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Keras, a high-level Python library for training ML models, has made a comeback with a complete rewrite and renewed commitment to multi-backend support. This post explores the value offering of Keras in the current AI/ML development era, highlighting its ease of use and shortcomings. The multi-framework support of Keras 3 is identified as its most valuable feature, allowing developers to switch between JAX, TensorFlow, and PyTorch backends seamlessly.
The post discusses the advantages of multi-framework support, including the ability to avoid the difficulty of choosing an AI/ML framework, enjoy the best of all worlds by leveraging the unique advantages of different frameworks, increase model adoption, and decouple the data input pipeline from the model execution. However, there are also potential disadvantages to consider, such as a potential drop in runtime performance, limitations of cross-framework support, and the overhead of maintaining cross-framework compatibility.
Experiments with ViT runtime and Gemma fine-tuning demonstrate the impact of framework-specific optimizations on runtime performance. While sticking with backend-agnostic Keras code may lead to a performance penalty, the multi-framework support of Keras 3 remains compelling for AI/ML development. The post suggests designing code to incorporate framework-specific optimizations as needed and balancing the use of Keras with dedicated mechanisms for framework-specific modifications.
Ultimately, the decision to adopt Keras 3 will depend on various project-specific factors, including the target audience, model deployment process, project timelines, and more. This post serves as an introduction to exploring the capabilities and considerations of using Keras 3 in AI/ML development.
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Source link: https://towardsdatascience.com/multi-framework-ai-ml-development-with-keras-3-cf7be29eb23d?source=rss——artificial_intelligence-5
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