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  1. Stackups
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  4. Machine Learning Tools
  5. Continuous Machine Learning vs Gym

Continuous Machine Learning vs Gym

OverviewComparisonAlternatives

Overview

Gym
Gym
Stacks54
Followers59
Votes0
GitHub Stars36.7K
Forks8.7K
Continuous Machine Learning
Continuous Machine Learning
Stacks21
Followers37
Votes0
GitHub Stars4.1K
Forks346

Gym vs Continuous Machine Learning: What are the differences?

Developers describe Gym as "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. On the other hand, Continuous Machine Learning is detailed as "CI/CD for Machine Learning Projects". Continuous Machine Learning (CML) is an open-source library for implementing continuous integration & delivery (CI/CD) in machine learning projects. Use it to automate parts of your development workflow, including model training and evaluation, comparing ML experiments across your project history, and monitoring changing datasets.

Gym and Continuous Machine Learning can be categorized as "Machine Learning" tools.

Gym is an open source tool with 21.3K GitHub stars and 6.09K GitHub forks. Here's a link to Gym's open source repository on GitHub.

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Detailed Comparison

Gym
Gym
Continuous Machine Learning
Continuous Machine Learning

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.

Continuous Machine Learning (CML) is an open-source library for implementing continuous integration & delivery (CI/CD) in machine learning projects. Use it to automate parts of your development workflow, including model training and evaluation, comparing ML experiments across your project history, and monitoring changing datasets.

Reinforcement learning; Compatible with any numerical computation library
GitFlow for data science; Auto reports for ML experiments; No additional services
Statistics
GitHub Stars
36.7K
GitHub Stars
4.1K
GitHub Forks
8.7K
GitHub Forks
346
Stacks
54
Stacks
21
Followers
59
Followers
37
Votes
0
Votes
0
Integrations
Theano
Theano
TensorFlow
TensorFlow
GitHub
GitHub
Git
Git
GitLab
GitLab
Google Cloud Platform
Google Cloud Platform
DVC
DVC

What are some alternatives to Gym, Continuous Machine Learning?

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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