PredictionIO vs PyTorch

Overview

PredictionIO

Stacks67

Followers110

Votes8

PyTorch

Stacks1.6K

Followers1.5K

Votes43

GitHub Stars94.7K

Forks25.8K

PredictionIO vs PyTorch: What are the differences?

Developers describe PredictionIO as "Open Source Machine Learning Server". PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery. On the other hand, PyTorch is detailed as "A deep learning framework that puts Python first". 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.

PredictionIO and PyTorch can be primarily classified as "Machine Learning" tools.

"Predict Future" is the primary reason why developers consider PredictionIO over the competitors, whereas "Developer Friendly" was stated as the key factor in picking PyTorch.

PredictionIO and PyTorch are both open source tools. PyTorch with 29.6K GitHub stars and 7.18K forks on GitHub appears to be more popular than PredictionIO with 11.8K GitHub stars and 1.92K GitHub forks.

Suggestic, cotobox, and Depop are some of the popular companies that use PyTorch, whereas PredictionIO is used by 500 Startups, Betaout, and Tokopedia. PyTorch has a broader approval, being mentioned in 21 company stacks & 46 developers stacks; compared to PredictionIO, which is listed in 5 company stacks and 5 developer stacks.

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CLI (Node.js)

Manual

Advice on PredictionIO, PyTorch

Developer at DCSIL

Oct 11, 2020

Decided

For data analysis, we choose a Python-based framework because of Python's simplicity as well as its large community and available supporting tools. We choose PyTorch over TensorFlow for our machine learning library because it has a flatter learning curve and it is easy to debug, in addition to the fact that our team has some existing experience with PyTorch. Numpy is used for data processing because of its user-friendliness, efficiency, and integration with other tools we have chosen. Finally, we decide to include Anaconda in our dev process because of its simple setup process to provide sufficient data science environment for our purposes. The trained model then gets deployed to the back end as a pickle.

99.3k views99.3k

Comments

Adithya

Student at PES UNIVERSITY

May 11, 2020

Needs advice

I have just started learning some basic machine learning concepts. So which of the following frameworks is better to use: Keras / TensorFlow/PyTorch. I have prior knowledge in python(and even pandas), java, js and C. It would be nice if something could point out the advantages of one over the other especially in terms of resources, documentation and flexibility. Also, could someone tell me where to find the right resources or tutorials for the above frameworks? Thanks in advance, hope you are doing well!!

107k views107k

Comments

cfvedova

Oct 10, 2020

Decided

A large part of our product is training and using a machine learning model. As such, we chose one of the best coding languages, Python, for machine learning. This coding language has many packages which help build and integrate ML models. For the main portion of the machine learning, we chose PyTorch as it is one of the highest quality ML packages for Python. PyTorch allows for extreme creativity with your models while not being too complex. Also, we chose to include scikit-learn as it contains many useful functions and models which can be quickly deployed. Scikit-learn is perfect for testing models, but it does not have as much flexibility as PyTorch. We also include NumPy and Pandas as these are wonderful Python packages for data manipulation. Also for testing models and depicting data, we have chosen to use Matplotlib and seaborn, a package which creates very good looking plots. Matplotlib is the standard for displaying data in Python and ML. Whereas, seaborn is a package built on top of Matplotlib which creates very visually pleasing plots.

72.8k views72.8k

Comments

Detailed Comparison

PredictionIO	PyTorch
PredictionIO is an open source machine learning server for software developers to create predictive features, such as personalization, recommendation and content discovery.	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.
Integrated with state-of-the-art machine learning algorithms. Fine-tune, evaluate and implement them scientifically.;Customize the modularized open codebase to fulfill any unique prediction requirement.;Built on top of scalable frameworks such as Hadoop and Cascading. Ready to handle data of any scale.;Build powerful features in minutes, not months. Streamline the data engineering process.	Tensor computation (like numpy) with strong GPU acceleration;Deep Neural Networks built on a tape-based autograd system
Statistics
GitHub Stars -	GitHub Stars 94.7K
GitHub Forks -	GitHub Forks 25.8K
Stacks 67	Stacks 1.6K
Followers 110	Followers 1.5K
Votes 8	Votes 43
Pros & Cons
Pros 8 Predict Future	Pros 15 Easy to use 11 Developer Friendly 10 Easy to debug 7 Sometimes faster than TensorFlow Cons 3 Lots of code 1 It eats poop
Integrations
No integrations available	Python

What are some alternatives to PredictionIO, PyTorch?

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.

Keras

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/

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

Polyaxon

An enterprise-grade open source platform for building, training, and monitoring large scale deep learning applications.

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 is an open source platform for managing the end-to-end machine learning lifecycle.

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.

Gluon

A new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.

Related Comparisons

"Predict Future" is the primary reason why developers consider PredictionIO over the competitors, whereas "Developer Friendly" was stated as the key factor in picking PyTorch.

PredictionIO vs PyTorch