Abstract: A random password policy for a specific user associated with an entity is generated based on a global password requirement. A new password created by the specific user based on the generated random password policy is identified. That the new password complies with a set of requirements specified by the generated random password policy is confirmed.

Abstract: A method, computer system, and a computer program product may perform hyperparameter tuning. Hyperparameter search input data may be received. A search algorithm to generate a set of hyperparameters may be invoked. A hyperparameter tuning resource allocator may receive physical resource data regarding a cluster. The hyperparameter tuning resource allocator may determine a required workload and may use a received number of parallel experiments to determine the required workload. The hyperparameter tuning resource allocator may determine whether an option to mandate the workload batch size was selected. The cluster may perform the number of parallel experiments to search and tune the set of hyperparameters. The hyperparameter tuning resource allocator may manage the cluster according to the physical resource data and the required workload and by using tensor swapping.

Abstract: A method, computer system, and a computer program product may perform hyperparameter tuning. Hyperparameter search input data may be received. A search algorithm to generate a set of hyperparameters may be invoked. A hyperparameter tuning resource allocator may receive physical resource data regarding a cluster. The hyperparameter tuning resource allocator may determine a required workload and may use a received number of parallel experiments to determine the required workload. The hyperparameter tuning resource allocator may determine whether an option to mandate the workload batch size was selected. The cluster may perform the number of parallel experiments to search and tune the set of hyperparameters. The hyperparameter tuning resource allocator may manage the cluster according to the physical resource data and the required workload and by using tensor swapping.

Abstract: A user manages security of one or more user devices by manipulating one or more sensors located in the user's mouth in a predetermined pattern corresponding to a password. The matching of the predetermined pattern to the password unlocks at least a portion of at least one user device.

Abstract: A user manages security of one or more user devices by manipulating one or more sensors located in the user's mouth in a predetermined pattern corresponding to a password. The matching of the predetermined pattern to the password unlocks at least a portion of at least one user device.

Abstract: A user manages security of one or more user devices by manipulating one or more sensors located in the user's mouth in a predetermined pattern corresponding to a password. The matching of the predetermined pattern to the password unlocks at least a portion of at least one user device.

Abstract: A user manages security of one or more user devices by manipulating one or more sensors located in the user's mouth in a predetermined pattern corresponding to a password. The matching of the predetermined pattern to the password unlocks at least a portion of at least one user device.

Abstract: The present invention provides a computer implemented method, system, and computer program product of deep learning via dynamic root solvers. In an embodiment, the present invention includes (1) forming an initial set of GPUs into an initial binary tree architecture, where the initial set includes initially idle GPUs and an initial root solver GPU as the root of the initial binary tree architecture, (2) calculating initial gradients and initial adjusted weight data, (3) choosing a first currently idle GPU as a current root solver GPU, (4) forming a current set of GPUs into a current binary tree architecture, where the current set includes the additional currently idle GPUs and the current root solver GPU as the root of the current binary tree architecture, (5) calculating current gradients and current adjusted weight data, and (6) transmitting an initial update to the weight data to the available GPUs.

Abstract: The present invention provides a computer implemented method, system, and computer program product of deep learning via dynamic root solvers. In an embodiment, the present invention includes (1) forming an initial set of GPUs into an initial binary tree architecture, where the initial set includes initially idle GPUs and an initial root solver GPU as the root of the initial binary tree architecture, (2) calculating initial gradients and initial adjusted weight data, (3) choosing a first currently idle GPU as a current root solver GPU, (4) forming a current set of GPUs into a current binary tree architecture, where the current set includes the additional currently idle GPUs and the current root solver GPU as the root of the current binary tree architecture, (5) calculating current gradients and current adjusted weight data, and (6) transmitting an initial update to the weight data to the available GPUs.

Abstract: A user manages security of one or more user devices by manipulating one or more sensors located in the user's mouth in a predetermined pattern corresponding to a password. The matching of the predetermined pattern to the password unlocks at least a portion of at least one user device.

Abstract: The present invention provides a computer implemented method, system, and computer program product of deep learning via dynamic root solvers. In an embodiment, the present invention includes (1) forming an initial set of GPUs into an initial binary tree architecture, where the initial set includes initially idle GPUs and an initial root solver GPU as the root of the initial binary tree architecture, (2) calculating initial gradients and initial adjusted weight data, (3) choosing a first currently idle GPU as a current root solver GPU, (4) forming a current set of GPUs into a current binary tree architecture, where the current set includes the additional currently idle GPUs and the current root solver GPU as the root of the current binary tree architecture, (5) calculating current gradients and current adjusted weight data, and (6) transmitting an initial update to the weight data to the available GPUs.

Abstract: The present invention provides a computer implemented method, system, and computer program product of deep learning via dynamic root solvers. In an embodiment, the present invention includes (1) forming an initial set of GPUs into an initial binary tree architecture, where the initial set includes initially idle GPUs and an initial root solver GPU as the root of the initial binary tree architecture, (2) calculating initial gradients and initial adjusted weight data, (3) choosing a first currently idle GPU as a current root solver GPU, (4) forming a current set of GPUs into a current binary tree architecture, where the current set includes the additional currently idle GPUs and the current root solver GPU as the root of the current binary tree architecture, (5) calculating current gradients and current adjusted weight data, and (6) transmitting an initial update to the weight data to the available GPUs.

Abstract: The present invention provides a computer implemented method, system, and computer program product of deep learning via dynamic root solvers. In an embodiment, the present invention includes (1) forming an initial set of GPUs into an initial binary tree architecture, where the initial set includes initially idle GPUs and an initial root solver GPU as the root of the initial binary tree architecture, (2) calculating initial gradients and initial adjusted weight data, (3) choosing a first currently idle GPU as a current root solver GPU, (4) forming a current set of GPUs into a current binary tree architecture, where the current set includes the additional currently idle GPUs and the current root solver GPU as the root of the current binary tree architecture, (5) calculating current gradients and current adjusted weight data, and (6) transmitting an initial update to the weight data to the available GPUs.

Abstract: A user manages security of one or more user devices by manipulating one or more sensors located in the user's mouth in a predetermined pattern corresponding to a password. The matching of the predetermined pattern to the password unlocks at least a portion of at least one user device.

Abstract: A user manages security of one or more user devices by manipulating one or more sensors located in the user's mouth in a predetermined pattern corresponding to a password. The matching of the predetermined pattern to the password unlocks at least a portion of at least one user device.

Abstract: A user manages security of one or more user devices by manipulating one or more sensors located in the user's mouth in a predetermined pattern corresponding to a password. The matching of the predetermined pattern to the password unlocks at least a portion of at least one user device.