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Published on 13 May 2024

Top 5 Deep Learning Frameworks You Should Know in 2024

TABLE OF CONTENTS

updated

Updated: 18 Jun 2024

As we enter 2024, the rapid pace of innovation in deep learning shows no signs of slowing down. The world's leading tech giants and startups are already developing deep learning models that can push the boundaries of what was once thought impossible, from multimodal AI models like self-driving cars to conversational AI assistants. However, the success of these groundbreaking projects depends on the ability to leverage deep learning frameworks that can efficiently train and deploy these intricate models. With the increasing complexity of deep learning architectures and the vast amounts of data required to train them, the choice of deep learning framework becomes imperative.

5 Deep Learning Frameworks in 2024

Here are 5 most popular deep learning frameworks that you should know in 2024:

1. TensorFlow

Initial Release Date

November 9, 2015

Platform

Linux, macOS, Windows, Android, JavaScript

Repository

github.com/tensorflow/tensorflow

Type

Machine learning library

TensorFlow, developed by Google is one of the most popular and versatile open-source deep learning frameworks. Since its inception, TensorFlow has gained widespread adoption across industries and academia, powering groundbreaking innovations in areas such as computer vision, natural language processing, and predictive modelling.

TensorFlow is a computational framework that excels at constructing and executing complex mathematical operations on large-scale data sets. Its flexible architecture allows developers to create intricate neural networks and deploy them across a wide range of platforms, from modest CPUs to massive distributed clusters. One of the key strengths of TensorFlow lies in its ability to effortlessly handle computationally intensive tasks. Its tensor-based computation model enables efficient parallelisation of operations, making it well-suited for tackling demanding deep learning workloads. The framework’s integration with powerful hardware accelerators like NVIDIA's GPUs significantly boosts performance, allowing for faster large AI model training and optimising AI inference.

TensorFlow's extensive library of pre-built components and models is another significant advantage. Developers can leverage a vast array of tools and deep learning algorithms, ranging from basic linear regression to cutting-edge generative AI models and transformer models. This rich ecosystem not only accelerates the development process but also encourages collaboration and knowledge sharing within the deep learning community.

The framework supports multiple programming languages, including Python, C++, and Java, catering to diverse developer preferences and requirements. This multi-language support fosters seamless integration with existing codebases and facilitates the deployment of TensorFlow-powered solutions across a wide range of systems and platforms.

2. PyTorch

Initial Release Date

September 2016

Platform

IA-32, x86-64, ARM64

Repository

github.com/pytorch/pytorch

Type

Library for machine learning and deep learning

Unlike many traditional deep learning frameworks that follow a static computational graph paradigm, PyTorch developed by Facebook follows an imperative style of programming. This approach allows developers to define and modify neural network architectures on the fly, providing a level of flexibility and experimentation that is unparalleled in the field of deep learning. By eliminating the need for pre-defining the entire computational graph, PyTorch allows researchers to iterate and refine their models rapidly

PyTorch leverages the battle-tested Torch library, which has been used in machine learning for over a decade. Torch's robust and optimised computational backend, coupled with PyTorch's user-friendly Python interface, creates a powerful combination that simplifies the development of complex deep learning models while maintaining high performance and efficiency.

One of PyTorch's standout features is its seamless integration with Python's extensive ecosystem of scientific computing libraries. This integration allows developers to leverage powerful tools like NumPy, SciPy, and Pandas, enabling them to preprocess data, visualise results, and integrate deep learning components into larger scientific pipelines with ease. The framework supports a wide range of hardware accelerators, including NVIDIA GPUs ensuring that computationally intensive deep learning workloads can be executed efficiently on state-of-the-art hardware. 

3. Keras

Initial Release Date

March 27, 2015

Platform

Cross-platform

Repository

github.com/keras-team/keras

Type

Frontend for TensorFlow

Keras, a high-level neural networks API is a solution that simplifies the process of building and experimenting with deep learning models while leveraging the computational power and scalability of TensorFlow. Originally designed as an interface for the Theano library, Keras has since evolved to support multiple backend engines, with TensorFlow being the default and most widely used option. This integration with TensorFlow allows Keras to experience the performance and scalability of one of the most popular deep-learning frameworks while providing a more intuitive and developer-friendly experience.

With a focus on rapid prototyping and experimentation, Keras enables researchers and developers to quickly construct and train complex neural network architectures using a concise and human-readable syntax. This approachable interface not only lowers the barrier to entry for those new to deep learning but also streamlines the development process for experienced practitioners, allowing them to iterate and refine their models efficiently.

Despite its user-friendly nature, Keras is remarkably powerful and scalable. Leveraging the capabilities of TensorFlow, Keras models can be deployed across a wide range of hardware configurations, from modest CPUs and GPUs to large-scale distributed clusters. This scalability ensures that deep learning projects can grow and adapt to increasing computational demands without sacrificing performance or requiring significant architectural changes.

Keras supports advanced features such as model serialisation, which allows for seamless sharing and deployment of trained models across different environments. This capability is particularly valuable in collaborative research settings and production deployments, where models need to be transferred between teams or integrated into larger systems.

4. Caffe

Stable Release Date

April 18,  2017

Platform

Linux, macOS, Windows

Repository

github.com/BVLC/caffe

Type

Library for deep learning

Developed by Berkeley AI Research (BAIR) and community contributors, Caffe has become a go-to choice for tackling complex image detection and classification tasks. The framework leverages cutting-edge techniques for GPU and CPU acceleration, enabling researchers and developers to train and deploy deep neural networks with unprecedented performance. 

One of the key strengths of Caffe lies in its modular architecture and extensive set of pre-built components. The framework provides a rich library of pre-trained models and layer types, allowing users to quickly construct and fine-tune complex neural network architectures tailored to their specific needs. This flexibility and extensibility have made Caffe a valuable tool for researchers exploring novel deep-learning techniques in computer vision and beyond.

Caffe has also found widespread adoption in startup prototypes and industrial use cases. Computer vision startups and established companies alike have leveraged Caffe's capabilities to build cutting-edge applications ranging from self-driving cars and drones to facial recognition systems and content moderation tools. The framework's robustness and scalability make it well-suited for deploying deep learning models in production environments, ensuring reliable performance and seamless integration with existing systems.

5. Deeplearning4j

Preview Release Date

May 13, 2020

Platform

CUDA, x86, ARM, PowerPC

Repository

github.com/deeplearning4j/deeplearning4j

Type

Natural language processing, deep learning, machine vision, artificial intelligence

Developed by Kondiut K. K. and contributors, Deeplearning4j allows developers to utilise the full potential of deep learning while leveraging the robustness, scalability, and cross-platform compatibility of the JVM. It provides a rich set of tools and libraries for building, training, and deploying deep neural networks across a wide range of applications. It consists of several interconnected projects, each addressing specific aspects of the deep learning workflow, from data preprocessing and feature engineering to model construction, training, and optimisation.

One of the standout features of Deeplearning4j is its comprehensive support for data preprocessing and feature engineering. The framework offers a powerful set of utilities for handling various data formats, cleaning and transforming datasets, and extracting meaningful features from raw data. This integrated approach streamlines the entire machine-learning pipeline, enabling developers to focus on model development rather than spending excessive time on data-wrangling tasks.

Building and tuning deep learning models is a central focus of Deeplearning4j. The framework provides a flexible and intuitive API for constructing complex neural network architectures, allowing developers to define and customise layers, activation functions, and optimisation algorithms. Deeplearning4j also supports a wide range of pre-trained models and transfer learning techniques, enabling developers to leverage existing knowledge and accelerate the development process.

What sets Deeplearning4j apart is its integration with the entire JVM ecosystem. It is compatible with multiple JVM languages, including Java, Scala, Kotlin, and Clojure, Deeplearning4j allows developers to leverage their existing knowledge and codebase while incorporating deep learning capabilities. 

Conclusion

As machine learning continues to dominate various domains, selecting the most suitable deep learning framework based on project requirements and computational resources becomes imperative. To efficiently train and deploy these complex models, you must choose the right GPU to maximise performance. At Hyperstack, you can access top-class NVIDIA's A100, H100 PCIe or H100 SXM to leverage their expertise in deploying and scaling deep learning solutions. We offer cost-effective GPUs optimised for DL frameworks—TensorFlow, PyTorch, and MXNet for faster neural network training.

FAQs

What is the importance of choosing the right deep learning framework? 

Deep learning models are becoming increasingly complex, requiring frameworks that can handle massive datasets and leverage advanced hardware accelerators. The right framework can significantly impact training times, scalability, and deployment efficiency, ultimately determining the success of your deep learning projects.

What are the advantages of using PyTorch?

PyTorch is renowned for its intuitive and Pythonic syntax, making it easier for developers to prototype and experiment with deep learning models. Its dynamic computation graph and strong GPU acceleration capabilities make it a top choice for research and rapid iteration.

What is the best GPU for deep learning?

We recommend using NVIDIA’s A100, H100 PCIe or H100 SXM for deep learning solutions. It's important to note that the choice of GPU will depend on specific project needs, including budget constraints, model complexity, and other factors. These GPUs are among the most powerful for deep learning tasks, but they may not be necessary or cost-effective for all projects.

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