graflag.models

Data models for GraFlag API responses.

class graflag.models.ClusterInfo(manager_ip: str, is_connected: bool, swarm_initialized: bool, worker_nodes: Dict[str, str]]=<factory>, shared_dir: str = '', shared_contents: List[str] = <factory>, services: List[Dict] = <factory>, error: str | None = None)[source]

Bases: object

Cluster status information.

manager_ip: str

is_connected: bool

swarm_initialized: bool

worker_nodes: List[Dict[str, str]]

shared_dir: str = ''

shared_contents: List[str]

services: List[Dict]

error: str | None = None

to_dict() → dict[source]

class graflag.models.MethodInfo(name: str, description: str = '', source_code: str = '', supported_data: str = '', parameters: Dict[str, ~typing.Any]=<factory>, has_dockerfile: bool = False, has_env: bool = False)[source]

Bases: object

Method metadata.

name: str

description: str = ''

source_code: str = ''

supported_data: str = ''

parameters: Dict[str, Any]

has_dockerfile: bool = False

has_env: bool = False

to_dict() → dict[source]

class graflag.models.DatasetInfo(name: str, path: str = '', size_mb: float = 0.0, file_count: int = 0)[source]

Bases: object

Dataset metadata.

name: str

path: str = ''

size_mb: float = 0.0

file_count: int = 0

to_dict() → dict[source]

class graflag.models.ExperimentInfo(name: str, method: str, dataset: str, timestamp: str, status: str, has_results: bool = False, has_evaluation: bool = False, results_path: str | None = None, evaluation_path: str | None = None, service_name: str | None = None)[source]

Bases: object

Experiment metadata and status.

name: str

method: str

dataset: str

timestamp: str

status: str

has_results: bool = False

has_evaluation: bool = False

results_path: str | None = None

evaluation_path: str | None = None

service_name: str | None = None

to_dict() → dict[source]

class graflag.models.ExperimentResults(experiment_name: str, method_name: str, dataset: str, metadata: Dict[str, ~typing.Any]=<factory>, execution_time_ms: float | None = None, peak_memory_mb: float | None = None, peak_gpu_memory_mb: float | None = None, result_type: str | None = None, scores_available: bool = False)[source]

Bases: object

Parsed experiment results.

experiment_name: str

method_name: str

dataset: str

metadata: Dict[str, Any]

execution_time_ms: float | None = None

peak_memory_mb: float | None = None

peak_gpu_memory_mb: float | None = None

result_type: str | None = None

scores_available: bool = False

to_dict() → dict[source]

class graflag.models.EvaluationResults(experiment_name: str, metrics: Dict[str, float]=<factory>, plots_available: List[str] = <factory>, evaluation_path: str | None = None)[source]

Bases: object

Parsed evaluation results.

experiment_name: str

metrics: Dict[str, float]

plots_available: List[str]

evaluation_path: str | None = None

to_dict() → dict[source]

class graflag.models.RunProgress(experiment_name: str, status: str, message: str = '', log_lines: List[str] = <factory>)[source]

Bases: object

Progress information for run execution.

experiment_name: str

status: str

message: str = ''

log_lines: List[str]

to_dict() → dict[source]