Popular GitHub Repositories for Nsight Compute

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.

The Compute Library is a set of computer vision and machine learning functions optimised for both Arm CPUs and GPUs using SIMD technologies.

The "official" unofficial DSPy framework. Build LLM powered agents and other workflows, based on the Stanford DSP paper.

Implementation of LambdaNetworks, a new approach to image recognition that reaches SOTA with less compute

Cramming the training of a (BERT-type) language model into limited compute.

Central place for the engineering/scaling WG: documentation, SLURM scripts and logs, compute environment and data.

Contains examples for the Movidius Neural Compute Stick.

A high-performance ML model serving framework, offers dynamic batching and CPU/GPU pipelines to fully exploit your compute machine

Pythonic tool for orchestrating machine-learning/high performance/quantum-computing workflows in heterogeneous compute environments.

LLaSA: Scaling Train-time and Inference-time Compute for LLaMA-based Speech Synthesis

Compute Sentence Embeddings Fast!

FlyCV is a high-performance library for processing computer visual tasks.

Triton Model Analyzer is a CLI tool to help with better understanding of the compute and memory requirements of the Triton Inference Server models.

Mobile AI Compute Engine Model Zoo

[High Performance / MAX 30 FPS] RaspberryPi3(RaspberryPi/Raspbian Stretch) or Ubuntu + Multi Neural Compute Stick(NCS/NCS2) + RealSense D435(or USB Camera or PiCamera) + MobileNet-SSD(MobileNetSSD) + Background Multi-transparent(Simple multi-class segmentation) + FaceDetection + MultiGraph + MultiProcessing + MultiClustering

Compute CNN receptive field size in pytorch in one line

High-Performance Python Compute Engine for Data and AI

AlignProp uses direct reward backpropogation for the alignment of large-scale text-to-image diffusion models. Our method is 25x more sample and compute efficient than reinforcement learning methods (PPO) for finetuning Stable Diffusion

Package to compute Mauve, a similarity score between neural text and human text. Install with `pip install mauve-text`.