Collection of best practices, reference architectures, model training examples and utilities to train large models on AWS.
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Updated
May 3, 2024 - Python
Collection of best practices, reference architectures, model training examples and utilities to train large models on AWS.
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application
Framework providing operating system abstractions and a range of shared networking (RDMA, TCP/IP) and memory services to common modern heterogeneous platforms.
GPU-accelerated Image Processing library using OpenCL
oneAPI Math Kernel Library (oneMKL) Interfaces
GPU-accelerated Image Processing library
On-device AI across mobile, embedded and edge for PyTorch
Gans Specialization course by deeplearning.ai: solved assignments and labs
C++ wrapper for the Nvidia C libraries (e.g. CUDA driver, nvrtc, cuFFT etc.)
NVIDIA GPU Operator creates/configures/manages GPUs atop Kubernetes
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Graphics Processing Units Molecular Dynamics
CUDA C++ Core Libraries
FlashInfer: Kernel Library for LLM Serving
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