backend

class Backend(cfg: dict[str, Any] | None = None)

Bases: ABC

type: str

abstractmethod classmethod default_config() → dict[str, Any]

Return a complete default configuration for this backend.

abstract property resource_specs: list[dict]

abstract property valid_launchers: set[str]

classmethod matches(arg: str) → bool

property name: str

abstractmethod submission_manager() → HPCSubmissionManager

abstractmethod launcher() → HPCLauncher

configure(cfg: dict[str, Any] | None = None) → dict[str, Any]

describe() → str

supports_subscheduling() → bool

validate() → None

property resource_index: dict[str, list[tuple[dict, str | None]]]

make_resource_index() → dict[str, list[tuple[dict, str | None]]]

Map resource type -> list of (resource_spec, parent_type)

resource_types() → list[str]

Return the types of resources available

count_per_node(rtype: str, default: int | None = None) → int

count_per_socket(rtype: str, default: int | None = None) → int

property node_count: int

property sockets_per_node: int

nodes_required(**rtypes: int) → int

Nodes required to run tasks tasks. A task can be thought of as a single MPI rank

canonical_type_name(rtype: str) → str

resource_view(*, ranks: int | None = None, ranks_per_socket: int | None = None) → dict[str, int]

Return basic information about how to allocate resources on this machine for a job requiring ranks ranks.

Parameters

ranksint

The number of ranks to use for a job

ranks_per_socketint

Number of ranks per socket, for performance use

Returns

view:

view[‘np’] view[‘ranks’] view[‘nodes’] view[‘sockets’] view[‘ranks_per_socket’]

walk_resources(rspec: dict, *, parent_type: str | None = None) → Generator[tuple[dict, str | None], None, None]