Systems and methods for process execution
Systems and methods disclose herein procedures for accelerated tree learning. In one class, the acceleration is based on self-adapting learning rates, while in another class, the acceleration is based on a plurality of learning rates, wherein each learning rate varies over the training; each learning rate increases linearly as a respective pseudo residual maintains a direction across sequential training iterations; and each learning rate decreases exponentially as the respective pseudo residual changes direction across sequential training iterations. The latter can be incorporated with other methodologies, such as momentum-augmented gradient boosting and Nesterov Accelerated Gradient Boosting. These systems and methods for accelerated tree learning exhibit a marked reduction in training time and resources required for gradient boosted trees.
1 . A computing apparatus, comprising:
a processor; and
a memory storing instructions that, when executed by the processor, configure the apparatus to:
obtain a request to trigger a deterministic process associated with an in-memory database stored in a random access memory;
determine that a result associated with the deterministic process does not exist in-memory;
in response to the determination that the result does not exist in-memory:
gather dependencies of the deterministic process; and
generate a hash for the gathered dependencies of the deterministic process;
determine whether the hash exists on a disk associated with the in-memory database;
where the hash exists on the disk:
retrieve the results from disk; and
where the hash does not exist on the disk:
trigger the deterministic process to generate results.
2 . The computing apparatus of claim 1 , wherein the memory stores instructions that, when executed by the processor, further configure the apparatus to:
store the generated results in-memory and/or on-disk: and
return the generated results,
where the hash does not exist on the disk.
3 . The computing apparatus of claim 1 , wherein the deterministic process is associated with the in-memory database comprising integrated analytics.
4 . The computing apparatus of claim 1 , wherein the deterministic process is associated with the in-memory database comprising a supply chain algorithm.
5 . The computing apparatus of claim 1 , wherein the deterministic process is associated with an in-memory versioned database.
6 . The computing apparatus of claim 1 , wherein the deterministic process comprises an embedded algorithm.
7 . A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that, when executed by a computer, cause the computer to:
obtain, by a processor, a request to trigger a deterministic process associated with an in-memory database stored in a random access memory;
determine that a result associated with the deterministic process does not exist in-memory;
in response to the determination that the result does not exist in-memory:
gather dependencies of the deterministic process; and
generate a hash for the gathered dependencies of the deterministic process;
determine whether the hash exists on a disk associated with the in-memory database;
where the hash exists on the disk:
retrieve, by the processor, the results from disk; and
where the hash does not exist on the disk:
trigger the deterministic process to generate results.
8 . The non-transitory computer-readable storage medium of claim 7 , further comprising instructions that, when executed by the processor, cause the computer to:
store the generated results in-memory and/or on-disk: and
return the generated results,
where the hash does not exist on the disk.
9 . The non-transitory computer-readable storage medium of claim 7 , wherein the deterministic process is associated with the in-memory database comprising integrated analytics.
10 . The non-transitory computer-readable storage medium of claim 7 , wherein the deterministic process is associated with the in-memory database comprising a supply chain algorithm.
11 . The non-transitory computer-readable storage medium of claim 7 , wherein the deterministic process is associated with an in-memory versioned database.
12 . The non-transitory computer-readable storage medium of claim 7 , wherein the deterministic process comprises an embedded algorithm.
13 . A computer-implemented method of process execution, comprising:
obtaining, by a processor, a request to trigger a deterministic process associated with an in-memory database stored in a random access memory;
determining, by the processor, that a result associated with the deterministic process does not exist in-memory;
in response to the determination that the result does not exist in-memory:
gathering, by the processor, one or more dependencies of the deterministic process; and
generating, by the processor, a hash for the one or more dependencies of the deterministic process;
determining, by the processor, whether the hash exists on a disk associated with the in-memory database;
where the hash exists on the disk:
retrieving, by the processor, the results from disk; and
where the hash does not exist on the disk:
triggering, by the processor, the deterministic process to generate results.
14 . The computer-implemented method of claim 13 , further comprising:
storing, by the processor, the generated results in-memory and/or on-disk: and
returning, by the processor, the generated results,
where the hash does not exist on the disk.
15 . The computer-implemented method of claim 13 , wherein the deterministic process is associated with the in-memory database comprising a supply chain algorithm.
16 . The computer-implemented method of claim 13 , wherein the deterministic process is associated with the in-memory database comprising supply chain algorithms.
17 . The computer-implemented method of claim 13 , wherein the deterministic process is associated with an in-memory versioned database.
18 . The computer-implemented method of claim 13 , wherein the deterministic process comprises an embedded algorithm.