Saving a PyTorch models state_dict into redis cache
I’m building a distributed parameter/server type architecture and wanting to communicate model updates through table solutions on Azure.
I’m having a hard time finding any useful information about saving a PyTorch models state_dict into a redis cache. I’ve given up on Azure Cosmos tables because of the size limit (64kb) per entity and looked toward redis since model state_dict params/weights are much larger, even for a small model.
Does anyone have any recommendations for me on how to pursue this? Or if this is even possible?

My solution (after @GuyKorland commented above) was RedisAI.
I implemented key-value mehanism for model data and communicated it that way between VMs.
for name, param in model.named_parameters():
    redisai_client.tensorset(f'{name}',param.data.numpy().cpu().detach())

