Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing critical section subgraphs in a computational graph system. One of the methods includes executing a lock operation including providing, by a task server, a request to a value server to create a shared critical section object. If the task server determines that the shared critical section object was created by the value server, the task server executes one or more other operations of the critical section subgraph in serial. The task server executes an unlock operation including providing, by the task server, a request to the value server to delete the shared critical section object.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing critical section subgraphs in a computational graph system. One of the methods includes executing a lock operation including providing, by a task server, a request to a value server to create a shared critical section object. If the task server determines that the shared critical section object was created by the value server, the task server executes one or more other operations of the critical section subgraph in serial. The task server executes an unlock operation including providing, by the task server, a request to the value server to delete the shared critical section object.

Abstract: A method for reducing idleness in a machine-learning training system can include performing operations by computing devices. A first set of training operations can access and prepare a plurality of training examples of a set of training data. A second set of training operations can train a machine-learned model based at least in part on the set of training data and can include one or more repeat iterations in which at least a portion of the second set of training operations is repeatedly performed such that the training example(s) are repeatedly used to train the machine-learned model. A rate of the repeat iteration(s) can be based at least in part on an echo factor that can be based at least in part on a comparison of a first computational time of the first set of training operations to a second computational time of the second set of training operations.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing critical section subgraphs in a computational graph system. One of the methods includes executing a lock operation including providing, by a task server, a request to a value server to create a shared critical section object. If the task server determines that the shared critical section object was created by the value server, the task server executes one or more other operations of the critical section subgraph in serial. The task server executes an unlock operation including providing, by the task server, a request to the value server to delete the shared critical section object.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing critical section subgraphs in a computational graph system. One of the methods includes executing a lock operation including providing, by a task server, a request to a value server to create a shared critical section object. If the task server determines that the shared critical section object was created by the value server, the task server executes one or more other operations of the critical section subgraph in serial. The task server executes an unlock operation including providing, by the task server, a request to the value server to delete the shared critical section object.

Abstract: A method for reducing idleness in a machine-learning training system can include performing operations by computing devices. A first set of training operations can access and prepare a plurality of training examples of a set of training data. A second set of training operations can train a machine-learned model based at least in part on the set of training data and can include one or more repeat iterations in which at least a portion of the second set of training operations is repeatedly performed such that the training example(s) are repeatedly used to train the machine-learned model. A rate of the repeat iteration(s) can be based at least in part on an echo factor that can be based at least in part on a comparison of a first computational time of the first set of training operations to a second computational time of the second set of training operations.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing critical section subgraphs in a computational graph system. One of the methods includes executing a lock operation including providing, by a task server, a request to a value server to create a shared critical section object. If the task server determines that the shared critical section object was created by the value server, the task server executes one or more other operations of the critical section subgraph in serial. The task server executes an unlock operation including providing, by the task server, a request to the value server to delete the shared critical section object.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a factorization model to learning features of model inputs of a trained model such that the factorization model is predictive of outcome for which the machine learned model is trained.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a factorization model to learning features of model inputs of a trained model such that the factorization model is predictive of outcome for which the machine learned model is trained.