CUDA vs Torch | What are the differences? | StackShare

CUDA vs Torch

Overview

CUDA

Stacks542

Followers215

Votes0

Torch

Stacks355

Followers61

Votes0

GitHub Stars9.1K

Forks2.4K

CUDA vs Torch: What are the differences?

Developers describe CUDA as "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. On the other hand, Torch is detailed as "An open-source machine learning library and a script language based on the Lua programming language". It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.

CUDA and Torch belong to "Machine Learning Tools" category of the tech stack.

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Detailed Comparison

CUDA	Torch
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.	It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
-	A powerful N-dimensional array; Lots of routines for indexing, slicing, transposing; Amazing interface to C, via LuaJIT; Linear algebra routines; Neural network, and energy-based models; Numeric optimization routines; Fast and efficient GPU support; Embeddable, with ports to iOS and Android backends
Statistics
GitHub Stars -	GitHub Stars 9.1K
GitHub Forks -	GitHub Forks 2.4K
Stacks 542	Stacks 355
Followers 215	Followers 61
Votes 0	Votes 0
Integrations
No integrations available	Python SQLFlow GraphPipe Flair Pythia Databricks Comet.ml

What are some alternatives to CUDA, Torch?

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.

scikit-learn

scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.

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.