Search

pllm.search searches immutable public configuration, not live sessions or secret material. A search space names exact Experiment.get_params(deep=True) paths; invalid paths, duplicate values, invalid combined configurations, and excessive cardinality fail before evaluation.

Candidate generation does not benchmark or rank anything. Evaluate candidates with a function that returns BenchmarkResult, then use an explicit metric direction or Pareto objective. PLLM rejects comparisons across incompatible model, workload, environment, numeric, privacy, or warm-state cohorts.

Python SDK example

from pllm import Deployment, ExecutionBudget, Experiment, MaskedLinearCpu, Model
from pllm.kernels import Cpu
from pllm.search import GridSearch, SearchSpace

experiment = Experiment(
    "thread-search",
    MaskedLinearCpu(
        Model("Qwen/Qwen2.5-0.5B-Instruct"),
        kernels=Cpu(threads=1),
    ),
    Deployment.local(root=".pllm/thread-search"),
    ExecutionBudget(requests=1, max_input_tokens=32, max_new_tokens=4),
)
space = SearchSpace(
    experiment,
    {"pipeline__kernels__threads": [1, 2, 4]},
)
candidates = GridSearch("docs-threads", space).candidates()

assert [candidate.parameters["pipeline__kernels__threads"] for candidate in candidates] == [1, 2, 4]
assert len({candidate.configuration_digest for candidate in candidates}) == 3

API: pllm.search

Persist the exact candidate experiment with each observation. Do not select a winner from latency alone when token counts, model fingerprints, environments, or other evidence-cohort fields differ.