Data transformation for a machine learning model
Data transformation caching in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: identifying, in dependence upon one or more machine learning models to be executed on the GPU servers, one or more transformations to apply to a dataset; generating, in dependence upon the one or more transformations, a transformed dataset; storing, within one or more of the storage systems, the transformed dataset; receiving a plurality of requests to transmit the transformed dataset to one or more of the GPU servers; and responsive to each request, transmitting, from the one or more storage systems to the one or more GPU servers without re-performing the one or more transformations on the dataset, the transformed dataset.
1. A method comprising:
generating a transformed dataset by applying one or more transformations to a dataset that are identified based on one or more expected input formats of data received as input data by one or more machine learning models to be executed on one or more servers;
storing, within one or more storage systems, the transformed dataset; and
responsive to receiving one or more requests to transmit the transformed dataset to one or more servers, transmitting, from the one or more storage systems to the one or more servers without reapplying the one or more transformations on the dataset, the transformed dataset including data in the one or more expected formats of data to be received as input data by the one or more machine learning models.
2. The method of claim 1 wherein generating, in dependence upon one or more transformations to be executed on one or more servers, a transformed dataset by applying the one or more transformations to a dataset further comprises generating, by a storage system in dependence upon the one or more transformations, transformed dataset.
3. The method of claim 1 wherein transmitting, from the one or more storage systems to the one or more servers without re-applying the one or more transformations on the dataset, the transformed dataset further comprises transmitting the transformed dataset from the one or more storage systems directly to application memory on a server.
4. The method of claim 3 wherein transmitting the transformed dataset from the one or more storage systems directly to application memory on the server further comprises transmitting the transformed data dataset from the one or more storage systems to the server via remote direct memory access (‘RDMA’).
5. The method of claim 1 further comprising executing, by one or more of the servers, one or more machine learning algorithms associated with the machine learning model using the transformed dataset as input.
6. The method of claim 1 further comprising:
scheduling, by a unified management plane, one or more transformations for one or more of the storage systems to apply to the dataset; and
scheduling, by the unified management plane, execution of one or more machine learning algorithms associated with the machine learning model by the one or more servers.
7. The method of claim 1 further comprising providing, by a unified management plane to the one or more servers, information describing the dataset, the one or more transformations applied to the dataset, and the transformed dataset.
8. An artificial intelligence infrastructure configured to carry out steps of:
generating a transformed dataset by applying one or more transformations to a dataset that are identified based on one or more expected input formats of data received as input data by one or more machine learning models to be executed on one or more servers;
storing, within one or more storage systems, the transformed dataset; and
responsive to receiving one or more requests to transmit the transformed dataset to one or more servers, transmitting, from the one or more storage systems to the one or more servers without reapplying the one or more transformations on the dataset, the transformed dataset including data in the one or more expected formats of data to be received as input data by the one or more machine learning models.
9. The artificial intelligence infrastructure of claim 8 wherein generating, in dependence upon one or more transformations to be executed on one or more servers, a transformed dataset by applying the one or more transformations to a dataset further comprises generating, by a storage system in dependence upon the one or more transformations, transformed dataset.
10. The artificial intelligence infrastructure of claim 8 wherein transmitting, from the one or more storage systems to the one or more servers without re-applying the one or more transformations on the dataset, the transformed dataset further comprises transmitting the transformed dataset from the one or more storage systems directly to application memory on a server.
11. The artificial intelligence infrastructure of claim 10 wherein transmitting the transformed dataset from the one or more storage systems directly to application memory on the server further comprises transmitting the transformed data dataset from the one or more storage systems to the server via remote direct memory access (‘RDMA’).
12. The artificial intelligence infrastructure of claim 8 wherein the artificial intelligence infrastructure is further configured to carry out the step of executing, by one or more of the servers, one or more machine learning algorithms associated with the machine learning model using the transformed dataset as input.
13. The artificial intelligence infrastructure of claim 8 wherein the artificial intelligence infrastructure is further configured to carry out the steps of:
scheduling, by a unified management plane, one or more transformations for one or more of the storage systems to apply to the dataset; and
scheduling, by the unified management plane, execution of one or more machine learning algorithms associated with the machine learning model by the one or more servers.
14. The artificial intelligence infrastructure of claim 8 wherein the artificial intelligence infrastructure is further configured to carry out the step of providing, by a unified management plane to the one or more servers, information describing the dataset, the one or more transformations applied to the dataset, and the transformed dataset.
15. An apparatus comprising a computer processor, a computer memory operatively coupled to the computer processor, the computer memory having disposed within it computer program instructions that, when executed by the computer processor, cause the apparatus to carry out steps of:
generating a transformed dataset by applying one or more transformations to a dataset that are identified based on one or more expected input formats of data received as input data by one or more machine learning models to be executed on one or more servers;
storing, within one or more storage systems, the transformed dataset; and
responsive to receiving one or more requests to transmit the transformed dataset to one or more servers, transmitting, from the one or more storage systems to the one or more servers without reapplying the one or more transformations on the dataset, the transformed dataset including data in the one or more expected formats of data to be received as input data by the one or more machine learning models.
16. The apparatus of claim 15 wherein generating, in dependence upon one or more transformations to be executed on one or more servers, a transformed dataset by applying the one or more transformations to a dataset further comprises generating, by a storage system in dependence upon the one or more transformations, transformed dataset.
17. The apparatus of claim 15 wherein transmitting, from the one or more storage systems to the one or more servers without re-applying the one or more transformations on the dataset, the transformed dataset further comprises transmitting the transformed dataset from the one or more storage systems directly to application memory on a server.
18. The apparatus of claim 15 further comprising computer program instructions that, when executed by the computer processor, cause the apparatus to carry out the steps of:
scheduling, by a unified management plane, one or more transformations for one or more of the storage systems to apply to the dataset; and
scheduling, by the unified management plane, execution of one or more machine learning algorithms associated with the machine learning model by the one or more servers.
19. The apparatus of claim 15 further comprising computer program instructions that, when executed by the computer processor, cause the apparatus to carry out the step of providing, by a unified management plane to the one or more servers, information describing the dataset, the one or more transformations applied to the dataset, and the transformed dataset.
20. The apparatus of claim 15 further comprising computer program instructions that, when executed by the computer processor, cause the apparatus to carry out the step of executing, by one or more of the servers, one or more machine learning algorithms associated with the machine learning model using the transformed dataset as input.