Abstract: An apparatus for artificial intelligence acceleration is provided. The apparatus includes a storage and compute system having a distributed, redundant key value store for metadata. The storage and compute system having distributed compute resources configurable to access, through a plurality of authorities, data in the solid-state memory, run inference with a deep learning model, generate vectors for the data and store the vectors in the key value store.

Abstract: Accelerating artificial intelligence workflows, including: receiving, from a computing process of an artificial intelligence workflow, a request for information stored on a data repository; issuing, from a user space of an operating system environment, parallel requests to the data repository using a network protocol that operates serially at the kernel level of the operating system environment; receiving, from the data repository, one or more responses to the parallel requests; and providing, to the computing process of the artificial intelligence workflow and based on the one or more responses to the parallel requests, a response to the request for information.

Abstract: Providing persistent storage to transient cloud computing services, including: creating a cloud computing instance, wherein the cloud computing instance is created on on-premises cloud infrastructure; and storing, in non-volatile storage in a storage system that is communicatively coupled to the on-premises cloud infrastructure, data associated with the cloud computing instance.

Abstract: Nonsequential readahead based on data access patterns, the method comprising: determining a set of access patterns for stored content; determining, based on the set of access patterns, a list of storage locations for content expected to be used; and prefetching, based on the list of storage locations for content expected to be used, one or more data objects.

Abstract: Executing a machine learning model in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: receiving, by a graphical processing unit (‘GPU’) server, a dataset transformed by a storage system that is external to the GPU server; and executing, by the GPU server, one or more machine learning algorithms using the transformed dataset as input.

Abstract: Nonsequential readahead for deep learning training that includes: receiving an indication of a list of batch storage locations for a batch of data objects; prefetching, for each storage location in the list of batch storage locations, storage content corresponding to the batch of data objects; and storing the storage content corresponding to the batch of data objects within a cache accessible to an artificial intelligence workflow.

Abstract: Providing persistent storage to transient cloud computing services, including: creating a cloud computing instance, wherein the cloud computing instance is created on on-premises cloud infrastructure; and storing, in non-volatile storage in a storage system that is communicatively coupled to the on-premises cloud infrastructure, data associated with the cloud computing instance.

Abstract: Nonsequential readahead based on data access patterns, the method comprising: determining a set of access patterns for stored content; determining, based on the set of access patterns, a list of storage locations for content expected to be used; and prefetching, based on the list of storage locations for content expected to be used, one or more data objects.

Abstract: Nonsequential readahead for deep learning training that includes: receiving an indication of a list of batch storage locations for a batch of data objects; prefetching, for each storage location in the list of batch storage locations, storage content corresponding to the batch of data objects; and storing the storage content corresponding to the batch of data objects within a cache accessible to an artificial intelligence workflow.

Abstract: Creating batches of training data for machine learning workflows, including: selecting, by an artificial intelligence and machine learning infrastructure system in accordance with a batch building policy, a subset of data objects stored in a data storage system, wherein the batch building policy describes parameters for selecting data types; and providing, to a deep learning computing system by the artificial intelligence and machine learning infrastructure system, one or more data objects that include the subset of data objects selected based on the parameters for selecting data types described by the batch building policy.

Abstract: Methods, apparatuses, and systems for ensuring timely restoration of an application, including: determining, based on a plurality of factors, a projected time to complete an application recovery operation from a first execution environment to a second execution environment and generating a report based on the projected time to complete the application recovery operation.

Abstract: Batch building for artificial intelligence workflows, including: issuing, responsive to a request for a batch of data objects, requests to a data repository for multiple data objects stored among one or more directories; selecting, in accordance with a batch building policy, a subset of data objects based on one or more responses to the requests; and providing, to the artificial intelligence workflow, a batch of data objects that includes the subset of data objects.

Abstract: Executing a machine learning model in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: receiving, by a graphical processing unit (‘GPU’) server, a dataset transformed by a storage system that is external to the GPU server; and executing, by the GPU server, one or more machine learning algorithms using the transformed dataset as input.

Abstract: Application placement for distributed applications, including: identifying, from amongst a plurality of disparate storage environments, a storage environment that contains data that can be utilized by a portion of a distributed application; and initiating execution of the portion of the distributed application in an execution environment that is communicatively coupled to the storage environment.

Abstract: Data transformation offloading in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: storing, within the storage system, a dataset; identifying, in dependence upon one or more machine learning models to be executed on the GPU servers, one or more transformations to apply to the dataset; and generating, by the storage system in dependence upon the one or more transformations, a transformed dataset.

Abstract: An apparatus for artificial intelligence acceleration is provided. The apparatus includes a storage and compute system having a distributed, redundant key value store for metadata. The storage and compute system having distributed compute resources configurable to access, through a plurality of authorities, data in the solid-state memory, run inference with a deep learning model, generate vectors for the data and store the vectors in the key value store.

Abstract: A storage system has storage devices and a storage controller that includes a processing device. The processing device is to receive a request for portions of data stored at locations of one or more storage devices. The processing device is to generate a file at a storage device. The file corresponds to a view of the portions of data stored at the locations of the one or more storage devices. The processing device is to identify the portions of data that are stored at the locations of the one or more storage devices. The processing device is to provide the view comprising the portions of data.