Unifying a quota representation and management for heterogenous resources

A computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:

• receive a user request with resource specifications from an external application;
• extract select resource management sections from the received user request;
• classify the extracted select resource management sections using a first machine learning (ML) model which is trained using a historical classification dataset;
• map the classified extracted select resource management sections to at least one integer value by using a second ML model which is trained using a historical integer value dataset;
• deduct the at least one integer value from a quota management tree to determine configuration specifications;
• and
• execute accelerators using the configuration specifications for artificial intelligence (AI) workloads.

A system comprising:

• a processor set, one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions executable to:
• receive a user request with resource specifications from an external application;
• extract select resource management sections from the received user request;
• classify the extracted select resource management sections using a first machine learning (ML) model which is trained using a historical classification dataset;
• map the classified extracted select resource management sections to at least one integer value by using a second ML model which is trained using a historical integer value dataset;
• deduct the at least one integer value from a quota management tree to determine configuration specifications;
• and
• execute accelerators using the configuration specifications for artificial intelligence (AI) workloads,
• wherein the first ML model comprises a decision tree model which utilizes a decision tree algorithm to classify the extracted select resource management sections.