Top 15 Deep Learning Frameworks You Need to Know in 2025

Have you ever wondered how AI handles massive data? Deep learning frameworks power AI development, enabling models for tasks like computer vision and natural language processing. The top deep learning frameworks provide tools to build, train, and scale complex neural networks. But which one is right for your next project?

In 2025, frameworks like deep learning with PyTorch are gaining momentum, but others like TensorFlow and JAX are also pushing the boundaries. Each has its strengths, making it crucial to understand their features and use cases.

This article will explore the top deep learning frameworks to watch in 2025, helping you decide which is best for your needs. Whether you’re researching or deploying AI models, these frameworks will be key to your success in the ever-evolving AI landscape.

Read on! 

What Are Deep Learning Frameworks?

Deep learning frameworks are specialized software tools that are made to make the process of building, training, and deploying neural networks simple and quick. These frameworks provide the infrastructure and libraries required for AI developers to efficiently handle complex tasks like image recognition, NLP, and more. 

Without these top deep learning framework options, developing deep learning would be significantly more time-consuming and technically challenging.

Top Deep Learning Framework Platform in 2025

The overall world of deep learning is swiftly changing, with numerous frameworks catering to a range of needs, from research to production-ready AI systems. 

In 2025, deep learning framework list will continue to shape the field, offering a combination of power, flexibility, and ease of use. These tools help developers and researchers alike accelerate model development, streamline training, and optimize deployment. 

Let’s take a closer look at the top deep-learning framework options you need to know in 2025.

1. TensorFlow

TensorFlow is a dominant framework developed by Google that is widely recognized for its versatility and scalability. It supports a range of machine learning tasks, from image classification to time-series forecasting. Its strong ecosystem, extensive documentation, and industry adoption contribute significantly to the deep learning framework popularity, making it a top choice for both researchers and developers in 2025.

Key Features:

• Highly scalable, designed for both research and production environments.
• TensorFlow Lite is for mobile applications, and TensorFlow.js is for browser-based solutions.
• TensorFlow Extended (TFX) enables end-to-end machine learning pipelines.
• Popular Use Cases: Computer vision, speech recognition, recommendation engines, and more

What’s New in 2025?: TensorFlow’s new optimizations improve training performance, while extended hardware support enhances GPU and TPU capabilities, allowing for faster model training and deployment.

2. PyTorch

PyTorch is one of the most used top deep learning framework options, particularly in the research community. Deep learning with PyTorch is known for its dynamic computational graph.

Key Features:

• Dynamic computation graph, making it flexible for research.
• Strong GPU support for faster training.
• Extensive libraries for natural language processing and computer vision.
• Popular Use Cases: Image and speech recognition, deep reinforcement learning, and natural language understanding. 

What’s New in 2025?: Deep learning with PyTorch continues to improve support for distributed training, making it an ideal choice for both small and large-scale AI projects. The new release focuses on better performance and integration with cloud services.

3. Keras

Keras is a high-level API that runs on top of TensorFlow, designed for fast prototyping and easy model creation. It simplifies the deep learning workflow without sacrificing performance.

Key Features:

• Easy-to-use API for rapid model building.
• Built-in support for common neural network architectures.
• Tight integration with TensorFlow.
• Popular Use Cases: Rapid prototyping, quick experimentation, and deep learning on smaller datasets.

 What’s New in 2025?: Keras has seen continuous improvements in model optimization and is now fully integrated with TensorFlow 2.x for enhanced performance and usability.

Also Read: Keras vs. PyTorch: Difference Between Keras & PyTorch

4. Sonnet

Sonnet is a top deep learning framework developed by DeepMind and designed for research into deep learning. It provides flexible and modular tools that allow you to experiment with cutting-edge architectures.

Key Features:

• Built for researchers who need to create complex neural network architectures.
• Extends TensorFlow to make it more modular and reusable.
• Focuses on composability and code readability.
• Popular Use Cases: Research in reinforcement learning and natural language processing.

What’s New in 2025?: Sonnet now supports even more advanced architectures, including self-supervised learning models, and has streamlined integration with TensorFlow 2.x.

5. MXNet

MXNet, backed by Apache, is an open-source framework known for its scalability and efficiency. It’s particularly well-suited for large-scale distributed machine learning.

Key Features:

• Supports both symbolic and imperative programming for flexibility.
• Optimized for multi-GPU and distributed training.
• Deep integration with AWS, making it ideal for cloud-based training.
• Popular Use Cases: Large-scale AI models, image and video processing, and NLP.

What’s New in 2025?: MXNet has seen updates to improve support for edge devices and low-latency applications, enhancing its deployment capabilities.

6. Swift for TensorFlow

Swift for TensorFlow offers deep integration with the Swift programming language, enabling fast and efficient machine learning model training and deployment.

Key Features:

• Designed for high performance and fast prototyping.
• Seamless integration with TensorFlow.
• Allows for native Swift code for deep learning models.
• Popular Use Cases: Machine learning on iOS and macOS devices, edge computing.

What’s New in 2025?: Swift for TensorFlow has introduced tools to make it simpler to use on mobile devices, with improved performance and resource efficiency.

7. Gluon

Gluon is a top deep learning framework that combines the ease of use of high-level APIs with the power of lower-level optimization. Developed by Microsoft and Amazon, Gluon is particularly popular for production applications.

Key Features:

• Simple, concise, and flexible API.
• Supports both dynamic and static neural networks.
• Strong integration with Apache MXNet.
• Popular Use Cases: Computer vision, natural language processing, and predictive analytics. 

What’s New in 2025?: Gluon has improved its ability to handle large-scale models with distributed computing, enhancing its use in real-time AI applications.

Also Read: Understanding 8 Types of Neural Networks in AI & Application

8. DL4J (Deeplearning4j)

DL4J is a Java-based deep learning framework designed for use in business environments, particularly those dealing with big data.
 Key Features:

• Optimized for Java and Scala.
• Integrates well with Hadoop and Spark for big data applications.
• Distributed deep learning capabilities.
• Popular Use Cases: Big data, time-series analysis, and natural language processing.

What’s New in 2025?: DL4J has enhanced integration with data processing pipelines, making it easier to scale deep learning models in large enterprise settings.

9. ONNX

ONNX (Open Neural Network Exchange) is an open-source format designed to facilitate the sharing and deployment of models across different frameworks.

Key Features:

• Supports cross-platform model deployment.
• Open-source and widely adopted by leading AI companies.
• Integrates with many major AI frameworks like PyTorch, TensorFlow, and Caffe.
• Popular Use Cases: Model conversion and deployment across different platforms.

What’s New in 2025?: ONNX has expanded its support for edge devices and enhanced performance for model interoperability between different frameworks.

Read More: Introducing Deep Learning with Python: Learn Deep Learning in Python

10. Chainer

Chainer is a flexible and intuitive top deep learning framework that enables projects with neural network architectures.

Key Features:

• Strong support for dynamic computation graphs.
• Allows easy manipulation of neural network layers.
• Focuses on flexibility and speed for research.
• Popular Use Cases: Reinforcement learning and deep generative models.

What’s New in 2025?: Chainer has improved its scalability and support for large datasets, making it more suitable for industrial applications.

11. The Microsoft Cognitive Toolkit (CNTK)

CNTK is a deep learning framework developed by Microsoft that is designed for speed and efficiency in large-scale applications.

Key Features:

• High performance with multi-GPU support.
• Deep integration with Microsoft Azure.
• Optimized for speech, vision, and text recognition. 
• Popular Use Cases: Speech recognition, image recognition, and large-scale AI applications.

What’s New in 2025?: CNTK has updated its cloud integration tools, making it easier to scale AI models across distributed systems.

12. Caffe

Caffe is a top deep learning framework known for its performance in image classification and convolutional neural networks (CNNs).

Key Features:

• High performance for image-based tasks.
• Optimized for deep convolutional networks.
• Strong support for hardware acceleration.
• Popular Use Cases: Image classification, computer vision, and multimedia applications.

What’s New in 2025?: Caffe has been updated for improved GPU support and faster model training.

Also Read: CNN vs RNN: Difference Between CNN and RNN

13. Theano

Although officially discontinued, Theano remains influential in AI development, particularly for academic and research purposes.

Key Features:

• Optimizes mathematical expressions for efficient computation.
• Deep integration with NumPy for numerical computing.
• Legacy support for deep learning applications.
• Popular Use Cases: Research in neural networks and machine learning algorithms.

What’s New in 2025?: Theano's concepts still influence modern frameworks, though they are no longer actively maintained.

14. Torch

Torch is a scientific computing framework that has been popular for academic research in AI. While overshadowed by deep learning with PyTorch, it remains influential.

Key Features:

• Simple, flexible, and fast for research.
• Offers a comprehensive set of tools for deep learning.
• Popular Use Cases: Research and academic projects in AI.

What’s New in 2025?: While deep learning with PyTorch has surpassed Torch, its legacy continues to shape the development of modern deep-learning libraries.

Also Read: Tensorflow vs Pytorch – Comparison, Features & Applications

15. GGML

GGML is an emerging top deep learning framework aimed at improving machine learning performance on mobile and edge devices.

Key Features:

• Optimized for on-device learning.
• Focus on low-latency, real-time AI applications.
• Popular Use Cases: Mobile AI, IoT, and edge computing.

What’s New in 2025?: GGML has introduced tools to enhance real-time inference on edge devices, making it ideal for AI applications on mobile phones and other hardware.

Also Read: Top Differences Between ML, Deep Learning, And NLP

Until now, you have an idea of different deep learning technologies, now let's dive into what makes a deep learning framework effective and why it's essential for your AI projects.

What Makes a Deep Learning Framework Effective?

An effective deep learning framework provides the right tools, scalability, and flexibility to build, train, and deploy AI models efficiently. One of the key factors influencing deep learning framework popularity is how well a framework balances usability, performance, and support. 

Let’s explore the key aspects that make these frameworks stand out.

• Ease of Use: Simplifies the process of designing and training models.
• Flexibility: Allows customization and experimentation with various architectures.
• Scalability: Can handle large datasets and complex models.
• Community Support: Large user bases and extensive documentation.

Key Features of Deep Learning Frameworks

Deep learning frameworks offer various features like automated differentiation, GPU acceleration, and modularity, which streamline the process of model creation and optimization. Here’s a closer look at these essential features.

• Pre-built Layers and Functions: Reduces coding time.
• GPU Acceleration: Speeds up training and model development.
• Interoperability: Works with various tools and libraries in the AI ecosystem.

Why You Need Various Top Deep Learning Framework Options for AI Projects

Choosing the right deep learning framework depends on your project’s specific needs, whether it’s performance, ease of use, or scalability. Let’s dive into why having multiple options is crucial for AI success.

• Efficiency: Develop and deploy models faster.
• Reproducibility: Standardize models for better results.
• Innovation: Leverage cutting-edge advancements in AI with ready-to-use tools.

Read More: Deep Learning Algorithm [Comprehensive Guide With Examples]

How to Choose the Right Deep Learning Framework for Your Project?

Choosing the right deep learning framework for your project can be a huge decision that can have a large role in both development speed and model performance. The best framework for deep learning is based on various factors such as the complexity of the tasks, team expertise, scalability, and deployment requirements. 

This section will take you through the major ideas to help you make the best choice for your deep learning project.

Factors to Consider When Choosing A Top Deep Learning Framework

When evaluating a top deep learning framework, consider the following:

• Ease of use: Does the framework offer an intuitive API and documentation?
• Scalability: Can the framework handle large datasets and be scaled for production?
• Flexibility: How easily can you customize and extend the framework?
• Supported hardware: Does it work well with GPUs and TPUs for faster training?

Key Considerations for Business Applications

For business-driven applications, certain factors are more critical:

• Model deployment: Is the framework optimized for quick deployment in cloud or on-premise environments?
• Compliance and privacy: Can it handle sensitive data securely and comply with industry standards?
• Support for real-time systems: Does it offer tools for developing real-time predictive models or applications?

Performance and Speed: Which Framework Delivers Better Results?

Performance is key, especially when working with large datasets or complex models. Frameworks like TensorFlow and PyTorch offer superior performance, but their speed often changes depending on the nature of the task. 

• TensorFlow: Known for high performance and scalability, it is great for large-scale applications.
• PyTorch: Often preferred for research and rapid prototyping due to its flexibility and dynamic graph.
• MXNet: Offers excellent parallel computing capabilities, making it ideal for multi-GPU and distributed training.

Community Support and Documentation: Why It Matters

The community surrounding a framework can significantly impact your project’s success. Look for:

• Active community: Frameworks like PyTorch and TensorFlow boast large, active communities, ensuring regular updates, bug fixes, and new features.
• Comprehensive documentation: Good documentation accelerates development and problem-solving.
• Online forums and tutorials: Resources like Stack Overflow, GitHub repositories, and dedicated online courses can be invaluable.

Integration with Existing AI Tools

Consider how well the deep learning framework integrates with your existing tools and workflows:

• Pre-trained models: Does the framework offer pre-trained models to help accelerate development?
• Compatibility with AI libraries: Look for integration with popular libraries for natural language processing, computer vision, or reinforcement learning (e.g., Hugging Face, OpenCV).
• Deployment tools: Ensure the framework is compatible with deployment tools such as Docker, Kubernetes, or cloud platforms like AWS and Google Cloud.

By evaluating these key areas—performance, ease of use, community support, and integration—you’ll be better positioned to select the right deep learning framework for your specific needs and objectives.

But wait, before you decide, let's wrap up with some insights on how upGrad can elevate your deep learning journey.

Also Read: Deep Learning vs Neural Networks: Difference Between Deep Learning and Neural Networks

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