npm vs XGBoost
Overview
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CLI (Node.js)
Manual
Detailed Comparison
npm is the command-line interface to the npm ecosystem. It is battle-tested, surprisingly flexible, and used by hundreds of thousands of JavaScript developers every day. | 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 |
| - | Flexible;
Portable;
Multiple Languages;
Battle-tested
|
Statistics | |
GitHub Stars 17.6K | GitHub Stars 27.6K |
GitHub Forks 3.0K | GitHub Forks 8.8K |
Stacks 137.4K | Stacks 192 |
Followers 82.2K | Followers 86 |
Votes 1.6K | Votes 0 |
Pros & Cons | |
Pros
Cons
| No community feedback yet |
Integrations | |
| No integrations available | |
What are some alternatives to npm, XGBoost?
RequireJS
RequireJS loads plain JavaScript files as well as more defined modules. It is optimized for in-browser use, including in a Web Worker, but it can be used in other JavaScript environments, like Rhino and Node. It implements the Asynchronous Module API. Using a modular script loader like RequireJS will improve the speed and quality of your code.
Browserify
Browserify lets you require('modules') in the browser by bundling up all of your dependencies.
Yarn
Yarn caches every package it downloads so it never needs to again. It also parallelizes operations to maximize resource utilization so install times are faster than ever.
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/
Component
Component's philosophy is the UNIX philosophy of the web - to create a platform for small, reusable components that consist of JS, CSS, HTML, images, fonts, etc. With its well-defined specs, using Component means not worrying about most frontend problems such as package management, publishing components to a registry, or creating a custom build process for every single app.
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
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
