Abstract: A deep learning job scheduling method includes obtaining a job request of a deep learning job, determining a target job description file template from a plurality of pre-stored job description file templates based on the job request, determining an identifier of a target job basic image from identifiers of a plurality of pre-stored job basic images based on the job request, generating a target job description file based on the target job description file template and the identifier of the target job basic image, sending the target job description file to a container scheduler, and selecting the target job basic image from the pre-stored job base images based on the target job description file, and creating at least one container for executing the job request.

Abstract: A data labeling method and apparatus, a computing device, and a storage medium are provided, which belong to the field of artificial intelligence technologies. The method includes: an AI platform determines a plurality of hard examples in an unlabeled image set and hard example attributes of the plurality of hard examples, where the hard example attribute includes a hard example coefficient; the AI platform displays at least one hard example in the plurality of hard examples and a corresponding hard example attribute to a user by using a display interface; and the AI platform obtains a labeling result obtained after the user confirms the at least one hard example in the display interface based on hard example coefficients of the plurality of hard examples. According to this application, data labeling efficiency is improved, and AI model optimization efficiency is improved.

Abstract: A deep learning job scheduling method includes obtaining a job request of a deep learning job, determining a target job description file template from a plurality of pre-stored job description file templates based on the job request, determining an identifier of a target job basic image from identifiers of a plurality of pre-stored job basic images based on the job request, generating a target job description file based on the target job description file template and the identifier of the target job basic image, sending the target job description file to a container scheduler, and selecting the target job basic image from the pre-stored job base images based on the target job description file, and creating at least one container for executing the job request.

Abstract: A streaming application upgrading method includes obtaining an updated logical model of a streaming application, and determining a to-be-adjusted stream by comparing the updated logical model with an initial logical model; generating an upgrading instruction according to the to-be-adjusted stream; and delivering the generated upgrading instruction to a worker node, so that the worker node adjusts, according to an indication of the upgrading instruction, a stream between PEs distributed on the worker node.

Abstract: A streaming application upgrading method and a stream computing system, where the method includes obtaining a updated logical model of a streaming application, determining a to-be-adjusted stream by comparing the updated logical model with an initial logical model, generating an upgrading instruction according to the to-be-adjusted stream, and delivering the generated upgrading instruction to a worker node such that the worker node adjusts, according to an indication of the upgrading instruction, a stream between process elements (PEs) distributed on the worker node. The method provided in the present disclosure can upgrade the streaming application online without interrupting a service.

Abstract: A streaming application upgrading method and a stream computing system, where the method includes obtaining a updated logical model of a streaming application, determining a to-be-adjusted stream by comparing the updated logical model with an initial logical model, generating an upgrading instruction according to the to-be-adjusted stream, and delivering the generated upgrading instruction to a worker node such that the worker node adjusts, according to an indication of the upgrading instruction, a stream between process elements (PEs) distributed on the worker node. The method provided in the present disclosure can upgrade the streaming application online without interrupting a service.