Top 15 Deep Learning Frameworks You Need to Know in 2025

This article ranks the top 15 deep learning frameworks in 2025, emphasizing GPU acceleration and deployment flexibility. Frameworks such as TensorFlow, PyTorch, and Keras are crucial for building high-performance models in areas like autonomous driving and medical imaging. 

Each framework offers unique features, including distributed training, real-time inference, and cloud platform integration. Learning these deep learning frameworks equips developers to optimize AI models and deploy them efficiently in diverse environments.

In this article, you’ll walk through the top deep learning frameworks in 2025, showing you which one suits different projects.

Want to build smart deep learning projects? Explore upGrad’s AI and Machine Learning Courses and gain the skills to develop real-life AI applications with confidence. Start today!

What are the Top 15 Deep Learning Frameworks? Features and Use Cases

Deep learning framework platforms are comprehensive environments that combine tools, libraries, and infrastructure to streamline the development and deployment of AI models. 

As AI projects become more complex, finding the right platform to manage everything from training to deployment can be overwhelming. The challenge lies in selecting the most suitable deep learning framework for your specific needs.

Working with deep learning frameworks isn’t just about building models. You need the right tools to efficiently process, train, and deploy your AI models to tackle complex problems. Here are three programs that can help you:

• Masters in Artificial Intelligence and Machine Learning - IIITB Program
• Executive Diploma in Machine Learning & AI
• Master’s Degree in Artificial Intelligence and Data Science

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

1. TensorFlow

Source: 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.

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.

Pros and Cons:

Use Case:

As a data scientist working on a facial recognition system for a security company, you can use TensorFlow to train a model that detects and identifies individuals in real-time from security footage. By using its scalability, you can efficiently deploy the model for large-scale surveillance, ensuring faster recognition while maintaining high accuracy across various devices, including mobile and cloud-based systems.

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.

Struggling with grouping data without labels? Explore upGrad’s free Unsupervised Learning: Clustering course and learn how to use TensorFlow to identify patterns and segment data effectively. Check it out now!

2. PyTorch

Source: docs. 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 natural language processing and computer vision.
• Popular Use Cases: Image and speech recognition, deep reinforcement learning, and natural language understanding. 

Pros and Cons:

Use Case:

As a game developer, you can use PyTorch to create an AI agent that learns to play your game. By training the agent with reinforcement learning, it can adapt to different in-game scenarios and improve over time. PyTorch’s dynamic graph makes it easy to tweak and experiment with different strategies, helping you build a smarter, more unpredictable AI opponent.

What’s New in 2025? PyTorch introduces enhanced support for distributed training, making it easier to scale models across multiple GPUs. The new version improves cloud integration for seamless deployment. Performance optimizations speed up training, especially for large models.   

3. Keras

Source: 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.

Pros and Cons:

Use Case:

As a developer building a recommendation system for an e-commerce platform, you can use Keras to quickly prototype and test different deep learning models. With its simple API, you can iterate and adjust the model to enhance product recommendations, providing users with more relevant and personalized shopping experiences, all while reducing development time.

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

Source: 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.

Pros and Cons:

Use Case:

As a machine learning researcher, you can use Sonnet to design custom neural network architectures for advanced reinforcement learning models. Its modularity allows you to experiment with complex algorithms, making it easier to test new theories and approaches in AI without worrying about cumbersome coding or system limitations.

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. ML.NET

Source: dotnet

ML.NET is an open-source, cross-platform machine learning framework developed by Microsoft. Unlike traditional deep learning frameworks, ML.NET is designed for .NET developers and is aimed at integrating machine learning capabilities directly into .NET applications.

Key Features

• Integration with .NET: Built for .NET developers, enabling easy integration with existing .NET applications.
• Support for Classical ML and Deep Learning: Handles a wide range of machine learning tasks, including deep learning, through TensorFlow and ONNX model integration.
• Cross-Platform: Works on Windows, Linux, and macOS, making it accessible for various environments.
• Custom Model Support: Allows you to import pre-trained models from TensorFlow, ONNX, and other frameworks.

Pros and Cons

Use Case

As a software engineer working in a .NET environment, you can use ML.NET to build a predictive model for customer behavior analysis. ML.NET’s integration with TensorFlow allows you to import a pre-trained deep learning model. 

You can use it directly within your .NET application, enabling real-time predictions based on customer interactions.

What’s New in 2025? ML.NET has expanded its capabilities by introducing better support for deep learning models and more advanced integration with TensorFlow and ONNX. The framework now supports more advanced model types, including neural networks for image and text classification, and includes performance improvements for faster model training.

6. JAX

Source: docs.jax

JAX is a deep learning framework developed by Google, built for high-performance machine learning research. It offers automatic differentiation (autograd) and optimization tools, alongside a core focus on performance and flexibility. 

Key Features

• Autograd: Automatic differentiation for gradients, essential for optimizing machine learning models.
• XLA (Accelerated Linear Algebra): Compiles and optimizes code for CPU, GPU, and TPU, making it highly efficient.
• NumPy Integration: Provides a familiar interface for users who are already accustomed to the NumPy library.
• Parallelism Support: JAX enables vectorized operations and distributed training, allowing users to scale models easily.

Pros and Cons

Use Case

As a climate scientist working on predicting weather patterns, you can use JAX to develop a machine learning model that simulates climate changes based on historical data. 

For example, JAX allows you to quickly test and modify complex equations that simulate the effects of different environmental factors. Using JAX’s parallelization features, you can run multiple experiments at once, testing different variables such as ocean temperature, air pressure, and CO2 levels.

What’s new in 2025? Updates have focused on optimizing performance on TPUs, providing even faster computation for large-scale models. The latest version of JAX also introduces enhanced support for distributed training, making it easier to scale models across multiple machines. 

Also Read: How to Build Your Own AI System: Step-by-Step Guide

7. Gluon

Source: Github

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. 

Pros and Cons: 

Use Case

As a data scientist working in retail, you can use Gluon to build a demand forecasting model for your company. With Gluon’s dynamic neural networks, you can quickly test different architectures to predict future sales based on seasonal data, promotions, and historical trends. 

Its high-level API will allow you to iterate on your models quickly. At the same time, the underlying power of MXNet ensures that your model can handle large volumes of data efficiently for real-time predictions.

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: Predictive Analytics vs Descriptive Analytics

8. DL4J (Deeplearning4j)

Source: 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.

Pros and Cons:

Use Case

As a financial services company working with large datasets of transactional data, you can use DL4J to build a fraud detection system that analyzes patterns in customer behavior and flags suspicious transactions. 

DL4J’s integration with Apache Spark allows you to scale your model and process massive amounts of data efficiently. Once the model is trained, it can be deployed directly into a production environment, providing real-time fraud detection within your company’s infrastructure.

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.

Also Read: Data Modeling for Real-Time Data in 2025: A Complete Guide

9. ONNX

Source: Github

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.

Pros and Cons:

Use Case

As an AI engineer working for a healthcare company, you can use ONNX to deploy a trained deep learning model across different platforms. For example, you might develop a medical image analysis model in PyTorch, convert it to ONNX format, and then deploy it on both cloud-based systems (using TensorFlow) and local edge devices (using MXNet) for real-time analysis. 

ONNX’s ability to convert and optimize the model ensures that it can run efficiently across multiple deployment environments without rewriting the model.

What’s New in 2025?The release of ONNX v1.18.0 introduced support for additional data types, including FLOAT4E2M1, and improved compatibility with Python 3.13.

Also Read: Automated Machine Learning Workflow: Best Practices and Optimization Tips

10. Chainer

Source: 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.

Pros and Cons

Use Case: 

As a robotics engineer, you can use Chainer to build a model that helps a robot navigate through complex environments. With Chainer's dynamic computation graphs, you can easily tweak the neural network architecture during testing to improve its ability to avoid obstacles in real-time. 

Multi-GPU training enables you to process vast amounts of sensory data efficiently, improving the robot's decision-making speed and accuracy.

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)

Source: Github

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.

Pros and Cons:

Use Case

As a software developer, you can use CNTK to build a highly accurate speech recognition model for a virtual assistant. CNTK's multi-GPU capabilities enable you to train the model efficiently on a large dataset of audio samples, thereby reducing the time required to develop a model capable of understanding various accents and languages. 

Once trained, you can easily deploy the model on Microsoft's Azure cloud platform for real-time inference.

What’s New in 2025? The latest version improves its integration with cloud services, particularly Microsoft Azure, offering better tools for model training and deployment. 

Also Read: Top 7 Open Source Deep Learning Libraries You Can Try Today

12. Caffe

Source: caffe

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

Pros and Cons: 

Use Case: 

As a developer working on a real-time image recognition system for a security company, you can use Caffe to quickly build and deploy a deep learning model that can detect intruders by analyzing surveillance footage. 

By using Caffe’s pre-trained models, you can fine-tune a network for your specific security requirements and run it efficiently on GPUs. 

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

Also read: Image Classification in CNN: Everything You Need to Know

13. Hugging Face Transformers

Source: huggingface

Hugging Face Transformers is a powerful library for NLP tasks like text classification, machine translation, and text generation. It integrates seamlessly with RNNs, enabling advanced sequential data processing for more accurate, efficient models.

It provides an easy-to-use interface to some of the most advanced pre-trained models, including BERT, GPT-3, and T5. 

Key Features

• Pre-trained Models: Offers access to a wide variety of state-of-the-art pre-trained models for NLP tasks.
• Cross-Framework Compatibility: Works seamlessly with both PyTorch and TensorFlow.
• Easy Fine-Tuning: Simplifies the process of fine-tuning large language models for specific tasks.
• Transformer-Based Models: Focuses on transformer models, which have proven highly effective in NLP tasks.
• Model Hub: A large community-driven hub to share and access models, datasets, and solutions.

Pros and Cons

Use Case

As a content moderator for a large social media platform, you can use Hugging Face Transformers to build a content moderation system that automatically detects harmful or inappropriate language in user posts. 

By using pre-trained models like BERT, you can fine-tune the model to recognize offensive content in different languages. Hugging Face's user-friendly API and pre-built models allow you to quickly deploy a solution that improves content moderation without having to train a model from scratch.

What’s new in 2025? The framework has enhanced its support for deployment, providing improved integration with cloud platforms such as AWS and Google Cloud, enabling developers to scale their models seamlessly.

14. FastAI

Source: fast.ai 

FastAI is a high-level deep learning library built on top of PyTorch, designed to simplify the process of building and deploying machine learning models. It aims to make deep learning more accessible by abstracting away many of the complexities of model building, while still offering powerful tools for customization and fine-tuning. 

Key Features

• Built on PyTorch: FastAI uses the flexibility and power of PyTorch while simplifying its use with high-level abstractions.
• Pre-trained Models: Offers a range of pre-trained models for tasks like image classification, language modeling, and tabular data analysis.
• Extensive Documentation and Tutorials: Provides easy-to-follow tutorials, making it a great framework for beginners and educational purposes.
• Flexible and Modular: Allows users to easily customize models and workflows to suit specific needs.

Pros and Cons

Use Case

As a marketing analyst for an e-commerce platform, you can use FastAI to build a model that predicts customer churn based on user activity data. By using FastAI’s pre-built models and user-friendly API, you can quickly create a deep learning model to identify which customers are at risk of leaving. 

This enables you to take proactive steps to improve customer retention and optimize your marketing strategies, all while using a framework that requires minimal code and configuration.

What’s New in 2025? The framework now includes new features for transfer learning, making it even easier to fine-tune models for specific tasks with minimal data.  

15. GGML

Source: ggml.ai

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.

Pros and Cons

Use Case

As a developer working on an autonomous drone navigation system, you can use GGML to implement real-time obstacle detection and decision-making directly on the drone’s onboard computer. With GGML's low-latency inference and optimization for edge devices, the drone can process camera feed and sensor data in real time without needing to rely on cloud-based processing. 

This enables it to make rapid decisions for navigation and collision avoidance while minimising resource consumption.

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: Cloud Computing Vs Edge Computing: Difference Between Cloud Computing & Edge Computing

Take time to experiment with different frameworks to see which one aligns with your goals. Start small, test different models, and gradually scale up as you gain more experience. 

Next, let’s look at key factors to consider when selecting the perfect deep learning framework for your project.

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. 

Do you need to deploy on mobile or edge devices? Are you focused on rapid prototyping or handling large-scale data? Your choice should align with your project's needs, team expertise, and the resources available. 

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

1. Project Requirements

What are you trying to build?

For instance, if you’re developing a chatbot for customer service, you might opt for Hugging Face Transformers for NLP tasks, as it provides pre-trained models like GPT and BERT that can be fine-tuned with minimal data.

However, for image classification or object detection in a security camera system, TensorFlow or PyTorch may be better choices due to their strong support for computer vision tasks and wide array of pre-trained models.

2. Ease of Use

How quickly can you develop a prototype?

Suppose you need to quickly test and prototype different models for a product recommendation engine on your e-commerce website. In that case, FastAI offers a user-friendly interface built on top of PyTorch. This makes it easier to fine-tune existing models with minimal code.

For more complex, research-heavy applications, such as reinforcement learning for autonomous systems, PyTorch may offer greater flexibility, though it might have a steeper learning curve.

3. Performance and Scalability

How will your model scale as the dataset grows?

When dealing with large-scale image data for an autonomous vehicle’s object recognition system, you’ll want a framework like TensorFlow that supports multi-GPU setups and can scale across clusters for distributed training.

For smaller-scale tasks like sentiment analysis for social media posts, a framework like Keras (built on TensorFlow) will suffice, as it provides fast prototyping with less concern about scaling for the initial stages.

4. Hardware Compatibility

What hardware do you need to run your models on?

If you’re deploying AI models on mobile devices or IoT devices for real-time processing, consider frameworks like GGML or TensorFlow Lite. These are specifically optimized for low-latency inference on mobile and edge devices.

On the other hand, if you plan to utilize cloud-based services or supercomputers to train massive models, TensorFlow or PyTorch with multi-GPU support may be more appropriate, as they can use distributed computing to speed up training.

5. Community and Support

What kind of support and resources are available?

PyTorch and TensorFlow have large, active communities, making it easier to find tutorials, forums, and pre-built models. This is especially beneficial if you need open-source models or want to troubleshoot issues quickly.

For more niche tasks, like reinforcement learning for gaming or robotics, Sonnet (built on TensorFlow) or Ray RLLib might be the right choice, even though they have smaller communities. They offer specialized tools tailored for complex use cases, but they come with less mainstream support.

6. Deployment and Production Readiness

Will you deploy your model to production?

For real-time AI applications like fraud detection or video analysis, use frameworks like TensorFlow or ONNX. These frameworks offer seamless deployment and integrate easily with cloud services like AWS, Google Cloud, and Azure.

Explore advanced topics like reinforcement learning, generative models, and neural architecture search to enhance your skills.

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

How Can upGrad Help You Advance in Deep Learning?

Deep learning frameworks, such as PyTorch and TensorFlow, offer powerful capabilities, each excelling in distinct areas. PyTorch’s flexibility and dynamic computation graph make it a go-to choice for research and experimentation, while TensorFlow's scalability and production-ready tools suit large-scale applications. 

To advance your skills, focus on learning advanced topics like model optimization, cloud deployment, and multi-GPU training.

However, you might face challenges in scaling your models, optimizing performance, or choosing the right hardware for deployment. For deeper insights and hands-on experience, upGrad’s AI and deep learning courses provide structured learning paths and expert guidance. 

In addition to the courses mentioned, here are some more resources to help you further elevate your skills:

• Generative AI Mastery Certificate for Software Development
• Advanced Prompt Engineering Course with ChatGPT
• Introduction to Generative AI

Not sure where to go next in your deep learning journey? upGrad’s personalized career guidance can help you explore the right learning path based on your goals. You can also visit your nearest upGrad center and start hands-on training today!

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