CuPy vs Jovian
Overview
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. | It is a better place for your data science projects, Jupyter notebooks, machine learning models, experiment logs, results, and more. |
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 | Collaboration platform built for data science; Simple Jupyter notebook versioning; Compare and analyze experiments; Reproduce and run anywhere, instantly; Designed for collaboration and teamwork; Comment and discuss ideas in context; Rich visual diffs & notebook comparison; Automate your workflow with integrations; Hosted on your private cloud infrastructure; Enterprise-grade security and privacy built-in |
Statistics | |
GitHub Stars 10.6K | GitHub Stars - |
GitHub Forks 967 | GitHub Forks - |
Stacks 7 | Stacks 3 |
Followers 27 | Followers 6 |
Votes 0 | Votes 0 |
Integrations | |
What are some alternatives to CuPy, Jovian?
Pandas
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.
NumPy
Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
PyXLL
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SciPy
Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
Anaconda
A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda.
Dataform
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PySpark
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Pentaho Data Integration
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Dask
It is a versatile tool that supports a variety of workloads. It is composed of two parts: Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads. Big Data collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of dynamic task schedulers.
KNIME
It is a free and open-source data analytics, reporting and integration platform. KNIME integrates various components for machine learning and data mining through its modular data pipelining concept.
