Population-based training of machine learning models

A system comprising:

• one or more computers and one or more storage devices on which are stored instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations comprising:
• maintaining a plurality of training sessions for training a machine learning model, wherein a training session of the plurality of training sessions comprises data defining (i) respective hyperparameter values for training the machine learning model and (ii) model parameter values of the machine learning model, and defines a session during which the machine learning model is trained by repeatedly updating the model parameter values of the machine learning model according to the respective hyperparameter values;
• assigning, to each worker of one or more workers, one or more respective training sessions of the plurality of training sessions, each worker comprising one or more processing units to train the machine learning model during the assigned one or more respective training sessions,
• wherein for each worker of the one or more workers and each of the one or more respective training sessions assigned to the worker, the worker is configured to receive the training session, initialize a respective candidate model with model parameter values defined by the training session, train the respective candidate model starting from the model parameter values of the training session and according to hyperparameter values defined by the training session to determine final model parameter values for the training session, and update the training session by replacing the model parameter values defined by the training session with the final model parameter values determined for the training session;
• repeatedly performing operations until meeting one or more termination criteria, the operations comprising:
• receiving an updated training session from a respective worker of the one or more workers, wherein the updated training session identifies a first training session assigned to the respective worker and the final model parameter values determined by the respective worker for the first training session assigned to the respective worker,
• selecting a second training session from the plurality of training sessions,
• selecting, based on comparing the updated training session and the second training session using a fitness evaluation function, either the updated training session or the second training session as a parent training session,
• generating a child training session from the selected parent training session, wherein the generating comprises:
• generating child hyperparameter values of the child training session for training the machine learning model by updating hyperparameter values that have been used to train the machine learning model in the parent training session, and
• generating child model parameters for the child training session by setting the child model parameters using the model parameter values of the parent training session, and
• assigning the child training session to an available worker;
• and
• selecting a candidate model trained by a worker of the one or more workers to be a trained model for the machine learning model.

One or more non-transitory computer-readable storage media encoded with instructions that, when executed by one or more computers, cause the one or more computers to perform operations comprising:

• maintaining a plurality of training sessions for training a machine learning model, wherein a training session of the plurality of training sessions comprises data defining (i) respective hyperparameter values for training the machine learning model and (ii) model parameter values of the machine learning model, and defines a session during which the machine learning model is trained by repeatedly updating the model parameter values of the machine learning model according to the respective hyperparameter values;
• assigning, to each worker of one or more workers, one or more respective training sessions of the plurality of training sessions, each worker comprising one or more processing units to train the machine learning model during the assigned one or more respective training sessions,
• wherein for each worker of the one or more workers and each of the one or more respective training sessions assigned to the worker, the worker is configured to receive the training session, initialize a respective candidate model with model parameter values defined by the training session, train the respective candidate model starting from the model parameter values of the training session and according to hyperparameter values defined by the training session to determine final model parameter values for the training session, and update the training session by replacing the model parameter values defined by the training session with the final model parameter values determined for the training session;
• repeatedly performing operations until meeting one or more termination criteria, the operations comprising:
• receiving an updated training session from a respective worker of the one or more workers, wherein the updated training session identifies a first training session assigned to the respective worker and the final model parameter values determined by the respective worker for the first training session assigned to the respective worker,
• selecting a second training session from the plurality of training sessions,
• selecting, based on comparing the updated training session and the second training session using a fitness evaluation function, either the updated training session or the second training session as a parent training session,
• generating a child training session from the selected parent training session, wherein the generating comprises:
• generating child hyperparameter values of the child training session for training the machine learning model by updating hyperparameter values that have been used to train the machine learning model in the parent training session, and
• generating child model parameters for the child training session by setting the child model parameters using the model parameter values of the parent training session, and
• assigning the child training session to an available worker;
• and
• selecting a candidate model trained by a worker of the one or more workers to be a trained model for the machine learning model.