scikit-learn vs CUDA

Overview

scikit-learn

Stacks1.3K

Followers1.1K

Votes45

GitHub Stars63.9K

Forks26.4K

CUDA

Stacks542

Followers215

Votes0

CUDA vs scikit-learn: What are the differences?

CUDA: It provides everything you need to develop GPU-accelerated applications. A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation; scikit-learn: Easy-to-use and general-purpose machine learning in Python. scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

CUDA and scikit-learn can be primarily classified as "Machine Learning" tools.

scikit-learn is an open source tool with 36.5K GitHub stars and 17.9K GitHub forks. Here's a link to scikit-learn's open source repository on GitHub.

Repro, MonkeyLearn, and Home61 are some of the popular companies that use scikit-learn, whereas CUDA is used by Cruise, Replica Labs, and Enthusiasts First. scikit-learn has a broader approval, being mentioned in 104 company stacks & 252 developers stacks; compared to CUDA, which is listed in 13 company stacks and 13 developer stacks.

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Detailed Comparison

scikit-learn	CUDA
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.	A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
Statistics
GitHub Stars 63.9K	GitHub Stars -
GitHub Forks 26.4K	GitHub Forks -
Stacks 1.3K	Stacks 542
Followers 1.1K	Followers 215
Votes 45	Votes 0
Pros & Cons
Pros 26 Scientific computing 19 Easy Cons 2 Limited	No community feedback yet

What are some alternatives to scikit-learn, CUDA?

TensorFlow

TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.

PyTorch

PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

Kubeflow

The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.

TensorFlow.js

Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

Streamlit

It is the app framework specifically for Machine Learning and Data Science teams. You can rapidly build the tools you need. Build apps in a dozen lines of Python with a simple API.

MLflow

MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

H2O

H2O.ai is the maker behind H2O, the leading open source machine learning platform for smarter applications and data products. H2O operationalizes data science by developing and deploying algorithms and models for R, Python and the Sparkling Water API for Spark.

PredictionIO

PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.