A computer-implemented method comprising: receiving data specifying a machine learning model to be placed for distributed processing on a plurality of hardware devices; generating, from the data specifying the machine learning model, a sequence of operation embeddings, wherein each operation embedding in the sequence characterizes one or more respective operations necessary to perform the processing of the machine learning model; processing the sequence of operation embeddings using a placement recurrent neural network in accordance with first values of a plurality of network parameters of the placement recurrent neural network, wherein the first values have been determined through training of the placement recurrent neural network using a reinforcement learning technique that uses a reward derived from execution times for different placements of operations across the plurality of hardware devices, wherein the placement recurrent neural network is configured to process the sequence of operation embeddings in accordance with the first values to generate a network output defining a placement of the operations characterized by the operation embeddings in the sequence across the plurality of hardware devices, wherein the placement recurrent neural network is configured to generate, for each of the operation embeddings in the sequence, a set of scores that includes a respective score for each of the plurality of hardware devices, and wherein processing the sequence of operation embeddings comprises selecting a hardware device for each of the operations using the set of scores for the operation embedding characterizing the operation; and scheduling the machine learning model for processing by the plurality of hardware devices by placing the operations on the plurality of hardware devices according to the placement defined by the network output, wherein the placement recurrent neural network comprises: an encoder recurrent neural network configured to process the sequence of operation embeddings to generate a respective encoder hidden state for each of the operation embeddings; and a decoder neural network configured to, for each of the operation embeddings: receive a decoder input; and process the decoder input and the encoder hidden states to generate the set of scores for the operation embedding. receiving data specifying a machine learning model to be placed for distributed processing on a plurality of hardware devices; generating, from the data specifying the machine learning model, a sequence of operation embeddings, wherein each operation embedding in the sequence characterizes one or more respective operations necessary to perform the processing of the machine learning model; processing the sequence of operation embeddings using a placement recurrent neural network in accordance with first values of a plurality of network parameters of the placement recurrent neural network, wherein the first values have been determined through training of the placement recurrent neural network using a reinforcement learning technique that uses a reward derived from execution times for different placements of operations across the plurality of hardware devices, wherein the placement recurrent neural network is configured to process the sequence of operation embeddings in accordance with the first values to generate a network output defining a placement of the operations characterized by the operation embeddings in the sequence across the plurality of hardware devices, wherein the placement recurrent neural network is configured to generate, for each of the operation embeddings in the sequence, a set of scores that includes a respective score for each of the plurality of hardware devices, and wherein processing the sequence of operation embeddings comprises selecting a hardware device for each of the operations using the set of scores for the operation embedding characterizing the operation; and wherein the placement recurrent neural network is configured to process the sequence of operation embeddings in accordance with the first values to generate a network output defining a placement of the operations characterized by the operation embeddings in the sequence across the plurality of hardware devices, wherein the placement recurrent neural network is configured to generate, for each of the operation embeddings in the sequence, a set of scores that includes a respective score for each of the plurality of hardware devices, and wherein processing the sequence of operation embeddings comprises selecting a hardware device for each of the operations using the set of scores for the operation embedding characterizing the operation; and scheduling the machine learning model for processing by the plurality of hardware devices by placing the operations on the plurality of hardware devices according to the placement defined by the network output, wherein the placement recurrent neural network comprises: an encoder recurrent neural network configured to process the sequence of operation embeddings to generate a respective encoder hidden state for each of the operation embeddings; and a decoder neural network configured to, for each of the operation embeddings: receive a decoder input; and process the decoder input and the encoder hidden states to generate the set of scores for the operation embedding. receive a decoder input; and process the decoder input and the encoder hidden states to generate the set of scores for the operation embedding.