gigl.distributed.utils.dist_sampler#

Sampler factory helpers shared across sampling producers.

Attributes#

SamplerInput

Union of all supported sampler input types.

SamplerRuntime

Union of all supported GiGL sampler runtime types.

Functions#

create_dist_sampler(*, data, sampling_config, ...)

Create a GiGL sampler runtime for one channel on one worker.

Module Contents#

gigl.distributed.utils.dist_sampler.create_dist_sampler(*, data, sampling_config, worker_options, channel, sampler_options, degree_tensors, current_device)[source]#

Create a GiGL sampler runtime for one channel on one worker.

Parameters:

  • data (graphlearn_torch.distributed.DistDataset) – The distributed dataset containing graph topology and features.

  • sampling_config (graphlearn_torch.sampler.SamplingConfig) – Configuration for sampling behavior (neighbors, edges, etc.).

  • worker_options (Union[graphlearn_torch.distributed.MpDistSamplingWorkerOptions, graphlearn_torch.distributed.RemoteDistSamplingWorkerOptions]) – Worker-level options (RPC settings, device placement, concurrency).

  • channel (graphlearn_torch.channel.ChannelBase) – The communication channel for passing sampled messages.

  • sampler_options (gigl.distributed.sampler_options.SamplerOptions) – Algorithm-specific options (k-hop or PPR).

  • degree_tensors (Optional[Union[torch.Tensor, dict[graphlearn_torch.typing.EdgeType, torch.Tensor]]]) – Pre-computed degree tensors required by PPR sampling. Must not be None when sampler_options is PPRSamplerOptions.

  • current_device (torch.device) – The device on which sampling will run.

Returns:

A configured sampler runtime, either DistNeighborSampler or DistPPRNeighborSampler.

Raises:

NotImplementedError – If sampler_options is an unsupported type.

Return type:

SamplerRuntime

gigl.distributed.utils.dist_sampler.SamplerInput[source]#

Union of all supported sampler input types.

gigl.distributed.utils.dist_sampler.SamplerRuntime[source]#

Union of all supported GiGL sampler runtime types.