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Grails VS Keras

Compare Grails VS Keras and see what are their differences

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Grails logo Grails

An Open Source, full stack, web application framework for the JVM

Keras logo Keras

Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
  • Grails Landing page
    Landing page //
    2021-10-17
  • Keras Landing page
    Landing page //
    2023-10-16

Grails features and specs

  • Rapid Development
    Grails promotes rapid development through its convention-over-configuration approach and powerful features, like scaffolding and GORM (Grails Object Relational Mapping), which speed up the coding process significantly.
  • Groovy Language Integration
    Being built on Groovy, a dynamic language for the Java platform, Grails provides the flexibility and expressiveness of Groovy while maintaining compatibility with Java libraries and tools.
  • Spring Boot Foundation
    Grails is built on top of Spring Boot, leveraging its robust dependency injection, security, and configuration management capabilities, which ensures the stability and scalability of applications.
  • Plugin Ecosystem
    Grails offers a rich ecosystem of plugins for extending the framework. This allows developers to easily integrate various functionalities without reinventing the wheel.
  • Convention-over-Configuration
    The framework emphasizes conventions for many aspects of the development process, reducing the need for extensive configuration and allowing developers to focus more on business logic.
  • Strong Community and Documentation
    Grails has a strong community and extensive documentation, which make it easier for developers to find solutions to problems, share knowledge, and get support.

Possible disadvantages of Grails

  • Learning Curve
    Despite its many conveniences, Grails has a steep learning curve, particularly for developers not familiar with Groovy or the underlying Spring framework.
  • Performance Overheads
    The abstraction layers and dynamic aspects of Groovy may introduce performance overheads, making Grails applications potentially slower than those built with more streamlined frameworks.
  • Limited Flexibility
    While Grails' conventions can be beneficial, they can also limit flexibility, forcing developers into certain patterns and practices even when they may not be ideal for all scenarios.
  • Less Popularity
    Compared to other frameworks like Spring Boot alone or Hibernate, Grails has a smaller market share, leading to fewer job opportunities and a smaller pool of resources.
  • Complex Debugging
    The dynamic nature of Groovy can sometimes make debugging more complex and challenging, especially for those accustomed to statically-typed languages like Java.
  • Dependency Management Issues
    Managing dependencies in Grails can occasionally be problematic, particularly when dealing with transitive dependencies or conflicts between plugins.

Keras features and specs

  • User-Friendly
    Keras provides a simple and intuitive interface, making it easy for beginners to start building and training models without needing extensive experience in deep learning.
  • Modularity
    Keras follows a modular design, allowing users to easily plug in different neural network components, such as layers, activation functions, and optimizers, to create complex models.
  • Pre-trained Models
    Keras includes a wide range of pre-trained models and offers easy integration with transfer learning techniques, reducing the time required to achieve good results on new tasks.
  • Integration with TensorFlow
    As part of TensorFlow’s ecosystem, Keras provides deep integration with TensorFlow functionalities, enabling users to leverage TensorFlow's powerful features and performance optimizations.
  • Extensive Documentation
    Keras has comprehensive and well-organized documentation, along with numerous tutorials and code examples, making it easier for developers to learn and use the framework.
  • Community Support
    Keras benefits from a large and active community, which provides support through forums, GitHub, and specialized user groups, facilitating the resolution of issues and sharing of best practices.

Possible disadvantages of Keras

  • Performance Limitations
    Due to its high-level abstraction, Keras may incur performance overheads, making it less suitable for scenarios requiring extremely fast execution and low-level optimizations.
  • Limited Low-Level Control
    The simplicity and abstraction of Keras can be a downside for advanced users who need fine-grained control over model components and custom operations, which may require them to resort to lower-level frameworks.
  • Scalability Issues
    In some complex applications and large-scale deployments, Keras might face scalability challenges, where more specialized or low-level frameworks could handle such tasks more efficiently.
  • Dependency on TensorFlow
    While the integration with TensorFlow is generally an advantage, it also means that the performance and features of Keras are closely tied to the development and updates of TensorFlow.
  • Lagging Behind Latest Research
    Keras, being a user-friendly high-level API, might not always incorporate the latest cutting-edge research advancements in deep learning as quickly as more research-oriented frameworks.

Analysis of Grails

Overall verdict

  • Grails is a good choice for developers who appreciate convention over configuration and are looking for a quick, efficient way to build web applications. Its integration with the JVM ecosystem makes it particularly appealing for those with existing Java knowledge or infrastructure. However, as with any technology, its suitability depends on specific project requirements and team expertise.

Why this product is good

  • Grails is considered a powerful web application framework built on Groovy and the Spring Framework. It promotes rapid development, convention over configuration, and is designed to be easy to learn for Java developers. Grails provides a variety of built-in features such as ORM (Object-Relational Mapping) with GORM, a robust plugin system, and seamless integration with third-party libraries and frameworks. It aims to boost productivity by simplifying tasks and reducing configuration overhead.

Recommended for

  • Java developers looking to increase productivity
  • Teams that prefer convention over configuration
  • Projects that require rapid development and prototyping
  • Developers interested in using the Groovy language
  • Applications that need seamless integration with the Spring Framework

Analysis of Keras

Overall verdict

  • Keras is a solid choice for deep learning projects, offering simplicity and flexibility without sacrificing performance. It is well-suited for educational purposes, research, and even deploying models in production environments.

Why this product is good

  • Keras is widely regarded as a good deep learning library because it provides a user-friendly API that allows for easy and fast prototyping of neural networks. It is built on top of other libraries like TensorFlow, making it robust and efficient for both beginners and experienced developers. Its modularity, extensibility, and compatibility with other tools and libraries make it a popular choice for developing deep learning models.

Recommended for

  • Beginners who are new to deep learning
  • Researchers looking for an easy-to-use platform for prototyping models
  • Developers working on projects that require quick experimentation and development
  • Individuals and companies deploying models into production environments

Grails videos

+ Add

BUYING MY SNEAKER GRAILS ON STOCKX!

More videos:

  • Review - TOP 5 SNEAKER GRAILS
  • Review - Top 5 Grails with Superpower Review | Berkfamily54comics

Keras videos

+ Add

3. Deep Learning Tutorial (Tensorflow2.0, Keras & Python) - Movie Review Classification

More videos:

  • Review - Movie Review Classifier in Keras | Deep Learning | Binary Classifier
  • Review - EKOR KERAS!! Review and Bike Check DARTMOOR HORNET 2018 // MTB Indonesia

Category Popularity

0-100% (relative to Grails and Keras)

Grails

Keras

Developer Tools
100 100%
Developer Tools
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Web Frameworks
100 100%
Web Frameworks
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Grails and Keras

Grails Reviews

17 Popular Java Frameworks for 2023: Pros, cons, and more
Although you have to write your code in Groovy, Grails works well with other Java-related technologies such as the Java Development Kit, Jakarta EE containers, Hibernate, and Spring. Under the hood, Grails is built on top of Spring Boot to make use of its productivity-friendly features like dependency injection. With Grails, you can achieve the same results with much less...
Source: raygun.com
10 Best Java Frameworks You Should Know
Grails is a web application framework developed using Apache Groovy Language. It is a Framework that follows the coding by convention method which provides a Standalone environment. Also, it supports instance development with no configuration required.
Source: www.simplilearn.com

Keras Reviews

10 Python Libraries for Computer Vision
TensorFlow and Keras are widely used libraries for machine learning, but they also offer excellent support for computer vision tasks. TensorFlow provides pre-trained models like Inception and ResNet for image classification, while Keras simplifies the process of building, training, and evaluating deep learning models.
Source: clouddevs.com
25 Python Frameworks to Master
Keras is a high-level deep-learning framework capable of running on top of TensorFlow, Theano, and CNTK. It was developed by François Chollet in 2015 and is designed to provide a simple and user-friendly interface for building and training deep learning models.
Source: kinsta.com
15 data science tools to consider using in 2021
Keras is a programming interface that enables data scientists to more easily access and use the TensorFlow machine learning platform. It's an open source deep learning API and framework written in Python that runs on top of TensorFlow and is now integrated into that platform. Keras previously supported multiple back ends but was tied exclusively to TensorFlow starting with...
Source: searchbusinessanalytics.techtarget.com

Social recommendations and mentions

Based on our record, Keras should be more popular than Grails. It has been mentiond 35 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Grails mentions (6)

  • Mastering Node.js
    Trails is a modern web application framework. It builds on the pedigree of Rails and Grails to accelerate development by adhering to a straightforward, convention-based, API-driven design philosophy. - Source: dev.to / 11 months ago
  • RIFE2 web framework under development
    And frameworks like Grails build conventions and helpers on top of Spring. Source: over 2 years ago
  • Web app in Java with Template Engine
    I don't have any direct experience and am only suggesting it because you mentioned RoR...But Grails (https://grails.org/) is basically the JVM version of RoR (Groovy on Rails -> Grails). Source: over 2 years ago
  • Libraries other than Spring Boot for creating web APIs
    Grails - Spring under the hood. Much less boilerplate. Opinionated, which helps keep things consistent. Uses Spring-Security plugin for authentication. Source: almost 3 years ago
  • "get-it-done" MVC web framework like Django in Java?
    Also, Grails, which a Rails like framework build on Groovy, a JVM scripting language. Source: over 3 years ago
View more

Keras mentions (35)

  • Top Programming Languages for AI Development in 2025
    The unchallenged leader in AI development is still Python. And Keras, and robust community support. - Source: dev.to / about 1 month ago
  • Top 8 OpenSource Tools for AI Startups
    If you need simplicity, Keras is a great high-level API built on top of TensorFlow. It lets you quickly prototype neural networks without worrying about low-level implementations. Keras is perfect for getting those first models up and running—an essential part of the startup hustle. - Source: dev.to / 7 months ago
  • Top 5 Production-Ready Open Source AI Libraries for Engineering Teams
    At its heart is TensorFlow Core, which provides low-level APIs for building custom models and performing computations using tensors (multi-dimensional arrays). It has a high-level API, Keras, which simplifies the process of building machine learning models. It also has a large community, where you can share ideas, contribute, and get help if you are stuck. - Source: dev.to / 8 months ago
  • Using Google Magika to build an AI-powered file type detector
    The core model architecture for Magika was implemented using Keras, a popular open source deep learning framework that enables Google researchers to experiment quickly with new models. - Source: dev.to / 12 months ago
  • My Favorite DevTools to Build AI/ML Applications!
    As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development. - Source: dev.to / about 1 year ago
View more

What are some alternatives?

When comparing Grails and Keras, you can also consider the following products

Ruby on Rails - Ruby on Rails is an open source full-stack web application framework for the Ruby programming...

TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

Django - The Web framework for perfectionists with deadlines

PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...

Meteor - Meteor is a set of new technologies for building top-quality web apps in a fraction of the time.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

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