TensorFlow implements Semantic Versioning 2.0, categorizing its version numbers into MAJOR, MINOR, and PATCH. Major changes may disrupt backwards compatibility, while minor updates offer new features and optimizations without breaking existing functionalities. PATCH versions are reserved for bug fixes. There are specific areas, such as SavedModels and GraphDef compatibility, where users should remain cautious, as they are not included in the semantic versioning framework. Users and developers must understand both the risks of migration and the potential for compatibility as they update TensorFlow.
TensorFlow primarily adheres to Semantic Versioning 2.0, characterized by a version format of MAJOR.MINOR.PATCH, reflecting varying degrees of compatibility and changes.
MAJOR version changes indicate potentially backwards incompatible alterations, whereas MINOR versions denote backwards compatible features and PATCH versions pertain to bug fixes.
The document elucidates specific cases where previous TensorFlow graphs and checkpoints may be migratable to newer releases, though major changes may disrupt compatibility.
Certain aspects such as the compatibility of SavedModels, graphs, checkpoints, and GraphDef are not covered under Semantic Versioning, emphasizing the need for user awareness.
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