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  1. Stackups
  2. AI
  3. Development & Training Tools
  4. Machine Learning Tools
  5. Caffe vs Neuropod

Caffe vs Neuropod

OverviewComparisonAlternatives

Overview

Caffe
Caffe
Stacks66
Followers73
Votes0
GitHub Stars34.7K
Forks18.6K
Neuropod
Neuropod
Stacks1
Followers4
Votes0
GitHub Stars939
Forks75

Caffe vs Neuropod: What are the differences?

Caffe: A deep learning framework. It is a deep learning framework made with expression, speed, and modularity in mind; Neuropod: Uber ATG's open source deep learning inference engine. It is a library that provides a uniform interface to run deep learning models from multiple frameworks in C++ and Python. It makes it easy for researchers to build models in a framework of their choosing while also simplifying productionization of these models.

Caffe and Neuropod belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by Caffe are:

  • Extensible code
  • Speed
  • Community

On the other hand, Neuropod provides the following key features:

  • Run models from any supported framework using one API
  • Build generic tools and pipelines
  • Fully self-contained models

Caffe is an open source tool with 30.4K GitHub stars and 18.3K GitHub forks. Here's a link to Caffe's open source repository on GitHub.

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Detailed Comparison

Caffe
Caffe
Neuropod
Neuropod

It is a deep learning framework made with expression, speed, and modularity in mind.

It is a library that provides a uniform interface to run deep learning models from multiple frameworks in C++ and Python. It makes it easy for researchers to build models in a framework of their choosing while also simplifying productionization of these models.

Extensible code; Speed; Community;
Run models from any supported framework using one API; Build generic tools and pipelines; Fully self-contained models; Efficient zero-copy operations
Statistics
GitHub Stars
34.7K
GitHub Stars
939
GitHub Forks
18.6K
GitHub Forks
75
Stacks
66
Stacks
1
Followers
73
Followers
4
Votes
0
Votes
0
Integrations
TensorFlow
TensorFlow
Keras
Keras
Amazon SageMaker
Amazon SageMaker
Pythia
Pythia
No integrations available

What are some alternatives to Caffe, Neuropod?

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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