Abstract: A method for storing data in a storage system having solid-state memory is provided. The method includes determining portions of the solid-state memory that have a faster access rate and portions of the solid-state memory that have a slower access rate, relative to each other or to a threshold. The method includes writing data bits of erasure coded data to the portions of the solid-state memory having the faster access rate, and writing one or more parity bits of the erasure coded data to the portions of the solid-state memory having the slower access rate. A storage system is also provided.

Abstract: Generating a transformed dataset for use by 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: storing, within one or more storage systems, a transformed dataset generated 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; and 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.

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: 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: Generating a transformed dataset for use by 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: storing, within one or more storage systems, a transformed dataset generated 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; and 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.

Abstract: Provided herein are dibenzocyclooctyne compounds useful as reagents in 1,3-dipolar cycloaddition reactions, and methods for their preparation.

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: Provided herein are dibenzocyclooctyne compounds useful as reagents in 1,3-dipolar cycloaddition reactions, and methods for their preparation.

Abstract: Generating a transformed dataset for use by 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: storing, within one or more storage systems, a transformed dataset generated 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; and 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.

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: 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.

Abstract: Provided herein are dibenzocyclooctyne compounds useful as reagents in 1,3-dipolar cycloaddition reactions, and methods for their preparation.

Abstract: An apparatus for suspending a prosthesis from a residual limb comprises a tubular liner adapted to receive the residual limb. A lanyard is secured at one end to the distal end of the liner. A hollow prosthetic socket defines a well configured to receive the residual limb and includes a socket seal disposed at the distal end of the socket. An upper surface of the socket seal defines a recess opening to the exterior of the socket through a side wall. A sealing element disposed along the length of the lanyard is configured to engage within the passage. The free end of the lanyard is pulled through passage such that the sealing element sealingly engages within the passage for preventing fluid communication through the passage. The liner at least partially contacts the interior surface of the socket for providing a fluid seal and creating negative pressure within the socket.

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: Storage layer data obfuscation, including: determining a subset of a dataset to obfuscate in accordance with a security policy; generating, based at least in part on the security policy, an obfuscated snapshot of the dataset that is representative of the dataset with the subset of the dataset obfuscated; and sending, to a target computer system, the obfuscated snapshot from which a restored version of the dataset includes the subset of the dataset obfuscated.

Abstract: Improving machine learning models in an artificial intelligence infrastructure includes: storing, within one or more storage systems of an artificial intelligence infrastructure, information describing a dataset and one or more transformations applied to the dataset resulting in a transformed dataset; and storing, within the one or more storage systems, information describing only portions of previous versions of a machine learning model that differ from a current version of the machine learning model, wherein the previous versions used the transformed dataset as input during one or more prior executions by the artificial intelligence infrastructure.

Abstract: Ensuring reproducibility in an artificial intelligence infrastructure that includes one or more storage systems and one or more graphical processing unit (‘GPU’) servers, including: identifying, by a unified management plane, one or more transformations applied to a dataset by the artificial intelligence infrastructure, wherein applying the one or more transformations to the dataset causes the artificial intelligence infrastructure to generate a transformed dataset; storing, within the one or more storage systems, information describing the dataset, the one or more transformations applied to the dataset, and the transformed dataset; identifying, by the unified management plane, one or more machine learning models executed by the artificial intelligence infrastructure using the transformed dataset as input; and storing, within the one or more storage systems, information describing one or more machine learning models executed using the transformed dataset as input.

Abstract: A method of applying a data format in a direct memory access transfer is provided. The method includes distributing user data throughout a plurality of storage nodes through erasure coding, wherein the plurality of storage nodes are housed within a single chassis that couples the storage nodes as a cluster, each of the plurality of storage nodes having nonvolatile solid-state memory for user data storage. The method includes reading a self-describing data portion from a first memory of the nonvolatile solid-state memory and extracting a destination from the self-describing data portion. The method includes writing data, from the self-describing data portion, to a second memory of the nonvolatile solid-state memory according to the destination.

Abstract: Provided herein are dibenzocyclooctyne compounds useful as reagents in 1,3-dipolar cycloaddition reactions, and methods for their preparation.