Top 10 Deep Learning Frameworks

Deep learning is an area of AI and machine learning that uses unclassified data to classify images, computer vision, natural language processing (NLP), and other complex tasks.

A neural network is called “deep” if it has at least three layers (one hidden layer). The network does deep learning on many hidden layers of computation.

Whether the network uses supervised, semi-supervised, or unsupervised learning, programming a deep learning network from scratch is time-consuming and requires a lot of high-level math and computation.

Today, there are a lot of Deep learning frameworks that make it easier to make a neural network. The frameworks come with specific workflows that have already been programmed. This makes it easy to build and train a deep learning network.

Most Popular Deep Learning Frameworks

• TensorFlow (TF)
• PyTorch
• Keras
• SciKit-Learn (SKLearn)
• Apache MXNet
• Eclipse Deeplearning4j (DL4J)
• MATLAB
• Sonnet
• Chainer
• Flux

Each framework has its own set of features and ways to use them. Here is a quick summary of each framework and what it does well.

TensorFlow

The initial release occurred in 2015 and was created by the Google Brain team for use in internal research and production. TensorFlow 2.0, the more recent version, was released in 2019.

Features

• Built-in primitive neural network executions

Numerous built-in functions used in neural network programming are available in TensorFlow. The tf.nn module for various neural network operations is one of these features.

• Eager Execution

Rapidly available results from the eager execution environment allow for real-time monitoring of neural network operations.

• Programming language variety

Programmers most frequently use TensorFlow in Python to guarantee stability. There is also support for other languages like JavaScipt, C++, and Java. Programming language flexibility enables a wider range of industrial applications.

Advantages

• Scalability

With TensorFlow, moving from shared memory to distributed memory is simple. Deep learning workloads multiply, and TensorFlow 2.0’s distributed solutions offer infinite scalability.

• Parallelism

TensorFlow divides training across various resources, including GPUs, CPUs, and TPUs (Tensor Processing Units). For deep learning networks with numerous layers and parameters, the feature is useful.

• Open source

TensorFlow is a free, open-source system that is accessible to all. TensorFlow is ready for development, using it to test it out, teach with it, or use neural networks in business applications.

• Large learning resources

Due to TensorFlow’s popularity as one of the most widely used deep learning frameworks, there is a wealth of free educational resources online. Google even offers CoLab, an in-browser notebook environment with GPU that are readily available and TensorFlow preinstalled.

PyTorch

A neural network and machine learning framework based on Torch is called PyTorch. PyTorch is a scientific computing framework primarily focused on computer vision and NLP (natural language processing) tasks.

The first version of PyTorch was released in 2016 and was created by Facebook’s AI Research Lab (FAIR). Both Python and C++ support the library, though Python’s library offers greater stability.

Features

• Built-in neural network executions

Similar to TensorFlow, PyTorch provides the torch.nn package, which includes several modules with common neural network functionality.

• Automatic differentiation

Because it is difficult to implement forward and backward passes through a deep learning network, PyTorch provides the autograd package for quick gradient computation.

• GPU accelerated computations

Similar computation capabilities exist in PyTorch and NumPy. PyTorch uses tensors as n-dimensional arrays in place of arrays. The key distinction is that PyTorch offers the massive computation speed acceleration necessary for deep learning through GPU-powered computation.

Advantages

• Parallelism

Parallel training on multiple resources through the torch.nn.parallel wrapper allows custom computational task distribution.

• Simple debugging

The debugging process for computational graphs is made easier by debugging integration with programs like pdb, ipdb, and Python IDEs like PyCharm.

• Library extensions

Since PyTorch has a large developer and research community, it is simple to add new APIs to the framework and improve PyTorch deep learning networks.

Keras

A library for artificial neural networks, Keras serves as TensorFlow’s high-level front end. Recently, Keras supported several back-ends, but the most recent version only supports TensorFlow.

The development of neural network building blocks is the Keras library’s primary focus. The library aims to make the creation of neural networks simpler and to support deep learning on JVMs, websites, and mobile devices.

Features

• Pre-labeled datasets

The Keras library contains widely used demonstration and learning datasets. The data is organized, labeled, and prepared for testing. Examples include scrawled numbers, IMDB ratings, Boston real estate costs, etc.

• Predefined parameters

The neural network layers and various loss and optimization functions in Keras are easily accessible. The framework also includes various preprocessing features for data preparation.

• Ease of use

For developers who are new to deep learning, Keras is very user-friendly. The robust framework is straightforward but not simplistic. The front-end framework helps advanced users by streamlining TensorFlow computation procedures.

Advantages

• Easy deployment

The code is straightforward and deployable. The documentation is thorough and well-organized. With only a few lines of code, Keras makes it possible to create a neural network.

• Built-in data parallelism

The framework can use multiple GPUs to distribute the neural network training.

• Pretrained models

It takes time to learn everything from scratch and put it into practice. Utilize the pre-trained Keras models and operate with a setup that is easily accessible.

• Community support

The Keras framework frequently appears in various data science and machine learning communities and coding competitions.

SciKit-Learn (SKLearn)

An open-source machine learning library based on NumPy, SciPy, and matplotlib is called SciKit-Learn (also referred to as SKLearn). Despite being a general-purpose machine learning library, the framework includes some deep learning functionalities.

Due to the lack of GPU support, SciKit-Learn is not frequently used for large-scale applications.

Features

• Multi-Layer Perceptron algorithm (MLP)

The multi-layer perceptron algorithm is a feature of SciKit-Learn. A subset of deep neural networks, the MLP algorithm typically has fewer hidden layers than a deep learning network and only supports feed-forward propagation. MLPs are a crucial foundational component of deep learning.

• Data splitting

Divide a dataset into a training group and a test group when working with datasets for any algorithm is a common task (typically 70-30 or 80-20). The train test split function in SKLearn is useful for this task.

• Built-in datasets

Datasets optimized for machine learning algorithms are included with SciKit-Learn. The Iris dataset, diabetes dataset, housing prices, etc. are a few examples of initial datasets.

Advantages

• Useful toolkit

SciKit-Learn provides a noteworthy toolkit for feature engineering and data preprocessing. The tools are helpful when combined with other potent deep learning frameworks, even when not used as a standalone framework.

• User friendly

The framework frequently appears in educational and learning settings because the code is so simple.

Apache MXNet

A deep learning framework built on open-source software called Apache MXNet. Numerous deep learning models are supported by the framework from the Apache Software Foundation.

MXNet is the deep learning framework of choice for AWS and is supported by numerous research organizations and cloud service providers.

Features

• Portability

Pre-trained networks can be deployed using the framework on low-end hardware. For instance, it is simple to transfer a deep learning network trained on a powerful machine to IoT, mobile, edge, or serverless devices.

• Scalability

On dynamic cloud infrastructure with numerous CPUs and GPUs, MXNet supports distribution.

• Flexible programming

There are both imperative and symbolic programming options. It is possible to track bugs, set checkpoints, change hyperparameters, and stop early during development.

Advantages

• Multi-lingual

MXNet provides support for eight programming languages for front-end development (Python, R, Scala, Clojure, Julia, Perl, MATLAB, and JavaScript). C++ is a language that can be used for back-end optimization. The variety of languages available offers numerous development applications.

• High-performance API

The framework manages near-linear scalability to manage large projects efficiently.

Eclipse Deeplearning4j (DL4J)

A collection of deep learning tools called Deeplearning4j runs on the JVM (Java Virtual Machine). The framework is Java-based, but it also has extra support and APIs for other languages. 

Numerous deep learning algorithms, each with a distributed parallel version, are supported by Deeplearning4j.

Features

• Cuda integration

GPU optimization is possible with CUDA and can be shared through Hadoop or OpenMP.

• Keras support

This deep learning framework works with TensorFlow and can import models from other Python frameworks using Keras.

• Hadoop & Spark integration

Integration of Apache Spark makes it possible to run deep learning pipelines directly on Spark clusters and to use deep learning on big data.

Advantages

• JVM compatibility

JVM compatibility works with any language that runs on the JVM, like Clojure or Scala.

• Distributed mindset

Spark clusters and the Hadoop ecosystem work well together, so you can use a wide range of distributed programming features.

Matlab

MATLAB is proprietary software that can be used for deep learning. The software is made for engineers, mathematicians, scientists, and other professionals who don’t have much experience in deep learning.

The framework’s goal is to make a deep learning network with as little coding as possible.

Features

• Automated deployment

MATLAB integrates with all kinds of environments and automates deployment on enterprise systems, embedded devices, clusters, or in-cloud.

• Non-programmer friendly

To create, visualize, and use deep learning models in MATLAB, you need to write very little code. Importing models that have already been trained is supported and easy to set up.

• Interactive labeling

The framework makes object labeling interactive, which speeds up the process and gets better results.

Advantages

• Interactive

Different apps for preprocessing data help automate the labeling of data. This makes it possible for raw IoT and edge device data to be processed directly.

• Collaborative

TensorFlow is built into MATLAB, and pre-trained models can be used immediately in the environment. Even though the software is not open source, it works well with open-source software.

Sonnet

Sonnet is a framework for deep learning that is built on TensorFlow 2. The goal of the module-based framework is to make it easy to create actions and structures for machine learning.

The sonnet was made by DeepMind researchers and can be used in many different ways to build neural networks.

Features

• Predefined network modules

Sonnet has predefined network modules, such as MLP, in addition to layer modules and constructs (Multi-Layer Perceptron).

• Simplicity

Everything in the framework is done through a single idea: snt.module. This simple but powerful tool makes sure that each model can work on its own.

Advantages

• Modular

By defining custom modules and declaring submodules internally during development, there are many ways to customize the code.

• TensorFlow Focused

Sonnet works with TensorFlow and fits in well with raw code and other high-level libraries. Details about TensorFlow can be accessed directly through the Sonnet framework.

Caffe

Caffe is a free, open-source framework for deep learning written in C++ and has a Python front-end. The framework was made by Yangqing Jia, a Ph.D. student at UC Berkley, as a project.

The framework is good at classifying and dividing images, but other deep learning architectures are also possible.

Features

• Speed

Caffe can process more than 60 million daily images with just one GPU. It is one of the fastest convolutional network frameworks, which makes it great for both research and business uses.

• Actively developed

The community around the framework is very active. There are custom distributions that are optimized for certain processors.

Advantages

• OpenMP Support

Caffe works with the OpenMP API, which allows for parallelism and multiple threads.

• Variety of interfaces

Interfaces like C, C++, Python, and MATLAB can be used with Caffe. The framework can also be used right from the command line.

• Pre-trained networks

The C++ library Caffe Model Zoo has ready-to-use networks that have already been trained.

Flux

Flux is a machine learning framework for Julia that focuses on making production pipelines that work well. The framework has an interface based on stacking layers to make models easier to understand.

The framework lets you work with other Julia packages and helps make machine learning models more secure.

Features

• GPU support

Through the CUDA.jl package, the GPU kernels can be changed right in Julia. Changing the code lets you change the GPU code, the gradients, and the layers.

• TPU support

For cloud computing, flux deep learning models are turned into TPUs. The code also runs right from Google Colab notebooks.

Advantages

• Extensibility

Flux has many packages that can be added to make the deep learning workflow better in different situations.

• Pretrained models with Model Zoo

Like Caffe, Flux’s Model Zoo has models that have already been trained for computer vision, text-based models, and games. Flux can be used in many different ways because of the models.

Most Popular Framework

Currently, the top four most popular frameworks are:

• PyTorch
• TensorFlow
• Scikit-Learn
• Keras

By looking at how many times each framework is searched for on Google, we can see that as of May 2022, PyTorch is the most searched deep learning network globally. The ML community likes the framework because it uses Python and is easier to use than other frameworks regarding deep learning (especially TensorFlow).

Each framework is good at doing something different, and some even work well together instead of trying to be the best. TensorFlow with Keras as the front-end and scikit-learn to prepare the data is a common way to do this.

Beginner friendly framework

Deep learning is hard to get good at, and when you’re just starting on the deep learning path, there’s a lot of information to take in.

These are the two most common frameworks used in education:

• Keras
• Scikit-Learn

These two frameworks rule the field of education because they make it easy to learn basic deep learning concepts and terms. Any deep learning framework can use the things you learn from them.

Once you know how to use these two frameworks well, it’s best to move on to a more mature environment like TensorFlow or PyTorch, which have a lot of documentation, information, and examples.

Conclusion

Businesses use the power of data by using different deep learning frameworks. Each framework offers something unique in the world of deep learning.

Most frameworks can be tried out for free because they are open source. Because AI and machine learning are becoming more popular, there are also a lot of courses and pieces of information online. Now, it’s easier than ever to get good at deep learning.