pllm.pipeline Python API

Pipeline records compose component identities into a digestible configuration while leaving live sessions, masks, and credentials outside the object graph. Presence documents API availability, not complete model, security, or deployment coverage; check implementation status before relying on a path.

Research context

This API is project infrastructure rather than an implementation of one specific paper. Relevant method pages link their primary sources separately.

Python SDK example

The example performs a bounded offline task and checks an observable result. It does not download a checkpoint or contact a provider.

import pllm.pipeline as pipeline
from pllm.sources import TinyModel

configured = pipeline.VerifiedMaskedLinearCpu(TinyModel("qwen2"))
assert configured.to_spec()["components"]["verification"]["component"] == "pllm/freivalds-verify/v1"

API: pllm.pipeline

Objects and signatures

Experiment

Immutable Experiment record carrying name, pipeline, deployment, budget for the workflow where pipeline records compose component identities into a digestible configuration while leaving live sessions, masks, and credentials outside the object graph.

• Canonical object: pllm.configuration.Experiment
• Kind: class
• Signature/type: __init__(self, name: str, pipeline: Pipeline, deployment: Deployment, budget: ExecutionBudget) -> None
• Public exports: pllm.Experiment, pllm.config.Experiment, pllm.pipeline.Experiment
• Public members: name: str; pipeline: Pipeline; deployment: Deployment; budget: ExecutionBudget; __init__(self, name: str, pipeline: Pipeline, deployment: Deployment, budget: ExecutionBudget) -> None; classmethod from_spec(cls, spec: Mapping[str, Any], *, providers: Iterable[ProviderDescriptor]=...) -> Self; classmethod from_file(cls, path: str \| Path, *, providers: Iterable[ProviderDescriptor]=...) -> Self; get_params(self, deep: bool=True) -> dict[str, Any]; with_params(self, **changes: object) -> Self; to_spec(self) -> dict[str, Any]; canonical_bytes(self) -> bytes; configuration_digest(self) -> str; resolve(self) -> ExperimentProfile

Pipeline

Immutable Pipeline record carrying model, components, profile for the workflow where pipeline records compose component identities into a digestible configuration while leaving live sessions, masks, and credentials outside the object graph.

• Canonical object: pllm.configuration.Pipeline
• Kind: class
• Signature/type: __init__(self, model: Model, components: Mapping[str, ComponentRef], profile: str \| None=...) -> None
• Public exports: pllm.Pipeline, pllm.config.Pipeline, pllm.pipeline.Pipeline
• Public members: model: Model; components: Mapping[str, ComponentRef]; profile: str \| None; __init__(self, model: Model, components: Mapping[str, ComponentRef], profile: str \| None=...) -> None; classmethod from_profile(cls, profile: str, *, model: Model, components: Mapping[str, ComponentRef]) -> Self; classmethod from_spec(cls, value: Mapping[str, Any], *, providers: Iterable[ProviderDescriptor]=...) -> Pipeline; get_params(self, deep: bool=True) -> dict[str, Any]; with_params(self, **changes: object) -> Self; to_spec(self) -> dict[str, Any]; canonical_bytes(self) -> bytes; digest(self) -> str

DirectFHEProfile

Pipeline(model: 'Model', components: 'Mapping[str, ComponentRef]', profile: 'str | None' = None).

• Canonical object: pllm.profiles.DirectFHEProfile
• Kind: class
• Signature/type: __init__(self, model: ModelSource, *, linear: DirectFHE=..., inference: InferenceRole=..., kernels: KernelBackend=...) -> None
• Public exports: pllm.DirectFHEProfile, pllm.pipeline.DirectFHEProfile, pllm.profiles.DirectFHEProfile
• Public members: PROFILE: str; __init__(self, model: ModelSource, *, linear: DirectFHE=..., inference: InferenceRole=..., kernels: KernelBackend=...) -> None; property linear: DirectFHE; property inference: InferenceRole; property kernels: KernelBackend; get_params(self, deep: bool=True) -> dict[str, Any]; with_params(self, **changes: object) -> DirectFHEProfile

MaskedLinearCpu

Pipeline(model: 'Model', components: 'Mapping[str, ComponentRef]', profile: 'str | None' = None).

• Canonical object: pllm.profiles.MaskedLinearCpu
• Kind: class
• Signature/type: __init__(self, model: ModelSource, *, linear: ProtocolMethod=..., preparation: PreparationProvider=..., inference: InferenceRole=..., kernels: KernelBackend=...) -> None
• Public exports: pllm.MaskedLinearCpu, pllm.pipeline.MaskedLinearCpu, pllm.profiles.MaskedLinearCpu
• Public members: PROFILE: str; __init__(self, model: ModelSource, *, linear: ProtocolMethod=..., preparation: PreparationProvider=..., inference: InferenceRole=..., kernels: KernelBackend=...) -> None; property linear: ProtocolMethod; property preparation: PreparationProvider; property inference: InferenceRole; property kernels: KernelBackend; get_params(self, deep: bool=True) -> dict[str, Any]; with_params(self, **changes: object) -> MaskedLinearCpu

ProprietaryBlinded

Pipeline(model: 'Model', components: 'Mapping[str, ComponentRef]', profile: 'str | None' = None).

• Canonical object: pllm.profiles.ProprietaryBlinded
• Kind: class
• Signature/type: __init__(self, model: ModelSource, *, linear: BlindedLinear=..., inference: InferenceRole=..., kernels: KernelBackend=...) -> None
• Public exports: pllm.ProprietaryBlinded, pllm.pipeline.ProprietaryBlinded, pllm.profiles.ProprietaryBlinded
• Public members: PROFILE: str; __init__(self, model: ModelSource, *, linear: BlindedLinear=..., inference: InferenceRole=..., kernels: KernelBackend=...) -> None; property linear: BlindedLinear; property inference: InferenceRole; property kernels: KernelBackend; get_params(self, deep: bool=True) -> dict[str, Any]; with_params(self, **changes: object) -> ProprietaryBlinded

ProprietaryGuarded

Pipeline(model: 'Model', components: 'Mapping[str, ComponentRef]', profile: 'str | None' = None).

• Canonical object: pllm.profiles.ProprietaryGuarded
• Kind: class
• Signature/type: __init__(self, model: ModelSource, *, linear: GuardedLinear=..., inference: InferenceRole=..., kernels: KernelBackend=...) -> None
• Public exports: pllm.ProprietaryGuarded, pllm.pipeline.ProprietaryGuarded, pllm.profiles.ProprietaryGuarded
• Public members: PROFILE: str; __init__(self, model: ModelSource, *, linear: GuardedLinear=..., inference: InferenceRole=..., kernels: KernelBackend=...) -> None; property linear: GuardedLinear; property inference: InferenceRole; property kernels: KernelBackend; get_params(self, deep: bool=True) -> dict[str, Any]; with_params(self, **changes: object) -> ProprietaryGuarded

VerifiedMaskedLinearCpu

Pipeline(model: 'Model', components: 'Mapping[str, ComponentRef]', profile: 'str | None' = None).

• Canonical object: pllm.profiles.VerifiedMaskedLinearCpu
• Kind: class
• Signature/type: __init__(self, model: ModelSource, *, linear: ProtocolMethod=..., preparation: PreparationProvider=..., inference: InferenceRole=..., kernels: KernelBackend=..., verification: VerificationScheme=...) -> None
• Public exports: pllm.VerifiedMaskedLinearCpu, pllm.pipeline.VerifiedMaskedLinearCpu, pllm.profiles.VerifiedMaskedLinearCpu
• Public members: PROFILE: str; __init__(self, model: ModelSource, *, linear: ProtocolMethod=..., preparation: PreparationProvider=..., inference: InferenceRole=..., kernels: KernelBackend=..., verification: VerificationScheme=...) -> None; property linear: ProtocolMethod; property preparation: PreparationProvider; property inference: InferenceRole; property kernels: KernelBackend; property verification: FreivaldsVerify; with_params(self, **changes: object) -> VerifiedMaskedLinearCpu