Search Results for "ray tracing python"
Sort By:
Showing 8 open source projects for "ray tracing python"
View related business solutions-
Next-Gen Encryption for Post-Quantum Security | CLEAR by Quantum KnightCLEAR by Quantum Knight is a FIPS-140-3 validated encryption SDK engineered for enterprises requiring top-tier security. Offering robust post-quantum cryptography, CLEAR secures files, streaming media, databases, and networks with ease across over 30 modern platforms. Its compact design, smaller than a single smartphone image, ensures maximum efficiency and low energy consumption.
-
Rezku Point of SaleRezku is an all-inclusive ordering platform and management solution for all types of restaurant and bar concepts. You can now get a fully custom branded downloadable smartphone ordering app for your restaurant exclusively from Rezku.
-
1
Ray
A unified framework for scalable computing
...Find the best model and reduce training costs by using the latest optimization algorithms. Deploy your machine learning models at scale with Ray Serve, a Python-first and framework agnostic model serving framework. Scale reinforcement learning (RL) with RLlib, a framework-agnostic RL library that ships with 30+ cutting-edge RL algorithms including A3C, DQN, and PPO. Easily build out scalable, distributed systems in Python with simple and composable primitives in Ray Core. -
2
fugue
A unified interface for distributed computing
Fugue is a unified interface for distributed computing that lets users execute Python, Pandas, and SQL code on Spark, Dask, and Ray with minimal rewrites. -
3
Daft
Distributed DataFrame for Python designed for the cloud
Daft is a framework for ETL, analytics and ML/AI at scale. Its familiar Python Dataframe API is built to outperform Spark in performance and ease of use. Daft plugs directly into your ML/AI stack through efficient zero-copy integrations with essential Python libraries such as Pytorch and Ray. It also allows requesting GPUs as a resource for running models. Daft runs locally with a lightweight multithreaded backend. -
4
StatsForecast
Fast forecasting with statistical and econometric models
StatsForecast is a Python library for time-series forecasting that delivers a suite of classical statistical and econometric forecasting models optimized for high performance and scalability. It is designed not just for academic experiments but for production-level time-series forecasting, meaning it handles forecasting for many series at once, efficiently, reliably, and with minimal overhead. The library implements a broad set of models, including AutoARIMA, ETS, CES, Theta, plus a battery... -
Award-Winning Medical Office Software Designed for Your SpecialtyRXNT is an ambulatory healthcare technology pioneer that empowers medical practices and healthcare organizations to succeed and scale through innovative, data-backed, AI-powered software.
-
5
mlforecast
Scalable machine learning for time series forecasting
mlforecast is a time-series forecasting framework built around machine-learning models, designed to make forecasting both efficient and scalable. It lets you apply any regressor that follows the typical scikit-learn API, for example, gradient-boosted trees or linear models, to time-series data by automating much of the messy feature engineering and data preparation. Instead of writing custom code to build lagged features, rolling statistics, and date-based predictors, mlforecast generates... -
6
PyCaret
An open-source, low-code machine learning library in Python
...This makes experiments exponentially fast and efficient. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, LightGBM, CatBoost, Optuna, Hyperopt, Ray, and few more. The design and simplicity of PyCaret are inspired by the emerging role of citizen data scientists, a term first used by Gartner. Citizen Data Scientists are power users who can perform both simple and moderately sophisticated analytical tasks that would previously have required more technical expertise. -
7
LLM Applications
A comprehensive guide to building RAG-based LLM applications
LLM Applications is a practical reference repository that demonstrates how to build production-grade applications powered by large language models. The project focuses particularly on Retrieval-Augmented Generation architectures, which combine language models with external knowledge sources to improve accuracy and reliability. It provides step-by-step guidance for constructing systems that ingest documents, split them into chunks, generate embeddings, index them in vector databases, and... -
8
Tangent
Source-to-source debuggable derivatives in pure Python
Existing libraries implement automatic differentiation by tracing a program's execution (at runtime, like PyTorch) or by staging out a dynamic data-flow graph and then differentiating the graph (ahead-of-time, like TensorFlow). In contrast, Tangent performs ahead-of-time autodiff on the Python source code itself, and produces Python source code as its output. Tangent fills a unique location in the space of machine learning tools.
- Previous
- You're on page 1
- Next
