General purpose GPU compute framework built on Vulkan to support 1000s of cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends). Blazing fast, mobile-enabled, asynchronous and optimized for advanced GPU data processing usecases. Backed by the Linux Foundation.
الميزات الرئيسية لـ komputeproject/kompute هي: Computation and Optimization, Parallel Programming Frameworks.
تشمل البدائل مفتوحة المصدر لـ komputeproject/kompute: horovod/horovod — Horovod is a distributed deep learning framework and gradient synchronizer designed to scale model training across… alpaka-group/alpaka — alpaka - Abstraction Library for Parallel Kernel Acceleration. arogozhnikov/einops — Einops is a tensor manipulation library that provides a framework-agnostic interface for reshaping, Einstein… bitsandbytes-foundation/bitsandbytes — bitsandbytes is a deep learning quantization tool and library designed to reduce the memory footprint of large… bloomen/transwarp — A header-only C++ library for task concurrency. adapter-hub/adapters — A Unified Library for Parameter-Efficient and Modular Transfer Learning.
Horovod is a distributed deep learning framework and gradient synchronizer designed to scale model training across multiple GPUs and compute nodes. It functions as a distributed training orchestrator and an elastic training engine, utilizing an MPI collective communication library to synchronize weights and gradients across TensorFlow, PyTorch, Keras, and MXNet models. The system distinguishes itself through dynamic elastic scaling, which allows it to adjust the number of active workers at runtime and recover from node failures. It optimizes communication efficiency using tensor fusion batchi
alpaka - Abstraction Library for Parallel Kernel Acceleration
Einops is a tensor manipulation library that provides a framework-agnostic interface for reshaping, Einstein summation, and multi-dimensional array operations. It serves as an abstraction layer that works across NumPy, PyTorch, TensorFlow, and JAX, allowing for tensor transformations without changing the API. The library distinguishes itself through a declarative notation system that uses readable string patterns to describe tensor rearrangements and reductions. This approach includes an extended Einstein summation interface that supports multi-letter axis names and a named dimension mapping
A Unified Library for Parameter-Efficient and Modular Transfer Learning