Best AI tools for< Cuda Engineer >

Juice Remote GPU is a software that enables AI and Graphics workloads on remote GPUs. It allows users to offload GPU processing for any CUDA or Vulkan application to a remote host running the Juice agent. The software injects CUDA and Vulkan implementations during runtime, eliminating the need for code changes in the application. Juice supports multiple clients connecting to multiple GPUs and multiple clients sharing a single GPU. It is useful for sharing a single GPU across multiple workstations, allocating GPUs dynamically to CPU-only machines, and simplifying development workflows and deployments. Juice Remote GPU performs within 5% of a local GPU when running in the same datacenter. It supports various APIs, including CUDA, Vulkan, DirectX, and OpenGL, and is compatible with PyTorch and TensorFlow. The team behind Juice Remote GPU consists of engineers from Meta, Intel, and the gaming industry.

vLLM is a fast and easy-to-use library for LLM inference and serving. It offers state-of-the-art serving throughput, efficient management of attention key and value memory, continuous batching of incoming requests, fast model execution with CUDA/HIP graph, and various decoding algorithms. The tool is flexible with seamless integration with popular HuggingFace models, high-throughput serving, tensor parallelism support, and streaming outputs. It supports NVIDIA GPUs and AMD GPUs, Prefix caching, and Multi-lora. vLLM is designed to provide fast and efficient LLM serving for everyone.

Deep Live Cam is a cutting-edge AI tool that enables real-time face swapping and one-click video deepfakes. It harnesses advanced AI algorithms to deliver high-quality face replacement with just a single image. The tool supports multiple execution platforms, including CPU, NVIDIA CUDA, and Apple Silicon, providing users with flexibility and optimized performance. Deep Live Cam promotes ethical use by incorporating safeguards to prevent processing of inappropriate content. Additionally, it benefits from an active open-source community, ensuring ongoing support and improvements to stay at the forefront of technology.

This repository documents how to optimize common algorithms based on CUDA. It includes subdirectories with code implementations for specific optimizations. The optimizations cover topics such as compiling PyTorch from source, NVIDIA's reduce optimization, OneFlow's elementwise template, fast atomic add for half data types, upsample nearest2d optimization in OneFlow, optimized indexing in PyTorch, OneFlow's softmax kernel, linear attention optimization, and more. The repository also includes learning resources related to deep learning frameworks, compilers, and optimization techniques.

Yalm (Yet Another Language Model) is an LLM inference implementation in C++/CUDA, emphasizing performance engineering, documentation, scientific optimizations, and readability. It is not for production use and has been tested on Mistral-v0.2 and Llama-3.2. Requires C++20-compatible compiler, CUDA toolkit, and LLM safetensor weights in huggingface format converted to .yalm file.

Primitive cuda kernel library for ppl.nn.llm, part of PPL.LLM system, tested on Ampere and Hopper, requires Linux on x86_64 or arm64 CPUs, GCC >= 9.4.0, CMake >= 3.18, Git >= 2.7.0, CUDA Toolkit >= 11.4. 11.6 recommended. Provides cuda kernel functionalities for deep learning tasks.

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ppl.llm.kernel.cuda is a primitive cuda kernel library for ppl.nn.llm system, designed for Ampere and Hopper architectures. It requires Linux running on x86_64 or arm64 CPUs with specific versions of GCC, CMake, Git, and CUDA Toolkit. Users can follow the provided Quick Start guide to install prerequisites, clone the source code, and build from source. The project is distributed under the Apache License, Version 2.0.

AITemplate (AIT) is a Python framework that transforms deep neural networks into CUDA (NVIDIA GPU) / HIP (AMD GPU) C++ code for lightning-fast inference serving. It offers high performance close to roofline fp16 TensorCore (NVIDIA GPU) / MatrixCore (AMD GPU) performance on major models. AITemplate is unified, open, and flexible, supporting a comprehensive range of fusions for both GPU platforms. It provides excellent backward capability, horizontal fusion, vertical fusion, memory fusion, and works with or without PyTorch. FX2AIT is a tool that converts PyTorch models into AIT for fast inference serving, offering easy conversion and expanded support for models with unsupported operators.

This repository is a collection of tools and resources for deep learning infrastructure, covering programming languages, algorithms, acceleration techniques, and engineering aspects. It provides information on various online tools for chip architecture, CPU and GPU benchmarks, and code analysis. Additionally, it includes content on AI compilers, deep learning models, high-performance computing, Docker and Kubernetes tutorials, Protobuf and gRPC guides, and programming languages such as C++, Python, and Shell. The repository aims to bridge the gap between algorithm understanding and engineering implementation in the fields of AI and deep learning.

Ahnlich is a tool that provides multiple components for storing and searching similar vectors using linear or non-linear similarity algorithms. It includes 'ahnlich-db' for in-memory vector key value store, 'ahnlich-ai' for AI proxy communication, 'ahnlich-client-rs' for Rust client, and 'ahnlich-client-py' for Python client. The tool is not production-ready yet and is still in testing phase, allowing AI/ML engineers to issue queries using raw input such as images/text and features off-the-shelf models for indexing and querying.

llm-resource is a comprehensive collection of high-quality resources for Large Language Models (LLM). It covers various aspects of LLM including algorithms, training, fine-tuning, alignment, inference, data engineering, compression, evaluation, prompt engineering, AI frameworks, AI basics, AI infrastructure, AI compilers, LLM application development, LLM operations, AI systems, and practical implementations. The repository aims to gather and share valuable resources related to LLM for the community to benefit from.

ScaleLLM is a cutting-edge inference system engineered for large language models (LLMs), meticulously designed to meet the demands of production environments. It extends its support to a wide range of popular open-source models, including Llama3, Gemma, Bloom, GPT-NeoX, and more. ScaleLLM is currently undergoing active development. We are fully committed to consistently enhancing its efficiency while also incorporating additional features. Feel free to explore our **_Roadmap_** for more details. ## Key Features * High Efficiency: Excels in high-performance LLM inference, leveraging state-of-the-art techniques and technologies like Flash Attention, Paged Attention, Continuous batching, and more. * Tensor Parallelism: Utilizes tensor parallelism for efficient model execution. * OpenAI-compatible API: An efficient golang rest api server that compatible with OpenAI. * Huggingface models: Seamless integration with most popular HF models, supporting safetensors. * Customizable: Offers flexibility for customization to meet your specific needs, and provides an easy way to add new models. * Production Ready: Engineered with production environments in mind, ScaleLLM is equipped with robust system monitoring and management features to ensure a seamless deployment experience.

NVIDIA Generative AI Examples are state-of-the-art examples that are easy to deploy, test, and extend. All examples run on the high performance NVIDIA CUDA-X software stack and NVIDIA GPUs. These examples showcase the capabilities of NVIDIA's Generative AI platform, which includes tools, frameworks, and models for building and deploying generative AI applications.

LLMFlex is a python package designed for developing AI applications with local Large Language Models (LLMs). It provides classes to load LLM models, embedding models, and vector databases to create AI-powered solutions with prompt engineering and RAG techniques. The package supports multiple LLMs with different generation configurations, embedding toolkits, vector databases, chat memories, prompt templates, custom tools, and a chatbot frontend interface. Users can easily create LLMs, load embeddings toolkit, use tools, chat with models in a Streamlit web app, and serve an OpenAI API with a GGUF model. LLMFlex aims to offer a simple interface for developers to work with LLMs and build private AI solutions using local resources.

Lance is a modern columnar data format optimized for ML workflows and datasets. It offers high-performance random access, vector search, zero-copy automatic versioning, and ecosystem integrations with Apache Arrow, Pandas, Polars, and DuckDB. Lance is designed to address the challenges of the ML development cycle, providing a unified data format for collection, exploration, analytics, feature engineering, training, evaluation, deployment, and monitoring. It aims to reduce data silos and streamline the ML development process.

The Machine Learning Road Map is a comprehensive guide designed to take individuals from various levels of machine learning knowledge to a basic understanding of machine learning principles using high-quality, free resources. It aims to simplify the complex and rapidly growing field of machine learning by providing a structured roadmap for learning. The guide emphasizes the importance of understanding AI for everyone, the need for patience in learning machine learning due to its complexity, and the value of learning from experts in the field. It covers five different paths to learning about machine learning, catering to consumers, aspiring AI researchers, ML engineers, developers interested in building ML applications, and companies looking to implement AI solutions.

Expert in CUDA for configuration, installation, troubleshooting, and programming.

CUDA and Triton expert with concise, accurate answers.