Abstract: In some examples, a data management system generates snapshots in a distributed file system based on a protocol or a user triggered event, The data management system identifies a snappable file in a distributed file system and a first data block in the snappable file, the first data block including data and attribute data. The system scans an index file to access the attribute data of the first data block and initiates construction of a patch file based on the accessed attribute data. The system repeats the scanning of the index file to access attribute data of at least a further second data block, the second data block including data and attribute data, and completes construction of the patch file based on the accessed attribute data of the first and second data blocks. The system generates conversion simulation information by collecting attribute data for all the data blocks of the constructed patch file, and writes the simulation information to a patch file image.

Abstract: In some examples, a data management system generates snapshots in a distributed file system based on a protocol or a user triggered event, The data management system identifies a snappable file in a distributed file system and a first data block in the snappable file, the first data block including data and attribute data. The system scans an index file to access the attribute data of the first data block and initiates construction of a patch file based on the accessed attribute data. The system repeats the scanning of the index file to access attribute data of at least a further second data block, the second data block including data and attribute data, and completes construction of the patch file based on the accessed attribute data of the first and second data blocks. The system generates conversion simulation information by collecting attribute data for all the data blocks of the constructed patch file, and writes the simulation information to a patch file image.

Abstract: Methods, systems, and computer programs are presented for backing up data to a distributed multichannel storage system. One method includes an operation for detecting, by a distributed backup system (DBS), a request for backing up data from a host to the distributed backup system. The DBS sends a request to a backup agent executing at the host to initiate the backup. The backup agent mounts an endpoint for writing to the DBS and executes a script that writes the data for the backup to the endpoint. Further, the method includes operations for creating a file system stack at the DBS associated with the endpoint, storing data received by the file system stack at the DBS, and detecting, by the DBS, an end of the backup. The DBS sends a command to the backup agent to end the backup and terminates the file system stack at the DBS.

Abstract: In some examples, a data management system generates snapshots in a distributed file system based on a protocol or a user triggered event, The data management system identifies a snappable file in a distributed file system and a first data block in the snappable file, the first data block including data and attribute data. The system scans an index file to access the attribute data of the first data block and initiates construction of a patch file based on the accessed attribute data. The system repeats the scanning of the index file to access attribute data of at least a further second data block, the second data block including data and attribute data, and completes construction of the patch file based on the accessed attribute data of the first and second data blocks. The system generates conversion simulation information by collecting attribute data for all the data blocks of the constructed patch file, and writes the simulation information to a patch file image.

Abstract: Makeup identification using deep learning in a digital medium environment is described. Initially, a user input is received to provide a digital image depicting a face which has a desired makeup characteristic. A discriminative neural network is trained to identify and describe makeup characteristics of the input digital image based on data describing differences in visual characteristics between pairs of images, which include a first image depicting a face with makeup applied and a second image depicting a face without makeup applied. The makeup characteristics identified by the discriminative neural network are displayed for selection to search for similar digital images that have the selected makeup characteristic. Once retrieved, the similar digital images can be displayed along with the input digital image having the desired makeup characteristic.

Abstract: Makeup identification using deep learning in a digital medium environment is described. Initially, a user input is received to provide a digital image depicting a face which has a desired makeup characteristic. A discriminative neural network is trained to identify and describe makeup characteristics of the input digital image based on data describing differences in visual characteristics between pairs of images, which include a first image depicting a face with makeup applied and a second image depicting a face without makeup applied. The makeup characteristics identified by the discriminative neural network are displayed for selection to search for similar digital images that have the selected makeup characteristic. Once retrieved, the similar digital images can be displayed along with the input digital image having the desired makeup characteristic.