Abstract: According to implementations of the subject matter described herein, there is provided a solution for predicting the resource usage of the deep learning model. In this solution, information about a deep learning model is obtained, the information comprising first information for describing the deep learning model and second information about an operating environment of a job associated with the deep learning model. The static resource usage of the job is determined based on the first information and a strategy of the job during runtime in the operating environment is determined. Afterwards, resource usage of the job during runtime in the operating environment is predicted based on the strategy and the static resource usage. With this solution, the usage of various resources of the deep learning model, such as computation power consumption, memory consumption, execution time, and the like, under a specific runtime strategy can be accurately predicted.

Abstract: Aspects of the technology described herein can facilitate computing on transient resources. An exemplary computing device may use a task scheduler to access information of a computational task and instability information of a transient resource. Moreover, the task scheduler can schedule the computational task to use the transient resource based at least in part on the rate of data size reduction of the computational task. Further, a checkpointing scheduler in the exemplary computing device can determine a checkpointing plan for the computational task based at least in part on a recomputation cost associated with the instability information of the transient resource. Resultantly, the overall utilization rate of computing resources is improved by effectively utilizing transient resources.

Abstract: In implementations of the subject matter described herein, a new approach for controlling and tracing an object across a plurality of parties is proposed. A rule set may be enabled by the confirmation of a plurality of parties. The rule set may define constraints on operations related to the object. Upon receipt of a request for an operation related to the object, the requested operation may be verified based on the rule set agreed by the plurality of parties. In response to verifying that requested operation is valid, the requested operation may be performed, and a record for the operation may be created and stored in a blockchain database accessible to the plurality of parties.

Abstract: In implementations of the subject matter described herein, a new approach for controlling and tracing an object across a plurality of parties is proposed. A rule set may be enabled by the confirmation of a plurality of parties. The rule set may define constraints on operations related to the object. Upon receipt of a request for an operation related to the object, the requested operation may be verified based on the rule set agreed by the plurality of parties. In response to verifying that requested operation is valid, the requested operation may be performed, and a record for the operation may be created and stored in a blockchain database accessible to the plurality of parties.

Abstract: In implementations of the subject matter described herein, a new approach for controlling and tracing an object across a plurality of parties is proposed. A rule set may be enabled by the confirmation of a plurality of parties. The rule set may define constraints on operations related to the object. Upon receipt of a request for an operation related to the object, the requested operation may be verified based on the rule set agreed by the plurality of parties. In response to verifying that requested operation is valid, the requested operation may be performed, and a record for the operation may be created and stored in a blockchain database accessible to the plurality of parties.

Abstract: Aspects of the technology described herein can facilitate computing on transient resources. An exemplary computing device may use a task scheduler to access information of a computational task and instability information of a transient resource. Moreover, the task scheduler can schedule the computational task to use the transient resource based at least in part on the rate of data size reduction of the computational task. Further, a checkpointing scheduler in the exemplary computing device can determine a checkpointing plan for the computational task based at least in part on a recomputation cost associated with the instability information of the transient resource. Resultantly, the overall utilization rate of computing resources is improved by effectively utilizing transient resources.

Abstract: Aspects of the technology described herein can facilitate computing on transient resources. An exemplary computing device may use a task scheduler to access information of a computational task and instability information of a transient resource. Moreover, the task scheduler can schedule the computational task to use the transient resource based at least in part on the rate of data size reduction of the computational task. Further, a checkpointing scheduler in the exemplary computing device can determine a checkpointing plan for the computational task based at least in part on a recomputation cost associated with the instability information of the transient resource. Resultantly, the overall utilization rate of computing resources is improved by effectively utilizing transient resources.

Abstract: Aspects of the technology described herein can facilitate computing on transient resources. An exemplary computing device may use a task scheduler to access information of a computational task and instability information of a transient resource. Moreover, the task scheduler can schedule the computational task to use the transient resource based at least in part on the rate of data size reduction of the computational task. Further, a checkpointing scheduler in the exemplary computing device can determine a checkpointing plan for the computational task based at least in part on a recomputation cost associated with the instability information of the transient resource. Resultantly, the overall utilization rate of computing resources is improved by effectively utilizing transient resources.