CuPy vs Facette
Overview
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Detailed Comparison
It is a web application to display time series data from various sources such as collectd, Graphite, InfluxDB or KairosDB on graphs, designed to be easy to setup and to use. | 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. |
Data Visualization, Time series data | 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 |
Statistics | |
GitHub Stars 1.2K | GitHub Stars 10.6K |
GitHub Forks 68 | GitHub Forks 967 |
Stacks 0 | Stacks 8 |
Followers 4 | Followers 27 |
Votes 0 | Votes 0 |
Integrations | |
| No integrations available | |
What are some alternatives to Facette, CuPy?
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