PyTorch and JAX dominate research while TensorFlow and OneFlow excel in large-scale AI training
Hugging Face simplifies access to advanced pre-trained models for faster development
ONNX, DL4J, and MXNet ensure compatibility and enterprise adoption across AI systems
Artificial intelligence has integrated into the workflows of various sectors today. Advanced AI models and neural networks are used to train machines such as voice assistants or self-driving cars to analyze situations and quickly respond to them.
The progress in the domain is mainly dedicated to deep learning frameworks, as they provide a structure for building and training AI models. This article will discuss some of the most important toolkits that are driving innovation in 2025.
Here are the best, most popular deep learning frameworks that are used by large corporations to automate processes:
TensorFlow, created by Google, is one of the most trusted frameworks that is used in healthcare, finance, and many other data-sensitive industries. The tool provides developers with an entire ecosystem where they can use Keras to quickly build applications, TensorFlow Lite to run apps on phones, and TensorFlow Extended for large-scale production. This flexibility keeps it relevant in almost every area of AI.
PyTorch is preferred by many researchers because it is simple and easy to experiment with. Companies are also using it in real-world applications as the platform is continuously improving with new feature updates that allow faster training and smoother deployment.
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Known for speed and efficiency, JAX is often used in scientific projects where performance matters the most. With features like automatic differentiation and just-in-time compilation, the tool can handle heavy workloads across multiple GPUs or TPUs. Researchers who need precise control over their models prefer JAX over the others.
MindSpore was developed by Huawei to work across different platforms such as cloud servers, edge devices, and even smaller gadgets. With automatic parallelism, the deep learning framework makes it easier to train massive models without extra effort. Additionally, MindSpore’s ability to switch between dynamic and static modes offers more flexibility to users.
OneFlow was built with large-scale training in mind. Splitting work across many devices can be complicated, but OneFlow makes it much simpler by letting developers focus on building models instead of struggling with setup. Its design feels familiar to users who already know PyTorch, which makes it easier to adopt.
Also Read: How to Choose the Right Deep Learning Framework for a Project
Many students and beginners start with Keras because it is easier to understand compared to other frameworks. While it is useful for smaller projects and quick testing, Keras is closely linked with TensorFlow, which helps beginners transition to advanced projects easily.
Hugging Face has changed how developers use AI models. The library provides pre-trained models for natural language processing, computer vision, and audio data, allowing engineers to build chatbots, translation systems, or image classifiers very quickly. The large collection of models is the platform’s biggest strength.
Deeplearning4j is popular in companies that use Java or Scala. It may not be as widely discussed as PyTorch or TensorFlow, but it still plays a role in enterprise-level projects. DL4J fits well into systems where Java is already the main language.
ONNX focuses on compatibility and allows users to run models built using one framework to work in another. This saves time and reduces the problem of being locked into a single platform. For example, a model created in PyTorch can later be used on a system that runs on TensorFlow.
MXNet is still relevant in 2025 as it supports multiple programming languages, including Python, making it flexible for developers with different backgrounds. The deep learning tool also offers distributed training, which is important for large-scale projects.
Each framework has its strengths.
PyTorch and JAX are strong choices for research.
TensorFlow and OneFlow are trustworthy picks for training at scale.
MindSpore offers performance on different kinds of devices.
Keras is often the starting point for beginners in the AI domain.
Hugging Face simplifies the use of advanced models.
ONNX, DL4J, and MXNet are important for compatibility in large-scale model deployment.
Deep learning tools are the most important aspect in the AI sector. With better tools, developers and engineers can build AI systems using cheaper budget plans and have the flexibility to scale production. These frameworks simplify complex operations and speed up the developmental cycles of future AI-based applications.