songtrace serve is long-lived but only wants the GPU in bursts, and until now
it kept everything it ever loaded. align cached wav2vec2 in a module dict that
was never cleared, torchcrepe caches its model on its own infer() function,
and even once the last reference to a model goes, PyTorch keeps the freed
blocks in its allocator rather than returning them to the driver. On a shared
box that is the whole GPU held against one idle process.
Stages now hand it back as soon as they are done with it. A stage that caches
a model registers how to drop it with @on_release, so models.py needs to know
nothing about the pipeline, and run_pipeline calls release() in a finally
after each stage — not once per job, because transcribe sits between separate
and align waiting on the Whisper server, which can be minutes of a GPU nothing
is using.
release() answers the bytes it handed back and the stage logs a line when that
is not zero, which is the only part of this visible from outside. It will not
initialise CUDA just to clean up after a run that never touched it.
The cost is reloading each model once per job rather than once per process,
seconds against stages that run for minutes. The CUDA context itself stays
until the process exits; releasing that would mean a subprocess per stage.
branderson