Table Of Contents
Table Of Contents



Creates a new KVStore.

For single machine training, there are two commonly used types:

local: Copies all gradients to CPU memory and updates weights there.

device: Aggregates gradients and updates weights on GPUs. With this setting, the KVStore also attempts to use GPU peer-to-peer communication, potentially accelerating the communication.

For distributed training, KVStore also supports a number of types:

dist_sync: Behaves similarly to local but with one major difference. With dist_sync, batch-size now means the batch size used on each machine. So if there are n machines and we use batch size b, then dist_sync behaves like local with batch size n * b.

dist_device_sync: Identical to dist_sync with the difference similar to device vs local.

dist_async: Performs asynchronous updates. The weights are updated whenever gradients are received from any machine. No two updates happen on the same weight at the same time. However, the order is not guaranteed.


name ({'local', 'device', 'nccl', 'dist_sync', 'dist_device_sync', 'dist_async'}) – The type of KVStore.


kv – The created KVStore.

Return type