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nemo_gym.health.checks
nemo_gym.health.checks
Artifact normalization and single-rollout health checks.
Module Contents
Functions
| Name | Description |
|---|---|
_agent_steps | Normalize canonical TrajectoryTurn records for structural checks. |
_agent_turn_hollow | - |
_bind_policy_calls | - |
_call_identity | - |
_call_locator | - |
_call_ref_key | - |
_canonical_model_call_references | Return explicit model-call references from canonical TrajectoryTurn records only. |
_canonical_trajectory | - |
_is_failed | - |
_is_successful | - |
_item_has_tool_call | - |
_model_call_failed | - |
_model_call_missing_token_counts | - |
_model_call_runaway_generation | - |
_model_call_zero_completion_tokens | - |
_nonempty | - |
_normalized_trajectory_calls | - |
_replay_identity | - |
_response_has_content | - |
_rollout_missing_agent_turns | - |
_rollout_token_count_mismatch | - |
_subject | - |
_token_count | - |
_trajectory_capture_mismatch | - |
_trajectory_has_any_gap | - |
_trajectory_has_gap | - |
_trajectory_reference_contradictions | - |
_transcript_tokens | - |
_usage_tokens | - |
normalize_ignored_checks | Normalize and validate check IDs supplied by library, CLI, or Hydra config. |
Data
API
nemo_gym.health.checks._agent_steps( trajectory: dict[str, typing.Any] ) -> list[nemo_gym.health.types._AgentStep]
Normalize canonical TrajectoryTurn records for structural checks.
nemo_gym.health.checks._agent_turn_hollow( trajectory: dict[str, typing.Any], subject: dict[str, int | str] ) -> list[nemo_gym.health.types.Finding]
nemo_gym.health.checks._bind_policy_calls( trajectory: dict[str, typing.Any], calls: list[dict[str, typing.Any]] ) -> nemo_gym.health.types._CallBindings
nemo_gym.health.checks._call_identity( call: dict[str, typing.Any] ) -> str | None
nemo_gym.health.checks._call_locator( call: dict[str, typing.Any], fallback: int ) -> dict[str, int | str]
nemo_gym.health.checks._call_ref_key( ref: typing.Any ) -> str | None
nemo_gym.health.checks._canonical_model_call_references( trajectory: dict[str, typing.Any] ) -> tuple[tuple[str, dict[str, typing.Any]], ...]
Return explicit model-call references from canonical TrajectoryTurn records only.
nemo_gym.health.checks._canonical_trajectory( record: dict[str, typing.Any] ) -> tuple[dict[str, typing.Any] | None, str | None]
nemo_gym.health.checks._is_failed( call: dict[str, typing.Any] ) -> bool
nemo_gym.health.checks._is_successful( call: dict[str, typing.Any] ) -> bool
nemo_gym.health.checks._item_has_tool_call( item: typing.Any ) -> bool
nemo_gym.health.checks._model_call_failed( bindings: nemo_gym.health.types._CallBindings, subject: dict[str, int | str] ) -> list[nemo_gym.health.types.Finding]
nemo_gym.health.checks._model_call_missing_token_counts( bindings: nemo_gym.health.types._CallBindings, subject: dict[str, int | str] ) -> list[nemo_gym.health.types.Finding]
nemo_gym.health.checks._model_call_runaway_generation( bindings: nemo_gym.health.types._CallBindings, subject: dict[str, int | str] ) -> list[nemo_gym.health.types.Finding]
nemo_gym.health.checks._model_call_zero_completion_tokens( bindings: nemo_gym.health.types._CallBindings, subject: dict[str, int | str] ) -> list[nemo_gym.health.types.Finding]
nemo_gym.health.checks._nonempty( value: typing.Any ) -> bool
nemo_gym.health.checks._normalized_trajectory_calls( trajectory: dict[str, typing.Any] ) -> list[dict[str, typing.Any]]
nemo_gym.health.checks._replay_identity( call: dict[str, typing.Any] ) -> str | None
nemo_gym.health.checks._response_has_content( response: typing.Any ) -> bool
nemo_gym.health.checks._rollout_missing_agent_turns( trajectory: dict[str, typing.Any], subject: dict[str, int | str] ) -> list[nemo_gym.health.types.Finding]
nemo_gym.health.checks._rollout_token_count_mismatch( record: dict[str, typing.Any], bindings: nemo_gym.health.types._CallBindings, subject: dict[str, int | str] ) -> list[nemo_gym.health.types.Finding]
nemo_gym.health.checks._subject( task_index: int | str, rollout_index: int | str | None = None ) -> dict[str, int | str]
nemo_gym.health.checks._token_count( call: dict[str, typing.Any], key: str ) -> int
nemo_gym.health.checks._trajectory_capture_mismatch( trajectory: dict[str, typing.Any], bindings: nemo_gym.health.types._CallBindings, subject: dict[str, int | str] ) -> list[nemo_gym.health.types.Finding]
nemo_gym.health.checks._trajectory_has_any_gap( trajectory: dict[str, typing.Any], codes: frozenset[str] ) -> bool
nemo_gym.health.checks._trajectory_has_gap( trajectory: dict[str, typing.Any], code: str ) -> bool
nemo_gym.health.checks._trajectory_reference_contradictions( trajectory: dict[str, typing.Any] ) -> list[dict[str, typing.Any]]
nemo_gym.health.checks._transcript_tokens( record: dict[str, typing.Any] ) -> tuple[int, int, bool]
nemo_gym.health.checks._usage_tokens( usage: typing.Any ) -> tuple[int | None, int | None]
nemo_gym.health.checks.normalize_ignored_checks( checks: collections.abc.Sequence[str] | str | None ) -> tuple[str, ...]
Normalize and validate check IDs supplied by library, CLI, or Hydra config.
nemo_gym.health.checks.CHECK_REGISTRY: tuple[CheckSpec, ...] = (CheckSpec(id='check_execution_error', evaluation_scope=(CheckScope.ROLLOUT), su...
nemo_gym.health.checks._AGENT_TOOL_CALL_TYPES = frozenset({'function_call', 'tool_call', 'tool_use', 'mcp_call', 'mcp_list_tools...
nemo_gym.health.checks._INCOMPLETE_MODEL_CALL_GAPS = frozenset({'model_calls_unavailable', 'model_call_capture_incomplete', 'model_ca...
nemo_gym.health.checks._LENGTH_LIMIT_FINISH_REASONS = frozenset({'length', 'max_output_tokens', 'max_tokens'})
nemo_gym.health.checks._REFERENCE_CONTRADICTION_GAPS = {'model_call_reference_unmatched': 'missing_captured_call', 'model_call_referenc...
nemo_gym.health.checks._ROLLOUT_CHECKS: dict[str, Callable[[dict[str, Any], dict[str, Any], _CallBindings, dict[str, int | str]], list[Finding]]] = {'check_execution_error': lambda record, trajectory, bindings, subject: [], 'rec...
nemo_gym.health.checks._ROLLOUT_SPECS = tuple(spec for spec in CHECK_REGISTRY if spec.evaluation_scope == CheckScope.ROL...
nemo_gym.health.checks._TASK_SPECS = tuple(spec for spec in CHECK_REGISTRY if spec.evaluation_scope == CheckScope.TAS...