A machine receives a first set of global parameters from a global parameter server. The first set of global parameters includes data that weights one or more operands used in an algorithm that models an entity type. Multiple learner processors in the machine execute the algorithm using the first set of global parameters and a mini-batch of data known to describe the entity type. The machine generates a consolidated set of gradients that describes a direction for the first set of global parameters in order to improve an accuracy of the algorithm in modeling the entity type when using the first set of global parameters and the mini-batch of data. The machine transmits the consolidated set of gradients to the global parameter server. The machine then receives a second set of global parameters from the global parameter server, where the second set of global parameters is a modification of the first set of global parameters based on the consolidated set of gradients.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method for parameter data sharing, the computer-implemented method comprising: receiving, by a first machine, a first set of global parameters from a global parameter server, wherein the first set of global parameters comprises data that weights one or more operands used in an algorithm that models an entity type; executing, by multiple learner processors in the first machine, the algorithm using the first set of global parameters and a first mini-batch of data known to describe the entity type; generating, by the first machine, a first consolidated set of gradients that describes a direction for the first set of global parameters in order to improve an accuracy of the algorithm in modeling the entity type when using the first set of global parameters and the first mini-batch of data known to describe the entity type; transmitting, from the first machine, the first consolidated set of gradients to the global parameter server; and receiving, by the first machine, a second set of global parameters from the global parameter server, wherein the second set of global parameters is a modification of the first set of global parameters based on the first consolidated set of gradients. 2. The computer-implemented method of claim 1, further comprising: receiving, by a second machine, the first set of global parameters from the global parameter server; executing, by multiple learner processors in the second machine, the algorithm using the first set of global parameters and a second mini-batch of data known to describe the entity type; generating, by the second machine, a second consolidated set of gradients that describes a direction for the first set of global parameters in order to improve the accuracy of the algorithm in modeling the entity type when using the first set of global parameters; transmitting, from the second machine, the second consolidated set of gradients to the global parameter server; and receiving, by the first machine and the second machine, a third set of global parameters from the global parameter server, wherein the third set of global parameters is a modification of the first set of global parameters based on the first consolidated set of gradients and the second consolidated set of gradients. 3. The computer-implemented method of claim 2, further comprising: testing, by a third machine, a set of unknown data using the third set of global parameters in order to determine whether the set of unknown data matches the entity type. 4. The computer-implemented method of claim 1, further comprising: generating each of the first consolidated set of gradients by a different learner processor in the first machine; writing, by each of the multiple learner processors in the first machine, each gradient generated by each of the multiple learner processors in the first machine; and consolidating, by the first machine, gradients generated by all of the multiple learner processors in the first machine in order to create the first consolidated set of gradients. 5. The computer-implemented method of claim 1, further comprising: reading, by all of the multiple learner processors in the first machine, the first set of global parameters and the second set of global parameters from a shared memory in the first machine. 6. The computer-implemented method of claim 1, further comprising: storing, by one or more processors, global parameters currently in use by the first machine in a first memory in the first machine; and storing, by one or more processors, global parameters being downloaded from the global parameter server for future use, in a second memory in the first machine. 7. The computer-implemented method of claim 1, wherein the first set of global parameters further weight results from one or more particular operators used in the algorithm that models the entity type. 8. A computer program product for parameter data sharing, the computer program product comprising a computer readable storage device having program instructions embodied therewith, the program instructions readable and executable by a computer to perform a method comprising: receiving, by a first machine, a first set of global parameters from a global parameter server, wherein the first set of global parameters comprises data that weights one or more operands used in an algorithm that models an entity type; executing, by multiple learner processors in the first machine, the algorithm using the first set of global parameters and a first mini-batch of data known to describe the entity type; generating, by the first machine, a first consolidated set of gradients that describes a direction for the first set of global parameters in order to improve an accuracy of the algorithm in modeling the entity type when using the first set of global parameters and the first mini-batch of data known to describe the entity type; transmitting, from the first machine, the first consolidated set of gradients to the global parameter server; and receiving, by the first machine, a second set of global parameters from the global parameter server, wherein the second set of global parameters is a modification of the first set of global parameters based on the first consolidated set of gradients. 9. The computer program product of claim 8, wherein the method further comprises: receiving, by a second machine, the first set of global parameters from the global parameter server; executing, by multiple learner processors in the second machine, the algorithm using the first set of global parameters and a second mini-batch of data known to describe the entity type; generating, by the second machine, a second consolidated set of gradients that describe a direction for the first set of global parameters in order to improve the accuracy of the algorithm in modeling the entity type when using the first set of global parameters; transmitting, from the second machine, the second consolidated set of gradients to the global parameter server; and receiving, by the first machine and the second machine, a third set of global parameters from the global parameter server, wherein the third set of global parameters is a modification of the first set of global parameters based on the first consolidated set of gradients and the second consolidated set of gradients. 10. The computer program product of claim 9, wherein the method further comprises: testing, by a third machine, a set of unknown data using the third set of global parameters in order to determine whether the set of unknown data matches the entity type. 11. The computer program product of claim 8, wherein the method further comprises: generating each of the first consolidated set of gradients by a different learner processor in the first machine; writing, by each of the multiple learner processors in the first machine, each gradient generated by each of the multiple learner processors in the first machine; and consolidating, by the first machine, gradients generated by all of the multiple learner processors in the first machine in order to create the first consolidated set of gradients. 12. The computer program product of claim 8, wherein the method further comprises: reading, by all of the multiple learner processors in the first machine, the first set of global parameters and the second set of global parameters from a shared memory in the first machine. 13. The computer program product of claim 8, wherein the method further comprises: storing global parameters currently in use by the first machine in a first memory in the first machine; and storing global parameters being downloaded from the global parameter server for future use, in a second memory in the first machine. 14. The computer program product of claim 8, wherein the first set of global parameters further weight results from one or more particular operators used in the algorithm that models the entity type. 15. The computer program product of claim 8, wherein the program instructions are provided as a service in a cloud environment. 16. A computer system comprising one or more processors, one or more computer readable memories, and one or more computer readable storage mediums, and program instructions stored on at least one of the one or more storage mediums for execution by at least one of the one or more processors via at least one of the one or more memories, the stored program instructions comprising: program instructions to receive, by a first machine, a first set of global parameters from a global parameter server, wherein the first set of global parameters comprises data that weights one or more operands used in an algorithm that models an entity type; program instructions to execute, by multiple learner processors in the first machine, the algorithm using the first set of global parameters and a first mini-batch of data known to describe the entity type; program instructions to generate, by the first machine, a first consolidated set of gradients that describes a direction for the first set of global parameters in order to improve an accuracy of the algorithm in modeling the entity type when using the first set of global parameters and the first mini-batch of data known to describe the entity type; program instructions to transmit, from the first machine, the first consolidated set of gradients to the global parameter server; and program instructions to receive, by the first machine, a second set of global parameters from the global parameter server, wherein the second set of global parameters is a modification of the first set of global parameters based on the first consolidated set of gradients. 17. The computer system of claim 16, further comprising: program instructions to receive, by a second machine, the first set of global parameters from the global parameter server; program instructions to execute, by multiple learner processors in the second machine, the algorithm using the first set of global parameters and a second mini-batch of data known to describe the entity type; program instructions to generate, by the second machine, a second consolidated set of gradients that describes a direction for the first set of global parameters in order to improve the accuracy of the algorithm in modeling the entity type when using the first set of global parameters; program instructions to transmit, from the second machine, the second consolidated set of gradients to the global parameter server; and program instructions to receive, by the first machine and the second machine, a third set of global parameters from the global parameter server, wherein the third set of global parameters is a modification of the first set of global parameters based on the first consolidated set of gradients and the second consolidated set of gradients. 18. The computer system of claim 17, further comprising: program instructions to test, by a third machine, a set of unknown data using the third set of global parameters in order to determine whether the set of unknown data matches the entity type. 19. The computer system of claim 16, further comprising: program instructions to generate each of the first consolidated set of gradients by a different learner processor in the first machine; program instructions to write, by each of the multiple learner processors in the first machine, each gradient generated by each of the multiple learner processors in the first machine; and program instructions to consolidate, by the first machine, gradients generated by all of the multiple learner processors in the first machine in order to create the first consolidated set of gradients. 20. The computer system of claim 16, further comprising: program instructions to read, by all of the multiple learner processors in the first machine, the first set of global parameters and the second set of global parameters from a shared memory in the first machine.