The disclosure provides a neural network training method and apparatus, an electronic device, a medium and a program product, and relates to the field of artificial intelligence, in particular to the fields of deep learning and distributed learning. The method includes: acquiring a neural network for deep learning; constructing a deep reinforcement learning model for the neural network; and determining, through the deep reinforcement learning model, a processing unit selection for the plurality of the network layers based on a duration for training each of the network layers by each type of the plurality of types of the processing units, and a cost of each type of the plurality of types of the processing units, wherein the processing unit selection comprises the type of the processing unit to be used for each of the plurality of the network layers, and the processing unit selection is used for making a total cost of the processing units used by the neural network below a cost threshold, in response to a duration for pipelining parallel computing for training the neural network being shorter than a present duration.