Abstract: Provided are a streaming media processing method based on inference service, an electronic device, and a storage medium, which relates to the field of artificial intelligence, and in particular, to the field of inference service of artificial intelligence models. The method includes: detecting, in a process of processing a k-th channel of streaming media through an i-th inference service pod, the i-th inference service pod, to obtain a detection result of the i-th inference service pod, i and k being positive integers; determining a replacement object of the i-th inference service pod, in the case where it is determined that the i-th inference service pod is in an abnormal state based on the detection result of the i-th inference service pod; and processing the k-th channel of streaming media through the replacement object of the i-th inference service pod.

Abstract: Provided are an inference service deployment method, a device and a storage medium, relating to the field of artificial intelligence technology, and in particular to the field of machine learning and inference service technology. The inference service deployment method includes: obtaining performance information of a runtime environment of a deployment end; selecting a target version of an inference service from a plurality of candidate versions of the inference service of a model according to the performance information of the runtime environment of the deployment end; and deploying the target version of the inference service to the deployment end.

Abstract: The invention relates to a field of nuclear reactor fuel temperature measurement and discloses a method of measuring nuclear reactor fuel temperature, comprising: S1: collect fission gas produced by nuclear reactors through a gas collection device, S2: measure pressure value and temperature value of the fission gas through pressure and temperature sensors, S3: obtain the corresponding fuel temperature by calculating the pressure value and temperature value, and the invention provides a method of measuring nuclear reactor fuel temperature, and it collects the fission gas discharged by fuels through a fission gas collection device, utilizes the sensitive relevance in a specific temperature range between the release amount of metal fuel fission gas and fuel temperature changes, and makes the pressure of metal fuel fission gas correspond to fuel temperature, thus to convert fuel temperature measurement which is difficult to achieve into fission gas pressure measurement which is easy to achieve.

Abstract: A method for model production includes acquiring a related operation for model production from a user interface layer of a model production system, and determining a software platform of the model production system; acquiring a model service corresponding to the related operation by invoking an application programming interface (API) corresponding to the related operation, wherein the API is located between the user interface layer and other layer in the model production system; performing the model service by invoking local resources of the software platform with a tool of the software platform adapted to the model service, to generate a target model; and applying the target model in a target usage scene.

Abstract: A method for running an inference service platform, includes: determining inference tasks to be allocated for the inference service platform, in which the inference service platform includes two or more inference service groups, versions of the inference service groups are different, and the inference service groups are configured to perform a same type of inference services; determining a flow weight of each of the inference service groups, in which the flow weight is configured to indicate a proportion of a number of inference tasks to which the corresponding inference service group need to be allocated in a total number of inference tasks; and allocating the corresponding number of inference tasks in the inference tasks to be allocated to each of the inference service groups based on the flow weight of each of the inference service groups; and performing the inference tasks by the inference service group.

Abstract: A method for management of an artificial intelligence development platform is provided. The artificial intelligence development platform is deployed with instances of a plurality of model services, and each of the model services is provided with one or more instances. The method includes: acquiring calling information of at least one model service; determining the activity of the at least one model service according to the calling information; and at least deleting all instances of the at least one model service in response to that the determined activity meets a first condition.

Abstract: A resource scheduling method and apparatus, a device, and a storage medium are provided, and relates to the field of computer technology, and in particular to the field of deep learning technology. The method includes: acquiring a graphics processing unit (GPU) topology relationship of a cluster according to GPU connection information of each of computing nodes in the cluster; and in a case where a task request, for applying for a GPU resource, for a target task is received, determining a target computing node of the target task and a target GPU in the target computing node according to the task request and the GPU topology relationship, to complete GPU resource scheduling of the target task. The present disclosure can optimize the resource scheduling.

Abstract: A method for management of an artificial intelligence development platform is provided. The artificial intelligence development platform is deployed with instances of a plurality of model services, and each of the model services is provided with one or more instances. The method includes: acquiring calling information of at least one model service; determining the activity of the at least one model service according to the calling information; and at least deleting all instances of the at least one model service in response to that the determined activity meets a first condition.

Abstract: The present invention provides a dual-machine hot standby disaster tolerance system for network service in virtualized environment. The system comprises a main server and a standby server, and the main server and the standby server are connected via network; a main VM runs on the main server; a standby VM runs on the standby server; the standby VM is in the alternative state of the application layer semantics of the main VM; the alternative state of the application layer semantics means that the standby VM can serve instead of the main server in view of the application layer semantics, and generate the correct output for any client request. The outputs of the main VM and standby VM are compared according to the alternative rule in order to determine whether a backup is needed, therefore efficiently reducing the backup frequency, and improving the system performance on the basis of ensuring rapid recovery; the present invention greatly reduces the system overhead and increases the system throughput.