CUDA vs Leaf

Overview

CUDA

Stacks542

Followers215

Votes0

Leaf

Stacks18

Followers42

Votes0

GitHub Stars5.5K

Forks269

CUDA vs Leaf: 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; Leaf: Machine learning framework in Rust. Leaf is a Machine Intelligence Framework engineered by software developers, not scientists. It was inspired by the brilliant people behind TensorFlow, Torch, Caffe, Rust and numerous research papers and brings modularity, performance and portability to deep learning. Leaf is lean and tries to introduce minimal technical debt to your stack.

CUDA and Leaf can be primarily classified as "Machine Learning" tools.

Leaf is an open source tool with 5.4K GitHub stars and 271 GitHub forks. Here's a link to Leaf's open source repository on GitHub.

According to the StackShare community, CUDA has a broader approval, being mentioned in 13 company stacks & 13 developers stacks; compared to Leaf, which is listed in 4 company stacks and 9 developer stacks.

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Detailed Comparison

CUDA	Leaf
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.	Leaf is a Machine Intelligence Framework engineered by software developers, not scientists. It was inspired by the brilliant people behind TensorFlow, Torch, Caffe, Rust and numerous research papers and brings modularity, performance and portability to deep learning. Leaf is lean and tries to introduce minimal technical debt to your stack.
Statistics
GitHub Stars -	GitHub Stars 5.5K
GitHub Forks -	GitHub Forks 269
Stacks 542	Stacks 18
Followers 215	Followers 42
Votes 0	Votes 0
Integrations
No integrations available	Rust

What are some alternatives to CUDA, Leaf?

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.