Integration of over 50 neural networks into one project

I integrated over 50 neural networks into one open-source project. Here are the things I wish I had known before starting.

Working with Memory

I cannot load one model into VRAM and reuse it for multiple tasks simultaneously. Face swapping, lip sync, and portrait animation all need facial recognition, but you still need three separate model loads. One model, one task, no exceptions. Once I accepted this, the whole architecture became cleaner.

The second surprise, that is CUDA out of memory is not a crash, it is a recoverable state. My solution was to catch the RuntimeError, call torch.cuda.empty_cache() and gc.collect(), wait a randomized delay between 20 and 180 seconds to avoid multiple tasks colliding, and retry. That random interval matters more than it sounds when you have concurrent users.

The third thing that took me too long to figure out like measure available VRAM before loading a model, not after it fails. A simple check with torch.cuda.get_device_properties() and psutil.virtual_memory() before each task launch prevents most failures entirely rather than recovering from them.

For memory-heavy models I introduced micro models, lightweight versions of the same task that queue incoming requests while the large model finishes and unloads. It adds latency but keeps the system stable under real load.

I wrote the full article with code samples for all of this after shipping the project. It covers task queue design, layer-wise model loading across GPU and CPU, module organization by task type, and a few other patterns I ended up using.