improved code generation.
Add example 95 to support green context SM partition
- Enables launching GEMM on stream with partial SM allocation.
Add Snake activation functor for EVT.
Fix SM100 F8F6F4 SS MMA (1SM and 2SM) traits to use typed op templates.
Add UE8M0 (uniform exponent distribution) initialization support in tensor fill utilities.
Add cvt.rn.bf16x2.e4m3x2 conversion instruction support to numeric_conversion.h.
Update example 93 with paged KV cache support for Blackwell low-latency GQA.
Fix some kernel issues:
- Fix l2_capacity=0 handling in Blackwell SM100/SM120 kernel templates
- Fix CUTLASS clang build issues
- Remove PipelineStorage shadowing in SM100 complex epilogue
- Fix build issue in SM90 epilogue fusion visitor TMA warpspecialized
- Fix missing convert fucntion in EVT for fp4 kernels
Fix some profiler issues:
- Add missing reference kernels for blockwise GEMM profiler.
- Avoid instantiate 2sm tma kernels where ctaN is none power of 64 when ctaN > 128 in profiler.

Note: CUTLASS 4.x builds are known to be down on Windows platforms for all CUDA toolkits. CUTLASS team is working on a fix.

See the CHANGELOG for details of all past releases and updates.

Performance

CUTLASS primitives are very efficient. When used to construct device-wide GEMM kernels, they exhibit nearly optimal utilization of peak theoretical throughput. The figure below shows CUTLASS 3.8's performance as a % of theoretical peak utilization on various input and output data types when run on NVIDIA Blackwell SM100 architecture GPU.

The two figures below show the continual CUTLASS performance improvements on an NVIDIA H100 (NVIDIA Hopper architecture) since CUTLASS 3.1. CUTLASS 3.5.1 was compiled with the CUDA 12.5u1 Toolkit. Tensor Core operations are implemented using CUDA's mma and wgmma instructions.

CuTe

CUTLASS 3.0 introduced a new core library, CuTe, to describe and manipulate tensors of threads and data. CuTe is a collection of C++ CUDA template abstractions for defining and operating on hierarchically multidimensional layouts of threads and data. CuTe provides Layout and Tensor objects that compactly package the type, shape, memory space, and layout of data, while performing the complicated indexing for the user. This lets programmers focus on the logical descriptions of their algorithms while CuTe does the mechanical bookkeeping for them. With these tools, we can quickly design, implement, and modify all dense linear algebra operations.

The core abstractions of CuTe are hierarchically multidimensional layouts which can be composed with data arrays to represent tensors. The representation of layouts is powerful enough to represent nearly everything we need to implement efficient dense linear algebra. Layouts can also be combined and manipulated via functional composition, on which we build a large set of common operations such as tiling and partitioning.

CUTLASS 3.0 and beyond adopts CuTe throughout the GEMM hierarchy in its templates. This greatly simplifies the design and improves code composability and readability. More documentation specific to CuTe can be found in its dedicated documentation directory.

Compatibility

Minimum requirements:

Architecture: Volta (compute capability 7.0)
Compiler: Must support at least C++17
CUDA Toolkit version: 11.4

CUTLASS requires a C++17 host compiler and performs best when built with the CUDA 12.8 Toolkit. It is also compatible with CUDA 11.4, CUDA 11.5, CUDA 11.6, CUDA 11.7, CUDA 11.8, and all other CUDA 12.x versions.

Operating Systems

We have tested the following environments.

Operating System	Compiler
Ubuntu 18.04	GCC 7.5.0
Ubuntu 20.04	GCC 10.3.0
Ubuntu 22.04	GCC 11.2.0

Note: GCC 8.5.0 has known regressions regarding fold expressions and overloaded operators. Using GCC 7.5.0 or (preferred) GCC >= 9 is recommended.

Note: CUTLASS 3.x builds are known to be down on Windows platforms for all CUDA toolkits. CUTLASS team is working on a fix.

Hardware

CUTLASS runs successfully on the following NVIDIA GPUs, and it is expected to be efficient on Volta, Turing, Ampere, Ada, and Hopper architecture based NVIDIA GPUs.

GPU	CUDA Compute Capability	Minimum CUDA Toolkit Required by CUTLASS-3
NVIDIA V100 Tensor Core GPU	7.0	11.4
NVIDIA TitanV	7.0	11.4
NVIDIA GeForce RTX 20x0 series	7.5	11.4
NVIDIA T4	7.5	11.4
NVIDIA A100 Tensor Core GPU	8.0	11.4
NVIDIA A10	8.6	11.4
NVIDIA GeForce RTX 30x0 series	8.6	11.4
NVIDIA GeForce RTX 40x0 series	8.9	11.8
NVIDIA L40	8.9	11.8
NVIDIA H100 Tensor Core GPU	9.0	11.8
NVIDIA H200 Tensor Core GPU	9.0	11.8
NVIDIA B200 Tensor Core GPU	10.0	12.8
NVIDIA B300 Tensor Core GPU	10.3	13.0
NVIDIA DRIVE Thor	11.0	13.0
NVIDIA GeForce RTX 50x0 series	12.0	12.8
NVIDIA DGX Spark	12.1	13.0

Target Architecture

In general, PTX code generated for one target architecture can be run on future architectures (i.e., it is forward compatible). However, CUDA 12.0 introduced the concept of "architecture-accelerated features" whose PTX does not have forward compatibility guarantees. Several Hopper and Blackwell PTX instructions fall under this category of architecture-accelerated features, and thus require a sm_90a or sm100a target architecture (note the "a" appended). For more details on this and other architecture-accelerated instructions, please refer to the CUDA Documentation.

The target architecture information is passed on to CUTLASS via the cmake flag CUTLASS_NVCC_ARCHS. In order to maximize performance on Hopper GH100, users are required to build CUTLASS with 90a as the target architecture. If a user accidentally builds a kernel which uses SM90a features (e.g. Hopper Tensor Core Instructions), using the SM90 target (note the lack of "a"), with either CUDA Toolkit 12 or 11.8, the kernel is expected to fail with a runtime error.

cmake .. -DCUTLASS_NVCC_ARCHS="90a"

cmake .. -DCUTLASS_NVCC_ARCHS="100a"

Note: The NVIDIA Blackwell SM100 architecture used in the datacenter products has a different compute capability than the one underpinning NVIDIA Blackwell GeForce RTX 50 series GPUs (SM120). As a result, kernels compiled for Blackwell SM100 architecture with arch conditional features (using sm100a) are not compatible with RTX 50 series GPUs.

Please refer to the functionality documentation for details on which kernels require which target architectures.

Documentation

CUTLASS is described in the following documents and the accompanying Doxygen documentation.

Quick Start Guide - basics of building and running CUTLASS
Functionality - summarizes functionality available in CUTLASS
Efficient GEMM in CUDA - describes how GEMM kernels may be implemented efficiently in CUDA
CUTLASS 3.x Design - describes the CUTLASS 3.x design, its benefits, and how CuTe enables us to write much more composable components
GEMM API 3.x - describes the CUTLASS 3.x GEMM model and C++ template concepts
GEMM API 2.x - describes the CUTLASS 2.x GEMM model and C++ template concepts
Implicit GEMM Convolution - describes 2-D and 3-D convolution in CUTLASS
Code Organization - describes the organization and contents of the CUTLASS project
Terminology - describes terms used in the code
Programming Guidelines - guidelines for writing efficient modern CUDA C++
Fundamental types - describes basic C++ classes used in CUTLASS to represent numeric quantities and arrays
Layouts - describes layouts of matrices and tensors in memory
Tile Iterators - describes C++ concepts for iterating over tiles of matrices in memory
CUTLASS Profiler - command-line driven profiling application
CUTLASS Utilities - additional templates used to facilitate rapid development
Dependent kernel launch - describes a new feature in Hopper which allows overlapping dependent kernels in the same stream, and how it is used in CUTLASS.

Resources

We have also described the structure of an efficient GEMM in our talk at the GPU Technology Conference 2018.

Building CUTLASS

CUTLASS is a header-only template library and does not need to be built to be used by other projects. Client applications should target CUTLASS's include/ directory in their include paths.

CUTLASS unit tests, examples, and utilities can be build with CMake. The minimum version of CMake is given in the Quickstart guide. Make sure the CUDACXX environment variable points to NVCC in the CUDA Toolkit installed on your system.

$ export CUDACXX=${CUDA_INSTALL_PATH}/bin/nvcc

Create a build directory within the CUTLASS project, then run CMake. By default CUTLASS will build kernels for CUDA architecture versions 5.0, 6.0, 6.1, 7.0, 7.5, 8.0, 8.6, 8.9, and 9.0. To reduce compile time you can specify the architectures to build CUTLASS for by changing the CMake configuration setting CUTLASS_NVCC_ARCHS.

$ mkdir build && cd build

$ cmake .. -DCUTLASS_NVCC_ARCHS=80               # compiles for NVIDIA's Ampere Architecture

From the build/ directory, compile and run the CUTLASS unit tests by building the target test_unit with make.

The unit tests are organized as several binaries mirroring the top-level namespaces of CUTLASS, and they may be executed in parallel via make's -j command line argument.

$ make test_unit -j
...
...
...
[----------] Global test environment tear-down
[==========] 946 tests from 57 test cases ran. (10812 ms total)
[  PASSED  ] 946 tests.

All tests should pass on supported platforms, though the exact number of tests may vary over time.

Project Structure

CUTLASS is arranged as a header-only library along with Utilities, Tools, Examples, and unit tests. Doxygen documentation provides a complete list of files, classes, and template concepts defined in the CUTLASS project.

A detailed explanation of the source code organization may be found in the CUTLASS documentation, but several main components are summarized below.

CUTLASS Template Library

include/                     # client applications should target this directory in their build's include paths

  cutlass/                   # CUDA Templates for Linear Algebra Subroutines and Solvers - headers only

    arch/                    # direct exposure of architecture features (including instruction-level GEMMs)

    conv/                    # code specialized for convolution

    epilogue/                # code specialized for the epilogue of gemm/convolution

    gemm/                    # code specialized for general matrix product computations

    layout/                  # layout definitions for matrices, tensors, and other mathematical objects in memory

    platform/                # CUDA-capable Standard Library components

    reduction/               # bandwidth-limited reduction kernels that do not fit the "gemm" model

    thread/                  # simt code that can be performed within a CUDA thread

    transform/               # code specialized for layout, type, and domain transformations

    *                        # core vocabulary types, containers, and basic numeric operations

  cute/                      # CuTe Layout, layout algebra, MMA/Copy atoms, tiled MMA/Copy

    algorithm/               # Definitions of core operations such as copy, gemm, and operations on cute::tuples

    arch/                    # Bare bones PTX wrapper structs for copy and math instructions

    atom/                    # Meta-information either link to or built from arch/ operators

      mma_atom.hpp           # cute::Mma_Atom and cute::TiledMma

      copy_atom.hpp          # cute::Copy_Atom and cute::TiledCopy

      *sm*.hpp               # Arch specific meta-information for copy and math operations

    *                        # Core library types such as Shape, Stride, Layout, Tensor, and associated operations

CUTLASS SDK Examples

CUTLASS SDK examples apply CUTLASS templates to implement basic computations.

Tools

tools/
  library/                   # CUTLASS Instance Library - contains instantiations of all supported CUTLASS templates
    include/
      cutlass/
        library/

  profiler/                  # CUTLASS Profiler         - command-line utility for executing operations in the
                             #                            CUTLASS Library

  util/                      # CUTLASS Utilities        - contains numerous helper classes for
    include/                 #                            managing tensors in device memory, reference
      cutlass/               #                            implementations for GEMM, random initialization
        util/                #                            of tensors, and I/O.

Test

The test/unit/ directory consist of unit tests implemented with Google Test that demonstrate basic usage of Core API components and complete tests of the CUTLASS GEMM computations.

Instructions for building and running the Unit tests are described in the Quickstart guide.

Performance Profiling

The tools/profiler/ directory contains a command-line utility for launching each of the GEMM kernels. It can be built as follows:

$ make cutlass_profiler -j16

Building all GEMM and Convolution kernels (long build times)

By default, only one tile size is instantiated for each data type, math instruction, and layout. To instantiate all, set the following environment variable when running CMake from an empty build/ directory. Beware, this results in tens of thousands of kernels and long build times. This would also result in a large binary size and on some platforms linker to fail on building the library. Therefore, it's highly recommended to generate only a subset of kernels as demonstrated in the sub-section below.

$ cmake .. -DCUTLASS_NVCC_ARCHS=90a -DCUTLASS_LIBRARY_KERNELS=all
...
$ make cutlass_profiler -j16

Building a subset of GEMM and Convolution kernels (reduced build times)

To compile strictly one kernel or a small set of kernels, a comma-delimited list of kernel names with wildcard characters may be used to reduce the set of kernels. The following examples show building exactly one or a subset of kernels for NVIDIA Ampere and Turing architecture:

Building a subset Tensor Core GEMM kernels

To compile a subset of Tensor Core GEMM kernels with FP32 accumulation and FP16 input targeting NVIDIA Ampere and Turing architecture, use the below cmake command line:

$ cmake .. -DCUTLASS_NVCC_ARCHS='75;80' -DCUTLASS_LIBRARY_KERNELS=cutlass_tensorop_s*gemm_f16_*_nt_align8
...
$ make cutlass_profiler -j16

Example command line for profiling a subset of Tensor Core GEMM kernels is as follows:

./tools/profiler/cutlass_profiler --kernels=cutlass_tensorop_s*gemm_f16_*_nt_align8 --m=3456 --n=4096 --k=4096

...
=============================
  Problem ID: 1

        Provider: CUTLASS
   OperationKind: gemm
       Operation: cutlass_tensorop_s1688gemm_f16_256x128_32x2_nt_align8

          Status: Success
    Verification: ON
     Disposition: Passed

reference_device: Passed
          cuBLAS: Passed

       Arguments: --gemm_kind=universal --m=3456 --n=4096 --k=4096 --A=f16:column --B=f16:row --C=f32:column --alpha=1  \
                  --beta=0 --split_k_slices=1 --batch_count=1 --op_class=tensorop --accum=f32 --cta_m=256 --cta_n=128  \
                  --cta_k=32 --stages=2 --warps_m=4 --warps_n=2 --warps_k=1 --inst_m=16 --inst_n=8 --inst_k=8 --min_cc=75  \
                  --max_cc=1024

           Bytes: 118489088  bytes
           FLOPs: 115992428544  flops

         Runtime: 1.55948  ms
          Memory: 70.7616 GiB/s

            Math: 74378.8 GFLOP/s



=============================
...

Building one CUDA Core GEMM kernel

To compile one SGEMM kernel targeting NVIDIA Ampere and Turing architecture, use the below cmake command line:

$ cmake .. -DCUTLASS_NVCC_ARCHS='75;80' -DCUTLASS_LIBRARY_KERNELS=cutlass_simt_sgemm_128x128_8x2_nn_align1
...
$ make cutlass_profiler -j16

Example command line for profiling single SGEMM CUDA kernel is as follows:

$ ./tools/profiler/cutlass_profiler --kernels=sgemm --m=3456 --n=4096 --k=4096

=============================
  Problem ID: 1

        Provider: CUTLASS
   OperationKind: gemm
       Operation: cutlass_simt_sgemm_128x128_8x2_nn_align1

          Status: Success
    Verification: ON
     Disposition: Passed

          cuBLAS: Passed

       Arguments: --m=3456 --n=4096 --k=4096 --A=f32:column --B=f32:column --C=f32:column --alpha=1 --beta=0 --split_k_slices=1  \
                  --batch_count=1 --op_class=simt --accum=f32 --cta_m=128 --cta_n=128 --cta_k=8 --stages=2 --warps_m=4  \
                  --warps_n=2 --warps_k=1 --inst_m=1 --inst_n=1 --inst_k=1 --min_cc=50 --max_cc=1024

           Bytes: 180355072  bytes
           FLOPs: 115992428544  flops

         Runtime: 6.73655  ms
          Memory: 24.934 GiB/s

            Math: 17218.4 GFLOP/s

=============================

Building a subset of Tensor Core Convolution kernels

To compile a subset of Tensor core convolution kernels implementing forward propagation (fprop) with FP32 accumulation and FP16 input targeting NVIDIA Ampere and Turing architecture, use the below cmake command line:

$ cmake .. -DCUTLASS_NVCC_ARCHS='75;80' -DCUTLASS_LIBRARY_KERNELS=cutlass_tensorop_s*fprop_optimized_f16
...
$ make cutlass_profiler -j16

Example command line for profiling a subset of Tensor Core convolution kernels is as follows:

$ ./tools/profiler/cutlass_profiler --kernels=cutlass_tensorop_s*fprop_optimized_f16 --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3

...
=============================
  Problem ID: 1

        Provider: CUTLASS
   OperationKind: conv2d
       Operation: cutlass_tensorop_s16816fprop_optimized_f16_128x128_32x5_nhwc

          Status: Success
    Verification: ON
     Disposition: Passed

reference_device: Passed

       Arguments: --conv_kind=fprop --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3 --p=224 --q=224 --pad_h=1 --pad_w=1  \
                  --stride_h=1 --stride_w=1 --dilation_h=1 --dilation_w=1 --Activation=f16:nhwc --Filter=f16:nhwc --Output=f32:nhwc  \
                  --conv_mode=cross --iterator_algorithm=optimized --alpha=1 --beta=0 --split_k_mode=serial --split_k_slices=1  \
                  --eq_gemm_provider=none --op_class=tensorop --accum=f32 --cta_m=128 --cta_n=128 --cta_k=32 --stages=5  \
                  --warps_m=2 --warps_n=2 --warps_k=1 --inst_m=16 --inst_n=8 --inst_k=16 --min_cc=80 --max_cc=1024

           Bytes: 1130659840  bytes
           FLOPs: 118482796544  flops

         Runtime: 0.711496  ms
          Memory: 1479.99 GiB/s

            Math: 166526 GFLOP/s

=============================
...

Building one Convolution CUDA kernel

To compile and run one CUDA Core convolution kernel implementing forward propagation (fprop) with F32 accumulation and FP32 input targeting NVIDIA Ampere and Turing architecture, use the below cmake command line:

$ cmake .. -DCUTLASS_NVCC_ARCHS='75;80' -DCUTLASS_LIBRARY_KERNELS=cutlass_simt_sfprop_optimized_128x128_8x2_nhwc
...
$ make cutlass_profiler -j16

Example command line for profiling one CUDA Core convolution kernel:

$ ./tools/profiler/cutlass_profiler --kernels=cutlass_simt_sfprop_optimized_128x128_8x2_nhwc --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3


=============================
  Problem ID: 1

        Provider: CUTLASS
   OperationKind: conv2d
       Operation: cutlass_simt_sfprop_optimized_128x128_8x2_nhwc

          Status: Success
    Verification: ON
     Disposition: Passed

reference_device: Passed

       Arguments: --conv_kind=fprop --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3 --p=224 --q=224 --pad_h=1 --pad_w=1  \
                  --stride_h=1 --stride_w=1 --dilation_h=1 --dilation_w=1 --Activation=f32:nhwc --Filter=f32:nhwc --Output=f32:nhwc  \
                  --conv_mode=cross --iterator_algorithm=optimized --alpha=1 --beta=0 --split_k_mode=serial --split_k_slices=1  \
                  --eq_gemm_provider=none --op_class=simt --accum=f32 --cta_m=128 --cta_n=128 --cta_k=8 --stages=2 --warps_m=4  \
                  --warps_n=2 --warps_k=1 --inst_m=1 --inst_n=1 --inst_k=1 --min_cc=50 --max_cc=1024

           Bytes: 2055798784  bytes
           FLOPs: 118482796544  flops

         Runtime: 7.34266  ms
          Memory: 260.752 GiB/s

            Math: 16136.2 GFLOP/s


=============================

More Details on Compiling CUTLASS Kernels and CUTLASS Profiler

Please follow the links for more CMake examples on selectively compiling CUTLASS kernels:
- GEMM CMake Examples
- Implicit GEMM convolution CMake Examples
Further details about the CUTLASS Profiler are described here.

About

CUTLASS is released by NVIDIA Corporation as Open Source software under the 3-clause "New" BSD license.

Contributors

The official list of CUTLASS developers and contributors is available here: CONTRIBUTORS.

Copyright

  Redistribution and use in source and binary forms, with or without
  modification, are permitted provided that the following conditions are met:

  1. Redistributions of source code must retain the above copyright notice, this
  list of conditions and the following disclaimer.

  2. Redistributions in binary form must reproduce the above copyright notice,
  this list of conditions and the following disclaimer in the documentation
  and/or other materials provided with the distribution.

  3. Neither the name of the copyright holder nor the names of its
  contributors may be used to endorse or promote products derived from
  this software without specific prior written permission.

  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
  DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
  FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
  DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
  SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
  OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

amnezia-vpn/amnezia-client

Amnezia VPN Client (Desktop+Mobile)

Amnezia VPN

The best client for self-hosted VPN

English | Русский

Amnezia is an open-source VPN client, with a key feature that enables you to deploy your own VPN server on your server.

Website | Alt website link | Documentation | Troubleshooting

Tip

If the Amnezia website is blocked in your region, you can use an Alternative website link.

All releases

Features

Very easy to use - enter your IP address, SSH login, password and Amnezia will automatically install VPN docker containers to your server and connect to the VPN.
Classic VPN-protocols: OpenVPN, WireGuard and IKEv2 protocols.
Protocols with traffic Masking (Obfuscation): OpenVPN over Cloak plugin, Shadowsocks (OpenVPN over Shadowsocks), AmneziaWG and XRay.
Split tunneling support - add any sites to the client to enable VPN only for them or add Apps (only for Android and Desktop).
Windows, MacOS, Linux, Android, iOS releases.
Support for AmneziaWG protocol configuration on Keenetic beta firmware.

Tech

AmneziaVPN uses several open-source projects to work:

Help us with translations

Download the most actual translation files.

Go to "Actions" tab, click on the first line. Then scroll down to the "Artifacts" section and download "AmneziaVPN_translations".

Unzip this file. Each *.ts file contains strings for one corresponding language.

Translate or correct some strings in one or multiple *.ts files and commit them back to this repository into the client/translations folder. You can do it via a web-interface or any other method you're familiar with.

Checking out the source code

Make sure to pull all submodules after checking out the repo.

git submodule update --init --recursive

Hacking guide

Want to contribute? Welcome!

Build requirements

CMake
Compiler and underlying build system, depending on the target:
- [Linux] Any of make and gcc
- [Apple] Xcode or Xcode command line tools
- [Windows] Visual Studio 2022 or VS 2022 Build Tools
- [Android] Android SDK and Ninja
Qt 6.10+ with the following modules:
- Core module for targeting platform (Desktop/Android/iOS)
- Qt 5 Compatibility module
- Qt Remote Objects
Conan package manager
- On MacOS is enough just to use homebrew or install it in .venv in project root
- Other systems must have it in PATH
(Optional) Installer dependencies:
- [Windows/Linux] Qt Installer Framework
- [Windows] WIX toolset

Building the project using scripts

Run scripts located in deploy directory
Basically, if dependencies are located in default installation paths, the scripts will find them automatically.
If they differ, specify them using the following variables:
- QT_INSTALL_DIR - Qt root installation folder
- QT_ROOT_PATH - Qt framework root directory
- QIF_ROOT_PATH - Qt Installer Framework root path
- ANDROID_HOME - Path to Android SDK root folder
- and others. Check scripts for more

Unix-like:

# Build executables for the host platform
deploy/build.sh

# Or just
deploy/build.sh

# Build executables and installers for the host platform
deploy/build.sh --installer all

# Build Android APK and AAB
deploy/build.sh -t android --aab

# Call for help
deploy/build.sh -h

Windows:

:: Build executables for Windows
deploy/build.bat

:: Build executables with IFW installer for Windows
deploy/build.bat --installer ifw

:: Build executables with IFW and WIX installer for Windows
deploy/build.bat --installer ifw --installer wix

:: Or just
deploy/build.bat --installer all

Developing the project in IDEs

Basically, you can use any IDE that handles CMake and Qt kits properly to run configure and build steps, and to navigate through the code nicely. For example:
- Qt Creator
- Visual Studio Code with Qt Extension Pack
- and so on
To use Xcode, you have to configure project first by using cmake. The easiest way to do it is to use Qt Creator for configuration. Then open AmneziaVPN.xcodeproj file from the build folder by using Xcode. Note that none of the files changed are saved - the files actually getting changed in build directory. Copy them manually if necessary
Android studio could be used in the same way - just configure the project by using cmake manually or by using Qt Creator. Open <build-dir>/client/android-build in Android studio then. Do not forget to copy the changes - everything you do is saved under the build directory actually.

Installing Android SDK

Android SDK could be installed using the following methods:
- Using Qt Creator. Use Preferences->SDKs
- Using Android studio. By default it installs necessary SDKs automatically during the installation
- Manually by using sdk-manager. Check this page for details

License

This project is licensed under the GNU General Public License v3.0 (see LICENSE) and also includes third-party components distributed under their own terms (see THIRD_PARTY_LICENSES.md).

Donate

Patreon: https://www.patreon.com/amneziavpn

Bitcoin: bc1qmhtgcf9637rl3kqyy22r2a8wa8laka4t9rx2mf
USDT BEP20: 0x6abD576765a826f87D1D95183438f9408C901bE4
USDT TRC20: TELAitazF1MZGmiNjTcnxDjEiH5oe7LC9d
XMR: 48spms39jt1L2L5vyw2RQW6CXD6odUd4jFu19GZcDyKKQV9U88wsJVjSbL4CfRys37jVMdoaWVPSvezCQPhHXUW5UKLqUp3
TON: UQDpU1CyKRmg7L8mNScKk9FRc2SlESuI7N-Hby4nX-CcVmns

Acknowledgments

This project is tested with BrowserStack. We express our gratitude to BrowserStack for supporting our project.

google/perfetto

Production-grade client-side tracing, profiling, and analysis for complex software systems.

Perfetto - System profiling, app tracing and trace analysis

Perfetto is an open-source suite of SDKs, daemons and tools which use tracing to help developers understand the behaviour of complex systems and root-cause functional and performance issues on client and embedded systems.

It is a production-grade tool that is the default tracing system for the Android operating system and the Chromium browser.

Core Components

Perfetto is not a single tool, but a collection of components that work together:

High-performance tracing daemons: For capturing tracing information from many processes on a single machine into a unified trace file.
Low-overhead tracing SDK: A C++17 library for direct userspace-to-userspace tracing of timings and state changes in your application.
Extensive OS-level probes: For capturing system-wide context on Android and Linux (e.g. scheduling states, CPU frequencies, memory profiling, callstack sampling).
Browser-based UI: A powerful, fully local UI for visualizing and exploring large, multi-GB traces on a timeline. It works in all major browsers, requires no installation, and can open traces from other tools.
SQL-based analysis library: A powerful engine that allows you to programmatically query traces using SQL to automate analysis and extract custom metrics.

Why Use Perfetto?

Perfetto was designed to be a versatile and powerful tracing system for a wide range of use cases.

For Android App & Platform Developers: Debug and root-cause functional and performance issues like slow startups, dropped frames (jank), animation glitches, low memory kills, and ANRs. Profile both Java/Kotlin and native C++ memory usage with heap dumps and profiles.
For C/C++ Developers (Linux, macOS, Windows): Use the Tracing SDK to instrument your application with custom trace points to understand its execution flow, find performance bottlenecks, and debug complex behavior. On Linux, you can also perform detailed CPU and native heap profiling.
For Linux Kernel & System Developers: Get deep insights into kernel behavior. Perfetto acts as an efficient userspace daemon for ftrace, allowing you to visualize scheduling, syscalls, interrupts, and custom kernel tracepoints on a timeline.
For Chromium Developers: Perfetto is the tracing backend for chrome://tracing. Use it to debug and root-cause issues in the browser, V8, and Blink.
For Performance Engineers & SREs: Analyze and visualize a wide range of profiling and tracing formats, not just Perfetto's. Use the powerful SQL interface to programmatically analyze traces from tools like Linux perf, macOS Instruments, Chrome JSON traces, and more.

Getting Started

We've designed our documentation to guide you to the right information as quickly as possible, whether you're a newcomer to performance analysis or an experienced developer.

New to tracing? If you're unfamiliar with concepts like tracing and profiling, start here:
- What is Tracing? - A gentle introduction to the world of performance analysis.
Ready to dive in? Our "Getting Started" guide is the main entry point for all users. It will help you find the right tutorials and documentation for your specific needs:
- How do I start using Perfetto? - Find your path based on your role and goals (e.g., Android App Developer, C/C++ Developer, etc.).
Want the full overview? For a comprehensive look at what Perfetto is, why it's useful, and who uses it, see our main documentation page:
- Perfetto Documentation Home

Debian Distribution

For users interested in the Debian distribution of Perfetto, the official source of truth and packaging efforts are maintained at Debian Perfetto Salsa Repository

Community & Support

Have questions? Need help?

GitHub Discussions: For Q&A and general discussions.
GitHub Issues: For bug reports.
Discord: For live chat with the community and developers.

We follow Google's Open Source Community Guidelines.

SerenityOS/serenity

The Serenity Operating System 🐞

SerenityOS

Graphical Unix-like operating system for 64-bit x86, Arm, and RISC-V computers.

FAQ | Documentation | Build Instructions

About

SerenityOS is a love letter to '90s user interfaces with a custom Unix-like core. It flatters with sincerity by stealing beautiful ideas from various other systems.

Roughly speaking, the goal is a marriage between the aesthetic of late-1990s productivity software and the power-user accessibility of late-2000s *nix. This is a system by us, for us, based on the things we like.

You can watch videos of the system being developed on YouTube:

Screenshot

Features

Modern 64-bit kernel with pre-emptive multi-threading
Browser with JavaScript, WebAssembly, and more (check the spec compliance for JS, CSS, and Wasm)
Security features (hardware protections, limited userland capabilities, W^X memory, pledge & unveil, (K)ASLR, OOM-resistance, web-content isolation, state-of-the-art TLS algorithms, ...)
System services (WindowServer, LoginServer, AudioServer, WebServer, RequestServer, CrashServer, ...) and modern IPC
Good POSIX compatibility (LibC, Shell, syscalls, signals, pseudoterminals, filesystem notifications, standard Unix utilities, ...)
POSIX-like virtual file systems (/proc, /dev, /sys, /tmp, ...) and ext2 file system
Network stack and applications with support for IPv4, TCP, UDP; DNS, HTTP, Gemini, IMAP, NTP
Profiling, debugging and other development tools (Kernel-supported profiling, CrashReporter, interactive GUI playground, HexEditor, HackStudio IDE for C++ and more)
Libraries for everything from cryptography to OpenGL, audio, JavaScript, GUI, playing chess, ...
Support for many common and uncommon file formats (PNG, JPEG, GIF, MP3, WAV, FLAC, ZIP, TAR, PDF, QOI, Gemini, ...)
Unified style and design philosophy, flexible theming system, custom (bitmap and vector) fonts
Games (Solitaire, Minesweeper, 2048, chess, Conway's Game of Life, ...) and demos (CatDog, Starfield, Eyes, mandelbrot set, WidgetGallery, ...)
Every-day GUI programs and utilities (Spreadsheet with JavaScript, TextEditor, Terminal, PixelPaint, various multimedia viewers and players, Mail, Assistant, Calculator, ...)

... and all of the above are right in this repository, no extra dependencies, built from-scratch by us :^)

Additionally, there are over three hundred ports of popular open-source software, including games, compilers, Unix tools, multimedia apps and more.

How do I read the documentation?

Man pages are available online at man.serenityos.org. These pages are generated from the Markdown source files in Base/usr/share/man and updated automatically.

When running SerenityOS you can use man for the terminal interface, or help for the GUI.

Code-related documentation can be found in the documentation folder.

How do I build and run this?

See the SerenityOS build instructions or the Ladybird build instructions.

The build system supports a cross-compilation build of SerenityOS from Linux, macOS, Windows (with WSL2) and many other *Nixes. The default build system commands will launch a QEMU instance running the OS with hardware or software virtualization enabled as supported.

Ladybird runs on the same platforms that can be the host for a cross build of SerenityOS and on SerenityOS itself.

Get in touch and participate!

Join our Discord server: SerenityOS Discord

Before opening an issue, please see the issue policy.

A general guide for contributing can be found in CONTRIBUTING.md.

Authors

Andreas Kling - awesomekling
Robin Burchell - rburchell
Conrad Pankoff - deoxxa
Sergey Bugaev - bugaevc
Liav A - supercomputer7
Linus Groh - linusg
Ali Mohammad Pur - alimpfard
Shannon Booth - shannonbooth
Hüseyin ASLITÜRK - asliturk
Matthew Olsson - mattco98
Nico Weber - nico
Brian Gianforcaro - bgianfo
Ben Wiederhake - BenWiederhake
Tom - tomuta
Paul Scharnofske - asynts
Itamar Shenhar - itamar8910
Luke Wilde - Lubrsi
Brendan Coles - bcoles
Andrew Kaster - ADKaster
thankyouverycool - thankyouverycool
Idan Horowitz - IdanHo
Gunnar Beutner - gunnarbeutner
Tim Flynn - trflynn89
Jean-Baptiste Boric - boricj
Stephan Unverwerth - sunverwerth
Max Wipfli - MaxWipfli
Daniel Bertalan - BertalanD
Jelle Raaijmakers - GMTA
Sam Atkins - AtkinsSJ
Tobias Christiansen - TobyAsE
Lenny Maiorani - ldm5180
sin-ack - sin-ack
Jesse Buhagiar - Quaker762
Peter Elliott - Petelliott
Karol Kosek - krkk
Mustafa Quraish - mustafaquraish
David Tuin - davidot
Leon Albrecht - Hendiadyoin1
Tim Schumacher - timschumi
Marcus Nilsson - metmo
Gegga Thor - Xexxa
kleines Filmröllchen - kleinesfilmroellchen
Kenneth Myhra - kennethmyhra
Maciej - sppmacd
Sahan Fernando - ccapitalK
Benjamin Maxwell - MacDue
Dennis Esternon - djwisdom
frhun - frhun
networkException - networkException
Brandon Jordan - electrikmilk
Lucas Chollet - LucasChollet
Timon Kruiper - FireFox317
Martin Falisse - martinfalisse
Gregory Bertilson - Zaggy1024
Erik Wouters - EWouters
Rodrigo Tobar - rtobar
Alexander Kalenik - kalenikaliaksandr
Tim Ledbetter - tcl3
Steffen T. Larssen - stelar7
Andi Gallo - axgallo
Simon Wanner - skyrising
FalseHonesty - FalseHonesty
Bastiaan van der Plaat - bplaat
Dan Klishch - DanShaders
Julian Offenhäuser - janso3
Sönke Holz - spholz
implicitfield - implicitfield

And many more! See here for a full contributor list. The people listed above have landed more than 100 commits in the project. :^)

License

SerenityOS is licensed under a 2-clause BSD license.

ggml-org/llama.cpp

LLM inference in C/C++

llama.cpp

Manifesto / ggml / ops

LLM inference in C/C++

Recent API changes

Hot topics

Hugging Face cache migration: models downloaded with -hf are now stored in the standard Hugging Face cache directory, enabling sharing with other HF tools.
guide : using the new WebUI of llama.cpp
guide : running gpt-oss with llama.cpp
[FEEDBACK] Better packaging for llama.cpp to support downstream consumers 🤗
Support for the gpt-oss model with native MXFP4 format has been added | PR | Collaboration with NVIDIA | Comment
Multimodal support arrived in llama-server: #12898 | documentation
VS Code extension for FIM completions: https://github.com/ggml-org/llama.vscode
Vim/Neovim plugin for FIM completions: https://github.com/ggml-org/llama.vim
Hugging Face Inference Endpoints now support GGUF out of the box! https://github.com/ggml-org/llama.cpp/discussions/9669
Hugging Face GGUF editor: discussion | tool
WebGPU support is now available in the browser, see a blog/demo introducing it here.

Quick start

Getting started with llama.cpp is straightforward. Here are several ways to install it on your machine:

Install llama.cpp using brew, nix or winget
Run with Docker - see our Docker documentation
Download pre-built binaries from the releases page
Build from source by cloning this repository - check out our build guide

Once installed, you'll need a model to work with. Head to the Obtaining and quantizing models section to learn more.

Example command:

# Use a local model file
llama-cli -m my_model.gguf

# Or download and run a model directly from Hugging Face
llama-cli -hf ggml-org/gemma-3-1b-it-GGUF

# Launch OpenAI-compatible API server
llama-server -hf ggml-org/gemma-3-1b-it-GGUF

Description

The main goal of llama.cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware - locally and in the cloud.

Plain C/C++ implementation without any dependencies
Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
AVX, AVX2, AVX512 and AMX support for x86 architectures
RVV, ZVFH, ZFH, ZICBOP and ZIHINTPAUSE support for RISC-V architectures
1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads GPUs via MUSA)
Vulkan and SYCL backend support
CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity

The llama.cpp project is the main playground for developing new features for the ggml library.

Models

Typically finetunes of the base models below are supported as well.

Instructions for adding support for new models: HOWTO-add-model.md

Text-only

Multimodal

Bindings

Python: ddh0/easy-llama
Python: abetlen/llama-cpp-python
Go: go-skynet/go-llama.cpp
Node.js: withcatai/node-llama-cpp
JS/TS (llama.cpp server client): lgrammel/modelfusion
JS/TS (Programmable Prompt Engine CLI): offline-ai/cli
JavaScript/Wasm (works in browser): tangledgroup/llama-cpp-wasm
Typescript/Wasm (nicer API, available on npm): ngxson/wllama
Ruby: yoshoku/llama_cpp.rb
Ruby: docusealco/rllama
Rust (more features): edgenai/llama_cpp-rs
Rust (nicer API): mdrokz/rust-llama.cpp
Rust (more direct bindings): utilityai/llama-cpp-rs
Rust (automated build from crates.io): ShelbyJenkins/llm_client
C#/.NET: SciSharp/LLamaSharp
C#/VB.NET (more features - community license): LM-Kit.NET
Scala 3: donderom/llm4s
Clojure: phronmophobic/llama.clj
React Native: mybigday/llama.rn
Java: kherud/java-llama.cpp
Java: QuasarByte/llama-cpp-jna
Zig: deins/llama.cpp.zig
Flutter/Dart: netdur/llama_cpp_dart
Flutter: xuegao-tzx/Fllama
PHP (API bindings and features built on top of llama.cpp): distantmagic/resonance (more info)
Guile Scheme: guile_llama_cpp
Swift srgtuszy/llama-cpp-swift
Swift ShenghaiWang/SwiftLlama
Delphi Embarcadero/llama-cpp-delphi
Go (no CGo needed): hybridgroup/yzma
Android: llama.android

UIs

(to have a project listed here, it should clearly state that it depends on llama.cpp)

AI Sublime Text plugin (MIT)
BonzAI App (proprietary)
cztomsik/ava (MIT)
Dot (GPL)
eva (MIT)
iohub/collama (Apache-2.0)
janhq/jan (AGPL)
johnbean393/Sidekick (MIT)
KanTV (Apache-2.0)
KodiBot (GPL)
llama.vim (MIT)
LARS (AGPL)
Llama Assistant (GPL)
LlamaLib (Apache-2.0)
LLMFarm (MIT)
LLMUnity (MIT)
LMStudio (proprietary)
LocalAI (MIT)
LostRuins/koboldcpp (AGPL)
MindMac (proprietary)
MindWorkAI/AI-Studio (FSL-1.1-MIT)
Mobile-Artificial-Intelligence/maid (MIT)
Mozilla-Ocho/llamafile (Apache-2.0)
nat/openplayground (MIT)
nomic-ai/gpt4all (MIT)
ollama/ollama (MIT)
oobabooga/text-generation-webui (AGPL)
PocketPal AI (MIT)
psugihara/FreeChat (MIT)
ptsochantaris/emeltal (MIT)
pythops/tenere (AGPL)
ramalama (MIT)
semperai/amica (MIT)
withcatai/catai (MIT)
Autopen (GPL)

Tools

akx/ggify – download PyTorch models from Hugging Face Hub and convert them to GGML
akx/ollama-dl – download models from the Ollama library to be used directly with llama.cpp
crashr/gppm – launch llama.cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption
gpustack/gguf-parser - review/check the GGUF file and estimate the memory usage
Styled Lines (proprietary licensed, async wrapper of inference part for game development in Unity3d with pre-built Mobile and Web platform wrappers and a model example)
unslothai/unsloth – 🦥 exports/saves fine-tuned and trained models to GGUF (Apache-2.0)

Infrastructure

Paddler - Open-source LLMOps platform for hosting and scaling AI in your own infrastructure
GPUStack - Manage GPU clusters for running LLMs
llama_cpp_canister - llama.cpp as a smart contract on the Internet Computer, using WebAssembly
llama-swap - transparent proxy that adds automatic model switching with llama-server
Kalavai - Crowdsource end to end LLM deployment at any scale
llmaz - ☸️ Easy, advanced inference platform for large language models on Kubernetes.
LLMKube - Kubernetes operator for llama.cpp with multi-GPU and Apple Silicon Metal support"

Games

Lucy's Labyrinth - A simple maze game where agents controlled by an AI model will try to trick you.

Supported backends

Backend	Target devices
Metal	Apple Silicon
BLAS	All
BLIS	All
SYCL	Intel GPU
OpenVINO [In Progress]	Intel CPUs, GPUs, and NPUs
MUSA	Moore Threads GPU
CUDA	Nvidia GPU
HIP	AMD GPU
ZenDNN	AMD CPU
Vulkan	GPU
CANN	Ascend NPU
OpenCL	Adreno GPU
IBM zDNN	IBM Z & LinuxONE
WebGPU	All
RPC	All
Hexagon [In Progress]	Snapdragon
VirtGPU	VirtGPU APIR

Obtaining and quantizing models

The Hugging Face platform hosts a number of LLMs compatible with llama.cpp:

You can either manually download the GGUF file or directly use any llama.cpp-compatible models from Hugging Face or other model hosting sites, by using this CLI argument: -hf <user>/<model>[:quant]. For example:

llama-cli -hf ggml-org/gemma-3-1b-it-GGUF

By default, the CLI would download from Hugging Face, you can switch to other options with the environment variable MODEL_ENDPOINT. The MODEL_ENDPOINT must point to a Hugging Face compatible API endpoint.

After downloading a model, use the CLI tools to run it locally - see below.

llama.cpp requires the model to be stored in the GGUF file format. Models in other data formats can be converted to GGUF using the convert_*.py Python scripts in this repo.

The Hugging Face platform provides a variety of online tools for converting, quantizing and hosting models with llama.cpp:

Use the GGUF-my-repo space to convert to GGUF format and quantize model weights to smaller sizes
Use the GGUF-my-LoRA space to convert LoRA adapters to GGUF format (more info: https://github.com/ggml-org/llama.cpp/discussions/10123)
Use the GGUF-editor space to edit GGUF meta data in the browser (more info: https://github.com/ggml-org/llama.cpp/discussions/9268)
Use the Inference Endpoints to directly host llama.cpp in the cloud (more info: https://github.com/ggml-org/llama.cpp/discussions/9669)

To learn more about model quantization, read this documentation

`llama-cli`

A CLI tool for accessing and experimenting with most of `llama.cpp`'s functionality.

Run in conversation mode

Models with a built-in chat template will automatically activate conversation mode. If this doesn't occur, you can manually enable it by adding -cnv and specifying a suitable chat template with --chat-template NAME

llama-cli -m model.gguf

# > hi, who are you?
# Hi there! I'm your helpful assistant! I'm an AI-powered chatbot designed to assist and provide information to users like you. I'm here to help answer your questions, provide guidance, and offer support on a wide range of topics. I'm a friendly and knowledgeable AI, and I'm always happy to help with anything you need. What's on your mind, and how can I assist you today?
#
# > what is 1+1?
# Easy peasy! The answer to 1+1 is... 2!

Run in conversation mode with custom chat template

# use the "chatml" template (use -h to see the list of supported templates)
llama-cli -m model.gguf -cnv --chat-template chatml

# use a custom template
llama-cli -m model.gguf -cnv --in-prefix 'User: ' --reverse-prompt 'User:'

Constrain the output with a custom grammar
```
llama-cli -m model.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:'

# {"appointmentTime": "8pm", "appointmentDetails": "schedule a a call"}
```
The grammars/ folder contains a handful of sample grammars. To write your own, check out the GBNF Guide.

For authoring more complex JSON grammars, check out https://grammar.intrinsiclabs.ai/

`llama-server`

A lightweight, OpenAI API compatible, HTTP server for serving LLMs.

Start a local HTTP server with default configuration on port 8080

llama-server -m model.gguf --port 8080

# Basic web UI can be accessed via browser: http://localhost:8080
# Chat completion endpoint: http://localhost:8080/v1/chat/completions

Support multiple-users and parallel decoding

# up to 4 concurrent requests, each with 4096 max context
llama-server -m model.gguf -c 16384 -np 4

Enable speculative decoding

# the draft.gguf model should be a small variant of the target model.gguf
llama-server -m model.gguf -md draft.gguf

Serve an embedding model

# use the /embedding endpoint
llama-server -m model.gguf --embedding --pooling cls -ub 8192

Serve a reranking model

# use the /reranking endpoint
llama-server -m model.gguf --reranking

Constrain all outputs with a grammar

# custom grammar
llama-server -m model.gguf --grammar-file grammar.gbnf

# JSON
llama-server -m model.gguf --grammar-file grammars/json.gbnf

`llama-perplexity`

A tool for measuring the perplexity ^[1] (and other quality metrics) of a model over a given text.

Measure the perplexity over a text file

llama-perplexity -m model.gguf -f file.txt

# [1]15.2701,[2]5.4007,[3]5.3073,[4]6.2965,[5]5.8940,[6]5.6096,[7]5.7942,[8]4.9297, ...
# Final estimate: PPL = 5.4007 +/- 0.67339

Measure KL divergence
```
# TODO
```

`llama-bench`

Benchmark the performance of the inference for various parameters.

Run default benchmark

llama-bench -m model.gguf

# Output:
# | model               |       size |     params | backend    | threads |          test |                  t/s |
# | ------------------- | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |
# | qwen2 1.5B Q4_0     | 885.97 MiB |     1.54 B | Metal,BLAS |      16 |         pp512 |      5765.41 ± 20.55 |
# | qwen2 1.5B Q4_0     | 885.97 MiB |     1.54 B | Metal,BLAS |      16 |         tg128 |        197.71 ± 0.81 |
#
# build: 3e0ba0e60 (4229)

`llama-simple`

A minimal example for implementing apps with `llama.cpp`. Useful for developers.

Basic text completion

llama-simple -m model.gguf

# Hello my name is Kaitlyn and I am a 16 year old girl. I am a junior in high school and I am currently taking a class called "The Art of

Contributing

Contributors can open PRs
Collaborators will be invited based on contributions
Maintainers can push to branches in the llama.cpp repo and merge PRs into the master branch
Any help with managing issues, PRs and projects is very appreciated!
See good first issues for tasks suitable for first contributions
Read the CONTRIBUTING.md for more information
Make sure to read this: Inference at the edge
A bit of backstory for those who are interested: Changelog podcast

XCFramework

The XCFramework is a precompiled version of the library for iOS, visionOS, tvOS, and macOS. It can be used in Swift projects without the need to compile the library from source. For example:

// swift-tools-version: 5.10
// The swift-tools-version declares the minimum version of Swift required to build this package.

import PackageDescription

let package = Package(
    name: "MyLlamaPackage",
    targets: [
        .executableTarget(
            name: "MyLlamaPackage",
            dependencies: [
                "LlamaFramework"
            ]),
        .binaryTarget(
            name: "LlamaFramework",
            url: "https://github.com/ggml-org/llama.cpp/releases/download/b5046/llama-b5046-xcframework.zip",
            checksum: "c19be78b5f00d8d29a25da41042cb7afa094cbf6280a225abe614b03b20029ab"
        )
    ]
)

The above example is using an intermediate build b5046 of the library. This can be modified to use a different version by changing the URL and checksum.

Completions

Command-line completion is available for some environments.

Bash Completion

$ build/bin/llama-cli --completion-bash > ~/.llama-completion.bash
$ source ~/.llama-completion.bash

Optionally this can be added to your .bashrc or .bash_profile to load it automatically. For example:

$ echo "source ~/.llama-completion.bash" >> ~/.bashrc

Dependencies

yhirose/cpp-httplib - Single-header HTTP server, used by llama-server - MIT license
stb-image - Single-header image format decoder, used by multimodal subsystem - Public domain
nlohmann/json - Single-header JSON library, used by various tools/examples - MIT License
miniaudio.h - Single-header audio format decoder, used by multimodal subsystem - Public domain
subprocess.h - Single-header process launching solution for C and C++ - Public domain

https://huggingface.co/docs/transformers/perplexity ↩︎

ElyPrismLauncher/Launcher

This fork of Prism Launcher adds integrated support for Ely.by accounts.

Caution

There's now malicious links in the wild that disguise themselves as this project.

Make sure that you download PineconeMC from elyprismlauncher.github.io and/or pineconemc.ru.

This fork of Prism Launcher adds integrated support for Ely.by accounts (MSA accounts can still be used)

This is not endorsed by Prism Launcher or Ely.by.

Why this fork?

There are already quite a few forks out in the wild that add Ely.by support and/or disable the Microsoft account requirement. This fork goes beyond simply adding a login-password prompt and authlib-injector download.

Modern and secure login: PineconeMC uses OAuth2 to log you in. This means that your credentials are never transferred to the launcher. Instead, you log into your account on the official Ely.by page in the browser, and Ely.by gives the launcher a token to access your account with limited privileges.
Skins support on servers: All other forks rely exclusively on authlib-injector to patch Minecraft to support Ely.by. But authlib-injector can't provide skins on servers that don't have a special skins plugin installed. PineconeMC uses Ely.by's official Authlib patches, allowing you to see skins anywhere

Installation

All downloads and instructions for PineconeMC can be found on the Releases page.
Last build status can be found in the GitHub Actions tab.

Development Builds

Please understand that these builds are not intended for most users. There may be bugs, and other instabilities. You have been warned.

There are development builds available through:

GitHub Actions (includes builds from pull requests opened by contributors)
nightly.link (this will always point only to the latest version of develop)

These have debug information in the binaries, so their file sizes are relatively larger.

Community & Support

Feel free to create a GitHub issue if you find a bug or want to suggest a new feature. We have a Discord server where other community members can help you:

Building

If you want to build PineconeMC yourself, check the build instructions.

The following comes from the original Prism Launcher README

Forking/Redistributing/Custom builds policy

You are free to fork, redistribute and provide custom builds as long as you follow the terms of the license (this is a legal responsibility), and if you made code changes rather than just packaging a custom build, please do the following as a basic courtesy:

Make it clear that your fork is not Prism Launcher and is not endorsed by or affiliated with the Prism Launcher project (https://prismlauncher.org).
Go through CMakeLists.txt and change Prism Launcher's API keys to your own or set them to empty strings ("") to disable them (this way the program will still compile but the functionality requiring those keys will be disabled).

If you have any questions or want any clarification on the above conditions please make an issue and ask us.

If you are just building Prism Launcher for your distribution, please make sure to set the Launcher_BUILD_PLATFORM to a slug representing your distribution. Examples are archlinux, fedora and nixpkgs.

Note that if you build this software without removing the provided API keys in CMakeLists.txt you are accepting the following terms and conditions:

If you do not agree with these terms and conditions, then remove the associated API keys from the CMakeLists.txt file by setting them to an empty string ("").

License

All launcher code is available under the GPL-3.0-only license.

The logo and related assets are under the CC BY-SA 4.0 license.

shadps4-emu/shadPS4

PlayStation 4 emulator for Windows, Linux, macOS and FreeBSD written in C++

shadPS4

Bloodborne by From Software	Hatsune Miku Project DIVA Future Tone by SEGA

Yakuza 0 by SEGA	DRIVECLUB™ by Evolution Studios

General information

shadPS4 is an early PlayStation 4 emulator for Windows, Linux and macOS written in C++.

Important

This is the emulator core, which does not include a GUI. If you just want to use the emulator as an end user, download the QtLauncher instead.

If you encounter problems or have doubts, do not hesitate to look at the Quickstart.
To verify that a game works, you can look at shadPS4 Game Compatibility.
To discuss shadPS4 development, suggest ideas or to ask for help, join our Discord server.
To get the latest news, go to our X (Twitter) or our website.
You can donate to the project via our Kofi page.

Status

Important

shadPS4 is early in development, don't expect a flawless experience.

Currently, the emulator can successfully run games like Bloodborne, Dark Souls Remastered, Red Dead Redemption, and many other games.

Why

This project began for fun. Given our limited free time, it may take some time before shadPS4 can run more complex games, but we're committed to making small, regular updates.

Building

Docker

For building shadPS4 in a containerized environment using Docker and VSCode, check the instructions here:
Docker Build Instructions

Windows

Check the build instructions for Windows.

Linux

Check the build instructions for Linux.

macOS

Check the build instructions for macOS.

Important

macOS users need at least macOS 15.4 to run shadPS4. Due to GPU issues there are currently heavy bugs on Intel Macs.

Usage examples

Important

For a user-friendly GUI, download the QtLauncher.

To get the list of all available commands and also a more detailed description of what each command does, please refer to the --help flag's output.

Below is a list of commonly used command patterns:

shadPS4 CUSA00001 # Searches for a game folder called CUSA00001 in the list of game install folders, and boots it.
shadPS4 --fullscreen true --config-clean CUSA00001    # the game argument is always the last one,
shadPS4 -g CUSA00001 --fullscreen true --config-clean # ...unless manually specified otherwise.
shadPS4 /path/to/game.elf # Boots a PS4 ELF file directly. Useful if you want to boot an executable that is not named eboot.bin.
shadPS4 CUSA00001 -- -flag1 -flag2 # Passes '-flag1' and '-flag2' to the game executable in argv.

Debugging and reporting issues

For more information on how to test, debug and report issues with the emulator or games, read the Debugging documentation.

Keyboard and Mouse Mappings

Note

Some keyboards may also require you to hold the Fn key to use the F* keys. Mac users should use the Command key instead of Control, and need to use Command+F11 for full screen to avoid conflicting with system key bindings.

Button	Function
F10	FPS Counter
Ctrl+F10	Video Debug Info
F11	Fullscreen
F12	Trigger RenderDoc Capture (or game-only screenshot if RenderDoc is unavailable)
Alt+F12	Capture screenshot including HUD/dialog overlays

Note

Xbox and DualShock controllers work out of the box.

Controller button	Keyboard equivalent
LEFT AXIS UP	W
LEFT AXIS DOWN	S
LEFT AXIS LEFT	A
LEFT AXIS RIGHT	D
RIGHT AXIS UP	I
RIGHT AXIS DOWN	K
RIGHT AXIS LEFT	J
RIGHT AXIS RIGHT	L
TRIANGLE	Numpad 8 or C
CIRCLE	Numpad 6 or B
CROSS	Numpad 2 or N
SQUARE	Numpad 4 or V
PAD UP	UP
PAD DOWN	DOWN
PAD LEFT	LEFT
PAD RIGHT	RIGHT
OPTIONS	RETURN
BACK BUTTON / TOUCH PAD	SPACE
L1	Q
R1	U
L2	E
R2	O
L3	X
R3	M

Keyboard and mouse inputs can be customized in the settings menu by clicking the Controller button, and further details and help on controls are also found there. Custom bindings are saved per-game. Inputs support up to three keys per binding, mouse buttons, mouse movement mapped to joystick input, and more.

Firmware files

shadPS4 can load some PlayStation 4 firmware files. The following firmware modules are supported and must be placed in shadPS4's sys_modules folder.

Modules	Modules	Modules	Modules
libSceAudiodec.sprx	libSceCesCs.sprx	libSceFont.sprx	libSceFontFt.sprx
libSceFreeTypeOt.sprx	libSceJpegDec.sprx	libSceJpegEnc.sprx	libSceJson.sprx
libSceJson2.sprx	libSceLibcInternal.sprx	libSceNgs2.sprx	libScePngEnc.sprx
libSceRtc.sprx	libSceSystemGesture.sprx	libSceUlt.sprx

Caution

The above modules are required to run the games properly and must be dumped from your legally owned PlayStation 4 console.

Main team

Logo is done by Xphalnos

Contributing

If you want to contribute, please read the CONTRIBUTING.md file.
Open a PR and we'll check it 😃

Special Thanks

A few noteworthy teams/projects who've helped us along the way are:

Panda3DS: A multiplatform 3DS emulator from our co-author wheremyfoodat. They have been incredibly helpful in understanding and solving problems that came up from natively executing the x64 code of PS4 binaries
fpPS4: The fpPS4 team has assisted massively with understanding some of the more complex parts of the PS4 operating system and libraries, by helping with reverse engineering work and research.
yuzu: Our shader compiler has been designed with yuzu's Hades compiler as a blueprint. This allowed us to focus on the challenges of emulating a modern AMD GPU while having a high-quality optimizing shader compiler implementation as a base.
felix86: A new x86-64 → RISC-V Linux userspace emulator
emudev.org: A network of people interested in the documentation, emulation, simulation and re-implementation of hardware near extinction . Belongs to my friend skmp and me (shadow) also a member of it

License

GPL-2.0 license

chriskohlhoff/asio

Asio C++ Library

asio version 1.38.0 Released Tuesday, 30 December 2025.

Visit https://think-async.com/ or see packaged doc/index.html for API documentation and a tutorial.

ceph/ceph

Ceph is a distributed object, block, and file storage platform

Ceph - a scalable distributed storage system

See https://ceph.com/ for current information about Ceph.

Status

Contributing Code

Most of Ceph is dual-licensed under the LGPL version 2.1 or 3.0. Some miscellaneous code is either public domain or licensed under a BSD-style license.

The Ceph documentation is licensed under Creative Commons Attribution Share Alike 3.0 (CC-BY-SA-3.0).

Some headers included in the ceph/ceph repository are licensed under the GPL. See the file COPYING for a full inventory of licenses by file.

All code contributions must include a valid "Signed-off-by" line. See the file SubmittingPatches.rst for details on this and instructions on how to generate and submit patches.

Assignment of copyright is not required to contribute code. Code is contributed under the terms of the applicable license.

Checking out the source

Clone the ceph/ceph repository from github by running the following command on a system that has git installed:

git clone git@github.com:ceph/ceph

Alternatively, if you are not a github user, you should run the following command on a system that has git installed:

git clone https://github.com/ceph/ceph.git

When the ceph/ceph repository has been cloned to your system, run the following commands to move into the cloned ceph/ceph repository and to check out the git submodules associated with it:

cd ceph
git submodule update --init --recursive --progress

Build Prerequisites

section last updated 06 Sep 2024

We provide the Debian and Ubuntu apt commands in this procedure. If you use a system with a different package manager, then you will have to use different commands.

#. Install curl:

apt install curl

#. Install package dependencies by running the install-deps.sh script:

./install-deps.sh

#. Install the python3-routes package:

apt install python3-routes

Building Ceph

These instructions are meant for developers who are compiling the code for development and testing. To build binaries that are suitable for installation we recommend that you build .deb or .rpm packages, or refer to ceph.spec.in or debian/rules to see which configuration options are specified for production builds.

To build Ceph, follow this procedure:

Make sure that you are in the top-level ceph directory that contains do_cmake.sh and CONTRIBUTING.rst.
Run the do_cmake.sh script:
```
./do_cmake.sh
```
See build types.
Move into the build directory:
```
cd build
```
Use the ninja buildsystem to build the development environment:
```
ninja -j3
```
Important

Ninja is the build system used by the Ceph project to build test builds. The number of jobs used by ninja is derived from the number of CPU cores of the building host if unspecified. Use the -j option to limit the job number if build jobs are running out of memory. If you attempt to run ninja and receive a message that reads g++: fatal error: Killed signal terminated program cc1plus, then you have run out of memory.

Using the -j option with an argument appropriate to the hardware on which the ninja command is run is expected to result in a successful build. For example, to limit the job number to 3, run the command ninja -j3. On average, each ninja job run in parallel needs approximately 2.5 GiB of RAM.

This documentation assumes that your build directory is a subdirectory of the ceph.git checkout. If the build directory is located elsewhere, point CEPH_GIT_DIR to the correct path of the checkout. Additional CMake args can be specified by setting ARGS before invoking do_cmake.sh. See cmake options for more details. For example:
```
ARGS="-DCMAKE_C_COMPILER=gcc-7" ./do_cmake.sh
```
To build only certain targets, run a command of the following form:
```
ninja [target name]
```
Install the vstart cluster:
```
ninja install
```

Build Types

do_cmake.sh by default creates a "debug build" of Ceph (assuming .git exists). A Debug build runtime performance may be as little as 20% of that of a non-debug build. Pass -DCMAKE_BUILD_TYPE=RelWithDebInfo to do_cmake.sh to create a non-debug build. The default build type is RelWithDebInfo once .git does not exist.

CMake mode	Debug info	Optimizations	Sanitizers	Checks	Use for
`Debug`	Yes	`-Og`	None	`ceph_assert`, `assert`	gdb, development
`RelWithDebInfo`	Yes	`-O2`, `-DNDEBUG`	None	`ceph_assert` only	production

CMake Options

The -D flag can be used with cmake to speed up the process of building Ceph and to customize the build.

Building without RADOS Gateway

The RADOS Gateway is built by default. To build Ceph without the RADOS Gateway, run a command of the following form:

cmake -DWITH_RADOSGW=OFF [path to top-level ceph directory]

Building with debugging and arbitrary dependency locations

Run a command of the following form to build Ceph with debugging and alternate locations for some external dependencies:

cmake -DCMAKE_INSTALL_PREFIX=/opt/ceph -DCMAKE_C_FLAGS="-Og -g3 -gdwarf-4" \
..

Ceph has several bundled dependencies such as Boost, RocksDB and Arrow. By default, cmake builds these bundled dependencies from source instead of using libraries that are already installed on the system. You can opt to use these system libraries, as long as they meet Ceph's version requirements. To use system libraries, use cmake options like WITH_SYSTEM_BOOST, as in the following example:

cmake -DWITH_SYSTEM_BOOST=ON [...]

To view an exhaustive list of -D options, invoke cmake -LH:

cmake -LH

Preserving diagnostic colors

If you pipe ninja to less and would like to preserve the diagnostic colors in the output in order to make errors and warnings more legible, run the following command:

cmake -DDIAGNOSTICS_COLOR=always ...

The above command works only with supported compilers.

The diagnostic colors will be visible when the following command is run:

ninja | less -R

Other available values for DIAGNOSTICS_COLOR are auto (default) and never.

Tips and Tricks

Use "debug builds" only when needed. Debugging builds are helpful for development, but they can slow down performance. Use -DCMAKE_BUILD_TYPE=Release when debugging isn't necessary.
Enable Selective Daemons when testing specific components. Don't start unnecessary daemons.
Preserve Existing Data skip cluster reinitialization between tests by using the -n flag.
To manage a vstart cluster, stop daemons using ./stop.sh and start them with ./vstart.sh --daemon osd.${ID} [--nodaemonize].
Restart the sockets by stopping and restarting the daemons associated with them. This ensures that there are no stale sockets in the cluster.
To track RocksDB performance, set export ROCKSDB_PERF=true and start the cluster by using the command ./vstart.sh -n -d -x --bluestore.
Build with vstart-base using debug flags in cmake, compile, and deploy via ./vstart.sh -d -n --bluestore.
To containerize, generate configurations with vstart.sh, and deploy with Docker, mapping directories and configuring the network.
Manage containers using docker run, stop, and rm. For detailed setups, consult the Ceph-Container repository.

Troubleshooting

Cluster Fails to Start: Look for errors in the logs under the out/ directory.
OSD Crashes: Check the OSD logs for errors.
Cluster in a Health Error State: Run the ceph status command to identify the issue.
RocksDB Errors: Look for RocksDB-related errors in the OSD logs.

Building a source tarball

To build a complete source tarball with everything needed to build from source and/or build a (deb or rpm) package, run

./make-dist

This will create a tarball like ceph-$version.tar.bz2 from git. (Ensure that any changes you want to include in your working directory are committed to git.)

Running a test cluster

From the ceph/ directory, run the following commands to launch a test Ceph cluster:

cd build
ninja vstart        # builds just enough to run vstart
../src/vstart.sh --debug --new -x --localhost --bluestore
./bin/ceph -s

Most Ceph commands are available in the bin/ directory. For example:

./bin/rbd create foo --size 1000
./bin/rados -p foo bench 30 write

To shut down the test cluster, run the following command from the build/ directory:

../src/stop.sh

Use the sysvinit script to start or stop individual daemons:

./bin/init-ceph restart osd.0
./bin/init-ceph stop

Running unit tests

To build and run all tests (in parallel using all processors), use ctest:

cd build
ninja
ctest -j$(nproc)

(Note: Many targets built from src/test are not run using ctest. Targets starting with "unittest" are run in ninja check and thus can be run with ctest. Targets starting with "ceph_test" can not, and should be run by hand.)

When failures occur, look in build/Testing/Temporary for logs.

To build and run all tests and their dependencies without other unnecessary targets in Ceph:

cd build
ninja check -j$(nproc)

To run an individual test manually, run ctest with -R (regex matching):

ctest -R [regex matching test name(s)]

(Note: ctest does not build the test it's running or the dependencies needed to run it)

To run an individual test manually and see all the tests output, run ctest with the -V (verbose) flag:

ctest -V -R [regex matching test name(s)]

To run tests manually and run the jobs in parallel, run ctest with the -j flag:

ctest -j [number of jobs]

There are many other flags you can give ctest for better control over manual test execution. To view these options run:

man ctest

Building Ceph using Containers

Ceph now provides tools to build the code, run unit tests, or build packages from within an OCI-style container using Podman or Docker! This allows one to build code for distributions other than the one you have on your system, avoids the need to install build dependencies for Ceph on your local system and provides an opportunity to test builds on platforms that are not yet supported by the official build infrastructure. For more details see the container build document.

Building the Documentation

Prerequisites

The list of package dependencies for building the documentation can be found in doc_deps.deb.txt:

sudo apt-get install `cat doc_deps.deb.txt`

Building the Documentation

To build the documentation, ensure that you are in the top-level /ceph directory, and execute the build script. For example:

admin/build-doc

Reporting Issues

To report an issue and view existing issues, please visit https://tracker.ceph.com/projects/ceph.

Neargye/magic_enum

Static reflection for enums (to string, from string, iteration) for modern C++, work with any enum type without any macro or boilerplate code

Magic Enum C++

Header-only C++17 library provides static reflection for enums, work with any enum type without any macro or boilerplate code.

If you like this project, please consider donating to one of the funds that help victims of the war in Ukraine: https://u24.gov.ua.

Documentation

Features & Examples

Basic

#include <magic_enum/magic_enum.hpp>
#include <iostream>

enum class Color { RED = -10, BLUE = 0, GREEN = 10 };

int main() {
  Color c1 = Color::RED;
  std::cout << magic_enum::enum_name(c1) << std::endl; // RED
  return 0;
}

Enum value to string

Color color = Color::RED;
auto color_name = magic_enum::enum_name(color);
// color_name -> "RED"

String to enum value

std::string color_name{"GREEN"};
auto color = magic_enum::enum_cast<Color>(color_name);
if (color.has_value()) {
  // color.value() -> Color::GREEN
}

// case insensitive enum_cast
auto color = magic_enum::enum_cast<Color>(value, magic_enum::case_insensitive);

// enum_cast with BinaryPredicate
auto color = magic_enum::enum_cast<Color>(value, [](char lhs, char rhs) { return std::tolower(lhs) == std::tolower(rhs); });

// enum_cast with default
auto color_or_default = magic_enum::enum_cast<Color>(value).value_or(Color::NONE);

Integer to enum value

int color_integer = 0;
auto color = magic_enum::enum_cast<Color>(color_integer);
if (color.has_value()) {
  // color.value() -> Color::BLUE
}

auto color_or_default = magic_enum::enum_cast<Color>(value).value_or(Color::NONE);

Indexed access to enum value

std::size_t i = 0;
Color color = magic_enum::enum_value<Color>(i);
// color -> Color::RED

Enum value sequence

constexpr auto colors = magic_enum::enum_values<Color>();
// colors -> {Color::RED, Color::BLUE, Color::GREEN}
// colors[0] -> Color::RED

Number of enum elements

constexpr std::size_t color_count = magic_enum::enum_count<Color>();
// color_count -> 3

Enum value to integer

Color color = Color::RED;
auto color_integer = magic_enum::enum_integer(color); // or magic_enum::enum_underlying(color);
// color_integer -> -10

Enum names sequence

constexpr auto color_names = magic_enum::enum_names<Color>();
// color_names -> {"RED", "BLUE", "GREEN"}
// color_names[0] -> "RED"

Enum entries sequence

constexpr auto color_entries = magic_enum::enum_entries<Color>();
// color_entries -> {{Color::RED, "RED"}, {Color::BLUE, "BLUE"}, {Color::GREEN, "GREEN"}}
// color_entries[0].first -> Color::RED
// color_entries[0].second -> "RED"

Enum fusion for multi-level switch/case statements

switch (magic_enum::enum_fuse(color, direction).value()) {
  case magic_enum::enum_fuse(Color::RED, Directions::Up).value(): // ...
  case magic_enum::enum_fuse(Color::BLUE, Directions::Down).value(): // ...
// ...
}

Enum switch runtime value as constexpr constant

Color color = Color::RED;
magic_enum::enum_switch([] (auto val) {
  constexpr Color c_color = val;
  // ...
}, color);

Enum iterate for each enum as constexpr constant

magic_enum::enum_for_each<Color>([] (auto val) {
  constexpr Color c_color = val;
  // ...
});

Check if enum contains

magic_enum::enum_contains(Color::GREEN); // -> true
magic_enum::enum_contains<Color>(2); // -> true
magic_enum::enum_contains<Color>(123); // -> false
magic_enum::enum_contains<Color>("GREEN"); // -> true
magic_enum::enum_contains<Color>("fda"); // -> false

Enum index in sequence

constexpr auto color_index = magic_enum::enum_index(Color::BLUE);
// color_index.value() -> 1
// color_index.has_value() -> true

Functions for flags

enum Directions : std::uint64_t {
  Left = 1,
  Down = 2,
  Up = 4,
  Right = 8,
};
template <>
struct magic_enum::customize::enum_range<Directions> {
  static constexpr bool is_flags = true;
};

magic_enum::enum_flags_name(Directions::Up | Directions::Right); // -> "Directions::Up|Directions::Right"
magic_enum::enum_flags_name(Directions::Up | Directions::Right, ','); // -> "Directions::Up,Directions::Right"
magic_enum::enum_flags_contains(Directions::Up | Directions::Right); // -> true
magic_enum::enum_flags_cast(3); // -> "Directions::Left|Directions::Down"
magic_enum::enum_flags_cast<Directions>("Left,Down", ','); // -> Directions::Left|Directions::Down

Enum type name

Color color = Color::RED;
auto type_name = magic_enum::enum_type_name<decltype(color)>();
// type_name -> "Color"

IOstream operator for enum

using magic_enum::iostream_operators::operator<<; // out-of-the-box ostream operators for enums.
Color color = Color::BLUE;
std::cout << color << std::endl; // "BLUE"

using magic_enum::iostream_operators::operator>>; // out-of-the-box istream operators for enums.
Color color;
std::cin >> color;

Bitwise operator for enum

enum class Flags { A = 1 << 0, B = 1 << 1, C = 1 << 2, D = 1 << 3 };
using namespace magic_enum::bitwise_operators; // out-of-the-box bitwise operators for enums.
// Support operators: ~, |, &, ^, |=, &=, ^=.
Flags flags = Flags::A | Flags::B & ~Flags::C;

Checks whether type is an Unscoped enumeration.

enum color { red, green, blue };
enum class direction { left, right };

magic_enum::is_unscoped_enum<color>::value -> true
magic_enum::is_unscoped_enum<direction>::value -> false
magic_enum::is_unscoped_enum<int>::value -> false

// Helper variable template.
magic_enum::is_unscoped_enum_v<color> -> true

Checks whether type is an Scoped enumeration.

enum color { red, green, blue };
enum class direction { left, right };

magic_enum::is_scoped_enum<color>::value -> false
magic_enum::is_scoped_enum<direction>::value -> true
magic_enum::is_scoped_enum<int>::value -> false

// Helper variable template.
magic_enum::is_scoped_enum_v<direction> -> true

Static storage enum variable to string This version is much lighter on the compile times and is not restricted to the enum_range limitation.
```
constexpr Color color = Color::BLUE;
constexpr auto color_name = magic_enum::enum_name<color>();
// color_name -> "BLUE"
```

containers::array array container for enums.

magic_enum::containers::array<Color, RGB> color_rgb_array {};
color_rgb_array[Color::RED] = {255, 0, 0};
color_rgb_array[Color::GREEN] = {0, 255, 0};
color_rgb_array[Color::BLUE] = {0, 0, 255};
magic_enum::containers::get<Color::BLUE>(color_rgb_array) // -> RGB{0, 0, 255}

containers::bitset bitset container for enums.

std::uint8_t incoming = 0b00000011;
auto color_bitset = magic_enum::containers::bitset<Color> {magic_enum::containers::raw_access, incoming};
color_bitset.set(Color::BLUE);
auto raw_value = color_bitset.to_ulong(magic_enum::containers::raw_access);

containers::set set container for enums.

auto color_set = magic_enum::containers::set<Color>();
bool empty = color_set.empty();
// empty -> true
color_set.insert(Color::GREEN);
color_set.insert(Color::BLUE);
color_set.insert(Color::RED);
std::size_t size = color_set.size();
// size -> 3

Improved UB-free "SFINAE-friendly" underlying_type.

magic_enum::underlying_type<color>::type -> int

// Helper types.
magic_enum::underlying_type_t<Direction> -> int

Remarks

magic_enum does not pretend to be a silver bullet for reflection for enums, it was originally designed for small enum.
Before use, read the limitations of functionality.

Integration

You should add the required file magic_enum.hpp, and optionally other headers from include dir or release archive. Alternatively, you can build the library with CMake.
If you are using vcpkg on your project for external dependencies, then you can use the magic-enum package.
If you are using Conan to manage your dependencies, merely add magic_enum/x.y.z to your conan's requires, where x.y.z is the release version you want to use.
If you are using Build2 to build and manage your dependencies, add depends: magic_enum ^x.y.z to the manifest file where x.y.z is the release version you want to use. You can then import the target using magic_enum%lib{magic_enum}.

Alternatively, you can use something like CPM which is based on CMake's Fetch_Content module.

CPMAddPackage(
    NAME magic_enum
    GITHUB_REPOSITORY Neargye/magic_enum
    GIT_TAG vx.y.z # Where `x.y.z` is the release version you want to use.
)

Bazel is also supported, simply add to your WORKSPACE file:

http_archive(
    name = "magic_enum",
    strip_prefix = "magic_enum-<commit>",
    urls = ["https://github.com/Neargye/magic_enum/archive/<commit>.zip"],
)

To use bazel inside the repository it's possible to do:

bazel build //...
bazel test //...
bazel run //example

(Note that you must use a supported compiler or specify it with export CC= <compiler>.)

If you are using Ros, you can include this package by adding <depend>magic_enum</depend> to your package.xml and include this package in your workspace. In your CMakeLists.txt add the following:
```
find_package(magic_enum CONFIG REQUIRED)
...
target_link_libraries(your_executable magic_enum::magic_enum)
```
C++20 modules are supported as an alternative to the header-only mode, requires CMake 3.28+.

Configure with CMake:
```
cmake -DMAGIC_ENUM_USE_MODULES=ON -G Ninja ...
```
Then use import instead of #include:
```
import magic_enum;

enum class Color { RED, GREEN, BLUE };
auto name = magic_enum::enum_name(Color::RED); // "RED"
```
Caveats:
- Do not mix #include <magic_enum/...> and import magic_enum; within the same link unit — ODR violation.
- import std; is opt-in: add -DCMAKE_CXX_STANDARD=23 -DCMAKE_CXX_IMPORT_STD=ON (requires CMake 3.30+).

Compiler compatibility

Clang/LLVM >= 5
MSVC++ >= 14.11 / Visual Studio >= 2017
Xcode >= 10
GCC >= 9
MinGW >= 9