RPCTransport#
- class torchrl.weight_update.RPCTransport(collector_info=None, collector_rref=None, collector_class=None, worker_rank=None)[source]#
RPC transport for communicating with a single RPC remote collector.
This transport handles weight updates for ONE specific remote collector via torch.distributed primitives (send/recv) with RPC used for signaling. Multiple transports are created for multiple collectors, following the same pattern as the DistributedCollector.
- receive_weights(timeout: float | None = None, *, weights: Any = None, model: Any = None, strategy: WeightStrategy | None = None) Any | None[source]#
Receive weights from sender using torch.distributed.
- 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_weights(weights: Any) None[source]#
Send weights to the remote collector using torch.distributed.
Uses torch.distributed.send() for the actual weight transfer and RPC for signaling the remote collector to receive.
Order is critical to avoid deadlock: 1. Signal receiver via RPC to start recv() (non-blocking) 2. Send weights via torch.distributed (blocking until recv completes)
- send_weights_async(weights: Any) None[source]#
Send weights to remote collector asynchronously.
Uses torch.distributed.isend() for the actual weight transfer and RPC for signaling. Use wait_ack() to wait for completion.
Order is critical to avoid deadlock: 1. Signal receiver via RPC to start recv() (non-blocking) 2. Send weights via torch.distributed.isend() (non-blocking) 3. wait_ack() waits for both to complete
- setup_connection_and_weights_on_receiver(*, worker_idx: int, weights: Any = None, model: Any = None, strategy: WeightStrategy | None = None) Any[source]#
No-op for RPCTransport - weights are received via receive().