Search Results for "cuda machine learning" - Page 17
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Showing 977 open source projects for "cuda machine learning"
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The Algorithms Python
All Algorithms implemented in Python
...Each implementation is designed with clarity in mind, favoring readability and comprehension over performance optimization. The project covers various domains including mathematics, cryptography, machine learning, sorting, graph theory, and more. With contributions from a large global community, it continually grows and improves through collaboration and peer review. This repository is an ideal reference for students, educators, and developers seeking hands-on experience with algorithmic concepts in Python. -
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Superduper
Superduper: Integrate AI models and machine learning workflows
Superduper is a Python-based framework for building end-2-end AI-data workflows and applications on your own data, integrating with major databases. It supports the latest technologies and techniques, including LLMs, vector-search, RAG, and multimodality as well as classical AI and ML paradigms. Developers may leverage Superduper by building compositional and declarative objects that out-source the details of deployment, orchestration versioning, and more to the Superduper engine. This... -
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KServe
Standardized Serverless ML Inference Platform on Kubernetes
KServe provides a Kubernetes Custom Resource Definition for serving machine learning (ML) models on arbitrary frameworks. It aims to solve production model serving use cases by providing performant, high abstraction interfaces for common ML frameworks like Tensorflow, XGBoost, ScikitLearn, PyTorch, and ONNX. It encapsulates the complexity of autoscaling, networking, health checking, and server configuration to bring cutting edge serving features like GPU Autoscaling, Scale to Zero, and Canary Rollouts to your ML deployments. ... -
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Instill Core
Instill Core is a full-stack AI infrastructure tool for data
Instill Core is an open-source, full-stack AI infrastructure platform designed to orchestrate data pipelines, machine learning models, and unstructured data processing into a unified, production-ready system. It provides an end-to-end solution that enables developers to build, deploy, and manage AI-powered applications without needing to manually stitch together multiple tools across the data and model lifecycle. The platform focuses heavily on handling unstructured data such as documents, images, audio, and video, transforming them into AI-ready formats through integrated ETL pipelines and processing workflows. ... -
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WeClone
One-stop solution for creating your digital avatar from chat history
...Developers can use the resulting model to create chatbots that simulate a specific user’s communication patterns for testing or research purposes. Overall, WeClone explores the idea of digital identity replication through machine learning and conversational modeling. -
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Optuna
A hyperparameter optimization framework
Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. It features an imperative, define-by-run style user API. Thanks to our define-by-run API, the code written with Optuna enjoys high modularity, and the user of Optuna can dynamically construct the search spaces for the hyperparameters. Optuna Dashboard is a real-time web dashboard for Optuna. You can check the optimization history, hyperparameter importances, etc. in graphs and tables. ... -
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openage
Open source clone of the Age of Empires II engine
openage is a free cross-platform RTS game engine that provides the mechanics of Age of Empires. Using modern technologies as C++17, OpenGL/GLSL, Python, Qt5 and CMake allows people using GNU/Linux, BSD, macOS or Windows to play the game natively. Our aim is to make openage a platform for the original Age of Empires games providing the same look and feel, but with more features for modding and multiplayer. openage uses an open API powered by our human-readable configuration language nyan. We... -
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Colab-MCP
An MCP server for interacting with Google Colab
...Instead of relying on manual notebook usage, the system allows MCP-compatible agents to execute code, manage files, install dependencies, and orchestrate entire development workflows within Colab’s cloud infrastructure. This approach bridges the gap between local AI agents and remote high-performance compute environments, allowing users to offload heavy workloads such as machine learning training, data analysis, and dependency-heavy tasks to Colab’s GPU and TPU resources. By exposing Colab as an MCP server, the tool enables seamless integration with a wide range of AI assistants and agent frameworks, creating a standardized interface for tool use and execution. -
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SafeClaw
Chat with it via text and voice
SafeClaw is an open-source, entirely local alternative to cloud-based AI assistants like OpenClaw, enabling users to build a personal assistant that runs on their own machine without incurring API usage charges or exposing data to third-party services. It emphasizes privacy and predictability by using traditional programming, rule-based intent parsing, and established machine learning tools rather than large language models, meaning there are no per-token API costs and deterministic behavior. The assistant offers features such as voice control using fully local speech-to-text (Whisper) and text-to-speech (Piper) capabilities, news aggregation with extractive summarization, and smart home or Bluetooth device control. ... -
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Amazon CodeGuru Profiler Python Agent
Amazon CodeGuru Profiler Python Agent
Amazon CodeGuru Profiler collects runtime performance data from your live applications and provides recommendations that can help you fine-tune your application performance. Using machine learning algorithms, CodeGuru Profiler can help you find your most expensive lines of code and suggest ways you can improve efficiency and remove CPU bottlenecks. CodeGuru Profiler provides different visualizations of profiling data to help you identify what code is running on the CPU, see how much time is consumed, and suggest ways to reduce CPU utilization. ... -
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SDGym
Benchmarking synthetic data generation methods
...Or write your own custom machine learning model. In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. Install SDGym using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device. -
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MaxText
A simple, performant and scalable Jax LLM
...MaxText includes ready-to-use configurations and reproducible training examples that help developers understand how to deploy large-scale AI workloads with modern machine learning infrastructure. -
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Writer Framework
No-code in the front, Python in the back. An open-source framework
...It follows a hybrid approach where user interfaces are created using a drag-and-drop editor while business logic is implemented in Python, allowing teams to balance speed and flexibility without sacrificing control. The framework is particularly focused on AI use cases, enabling developers to integrate large language models, knowledge graphs, and custom machine learning workflows into user-facing applications. Its architecture enforces a clear separation of concerns between frontend and backend, which improves maintainability and scalability as applications grow in complexity. The system is designed to support rapid prototyping, enabling developers to iterate on UI and backend logic independently and deploy changes quickly. -
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Scientific Agent Skills
A set of ready to use Agent Skills for research, science, engineering
...It supports any AI agent compatible with the Agent Skills standard, including tools such as Cursor, Claude Code, Codex, and Gemini CLI. The repository includes 135 skills across scientific domains such as genomics, cheminformatics, clinical research, medical imaging, machine learning, physics, materials science, geospatial analysis, and scientific writing. Each skill provides curated documentation, examples, best practices, and integration guidance so agents can execute complex workflows more reliably. It is especially useful for researchers who need AI assistance with databases, Python libraries, literature review, data analysis, and scientific communication. ... -
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MuJoCo
Multi-Joint dynamics with Contact. A general purpose physics simulator
MuJoCo, developed and maintained by Google DeepMind, is a high-performance physics engine designed for simulating complex, articulated systems that interact through contact. It is widely used in research fields such as robotics, biomechanics, computer graphics, animation, and machine learning, where fast and accurate physics simulations are essential. The engine provides a robust C API optimized for real-time computation, making it suitable for scientific research and advanced simulation environments. MuJoCo’s core architecture is performance-tuned and utilizes preallocated data structures created through an XML-based compiler. ... -
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Mosec
A high-performance ML model serving framework, offers dynamic batching
Mosec is a high-performance and flexible model-serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API. -
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Sparrow
Structured data extraction and instruction calling with ML, LLM
Sparrow is an open-source platform designed to extract structured information from documents, images, and other unstructured data sources using machine learning and large language models. The system focuses on transforming complex documents such as invoices, receipts, forms, and scanned pages into structured formats like JSON that can be processed by downstream applications. It combines several components, including OCR pipelines, vision-language models, and LLM-based reasoning modules to identify and extract meaningful data fields from heterogeneous document layouts. ... -
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X's Recommendation Algorithm
Source code for the X Recommendation Algorithm
...While certain components (such as safety layers, spam detection, or private data) are excluded, the release provides valuable insights into the design of real-world machine learning–driven ranking systems. The project is intended as a reference for researchers, developers, and the public to study, experiment with, and better understand the mechanisms behind social media content. -
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Pyper
Concurrent Python made simple
Pyper is a Python-native orchestration and scheduling framework designed for modern data workflows, machine learning pipelines, and any task that benefits from a lightweight DAG-based execution engine. Unlike heavier platforms like Airflow, Pyper aims to remain lean, modular, and developer-friendly, embracing Pythonic conventions and minimizing boilerplate. It focuses on local development ergonomics and seamless transition to production environments, making it ideal for small teams and individuals needing a programmable and flexible orchestration solution without the overhead of enterprise systems. -
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Book2_Beauty-of-Data-Visualization
Machine Learning, Criticism and Correction
Book2_Beauty-of-Data-Visualization is an open educational project that teaches the principles and techniques of effective data visualization using Python and modern plotting libraries. The repository focuses on both the technical and aesthetic aspects of visual analytics, helping learners understand how to communicate data clearly and persuasively. It includes practical examples that demonstrate how different chart types reveal patterns, trends, and distributions in real datasets. The... -
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JAX Toolbox
Public CI, Docker images for popular JAX libraries
JAX Toolbox is a development toolkit designed to streamline and optimize the use of JAX for machine learning and high-performance computing on NVIDIA GPUs. It provides prebuilt Docker images, continuous integration pipelines, and optimized example implementations that help developers quickly set up and run JAX workloads without complex configuration. The project supports popular JAX-based frameworks and models, including architectures used for large-scale pretraining such as GPT and LLaMA variants. ... -
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Hugging Face Skills
Definitions for AI/ML tasks like dataset creation
Hugging Face Skills is a repository of standardized task definitions that package instructions, scripts, and resources so coding agents can reliably perform AI and machine learning workflows. Each skill is a self-contained folder with structured metadata and guidance that tells an agent how to execute tasks such as dataset creation, model training, evaluation, or Hub operations. The project is designed to be interoperable across major agent ecosystems, including Claude Code, OpenAI Codex, Gemini CLI, and Cursor, making it a cross-platform building block for agent automation. ... -
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Covalent workflow
Pythonic tool for running machine-learning/high performance workflows
Covalent is a Pythonic workflow tool for computational scientists, AI/ML software engineers, and anyone who needs to run experiments on limited or expensive computing resources including quantum computers, HPC clusters, GPU arrays, and cloud services. Covalent enables a researcher to run computation tasks on an advanced hardware platform – such as a quantum computer or serverless HPC cluster – using a single line of code. Covalent overcomes computational and operational challenges inherent... -
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Amazon Braket PennyLane Plugin
A plugin for allowing Xanadu PennyLane to use Amazon Braket devices
...The Amazon Braket Python SDK is an open-source library that provides a framework to interact with quantum computing hardware devices and simulators through Amazon Braket. PennyLane is a machine learning library for optimization and automatic differentiation of hybrid quantum-classical computations. Once the Pennylane-Braket plugin is installed, the provided Braket devices can be accessed straight away in PennyLane, without the need to import any additional packages. While the local device helps with small-scale simulations and rapid prototyping, the remote device allows you to run larger simulations or access quantum hardware via the Amazon Braket service. -
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fastdup
An unsupervised and free tool for image and video dataset analysis
fastdup is a powerful free tool designed to rapidly extract valuable insights from your image & video datasets. Assisting you to increase your dataset images & labels quality and reduce your data operations costs at an unparalleled scale.
