11/12/2023 0 Comments Batch Text Replacer 2.15 downloadingTF-TRT ingests, via its Python or C++ APIs, a TensorFlow SavedModel created from a trained TensorFlow model (see Build and load a SavedModel). This allows for a seamless workflow from model definition, to training, to deployment on NVIDIA devices.įor expert users requiring complete control of TensorRT’s capabilities, exporting the TensorFlow model to ONNX and directly using TensorRT is recommended. The most important benefit of using TF-TRT is that a user can create and test their model on TensorFlow, and leverage the performance acceleration provided by TensorRT, with just a few additional lines of code, without having to develop in C++ using TensorRT directly. To learn more about the optimizations provided by TensorRT, please visit TensorRT’s page. This allows the use of TensorFlow’s rich feature set, while optimizing the graph wherever possible with TensorRT, providing both flexibility and performance. These compatible subgraphs are optimized and executed by TensorRT, relegating the execution of the rest of the graph to native TensorFlow. TF-TRT automatically partitions a TensorFlow graph into subgraphs based on compatibility with TensorRT.
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