用法: torch.jit.optimize_for_inference(mod) 执行一组优化传递以优化模型以进行推理。如果模型尚未冻结,optimize_for_inference 将自动调用torch.jit.freeze。 除了应该在任何环境下加速您的模型的通用优化之外,为推理做准备还将烘焙构建特定设置,例如 CUDNN 或 MKLDNN 的存在,并且将来可能会进行转换,从而在一台机器...
Improve efficiency of your models and thus use less resources for inference by compiling the models into optimized forms. Implementation plan Use open-source model compilers - Libraries such as Treelite (for decision tree ensembles) improve the prediction throughput of models, due to more efficient...
🐛 Describe the bug torch.jit.optimize_for_inference allows to pass other_methods=["f"] to specify what methods/attributes to optimize. But there is no way of PREVENTING it from optimizing the forward method, which will then error out if ...
optimize_dl_model_for_inference( : : DLModelHandle, DLDeviceHandle, Precision, DLSamples, GenParam : DLModelHandleConverted, ConversionReport)DescriptionThe operator optimize_dl_model_for_inference optimizes the input model DLModelHandle for inference on the device DLDeviceHandle and returns the ...
Inspired by the TensorFlow for Poets, I have been exporting models optimized for inference with the freeze_graph and optimize_for_inference. I have run into an issue where some of the nodes required for inference get dropped by optimize_...
问如何在运行optimize_for_inference.py时修复“google.protobuf.message.DecodeError:解析消息时出错”EN...
const ops on the inference graph and outputs a frozen graph. With all weights frozen in the resulting inference graph, you can expect improved inference time. After the graph has been frozen, additional transformations by usingoptimize_for_inference toolcan help optimize the graph for inference....
TensorFlow-TensorRT (TF-TRT) is a deep-learning compiler for TensorFlow that optimizes TF models for inference on NVIDIA devices. TF-TRT is the TensorFlow integration for NVIDIA’s TensorRT (TRT) High-Performance Deep-Learning Inference SDK, allowing... ...
Six years ago, NVIDIA set out to create an AI inference server specifically designed for developers building high-throughput, latency-critical production applications. At the time, many developers were grappling with custom, framework-specific servers that increased complexity, drove up operational costs...
It enhances optimize_for_inference script with function of removing dropouts. Since dropouts are only valuable during training, it makes sense to remove them for inference. Sorry, something went wrong. googlebot added the cla: yes label May 14, 2018 Contributor concretevitamin commented May 17...