unilab.training.cuda_process_sharing.CudaProcessSharingEvidence

class unilab.training.cuda_process_sharing.CudaProcessSharingEvidence[source]

Bases: object

Structured per-rank evidence for a requested mode plus ambient state.

pipe_directory_env is the raw CUDA_MPS_PIPE_DIRECTORY value (None when unset); pipe_directory/control_pipe/control_pipe_kind/ server_pid describe the effective pipe this rank’s CUDA contexts would use. ambient_server is true only for an unconfigured run whose effective control pipe answered a daemon query — the transparent-MPS case.

Parameters:

Methods

__init__(configured, effective, validated[, ...])

to_dict()

Return the JSON-shaped diagnostics stored in a runtime manifest.

Attributes

ambient_server

collector_device

collector_gpu_uuid

control_pipe

control_pipe_kind

learner_device

learner_gpu_uuid

pipe_directory

pipe_directory_env

rank

server_pid

world_size

configured

effective

validated

configured: str | None
effective: str | None
validated: bool
rank: int = 0
world_size: int = 1
learner_device: str | None = None
collector_device: str | None = None
learner_gpu_uuid: str | None = None
collector_gpu_uuid: str | None = None
pipe_directory_env: str | None = None
pipe_directory: str | None = None
control_pipe: str | None = None
control_pipe_kind: str | None = None
server_pid: int | None = None
ambient_server: bool = False
to_dict()[source]

Return the JSON-shaped diagnostics stored in a runtime manifest.

Return type:

dict[str, Any]

__init__(configured, effective, validated, rank=0, world_size=1, learner_device=None, collector_device=None, learner_gpu_uuid=None, collector_gpu_uuid=None, pipe_directory_env=None, pipe_directory=None, control_pipe=None, control_pipe_kind=None, server_pid=None, ambient_server=False)
Parameters: