DistributedTransport¶
- class torchrl.weight_update.DistributedTransport(*, weights_buffer: TensorDictBase, store: Store = None, rank: int | None = None, sync: bool = True)[source]¶
torch.distributed transport for communicating with a single distributed worker.
This transport handles weight updates for ONE specific distributed worker via torch.distributed send/recv. Multiple transports are created for multiple workers, following the same pattern as multiprocess collectors.
- receive_initial_weights() Any[source]¶
Receive initial weights during connect() without TCPStore signaling.
This is used for the initial weight sync during connect() to avoid interfering with the main collection loop’s TCPStore-based coordination.
- Returns:
The received weights TensorDict.
- receive_weights(timeout: float | None = None, *, weights: Any = None, model: Any = None, strategy: torchrl.weight_update.weight_sync_schemes.WeightStrategy | None = None) Any | None[source]¶
Receive weights via torch.distributed and apply them to the model.
The surrounding collector loop is responsible for checking the TCPStore for the "update_weights" instruction. When this method is called we assume that a weight update has been requested and the sender has already performed the corresponding
send().- Parameters:
timeout – Maximum time to wait for weights (seconds). If None, blocks until weights are received.
weights – Pre-allocated weight buffer to receive into.
model – The model to apply weights to.
strategy – Strategy for applying weights to the model.
- Returns:
The received weights, or None if timeout expires.
- send_initial_weights(weights: Any) None[source]¶
Send initial weights during connect() without TCPStore signaling.
This is used for the initial weight sync during connect() to avoid interfering with the main collection loop’s TCPStore-based coordination.
- send_weights_async(weights: Any) None[source]¶
Send weights to distributed worker without waiting for acknowledgment.
Use wait_ack() to wait for acknowledgment after sending to all workers.
- setup_connection_and_weights_on_receiver(*, worker_idx: int, weights: Any = None, model: Any = None, strategy: torchrl.weight_update.weight_sync_schemes.WeightStrategy | None = None) Any[source]¶
No-op for DistributedTransport - handled by scheme.
