Caffe2 vs Kubeflow

Overview

Caffe2

Stacks49

Followers83

Votes2

Kubeflow

Stacks205

Followers585

Votes18

Caffe2 vs Kubeflow: What are the differences?

Caffe2: Open Source Cross-Platform Machine Learning Tools (by Facebook). Caffe2 is deployed at Facebook to help developers and researchers train large machine learning models and deliver AI-powered experiences in our mobile apps. Now, developers will have access to many of the same tools, allowing them to run large-scale distributed training scenarios and build machine learning applications for mobile; Kubeflow: Machine Learning Toolkit for Kubernetes. 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.

Caffe2 and Kubeflow can be primarily classified as "Machine Learning" tools.

Caffe2 and Kubeflow are both open source tools. It seems that Caffe2 with 8.46K GitHub stars and 2.13K forks on GitHub has more adoption than Kubeflow with 7.04K GitHub stars and 1.03K GitHub forks.

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Detailed Comparison

Caffe2	Kubeflow
Caffe2 is deployed at Facebook to help developers and researchers train large machine learning models and deliver AI-powered experiences in our mobile apps. Now, developers will have access to many of the same tools, allowing them to run large-scale distributed training scenarios and build machine learning applications for mobile.	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.
Statistics
Stacks 49	Stacks 205
Followers 83	Followers 585
Votes 2	Votes 18
Pros & Cons
Pros 1 Mobile deployment 1 Open Source	Pros 9 System designer 3 Kfp dsl 3 Customisation 3 Google backed 0 Azure
Integrations
No integrations available	Kubernetes Jupyter TensorFlow

What are some alternatives to Caffe2, Kubeflow?

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/

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.

Caffe2 vs Kubeflow