Search Results for "compiler python" - Page 2
Sort By:
Showing 143 open source projects for "compiler python"
View related business solutions-
Gen AI apps are built with MongoDB AtlasMongoDB Atlas is the developer-friendly database used to build, scale, and run gen AI and LLM-powered apps—without needing a separate vector database. Atlas offers built-in vector search, global availability across 115+ regions, and flexible document modeling. Start building AI apps faster, all in one place.
-
Simple, Secure Domain RegistrationRegister or renew your domain and pay only what we pay. No markups, hidden fees, or surprise add-ons. Choose from over 400 TLDs (.com, .ai, .dev). Every domain is integrated with Cloudflare's industry-leading DNS, CDN, and free SSL to make your site faster and more secure. Simple, secure, at-cost domain registration.
-
1
APKiD
Android Application Identifier for Packers, Protectors and Obfuscators
APKiD gives you information about how an APK was made. It identifies many compilers, packers, obfuscators, and other weird stuff. It's PEiD for Android. -
2
JUDI.jl
Julia Devito inversion
JUDI is a framework for large-scale seismic modeling and inversion and is designed to enable rapid translations of algorithms to fast and efficient code that scales to industry-size 3D problems. The focus of the package lies on seismic modeling as well as PDE-constrained optimization such as full-waveform inversion (FWI) and imaging (LS-RTM). Wave equations in JUDI are solved with Devito, a Python domain-specific language for automated finite-difference (FD) computations. JUDI's modeling... -
3
JupyterLab LaTeX
JupyterLab extension for live editing of LaTeX documents
An extension for JupyterLab which allows for live-editing of LaTeX documents. To use, right-click on an open .tex document within JupyterLab, and select Show LaTeX Preview. This extension includes both a notebook server extension (which interfaces with the LaTeX compiler) and a lab extension (which provides the UI for the LaTeX preview). The Python package named jupyterlab_latex provides both of them as a prebuilt extension. -
4
Jittor
Jittor is a high-performance deep learning framework
Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just in time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code specialized for your model. Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement... -
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
5
Eris
A NodeJS Discord library
A Node.js wrapper for interfacing with Discord. You will need NodeJS 10.4+. If you need voice support you will also need Python 2.7 and a C++ compiler. Create a directory for your bot, and change to that directory in your command line. If you want to be more updated (at the expense of stability), you can install the beta builds instead. Eris supports a few optional libraries that could potentially improve bot performance but may require additional dependencies. If you want Eris to use any... -
6
CWhy
Explains and suggests fixes for compile-time errors for C, C++, C#, Go
Explains and suggests fixes for compiler error messages for a wide range of programming languages, including C, C++, C#, Go, Java, LaTeX, PHP, Python, Ruby, Rust, Swift, and TypeScript. CWhy needs to be connected to an OpenAI account or an Amazon Web Services account. Your account will need to have a positive balance for this to work (check your OpenAI balance). CWhy currently defaults to GPT-4, and falls back to GPT-3.5-turbo if a request error occurs. For the newest and best model (GPT-4... -
7
BetaML.jl
Beta Machine Learning Toolkit
The Beta Machine Learning Toolkit is a package including many algorithms and utilities to implement machine learning workflows in Julia, Python, R and any other language with a Julia binding. All models are implemented entirely in Julia and are hosted in the repository itself (i.e. they are not wrapper to third-party models). If your favorite option or model is missing, you can try to implement it yourself and open a pull request to share it (see the section Contribute below) or request its... -
8
F´
A flight software and embedded systems framework
... of the user’s system must be met, Linux or Mac OS X operating system (or Windows Subsystem for Linux on Windows), CMake, Bash or Bash compatible shell, Clang or GCC compiler, Python 3 and PIP. F´ can be quickly installed and ready to use by cloning the GitHub repository, installing Python code (typically in a virtual environment), and building one of our reference applications. -
9
vyper
Pythonic Smart Contract Language for the EVM
See Installing Vyper to install vyper. See Tools and Resources for an additional list of framework and tools with vyper support. See Documentation for the documentation and overall design goals of the Vyper language. See Learn.Vyperlang.org for learning Vyper by building a Pokémon game. See try.vyperlang.org to use Vyper in a hosted jupyter environment! There is also an online compiler available you can use to experiment with the language and compile to bytecode and/or IR. While the vyper... -
Get the most trusted enterprise browserDefend against security incidents with Chrome Enterprise. Create customizable controls, manage extensions and set proactive alerts to keep your data and employees protected without slowing down productivity.
-
10
torchtext
Data loaders and abstractions for text and NLP
We recommend Anaconda as a Python package management system. Please refer to pytorch.org for the details of PyTorch installation. LTS versions are distributed through a different channel than the other versioned releases. Alternatively, you might want to use the Moses tokenizer port in SacreMoses (split from NLTK). You have to install SacreMoses. To build torchtext from source, you need git, CMake and C++11 compiler such as g++. When building from source, make sure that you have the same C... -
11
XLS
XLS: Accelerated HW Synthesis
XLS is an open-source toolkit for building high-level hardware with a modern compiler stack that spans from a functional DSL to optimized IR and hardware generation. At the front end, DSLX lets you describe algorithms with strong typing and familiar control flow while remaining synthesis-friendly. The compiler lowers DSLX into a rich intermediate representation, applies aggressive optimization and scheduling passes, and can either JIT the design for software simulation or emit Verilog for FPGA... -
12
Fantomas
FSharp source code formatter
F# source code formatter, inspired by scalariform for Scala, ocp-indent for OCaml and PythonTidy for Python. This project aims at formatting F# source files based on a given configuration. Fantomas will ensure correct indentation and consistent spacing between elements in the source files. We assume that the source files are parsable by F# compiler before feeding into the tool. Fantomas follows two F# style guides: the F# code formatting guidelines from Microsoft by default and the G-Research F... -
13
Transcrypt
Python 3.7 to JavaScript compiler
Python in the browser, precompiled for speed. Transcrypt started out as a personal repo, owned by Jacques de Hooge. As the project caught on and the number of people contributing issues, ideas and code grew, the repo was transferred to the QQuick organization, to be able to form a developer team on GitHub. There's a simple parallel between the Python and the JavaScript code. In combination with the use of source maps, this enables efficient debugging. Also, code can be tested from the command... -
14
tvm
Open deep learning compiler stack for cpu, gpu, etc.
Apache TVM is an open source machine learning compiler framework for CPUs, GPUs, and machine learning accelerators. It aims to enable machine learning engineers to optimize and run computations efficiently on any hardware backend. The vision of the Apache TVM Project is to host a diverse community of experts and practitioners in machine learning, compilers, and systems architecture to build an accessible, extensible, and automated open-source framework that optimizes current and emerging... -
15
AWS Neuron
Powering Amazon custom machine learning chips
AWS Neuron is a software development kit (SDK) for running machine learning inference using AWS Inferentia chips. It consists of a compiler, run-time, and profiling tools that enable developers to run high-performance and low latency inference using AWS Inferentia-based Amazon EC2 Inf1 instances. Using Neuron developers can easily train their machine learning models on any popular framework such as TensorFlow, PyTorch, and MXNet, and run it optimally on Amazon EC2 Inf1 instances. You can... -
16
Material Theme
A theme for Sublime Text 3 by Mattia Astorino
This theme brings the Material Design visual language to your Sublime Text 3. If you have problems, first search for a similar issue and then report a new one. If you want to enable the white panels and inputs you can install the addon package through Package Control, search for "Material theme white panels". You have to disable it if you want to use the Lighter theme style. This UI theme uses a custom compiler build on Gulp and JS. If you want to edit the UI you must first install the compiler... -
17
OneFlow
OneFlow is a deep learning framework designed to be user-friendly
OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient. An extension for OneFlow to target third-party compiler, such as XLA, TensorRT and OpenVINO etc.CUDA runtime is statically linked into OneFlow. OneFlow will work on a minimum supported driver, and any driver beyond. For more information. Distributed performance (efficiency) is the core technical difficulty of the deep learning framework. OneFlow focuses on performance improvement and heterogeneous... -
18
PyQuil
A Python library for quantum programming using Quil
PyQuil is a Python library for quantum programming using Quil, the quantum instruction language developed at Rigetti Computing. PyQuil serves three main functions. PyQuil has a ton of other features, which you can learn more about in the docs. However, you can also keep reading below to get started with running your first quantum program. Without installing anything, you can quickly get started with quantum programming by exploring our interactive Jupyter Notebook tutorials and examples. To run... -
19
Perf Book
The book "Performance Analysis and Tuning on Modern CPU"
... walks through vectorization, memory layout, data-oriented design, and algorithmic choices, illustrating when compiler flags, intrinsics, or hand-rolled assembly make sense. It also demonstrates tool-driven workflows—using profilers and PMU events—to locate true bottlenecks and validate that changes actually help. Throughout, the emphasis is on a methodical loop of hypothesize → measure → change → re-measure, rather than folklore or premature micro-optimizations. -
20
Essentials-of-Compilation
A book about compiling Racket and Python to x86-64 assembly
LaTeX source for two companion books—one compiling a subset of Racket, the other a subset of Python—both targeting x86‑64 assembly, used in Indiana University’s compiler course and published by MIT Press. -
21
Swift Numerics
Advanced mathematical types and functions for Swift
... in Swift. API design follows Swift’s emphasis on value semantics and protocol-oriented programming, enabling compiler optimizations and predictable performance. The modules are factored to keep dependencies minimal and to allow adopters to pull in only what they need. As a result, Swift Numerics underpins higher-level libraries in simulation, signal processing, and machine learning written in pure Swift. -
22
PyTorch/XLA
Enabling PyTorch on Google TPU
PyTorch/XLA is a Python package that uses the XLA deep learning compiler to connect the PyTorch deep learning framework and Cloud TPUs. You can try it right now, for free, on a single Cloud TPU with Google Colab, and use it in production and on Cloud TPU Pods with Google Cloud. Take a look at one of our Colab notebooks to quickly try different PyTorch networks running on Cloud TPUs and learn how to use Cloud TPUs as PyTorch devices. We are also introducing new TPU VMs for more transparent... -
23
Okteto
Develop your applications directly in your Kubernetes Cluster
... applications. You write your code locally and okteto detects the changes and instantly updates your Kubernetes applications. Okteto allows you to develop inside a container. When you run out to up your Kubernetes deployment is replaced by a development container that contains your development tools (e.g. maven and jdk, or npm, python, go compiler, debuggers, etc). This development container can use any docker image. -
24
JavaCPP
The missing bridge between Java and native C++
JavaCPP provides efficient access to native C++ inside Java, not unlike the way some C/C++ compilers interact with assembly language. No need to invent new languages such as with SWIG, SIP, C++/CLI, Cython, or RPython. Instead, similar to what cppyy strives to do for Python, it exploits the syntactic and semantic similarities between Java and C++. Under the hood, it uses JNI, so it works with all implementations of Java SE, in addition to Android, Avian, and RoboVM (instructions). More... -
25
PyKCS11: a complete PKCS#11 wrapper for Python, created using the SWIG compiler. API documentation: http://pkcs11wrap.sourceforge.net/api/
