CUDA vs MLflow

Overview

CUDA

Stacks528

Followers215

Votes0

MLflow

Stacks222

Followers524

Votes9

GitHub Stars22.8K

Forks5.0K

CUDA vs MLflow: 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; MLflow: An open source machine learning platform. MLflow is an open source platform for managing the end-to-end machine learning lifecycle.

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

MLflow is an open source tool with 30 GitHub stars and 13 GitHub forks. Here's a link to MLflow's open source repository on GitHub.

Detailed Comparison

CUDA	MLflow
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.	MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
-	Track experiments to record and compare parameters and results; Package ML code in a reusable, reproducible form in order to share with other data scientists or transfer to production; Manage and deploy models from a variety of ML libraries to a variety of model serving and inference platforms
Statistics
GitHub Stars -	GitHub Stars 22.8K
GitHub Forks -	GitHub Forks 5.0K
Stacks 528	Stacks 222
Followers 215	Followers 524
Votes 0	Votes 9
Pros & Cons
No community feedback yet	Pros 5 Code First 4 Simplified Logging

What are some alternatives to CUDA, MLflow?

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.

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.