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gpu.cpp is a lightweight C++ library for executing low-level general-purpose GPU computation across different hardware vendors and operating systems. It functions as a portable GPU wrapper, kernel orchestrator, and tensor management system using the WebGPU specification to abstract device initialization, buffer transfers, and compute shader dispatching. The library provides a framework for defining compute kernels from shader code and managing their asynchronous dispatch and synchronization. It enables the execution of cross-platform compute shaders and the orchestration of GPU tasks through
IREE is an MLIR-based compiler toolchain and runtime designed to translate machine learning models from various frameworks into optimized binaries for execution across diverse hardware targets. It provides a unified pipeline to ingest models from PyTorch, TensorFlow, JAX, and ONNX, lowering them into a common intermediate representation for deployment on CPUs, GPUs, and bare-metal embedded systems. The project distinguishes itself through a bytecode virtual machine and a hardware abstraction layer that decouple high-level model logic from specific hardware instruction sets. It supports sophis
Sglang is a high-performance inference engine and serving system designed for large language and multimodal models. It provides a programmable interface for orchestrating complex generation workflows, enabling developers to coordinate multi-turn dialogues, tool invocations, and reasoning chains through a domain-specific language. The platform is built to support production-scale deployments, offering an OpenAI-compatible API that allows for integration with existing application ecosystems. The system distinguishes itself through a disaggregated architecture that separates compute-intensive pr
ExecuTorch is a lightweight C++ runtime for deploying PyTorch models on mobile, embedded, and edge hardware. It provides an ahead-of-time compilation pipeline that exports, quantizes, and lowers model graphs into compact serialized programs, then executes them through a minimal runtime with hardware acceleration and on-device large language model inference capabilities. The project distinguishes itself through a hardware accelerator delegate system that partitions model subgraphs and offloads computation to specialized backends including NPUs, GPUs, and DSPs from Apple, Arm, Intel, MediaTek,
This repository provides a collection of practical demonstrations and implementation guides for machine learning tasks using TensorFlow.js. It serves as a resource for developers to explore model architectures, training workflows, and data manipulation techniques across domains such as computer vision, natural language processing, and reinforcement learning.
The main features of tensorflow/tfjs-examples are: Manual Memory Management, Core Model APIs, Model Execution APIs, Asynchronous Training Utilities, Hardware-Accelerated Compute Backends, Model Inference and Serving, Tensor Memory Management.
Projects with overlapping indexed features include: answerdotai/gpu.cpp — gpu.cpp is a lightweight C++ library for executing low-level general-purpose GPU computation across different hardware… iree-org/iree — IREE is an MLIR-based compiler toolchain and runtime designed to translate machine learning models from various… sgl-project/sglang — Sglang is a high-performance inference engine and serving system designed for large language and multimodal models. It… pytorch/executorch — ExecuTorch is a lightweight C++ runtime for deploying PyTorch models on mobile, embedded, and edge hardware. It… vllm-project/vllm — vLLM is a high-throughput inference engine designed for the efficient serving and execution of large language models.… seldonio/seldon-core — Seldon Core is a Kubernetes-based machine learning model server and MLOps inference framework. It functions as a…