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  5. .NET for Apache Spark vs GraphPipe

.NET for Apache Spark vs GraphPipe

OverviewComparisonAlternatives

Overview

GraphPipe
GraphPipe
Stacks2
Followers16
Votes0
GitHub Stars718
Forks103
.NET for Apache Spark
.NET for Apache Spark
Stacks31
Followers46
Votes0
GitHub Stars2.1K
Forks329

.NET for Apache Spark vs GraphPipe: What are the differences?

Developers describe .NET for Apache Spark as "Makes Apache Spark™ Easily Accessible to .NET Developers". With these .NET APIs, you can access the most popular Dataframe and SparkSQL aspects of Apache Spark, for working with structured data, and Spark Structured Streaming, for working with streaming data. On the other hand, GraphPipe is detailed as "Machine Learning Model Deployment Made Simple, by Oracle". GraphPipe is a protocol and collection of software designed to simplify machine learning model deployment and decouple it from framework-specific model implementations.

.NET for Apache Spark and GraphPipe can be primarily classified as "Machine Learning" tools.

.NET for Apache Spark and GraphPipe are both open source tools. .NET for Apache Spark with 1.11K GitHub stars and 108 forks on GitHub appears to be more popular than GraphPipe with 645 GitHub stars and 91 GitHub forks.

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Detailed Comparison

GraphPipe
GraphPipe
.NET for Apache Spark
.NET for Apache Spark

GraphPipe is a protocol and collection of software designed to simplify machine learning model deployment and decouple it from framework-specific model implementations.

With these .NET APIs, you can access the most popular Dataframe and SparkSQL aspects of Apache Spark, for working with structured data, and Spark Structured Streaming, for working with streaming data.

A minimalist machine learning transport specification based on flatbuffers; Simple, efficient reference model servers for Tensorflow, Caffe2, and ONNX.; Efficient client implementations in Go, Python, and Java.
-
Statistics
GitHub Stars
718
GitHub Stars
2.1K
GitHub Forks
103
GitHub Forks
329
Stacks
2
Stacks
31
Followers
16
Followers
46
Votes
0
Votes
0
Integrations
TensorFlow
TensorFlow
PyTorch
PyTorch
Caffe2
Caffe2
Apache Spark
Apache Spark
.NET
.NET
F#
F#
C#
C#
Ubuntu
Ubuntu

What are some alternatives to GraphPipe, .NET for Apache Spark?

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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