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  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. CUDA vs TransmogrifAI

CUDA vs TransmogrifAI

OverviewComparisonAlternatives

Overview

CUDA
CUDA
Stacks542
Followers215
Votes0
TransmogrifAI
TransmogrifAI
Stacks8
Followers20
Votes0
GitHub Stars2.3K
Forks401

CUDA vs TransmogrifAI: 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; TransmogrifAI: Automated machine learning for structured data (by Salesforce). TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Spark with minimal hand tuning.

CUDA and TransmogrifAI can be categorized as "Machine Learning" tools.

TransmogrifAI is an open source tool with 1.61K GitHub stars and 278 GitHub forks. Here's a link to TransmogrifAI's open source repository on GitHub.

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Detailed Comparison

CUDA
CUDA
TransmogrifAI
TransmogrifAI

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.

TransmogrifAI (pronounced trăns-mŏgˈrə-fī) is an AutoML library for building modular, reusable, strongly typed machine learning workflows on Spark with minimal hand tuning

Statistics
GitHub Stars
-
GitHub Stars
2.3K
GitHub Forks
-
GitHub Forks
401
Stacks
542
Stacks
8
Followers
215
Followers
20
Votes
0
Votes
0
Integrations
No integrations available
Apache Spark
Apache Spark

What are some alternatives to CUDA, TransmogrifAI?

TensorFlow

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

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

PyTorch

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

Keras

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

Kubeflow

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

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

Polyaxon

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

Streamlit

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

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

H2O

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.

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