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  1. Stackups
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  4. Machine Learning As A Service
  5. Gym vs Paperspace

Gym vs Paperspace

OverviewComparisonAlternatives

Overview

Paperspace
Paperspace
Stacks4
Followers20
Votes0
Gym
Gym
Stacks54
Followers59
Votes0
GitHub Stars36.7K
Forks8.7K

Paperspace vs Gym: What are the differences?

Paperspace: The way to access and manage limitless computing power in the cloud. It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use it to iterate faster and collaborate on intelligent, real-time prediction engines; Gym: Open source interface to reinforcement learning tasks. It is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games like Pong or Pinball.

Paperspace can be classified as a tool in the "Machine Learning as a Service" category, while Gym is grouped under "Machine Learning Tools".

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Detailed Comparison

Paperspace
Paperspace
Gym
Gym

It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Tens of thousands of individuals, startups and enterprises use it to iterate faster and collaborate on intelligent, real-time prediction engines.

It is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games like Pong or Pinball.

Intelligent alert; Two-factor authentication; Share drives; Unlimited power; Multiple monitors; Remote access; Simple management.
Reinforcement learning; Compatible with any numerical computation library
Statistics
GitHub Stars
-
GitHub Stars
36.7K
GitHub Forks
-
GitHub Forks
8.7K
Stacks
4
Stacks
54
Followers
20
Followers
59
Votes
0
Votes
0
Integrations
Golang
Golang
Swift
Swift
Postman
Postman
Airtable
Airtable
Azure IoT Hub
Azure IoT Hub
Theano
Theano
TensorFlow
TensorFlow

What are some alternatives to Paperspace, Gym?

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/

NanoNets

NanoNets

Build a custom machine learning model without expertise or large amount of data. Just go to nanonets, upload images, wait for few minutes and integrate nanonets API to your application.

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.

Inferrd

Inferrd

It is the easiest way to deploy Machine Learning models. Start deploying Tensorflow, Scikit, Keras and spaCy straight from your notebook with just one extra line.

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