Improve Python code (multiprocessing, asyncio, threading) + docker
34 USD 1 .The code receives the parameter at the start - % of the processor load.50% and use it in the work.
2ndAdd processing of several tasks at the same time (until the processor is loaded to the target value)
ThreeThe code downloads the "models" - when the model is failed to download or start - we drop to the previous version
4 ."Return" use of recorded models (caused by slow initialization)
and 5.Save the whole code in one file
6 .Correct display of the current state with regard to multi-precision
7 .In the case of the flow of "finding" - after ~ hour it is forced to complete
8 .Removal of unused models from memory after an hour of inactivity
The 9.Docker image if the server does not transmit new tasks within 20 minutes - stop and download everything
The 10.Temporary files (e.g. uploaded audio) – carefully delete (with the view of multi-precision)
11 of 11.We do not send the request to the suitcase without the availability.Comment: The server for the "Next" request returns the ID of the next task within a few seconds.If it does not respond - there are no tasks (as the server itself will send a new team without a request from the client).The image for the PC should work constantly and in case of failure to be recovered.Control the use of operating memory: vosk can consume a lot of resources
Socket Server to test to collect on its own (logic is simple: after receiving the next command will issue a new task (or will not issue), also will issue a new task immediately after the connection (if there is)
Applications 1
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