Top 9 Machine Learning Frameworks In Julia

Julia is a high-level, dynamic programming language which is fast, flexible, easy-to-use, scalable, and supports high-speed mathematical computation. The programming language also supports all hardware, including GPUs and TPUs on every cloud. Julia uses multiple dispatches as a paradigm, making it easy to express many object-oriented and functional programming patterns. In one of our articles, we discussed how this language is making AI and machine learning better.

In this article, we list down top 9 machine learning frameworks in Julia, one must know.

(The libraries are listed according to their stars on GitHub)

1| Flux

About: Flux is deep learning and machine learning library that provides a single, intuitive way to define models, just like mathematical notation. Any existing Julia libraries are differentiable and can be incorporated directly into Flux models. The intuitive features include compiled eager code, differentiable programming, GPU support, ONNX, among others.    

Click here to know more.

2| Mocha.jl

About: Mocha.jl is a deep learning library for the Julia programming language, which includes a number of features mentioned below:

  • Written in Julia and for Julia: Mocha.jl is completely written in Julia. This means that the library has native Julia interfaces and is capable of interacting with core Julia functionality as well as other Julia packages.
  • Minimum dependencies: This library includes minimum dependencies to use Julia as backend, and there is zero need for root privileges or installation of any external dependencies. 
  • Multiple backends: This library comes with a GPU backend, combining customised kernels with highly efficient libraries from NVIDIA such as cuBLAS, cuDNN, etc.
  • Modularity and correctness: This library is implemented in a modular architecture. 

Click here to know more.

3| Knet

About: Knet is a deep learning framework implemented in the Julia programming language.  Knet allows models to be defined by just describing their forward computation in plain Julia, allowing the use of loops, conditionals, recursion, closures, tuples, dictionaries, array indexing, concatenation and other high-level language features. The library supports GPU operations and automates differentiation using dynamic computational graphs for models defined in plain Julia. 

Click here to know more.

4| TensorFlow.jl

About: TensorFlow.jl is also a Julia wrapper for popular open-source machine learning TensorFlow. This wrapper can be used for various purposes such as fast ingestion of data, especially data in uncommon formats, fast postprocessing of inference results, such as calculating various statistics and visualisations that do not have a canned vectorized implementation. 

Click here to know more.

5| ScikitLearn.jl

About: ScikitLearn.jl is a Julia wrapper for the popular Python library Scikit-learn. It implements the Scikit-learn interface and algorithms in Julia.  It provides a uniform interface for training and using models, as well as a set of tools for chaining (pipelines), evaluating, and tuning model hyperparameters. It supports both models from the Julia ecosystem and those of the Scikit-learn library.  

Click here to know more.

6| MXNet.jl

About: MXNet.jl is the Apache MXNet Julia package that brings flexible and efficient GPU computing and state-of-art deep learning to Julia. The features of this library include efficient tensor and matrix computation across multiple devices, including multiple CPUs, GPUs and distributed server nodes. It also has flexible symbolic manipulation to composite and construction of state-of-the-art deep learning models. 

Click here to know more.

7| MLBase.jl

About: MLBase.jl is a Julia package that provides useful tools for machine learning applications. It provides a collection of useful tools to support machine learning programs, including data manipulation and preprocessing, score-based classification, performance evaluation, cross-validation and model tuning.   

Click here to know more.

8| Merlin

About: Merlin is a deep learning framework written in Julia. The library aims to provide a fast, flexible and compact deep learning library for machine learning. The requirements of this library are Julia 0.6 and g++ for OSX or Linux. The library runs on CPUs and CUDA GPUs.

Click here to know more.

9| Strada 

About: Strada is an open-source deep learning library for Julia, based on the popular Caffe framework. The library supports convolutional and recurrent neural network training, both on CPUs and GPUs. Some of the features of this library include flexibility, support for Caffe features, integration with Julia and other such.  

Click here to know more.

More Great AIM Stories

Ambika Choudhury
A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.

More Stories

OUR UPCOMING EVENTS

8th April | In-person Conference | Hotel Radisson Blue, Bangalore

Organized by Analytics India Magazine

View Event >>

30th Apr | Virtual conference

Organized by Analytics India Magazine

View Event >>

MORE FROM AIM
Yugesh Verma
All you need to know about Graph Embeddings

Embeddings can be the subgroups of a group, similarly, in graph theory embedding of a graph can be considered as a representation of a graph on a surface, where points of that surface are made up of vertices and arcs are made up of edges

Yugesh Verma
A beginner’s guide to Spatio-Temporal graph neural networks

Spatio-temporal graphs are made of static structures and time-varying features, and such information in a graph requires a neural network that can deal with time-varying features of the graph. Neural networks which are developed to deal with time-varying features of the graph can be considered as Spatio-temporal graph neural networks. 

Vijaysinh Lendave
How to Evaluate Recommender Systems with RGRecSys?

A recommender system, sometimes known as a recommendation engine, is a type of information filtering system that attempts to forecast a user’s “rating” or “preference” for an item. In this post, we will look at RGRecSys, a library that performs constraint evaluation of recommender systems.

3 Ways to Join our Community

Discord Server

Stay Connected with a larger ecosystem of data science and ML Professionals

Telegram Channel

Discover special offers, top stories, upcoming events, and more.

Subscribe to our newsletter

Get the latest updates from AIM