CuPy vs Kaggle
Overview
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CLI (Node.js)
Manual
Detailed Comparison
It is an open-source matrix library accelerated with NVIDIA CUDA. CuPy provides GPU accelerated computing with Python. It uses CUDA-related libraries including cuBLAS, cuDNN, cuRand, cuSolver, cuSPARSE, cuFFT and NCCL to make full use of the GPU architecture. | Machine learners share, stress test, and stay up-to-date on all the latest ML techniques and technologies. Discover a huge repository of community-published models, data & code for your next project. |
It's interface is highly compatible with NumPy in most cases it can be used as a drop-in replacement; Supports various methods, indexing, data types, broadcasting and more; You can easily make a custom CUDA kernel if you want to make your code run faster, requiring only a small code snippet of C++; It automatically wraps and compiles it to make a CUDA binary; Compiled binaries are cached and reused in subsequent runs | Find all the resources and knowledge needed for your next real-world ML project;
High-quality public datasets. Everything from avocado prices to video game sales;
Public notebooks and access to a powerful notebook environment with no cost GPUs & TPUs;
Pre-trained, ready-to-deploy ML models;
Build your ML skills in a supportive and helpful community |
Statistics | |
GitHub Stars 10.6K | GitHub Stars - |
GitHub Forks 967 | GitHub Forks - |
Stacks 8 | Stacks 10 |
Followers 27 | Followers 7 |
Votes 0 | Votes 0 |
Integrations | |
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Tinker
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