Abstract: The present application discloses a data caching method, system and device in an AI cluster, and a computer medium. The method comprises: determining a target data set to be cached; obtaining a weight value of the target data set on each cluster node in the AI cluster, determining a target cluster node for caching the target data set; obtaining a target shortest path from remaining cluster nodes comprising nodes except the target cluster node in the AI cluster, and on the basis of the weight value, the target shortest path, and the preceding node, determining a cache path for caching the target cluster node, so as to cache the target data set to the target cluster node according to the cache path.

Abstract: A mass file retrieval method and apparatus based on an AI training platform, and a device. The method includes: an AI training platform acquiring a retrieval task issued by a user, the AI training platform generating a retrieval thread flow according to the retrieval task, and controlling a service logic of a retrieval process according to the retrieval thread flow; and the AI training platform, performing sequential encoding on files in a database in units of folders, generating ordered queue folders, extracting retrieval key words from the retrieval task, and performing keyword retrieval on each ordered queue folder by combining a binary search and depth-first traversal.

Abstract: A mass file retrieval method and apparatus based on an AI training platform, and a device. The method includes: an AI training platform acquiring a retrieval task issued by a user, the AI training platform generating a retrieval thread flow according to the retrieval task, and controlling a service logic of a retrieval process according to the retrieval thread flow; and the AI training platform, performing sequential encoding on files in a database in units of folders, generating ordered queue folders, extracting retrieval key words from the retrieval task, and performing keyword retrieval on each ordered queue folder by combining a binary search and depth-first traversal.