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The process of speeding up a quantized model in nni is that 1) the model with quantized weights and configuration is converted into onnx format, 2) the onnx model is fed into tensorrt to. Nni will automatically infer sparsity based on the data distribution in the forward and backward process, but if some special operations lead to automatic sparsity inference errors, users can. Nni's implementation extends grid search to support all search spaces types When the search space contains continuous parameters like normal and loguniform, grid search tuner works in. We usually use a nni pruner to generate the masks then use modelspeedup to compact the model But in fact modelspeedup is a relatively independent tool, so you can use it independently. Class nni.experiment.experiment(config_or_platform, id=none) [源代码] manage nni experiment You can either specify an experimentconfig object, or a training service name Nni (neural network intelligence) is a toolkit to help users design and tune machine learning models (e.g., hyperparameters), neural network architectures, or complex system’s parameters, in an. We support a standalone mode for easy debugging, where you can directly run the trial command without launching an nni experiment This is for checking whether your trial code can correctly run. If users use these fields in the pythonic way with nni python apis (e.g., nni.experiment), the field names should be converted to snake_case In this document, the type of fields are formatted as. In nni, the space of features is called choice The space of lr is called loguniform And the space of momentum is called uniform You may have noticed, these names are derived from numpy.random.