Search Results for "python compile source code" - Page 28
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Showing 2729 open source projects for "python compile source code"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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No-Nonsense Code-to-Cloud Security for Devs | AikidoAikido provides a unified security platform for developers, combining 12 powerful scans like SAST, DAST, and CSPM. AI-driven AutoFix and AutoTriage streamline vulnerability management, while runtime protection blocks attacks.
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Synthetic Data Vault (SDV)
Synthetic Data Generation for tabular, relational and time series data
The Synthetic Data Vault (SDV) is a Synthetic Data Generation ecosystem of libraries that allows users to easily learn single-table, multi-table and timeseries datasets to later on generate new Synthetic Data that has the same format and statistical properties as the original dataset. Synthetic data can then be used to supplement, augment and in some cases replace real data when training Machine Learning models. Additionally, it enables the testing of Machine Learning or other data dependent... -
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git-delete-merged-branches
Command-line tool to delete merged Git branches
... Git hosting. Takes safety seriously. Deletion is a sharp knife that requires care. While git reflog would have your back in most cases, git-delete-merged-branches takes safety seriously. git push is used with --force-with-lease so if the server and you have a different understanding of that branch, it is not deleted. There is no use of os.system or shell code to go wrong. -
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Qiling
Qiling Advanced Binary Emulation Framework
Cross-platform and multi-arch ultra lightweight emulator. Supported OS: Linux, MacOS, Windows, FreeBSD, DOS and UEFI. Support Arch: x86(16/32/64), ARM(64) MIPS, EVM and WASM. It also support Linux Kernel Module(.ko) , Windows Driver(.sys) and MacOS Kernel(.kext) via Demigod. Binary instrumentation and API are Qiling Framework's main focus and priority. It is designed for reverse engineers - thus there is no need to rebuild another sand boxing tool. Using Qiling Framework saves you time. The... -
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Nerves
Craft and deploy bulletproof embedded software in Elixir
Nerves is the open-source platform and infrastructure you need to build, deploy, and securely manage your fleet of IoT devices at speed and scale. Nerves is written in Elixir, but you don’t have to rewrite everything in Elixir to get the advantages of Nerves, simply bring your own code (like C, C++, Python, Rust, and more) and scale up. Nerves use the Erlang runtime system, known for being distributed, fault-tolerant, soft real-time, and highly available. Nerves has the tools you need to manage... -
Secure remote access solution to your private network, in the cloud or on-prem.OpenVPN is here to bring simple, flexible, and cost-effective secure remote access to companies of all sizes, regardless of where their resources are located.
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Determined
Determined, deep learning training platform
The fastest and easiest way to build deep learning models. Distributed training without changing your model code. Determined takes care of provisioning machines, networking, data loading, and fault tolerance. Build more accurate models faster with scalable hyperparameter search, seamlessly orchestrated by Determined. Use state-of-the-art algorithms and explore results with our hyperparameter search visualizations. Interpret your experiment results using the Determined UI and TensorBoard... -
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NeuralProphet
A simple forecasting package
NeuralProphet bridges the gap between traditional time-series models and deep learning methods. It's based on PyTorch and can be installed using pip. A Neural Network based Time-Series model, inspired by Facebook Prophet and AR-Net, built on PyTorch. You can find the datasets used in the tutorials, including data preprocessing examples, in our neuralprophet-data repository. The documentation page may not we entirely up to date. Docstrings should be reliable, please refer to those when in... -
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BentoML
Unified Model Serving Framework
BentoML simplifies ML model deployment and serves your models at a production scale. Support multiple ML frameworks natively: Tensorflow, PyTorch, XGBoost, Scikit-Learn and many more! Define custom serving pipeline with pre-processing, post-processing and ensemble models. Standard .bento format for packaging code, models and dependencies for easy versioning and deployment. Integrate with any training pipeline or ML experimentation platform. Parallelize compute-intense model inference workloads... -
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PML
The easiest way to use deep metric learning in your application
This library contains 9 modules, each of which can be used independently within your existing codebase, or combined together for a complete train/test workflow. To compute the loss in your training loop, pass in the embeddings computed by your model, and the corresponding labels. The embeddings should have size (N, embedding_size), and the labels should have size (N), where N is the batch size. The TripletMarginLoss computes all possible triplets within the batch, based on the labels you... -
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AutoGluon
AutoGluon: AutoML for Image, Text, and Tabular Data
AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on automated stack ensembling, deep learning, and real-world applications spanning image, text, and tabular data. Intended for both ML beginners and experts, AutoGluon enables you to quickly prototype deep learning and classical ML solutions for your raw data with a few lines of code. Automatically utilize state-of-the-art techniques (where appropriate) without expert knowledge. Leverage automatic hyperparameter tuning, model... -
Photo and Video Editing APIs and SDKsUnlock Picsart's full editing suite by embedding our Editor SDK directly into your platform. Offer your users the power of a full design suite without leaving your site.
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PyG
Graph Neural Network Library for PyTorch
... graph learning via Quiver, a large number of common benchmark datasets (based on simple interfaces to create your own), the GraphGym experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. All it takes is 10-20 lines of code to get started with training a GNN model (see the next section for a quick tour). -
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SageMaker Training Toolkit
Train machine learning models within Docker containers
Train machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime... -
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CapRover
Scalable PaaS (automated Docker+nginx), aka Heroku on Steroids
CapRover is an extremely easy-to-use app/database deployment & web server manager for your NodeJS, Python, PHP, ASP.NET, Ruby, MySQL, MongoDB, Postgres, WordPress (and etc...) applications! It's blazingly fast and very robust as it uses Docker, Nginx, LetsEncrypt and NetData under the hood behind its simple-to-use interface. For a developer who does not like spending hours and days setting up a server, building tools, sending code to the server, building it, getting an SSL certificate... -
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Ansible Molecule
Molecule aids in the development and testing of Ansible roles
....x, we will also test our code with 2.8.x. Depending on the driver chosen, you may need to install additional OS packages. See INSTALL.rst, which is created when initializing a new scenario. Ansible is not listed as a direct dependency of molecule package because we only call it as a command-line tool. You may want to install it using your distribution package installer. It is your responsibility to assure that soft dependencies of Ansible are available on your controller or host machines. -
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DeepSeed
Deep learning optimization library making distributed training easy
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. DeepSpeed delivers extreme-scale model training for everyone, from data scientists training on massive supercomputers to those training on low-end clusters or even on a single GPU. Using current generation of GPU clusters with hundreds of devices, 3D parallelism of DeepSpeed can efficiently train deep learning models with trillions of parameters. With just a single GPU,... -
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TensorBoardX
tensorboard for pytorch (and chainer, mxnet, numpy, etc.)
... of functionality on top of tensorboard such as dataset management, diffing experiments, seeing the code that generated the results and more. Create special chart by collecting charts tags in ‘scalars’. Note that this function can only be called once for each SummaryWriter() object. Because it only provides metadata to tensorboard, the function can be called before or after the training loop. -
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Recommenders
Best practices on recommendation systems
...-of-the-art algorithms are included for self-study and customization in your own applications. Please see the setup guide for more details on setting up your machine locally, on a data science virtual machine (DSVM) or on Azure Databricks. Independent or incubating algorithms and utilities are candidates for the contrib folder. This will house contributions which may not easily fit into the core repository or need time to refactor or mature the code and add necessary tests. -
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Pelican
Static site generator that supports Markdown and reST syntax
Pelican is a static site generator that requires no database or server-side logic. Chronological content (e.g., articles, blog posts) as well as static pages. Integration with external services. Site themes (created using Jinja2 templates). Publication of articles in multiple languages. Generation of Atom and RSS feeds. Code syntax highlighting via Pygments. Import existing content from WordPress, Dotclear, or RSS feeds. Fast rebuild times due to content caching and selective output writing... -
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Algorithm Visualizer
Interactive Online Platform that Visualizes Algorithms from Code
... each example, lists required environment variables or credentials (e.g., Twilio/Gmail where applicable), and gives cron examples so readers can run the scripts in a real environment. The project is intentionally informal and educational: it’s meant for experimentation, learning language-interop, and having fun rather than production-grade automation. Many implementations exist across languages (shell, Ruby, Python, Node, PowerShell, Go, Java, and more) and contributors are encouraged to add further -
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glbinding
A C++ binding for the OpenGL API, generated using the gl.xml specifica
... as tools and examples for quick-starting your projects. Based on the OpenGL API specification (gl.xml) glbinding is generated using Python scripts and templates that can be easily adapted to fit custom needs. -
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NBInclude.jl
import code from IJulia Jupyter notebooks into Julia programs
NBInclude is a package for the Julia language that allows you to include and execute IJulia (Julia-language Jupyter) notebook files just as you would include an ordinary Julia file. The goal of this package is to make notebook files just as easy to incorporate into Julia programs as ordinary Julia (.jl) files, giving you the advantages of a notebook (integrated code, formatted text, equations, graphics, and other results) while retaining the modularity and re-usability of .jl files. -
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CxxWrap
Package to make C++ libraries available in Julia
This package aims to provide a Boost. Python-like wrapping for C++ types and functions to Julia. The idea is to write the code for the Julia wrapper in C++, and then use a one-liner on the Julia side to make the wrapped C++ library available there. The mechanism behind this package is that functions and types are registered in C++ code that is compiled into a dynamic library. This dynamic library is then loaded into Julia, where the Julia part of this package uses the data provided through a C... -
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Extism
The Universal Plug-in System. Extend anything with WebAssembly
Extism is a plug-in system for everyone. We've carefully designed it to be flexible, fitting into codebases of all shapes and sizes, but opinionated enough so that things Just Work™ the way they should. Extism's goal is to make all software programmable. You can use Extism in your codebase, regardless of the programming language. We support several environments through our official Host SDKs, and are adding more language support all the time. A plug-in system is software that enables your... -
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Swirl
Swirl queries any number of data sources with APIs
Swirl queries any number of data sources with APIs and uses spaCy and NLTK to re-rank the unified results without extracting and indexing anything! Includes zero-code configs for Apache Solr, ChatGPT, Elastic Search, OpenSearch, PostgreSQL, Google BigQuery, RequestsGet, Google PSE, NLResearch.com, Miro & more! SWIRL adapts and distributes queries to anything with a search API - search engines, databases, noSQL engines, cloud/SaaS services etc - and uses AI (Large Language Models) to re-rank... -
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dap-mode
Emacs Debug Adapter Protocol
Emacs client/library for Debug Adapter Protocol is a wire protocol for communication between client and Debug Server. It's similar to the LSP but provides integration with debug server. The API considered unstable until 1.0 release is out. It is tested against Java, Python, Ruby, Elixir and LLDB (C/C++/Objective-C/Swift). The main entry points are dap-debug and dap-debug-edit-template. The first one asks for a registered debug template and starts the configuration using the default values... -
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System Design Primer
Learn how to design large-scale systems
System Design Primer is a curated, open source collection of resources that helps engineers learn how to design large-scale systems. The project is structured as a comprehensive guide covering core system design concepts, trade-offs, and patterns necessary for building scalable, reliable, and maintainable systems. It offers both theoretical foundations—such as scalability principles, the CAP theorem, and consistency models—and practical exercises, including real-world system design interview...
