ArrayFire
tfjs
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ArrayFire | tfjs | |
---|---|---|
6 | 29 | |
4,404 | 18,110 | |
1.2% | 0.7% | |
7.8 | 8.6 | |
22 days ago | 8 days ago | |
C++ | TypeScript | |
BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
ArrayFire
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Learn WebGPU
Loads of people have stated why easy GPU interfaces are difficult to create, but we solve many difficult things all the time.
Ultimately I think CPUs are just satisfactory for the vast vast majority of workloads. Servers rarely come with any GPUs to speak of. The ecosystem around GPUs is unattractive. CPUs have SIMD instructions that can help. There are so many reasons not to use GPUs. By the time anyone seriously considers using GPUs they're, in my imagination, typically seriously starved for performance, and looking to control as much of the execution details as possible. GPU programmers don't want an automagic solution.
So I think the demand for easy GPU interfaces is just very weak, and therefore no effort has taken off. The amount of work needed to make it as easy to use as CPUs is massive, and the only reason anyone would even attempt to take this on is to lock you in to expensive hardware (see CUDA).
For a practical suggestion, have you taken a look at https://arrayfire.com/ ? It can run on both CUDA and OpenCL, and it has C++, Rust and Python bindings.
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seeking C++ library for neural net inference, with cross platform GPU support
What about Arrayfire. https://github.com/arrayfire/arrayfire
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[D] Deep Learning Framework for C++.
Low-overhead — not our goal, but Flashlight is on par with or outperforming most other ML/DL frameworks with its ArrayFire reference tensor implementation, especially on nonstandard setups where framework overhead matters
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[D] Neural Networks using a generic GPU framework
Looking for frameworks with Julia + OpenCL I found array fire. It seems quite good, bonus points for rust bindings. I will keep looking for more, Julia completely fell off my radar.
- Windows 11 va bloquer les bidouilles qui facilitent l'emploi d'un navigateur alternatif à Edge
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Arrayfire progressive performance decline?
Your Problem may be the lazy evaluation, see this issue: https://github.com/arrayfire/arrayfire/issues/1709
tfjs
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JavaScript Libraries for Implementing Trendy Technologies in Web Apps in 2024
TensorFlow.js
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Deep Learning in JavaScript
Many people seem to be unaware of tensorflow.js, an official JS implementation of TF
https://github.com/tensorflow/tfjs
I'd love to see PyTorch in JS, but I think unless you get it running on the GPU it won't be able to do much.
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Machine Learning in NodeJS || Part 1: TensorflowJS Basics
TensorflowJS GitHub Repository
- PyTorch Primitives in WebGPU for the Browser
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I want to talk about WebGPU
Also, Tensorflow.js WebGPU backend has been in the works for quite some time: https://github.com/tensorflow/tfjs/tree/master/tfjs-backend-...
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WebGPU Fundamentals
It's a pity that tfjs never truly developed any decent ops. E.g. you need lgamma to implement the cap for zero-inflated poisson regression and tfjs simply doesn't have that: https://github.com/tensorflow/tfjs/issues/2011
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Chrome Ships WebGPU
People have been doing it for long with WebGL, see eg https://github.com/tensorflow/tfjs and https://cloudblogs.microsoft.com/opensource/2021/09/02/onnx-...
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How to get rotation (yaw/pitch/roll) from face detection keypoints?
thanks, no not unity, going to show it as a demo with threejs + tensorflow on the web. I found a github request to add face orientation https://github.com/tensorflow/tfjs/issues/3835 looks like they assigned someone to add it but doesn't look like its available yet, but there's some posts about the math I can use to get rotations based on some of the landmarks
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[P] Supporting neural network inference in web browsers
There already exist a wide variety of neural network inference engines that run in web browsers (e.g. TensorFlow.js and, my personal favorite for use with PyTorch models, ONNX Runtime Web), but pre- and post-processing has always required imperative manipulations on flat buffers rather than a clean ndarray interface.
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Tensorflow JS model crashing on mobile
Full docs and code: https://github.com/tensorflow/tfjs/tree/master/e2e/benchmarks/local-benchmark
What are some alternatives?
Thrust - [ARCHIVED] The C++ parallel algorithms library. See https://github.com/NVIDIA/cccl
face-api.js - JavaScript API for face detection and face recognition in the browser and nodejs with tensorflow.js
Boost.Compute - A C++ GPU Computing Library for OpenCL
webhl - WebHL is a fork of hlviewer.js that uses the File System Access API to load game assets direct from your computer rather than from a server.
VexCL - VexCL is a C++ vector expression template library for OpenCL/CUDA/OpenMP
lightweight-human-pose-estimation.pytorch - Fast and accurate human pose estimation in PyTorch. Contains implementation of "Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose" paper.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
BlazePose-tensorflow - A third-party Tensorflow Implementation for paper "BlazePose: On-device Real-time Body Pose tracking".
CUB - THIS REPOSITORY HAS MOVED TO github.com/nvidia/cub, WHICH IS AUTOMATICALLY MIRRORED HERE.
openpose - OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
Taskflow - A General-purpose Parallel and Heterogeneous Task Programming System
firecracker - Secure and fast microVMs for serverless computing.