Brancher vs CuPy
Overview
Detailed Comparison
It is a user-centered Python package for differentiable probabilistic inference. It allows to design and train differentiable Bayesian models using stochastic variational inference. It is based on the deep learning framework PyTorch. | 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. |
Flexible; Integrated | 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 203 | GitHub Stars 10.6K |
GitHub Forks 29 | GitHub Forks 967 |
Stacks 0 | Stacks 7 |
Followers 8 | Followers 27 |
Votes 0 | Votes 0 |
Integrations | |
| No integrations available | |
What are some alternatives to Brancher, CuPy?
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
Integrate Python into Microsoft Excel. Use Excel as your user-facing front-end with calculations, business logic and data access powered by Python. Works with all 3rd party and open source Python packages. No need to write any VBA!
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
Dataform helps you manage all data processes in your cloud data warehouse. Publish tables, write data tests and automate complex SQL workflows in a few minutes, so you can spend more time on analytics and less time managing infrastructure.
PySpark
It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.
Pentaho Data Integration
It enable users to ingest, blend, cleanse and prepare diverse data from any source. With visual tools to eliminate coding and complexity, It puts the best quality data at the fingertips of IT and the business.
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.
