What's new in TensorFlow 2.19

2025-03-13 · Source: The TensorFlow Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, quick

Summary

TensorFlow 2.19 has been released, introducing several key updates for developers. This version discontinues the direct release of libtensorflow packages, though they remain accessible via PyPI. Significant changes include modifications to the C++ API in LiteRT, where `tflite::Interpreter::kTensorsReservedCapacity` and `tflite::Interpreter::kTensorsCapacityHeadroom` are now const references to enhance API compatibility and implementation flexibility. Additionally, the TF-Lite `tfl.Cast` operation now supports bfloat16 in its runtime kernel. Users are also advised that `tf.lite.Interpreter` will be deprecated in TF 2.20, with a new location at `ai_edge_litert.interpreter`.

Key takeaway

For embedded systems developers utilizing TensorFlow Lite, you should update your dependencies to TensorFlow 2.19 and begin migrating any usage of `tf.lite.Interpreter` to `ai_edge_litert.interpreter` before the TF 2.20 release. Be aware that libtensorflow packages are now sourced from PyPI, impacting your build processes.

Key insights

TensorFlow 2.19 updates LiteRT C++ API, adds bfloat16 support, and deprecates `tf.lite.Interpreter`.

Principles

• Prioritize API compatibility
• Support flexible constant values

In practice

• Use PyPI for libtensorflow
• Migrate from `tf.lite.Interpreter`

Topics

• TensorFlow 2.19
• TensorFlow LiteRT
• Keras 3.0
• bfloat16 Support
• API Deprecation

Best for: NLP Engineer, Computer Vision Engineer, Machine Learning Engineer, AI Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by The TensorFlow Blog.