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from nnabla.contrib.context import extension_contextnn.set_default_context(extension_context('cuda.cudnn'))

# NNnn.set_auto_forward(True)

for i in range(max_iter):…loss.backward(clear_buffer=True)# comm.allreduce(division=False, inplace=False)solver.update()

from nnabla.contrib.context import extension_contextnn.set_default_context(extension_context('cuda.cudnn'))

template <class T>void AffineCuda<T>::forward_impl(const Variables &inputs, const Variables &outputs) {cuda_set_device(std::stoi(this->ctx_.device_id));const T *x = inputs[0]->get_data_pointer<T>(this->ctx_);const T *w = inputs[1]->get_data_pointer<T>(this->ctx_);T *y = outputs[0]->cast_data_and_get_pointer<T>(this->ctx_);// y = x * w....

}template <class T>void AffineCuda<T>::backward_impl(const Variables &inputs, const Variables &outputs,

const vector<bool> &propagate_down, const vector<bool> &accum) {cuda_set_device(std::stoi(this->ctx_.device_id));const T *dy = outputs[0]->get_grad_pointer<T>(this->ctx_);if (propagate_down[0]) {T *dx = inputs[0]->cast_grad_and_get_pointer<T>(this->ctx_);const T *w = inputs[1]->get_data_pointer<T>(this->ctx_);// dx += dy * w^t...

}

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python "(path of Neural Network Console)/libs/nnabla/python/src/nnabla/utils/cli/cli.py" forward-c Network definition file included in the training result folder (net.nntxt)-p Parameter file included in the training result folder (parameters.h5)-d Dataset CSV file of input data-o Inference result output folder

import nnabla as nnnn.load_parameters('./parameters.h5')

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