Search Results for "python games code" - Page 3
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649 projects for "python games code" with 2 filters applied:
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Yandex Music API
Non-official Python library for works with API service Index
This library provides Python interface for anyone undocumented and self-made API Yandex Music. It is compatible with Python 3.7 + and supports working with both synchronous and asyncio code. In addition to implementing a clean API, this library has a number of — high-level wrapping classes in order to make the development of customers and scripts simple and understandable. All documentation was written from scratch based on logical analysis during reverse development (reverse engineering) API. -
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Gideros
Gideros Release version
Gideros is a cross-platform development environment for creating amazing games and interactive applications in 2D or 3D. It is easy to pick up, quick to develop, and robust to deploy. Code your game once and deploy it to Android, iOS, MacOS, tvOS, Windows, HTML 5, and more. Apart from supporting multiple platforms, Gideros also provides automatic screen scaling and automatic selection of proper image resolution, which makes supporting different screen resolutions, aspect ratios, and universal... -
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Hypothesis
The property-based testing library for Python
Hypothesis is a powerful library for property-based testing in Python. Instead of writing specific test cases, users define properties and Hypothesis generates random inputs to uncover edge cases and bugs. It integrates with unittest and pytest, shrinking failing examples to minimal reproducible cases. Widely adopted in production systems, Hypothesis boosts code reliability by exploring input spaces far beyond manually crafted tests. -
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parallel-ssh
Asynchronous parallel SSH client library.
parallel-ssh is an asynchronous parallel SSH library designed for large-scale automation. It differentiates itself from alternatives, other libraries and higher-level frameworks like Ansible or Chef in several ways. -
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Jupyter Notebook Tools for Sphinx
Sphinx source parser for Jupyter notebooks
nbsphinx is a Sphinx extension that provides a source parser for *.ipynb files. Custom Sphinx directives are used to show Jupyter Notebook code cells (and of course their results) in both HTML and LaTeX output. Un-evaluated notebooks – i.e. notebooks without stored output cells – will be automatically executed during the Sphinx build process. -
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fvcore
Collection of common code shared among different research projects
fvcore is a lightweight utility library that factors out common performance-minded components used across Facebook/Meta computer-vision codebases. It provides numerics and loss layers (e.g., focal loss, smooth-L1, IoU/GIoU) implemented for speed and clarity, along with initialization helpers and normalization layers for building PyTorch models. Its common modules include timers, logging, checkpoints, registry patterns, and configuration helpers that reduce boilerplate in research code... -
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Odigos
Distributed tracing without code changes
Odigos supports any application written in Java, Python, .NET, Node.js and Go. Historically, compiled languages like Go have been difficult to instrument without code changes. Odigos solves this problem by uniquely leveraging eBPF. Odigos currently supports all the popular managed and open source destinations. By producing data in the OpenTelemetry format, Odigos can be used with any observability tool that supports OTLP. Odigos automatically scales OpenTelemetry collectors based... -
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Smallpond
A lightweight data processing framework built on DuckDB and 3FS
smallpond is a lightweight distributed data processing framework built by DeepSeek, designed to scale DuckDB workloads over clusters using their 3FS (Fire-Flyer File System) backend. The idea is to preserve DuckDB’s fast analytics engine but lift it from single-node to multi-node settings, giving you the ability to operate on large datasets (e.g. petabyte scale) without moving to a heavyweight system like Spark. Users write Python-like code (via DataFrame APIs or SQL strings) to express... -
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pybind11
Seamless operability between C++11 and Python
Pybind11 is a lightweight, header‑only C++ library that simplifies and streamlines creating Python bindings for C++11+ code. It uses template meta-programming to automatically infer types, minimizing boilerplate compared to Boost.Python, and works seamlessly with major build systems -
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Lets-Plot
Multiplatform plotting library based on Grammar of Graphics
Lets-Plot is a multiplatform plotting library based on the Grammar of Graphics. The library' design is heavily influenced by Leland Wilkinson work The Grammar of Graphics describing the deep features that underlie all statistical graphics. -
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OneFile
Curates delightful open-source projects
OneFile curates delightful open-source projects that each consist of a single file, highlighting how much you can learn from compact, self-contained code. The collection spans tiny games, utilities, servers, interpreters, and teaching demos where the entire idea fits in one readable source. This “one file” constraint makes every example approachable—you can open it, grasp the full control flow, and experiment without navigating a large codebase. It’s ideal for learners seeking quick wins... -
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Tokenize.jl
Tokenization for Julia source code
Tokenize is a Julia package that serves a similar purpose and API as the tokenize module in Python but for Julia. This is to take a string or buffer containing Julia code, perform lexical analysis and return a stream of tokens. -
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EKS Best Practices
A best practices guide for day 2 operations
The Amazon EKS Best Practices Guide is a public repository containing comprehensive documentation and guidance for operating production-grade Kubernetes clusters on AWS’s managed service, Amazon EKS. Rather than a code library, it serves as a reference catalogue of patterns, anti-patterns, checklists and architectures across domains such as security, reliability, scalability, networking, cost optimization and hybrid cloud deployments. The repository is maintained by AWS but open... -
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Tunix
A JAX-native LLM Post-Training Library
Tunix is a JAX-native library for post-training large language models, bringing supervised fine-tuning, reinforcement learning–based alignment, and knowledge distillation into one coherent toolkit. It embraces JAX’s strengths—functional programming, jit compilation, and effortless multi-device execution—so experiments scale from a single GPU to pods of TPUs with minimal code changes. The library is organized around modular pipelines for data loading, rollout, optimization, and evaluation... -
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Flama
Fire up your models with the flame
Flama is a python library which establishes a standard framework for development and deployment of APIs with special focus on machine learning (ML). The main aim of the framework is to make ridiculously simple the deployment of ML APIs, simplifying (when possible) the entire process to a single line of code. The library builds on Starlette, and provides an easy-to-learn philosophy to speed up the building of highly performant GraphQL, REST and ML APIs. Besides, it comprises an ideal solution... -
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Public APIs
A collective list of free APIs
public-apis is a collaboratively maintained repository that provides an extensive, categorized list of publicly available APIs for developers. Curated by community contributors and the team at APILayer, it serves as a centralized resource for discovering APIs across a wide range of domains, including data, machine learning, weather, entertainment, and finance. The project aims to make API exploration and integration more accessible by offering a single, organized index of open and... -
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MetricFlow
MetricFlow allows you to define, build, and maintain metrics in code
MetricFlow is an open-source semantic layer engine designed to help organizations define, manage, and query business metrics in a consistent, governed way. It works alongside a data stack—typically built with dbt—and allows you to express metrics as YAML‐based definitions tied to semantic models and dimension tables, rather than embedding logic ad-hoc across many dashboards or scripts. When a user or tool requests a metric (e.g., “monthly revenue by region”), MetricFlow generates optimized,... -
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TensorStore
Library for reading and writing large multi-dimensional arrays
TensorStore is a high-performance library for reading and writing N-dimensional arrays that live in many different storage systems, from local files to cloud object stores. It separates the logical view (shape, dtype, chunking) from the physical layout so the same code can target Zarr, N5, TIFF pyramids, or custom backends. Rich indexing, slicing, and broadcasting operations make it feel like a familiar array API, while asynchronous I/O pipelines stream chunks efficiently in parallel... -
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CoreNet
CoreNet: A library for training deep neural networks
CoreNet is Apple’s internal deep learning framework for distributed neural network training, designed for high scalability, low-latency communication, and strong hardware efficiency. It focuses on enabling large-scale model training across clusters of GPUs and accelerators by optimizing data flow and parallelism strategies. CoreNet provides abstractions for data, tensor, and pipeline parallelism, allowing models to scale without code duplication or heavy manual configuration. Its distributed... -
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Prompt Engineering Interactive Tutorial
Anthropic's Interactive Prompt Engineering Tutorial
... the way you would debug code. Lessons include building prompts from scratch for common tasks like extraction, classification, transformation, and step-by-step reasoning, with checkpoints that let you compare your outputs against solid baselines. You’ll also practice advanced patterns such as tool use, constrained generation, and response validation so outputs are trustworthy and machine-consumable. -
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OpenAI Cookbook
Examples and guides for using the OpenAI API
openai-cookbook is a repository containing example code, tutorials, and guidance for how to build real applications on top of the OpenAI API. It covers a wide range of use cases: prompt engineering, embeddings and semantic search, fine-tuning, agent architectures, function calling, working with images, chat workflows, and more. The content is primarily in Python (notebooks, scripts), but the conceptual guidance is applicable across languages. The repository is kept up to date and often expanded... -
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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... -
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libgit2
A cross-platform, portable, linkable Git implementation library
... library for Xcode projects to simplify distribution significantly. libgit2 is used for powering Git GUI clients, such as gmaster and GitKraken and on Git hosting providers such as GitLab, Azure, GitHub, DevOps, among others. By clicking "merge pull request", we perform the merge. It is licensed under the GPLv2 license, so you can link it in its unmodified state with any type of software without releasing its source code. -
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GPT-Code UI
An open source implementation of OpenAI's ChatGPT Code interpreter
An open source implementation of OpenAI's ChatGPT Code interpreter. Simply ask the OpenAI model to do something and it will generate & execute the code for you. You can put a .env in the working directory to load the OPENAI_API_KEY environment variable. For Azure OpenAI Services, there are also other configurable variables like deployment name. See .env.azure-example for more information. Note that model selection on the UI is currently not supported for Azure OpenAI Services. -
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Playground Cheatsheet for Python
Playground and cheatsheet for learning Python
learn-python is another repository by Oleksii Trekhleb that serves as both a playground and an interactive cheatsheet for learning Python. It contains numerous Python scripts organized by topic (lists, dictionaries, loops, functions, classes, modules, etc.), each with code examples, explanations, test assertions, and links to further readings. The design supports “learn by doing”: you can modify the code, run the tests, see how behavior changes, and thus internalize Python language features...
