RayTransport

class torchrl.weight_update.RayTransport(*, remote_actor=None, worker_idx: int | None = None, backend: str = 'gloo', connection_info_name: str = 'connection_info', model_id: str | None = None)

Ray transport for communicating with a single Ray actor.

This transport handles weight updates for ONE specific remote actor using torch.distributed for efficient weight transfer. Ray is used for signaling/coordination, while the actual weight data is transferred via torch.distributed send/recv operations.

Multiple transports are created for multiple actors, following the same pattern as multiprocess collectors.

Parameters:

• remote_actor – The Ray actor handle for the remote collector/transform.

• worker_idx (int, optional) – The worker index for this remote actor. Defaults to 0.

• backend (str) – The torch.distributed backend to use (“gloo” or “nccl”). Defaults to “gloo”.

• connection_info_name (str) – Name of the Ray actor storing connection info. Defaults to “connection_info”.

• model_id (str, optional) – The model identifier for weight synchronization.

receive_weights(timeout: float | None = None, *, weights: Any = None, model: Any = None, strategy: torchrl.weight_update.weight_sync_schemes.WeightStrategy | None = None) → Any | None

Receive weights from sender via 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

Send weights to the remote actor via torch.distributed.

This method: 1. Signals the remote actor to start receiving via Ray remote call 2. Sends weights via torch.distributed.isend 3. Waits for both to complete

Parameters:

weights – The weights to send (typically a TensorDict).

send_weights_async(weights: Any) → None

Send weights to Ray actor without waiting for completion.

Use wait_ack() to wait for completion after sending to all actors.

Parameters:

weights – The weights to send (typically a TensorDict).

set_model(model: Any) → None

Set the model for receiving weights.

Parameters:

model – The model to receive weights into.

setup_connection_and_weights_on_receiver(*, worker_idx: int, strategy: torchrl.weight_update.weight_sync_schemes.WeightStrategy | None = None, model: Any | None = None, weights: Any | None = None) → Any

Join torch.distributed process group and receive initial weights.

This method: 1. Retrieves connection info from the shared Ray actor 2. Initializes torch.distributed process group with rank=worker_idx+1 3. Receives weights if model is stateful

Parameters:

• worker_idx (int) – The worker index for this transport.

• strategy (WeightStrategy, optional) – The weight transmission strategy.

• model (nn.Module or compatible, optional) – The model to receive weights for.

• weights (TensorDict, optional) – Pre-allocated buffer for receiving weights.

Returns:

The received weights (TensorDict) if model is stateful, None otherwise.

setup_connection_and_weights_on_sender() → None

Initialize torch.distributed on sender side for this worker’s rank.

This is called by the scheme after it has created the connection info Ray actor. The actual init_process_group happens in the scheme since it’s a collective operation that needs to happen for rank 0.

Note

This method exists for interface compatibility but the real work happens in the scheme’s _setup_distributed_connection_sender().

wait_ack() → None

Wait for Ray actor to finish applying weights.

Raises:

RuntimeError – If no pending future exists (i.e., send_weights_async() was not called before this method).