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  1. Stackups
  2. DevOps
  3. Build Automation
  4. Python Build Tools
  5. XGBoost vs pyup

XGBoost vs pyup

OverviewComparisonAlternatives

Overview

pyup
pyup
Stacks13
Followers20
Votes0
GitHub Stars468
Forks65
XGBoost
XGBoost
Stacks192
Followers86
Votes0
GitHub Stars27.6K
Forks8.8K

pyup vs XGBoost: What are the differences?

Developers describe pyup as "Manage Python project dependencies by sending automated pull requests whenever a dependencies releases a new version". We help you to keep track of dependency updates by sending you automated pull requests directly to your GitHub repo whenever a new update comes out. On the other hand, XGBoost is detailed as "Scalable and Flexible Gradient Boosting". Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow.

pyup belongs to "Python Build Tools" category of the tech stack, while XGBoost can be primarily classified under "Machine Learning Tools".

Some of the features offered by pyup are:

  • Notifications on every requirement update
  • Python dependency management made easy
  • Plays nice with GitHub integrations

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

  • Flexible
  • Portable
  • Multiple Languages

pyup is an open source tool with 328 GitHub stars and 50 GitHub forks. Here's a link to pyup's open source repository on GitHub.

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Detailed Comparison

pyup
pyup
XGBoost
XGBoost

We help you to keep track of dependency updates by sending you automated pull requests directly to your GitHub repo whenever a new update comes out.

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Flink and DataFlow

Notifications on every requirement update;Python dependency management made easy;Plays nice with GitHub integrations
Flexible; Portable; Multiple Languages; Battle-tested
Statistics
GitHub Stars
468
GitHub Stars
27.6K
GitHub Forks
65
GitHub Forks
8.8K
Stacks
13
Stacks
192
Followers
20
Followers
86
Votes
0
Votes
0
Integrations
GitHub
GitHub
Python
Python
Python
Python
C++
C++
Java
Java
Scala
Scala
Julia
Julia

What are some alternatives to pyup, XGBoost?

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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