Ai model development method and apparatus

An artificial intelligence (AI) model development method, comprising:

• obtaining, by a development platform, platform indication information, service indication information, and parameter requirement information of a to-be-generated AI model, wherein the platform indication information is used to indicate a running platform on which the to-be-generated AI model is run, the service indication information is used to indicate a first service to be executed by the to-be-generated AI model, and the parameter requirement information is used to indicate a requirement on a performance parameter of the to-be-generated AI model;
• selecting, by the development platform from an operator database, a plurality of available operators that correspond to the running platform and that are used to execute the first service, wherein the operator database stores a plurality of operators used to generate an AI model;
• selecting, by the development platform, an operator from the plurality of available operators, and setting a parameter of the selected operator, to generate a first candidate policy, wherein the first candidate policy comprises a plurality of operators;
• performing, by the development platform, sample training on the first candidate policy to obtain a first candidate AI model;
• invoking, by the development platform, a simulator component of the running platform to run the first candidate AI model to execute processing on test data of the first service, so as to obtain a first running parameter;
• and
• if the first running parameter matches the parameter requirement information, determining, by the development platform, that the first candidate AI model is a target AI model.

A development platform, wherein the development platform comprises a processor, a memory, and a plurality of simulator components;

• the memory and the plurality of simulator components are coupled to the processor;
• the plurality of simulator components comprise a simulator component of a running platform;
• the memory is configured to store computer program code;
• the computer program code comprises a computer instruction;
• and when the processor executes the foregoing computer instruction,
• the processor is configured to:
• obtain platform indication information, service indication information, and parameter requirement information of a to-be-generated artificial intelligence AI model, wherein the platform indication information is used to indicate a running platform on which the to-be-generated AI model is run, the service indication information is used to indicate a first service to be executed by the to-be-generated AI model, and the parameter requirement information is used to indicate a requirement on a performance parameter of the to-be-generated AI model;
• select, from an operator database, a plurality of available operators that correspond to the running platform and that are used to execute the first service, wherein the operator database stores a plurality of operators used to generate an AI model;
• select an operator from the plurality of available operators, and set a parameter of the selected operator, to generate a first candidate policy, wherein the first candidate policy comprises a plurality of operators;
• and perform sample training on the first candidate policy to obtain a first candidate AI model;
• and
• the processor is further configured to:
• invoke the simulator component of the running platform in the plurality of simulator components to run the first candidate AI model to execute processing on test data of the first service, so as to obtain a first running parameter;
• and if the first running parameter matches the parameter requirement information, determine that the first candidate AI model is a target AI model.

A development platform, comprising a constraint library, a policy generator, an artificial intelligence (AI) model generator, an evaluator, a code generator, and a plurality of simulator components, wherein

• the constraint library is configured to provide platform indication information, service indication information, and parameter requirement information of a to-be-generated AI model, wherein the platform indication information is used to indicate a running platform on which the to-be-generated AI model is run, the service indication information is used to indicate a first service to be executed by the to-be-generated AI model, and the parameter requirement information is used to indicate a requirement on a performance parameter of the to-be-generated AI model;
• the policy generator is configured to:
• select, from an operator database based on the platform indication information and the service indication information that are provided by the constraint library, a plurality of available operators that correspond to the running platform and that are used to execute the first service, wherein the operator database stores a plurality of operators used to generate an AI model;
• and select an operator from the plurality of available operators, and set a parameter of the selected operator, to generate a first candidate policy, wherein the first candidate policy comprises a plurality of operators;
• the AI model generator is configured to perform sample training on the first candidate policy generated by the policy generator, to obtain a first candidate AI model;
• the evaluator is configured to:
• invoke the simulator component of the running platform to run the first candidate AI model generated by the AI model generator, to execute processing on test data of the first service, so as to obtain a first running parameter;
• and determine whether the first running parameter matches the parameter requirement information provided by the constraint library, wherein the simulator component of the running platform is one of a plurality of simulator components;
• and
• the code generator is configured to:
• if the evaluator determines that the first running parameter matches the parameter requirement information, generate, based on the first candidate AI model generated by the AI model generator, model code runnable on the running platform.