General machine learning model, and model file generation and parsing method

A system for generating a machine learning secondary model file, wherein the machine learning secondary model file comprises a secondary model and a secondary model directory, comprising:

• a model filler configured to obtain a general-purpose machine learning model;
• wherein, the general-purpose machine learning model is generated by integrating a stack data and a heap data, the stack data and the heap data are respectively generated by aggregating the task instructions and the model parameters of task parameters of a machine learning task;
• a model storage optimizer configured to perform storage optimization on the general-purpose machine learning model to generate the secondary model;
• a storage offset calculating module configured to calculate a storage offset of the secondary model;
• a directory generator configured to generate a secondary model directory according to the secondary model and the storage offset of the secondary model;
• and
• a file generator configured to generate a machine learning secondary model file according to the secondary model and the secondary model directory;
• a file generator further configured to obtain a file header and a file tailer of the machine learning secondary model file and generate the machine learning secondary model file according to the file header, the secondary model directory, the general-purpose machine learning model, and the file tail;
• wherein the storage offset calculating module is further configured to:
• obtain a size of a storage space required for each general-purpose machine learning model and a count of the secondary models,
• obtain a storage order of the secondary models, and
• calculate a storage offset of each secondary model according to the size of the storage space required for each secondary model, the count of the secondary models, and the storage order of the secondary models.

A non-transitory computer-readable medium storing instructions that, when executed by a computer, cause the computer to perform operations for generating a machine learning secondary model file, comprising:

• obtaining a general-purpose machine learning model;
• wherein, the general-purpose machine learning model is generated by integrating a stack data and a heap data, the stack data and the heap data are respectively generated by aggregating the task instructions and the model parameters of task parameters of a machine learning task;
• performing storage optimization on the general-purpose machine learning model to generate the secondary model;
• calculating a storage offset of the secondary model;
• generating a secondary model directory according to the secondary model and the storage offset of the secondary model;
• and
• generating a machine learning secondary model file according to the secondary model and the secondary model directory;
• obtaining a file header and a file tailer of the machine learning secondary model file;
• and
• generating the machine learning secondary model file according to the file header, the secondary model directory, the general-purpose machine learning model, and the file tail;
• wherein calculating a storage offset of the secondary model further comprising:
• obtaining a size of a storage space required for each general-purpose machine learning model and a count of the secondary models,
• obtaining a storage order of the secondary models, and
• calculating a storage offset of each secondary model according to the size of the storage space required for each secondary model, the count of the secondary models, and the storage order of the secondary models.