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

Caffe vs Pipelines

OverviewComparisonAlternatives

Overview

Caffe
Caffe
Stacks66
Followers73
Votes0
GitHub Stars34.7K
Forks18.6K
Pipelines
Pipelines
Stacks29
Followers72
Votes0
GitHub Stars4.0K
Forks1.8K

Pipelines vs Caffe: What are the differences?

Pipelines: Machine Learning Pipelines for Kubeflow. Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable. Kubeflow pipelines are reusable end-to-end ML workflows built using the Kubeflow Pipelines SDK; Caffe: A deep learning framework. It is a deep learning framework made with expression, speed, and modularity in mind.

Pipelines and Caffe can be categorized as "Machine Learning" tools.

Pipelines and Caffe are both open source tools. It seems that Caffe with 29.2K GitHub stars and 17.6K forks on GitHub has more adoption than Pipelines with 1.16K GitHub stars and 336 GitHub forks.

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Detailed Comparison

Caffe
Caffe
Pipelines
Pipelines

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

Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable. Kubeflow pipelines are reusable end-to-end ML workflows built using the Kubeflow Pipelines SDK.

Extensible code; Speed; Community;
-
Statistics
GitHub Stars
34.7K
GitHub Stars
4.0K
GitHub Forks
18.6K
GitHub Forks
1.8K
Stacks
66
Stacks
29
Followers
73
Followers
72
Votes
0
Votes
0
Integrations
TensorFlow
TensorFlow
Keras
Keras
Amazon SageMaker
Amazon SageMaker
Pythia
Pythia
Argo
Argo
Kubernetes
Kubernetes
Kubeflow
Kubeflow
TensorFlow
TensorFlow

What are some alternatives to Caffe, Pipelines?

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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