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Awesome GitHub RepositoriesLarge Model Export Utilities
Tools for splitting and saving large model architectures to bypass filesystem and protobuf size limitations.
Distinct from Large Model Optimizations: Distinct from Large Model Optimizations: focuses on the physical storage and export process rather than runtime quantization or device mapping.
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Awesome Large Model Export Utilities GitHub Repositories
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- tensorflow/serving
tensorflow/serving
6,351Auf GitHub ansehenTensorFlow Serving is a high-performance machine learning inference server designed to deploy TensorFlow models to production environments. It functions as a complete serving system that executes predictions on input data through a graph executor, providing network endpoints that eliminate the need for a separate runtime environment for client applications. The system is distinguished by its model version manager, which organizes and selects specific model versions within a directory hierarchy. It uses a filesystem watcher to detect new model versions and trigger automatic updates without int
Supports splitting protobuf files during the saving process to bypass file size limits for large model architectures.
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