Use when composing the Search(...) call and calling .start(). Covers the four worker classes (MultiProcessWorker / IsoMultiProcessWorker / RayWorker / AsyncWorker) and when to pick each, SearchConfiguration sizing rules, dump_results checkpointing, tracker_config choice (Disabled / Loguru / MLflow), num_workers/task_timeout semantics, and GPU clock locking for stable measurements. Triggers on "Search()", "tuner.start()", "pool_size", "num_workers", "task_timeout", "IsoMultiProcessWorker", "RayWorker", "dump_results", "MLflow", "GPU clocks".
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name
compileiq-run-search
description
Use when composing the Search(...) call and calling .start(). Covers the four worker classes (MultiProcessWorker / IsoMultiProcessWorker / RayWorker / AsyncWorker) and when to pick each, SearchConfiguration sizing rules, dump_results checkpointing, tracker_config choice (Disabled / Loguru / MLflow), num_workers/task_timeout semantics, and GPU clock locking for stable measurements. Triggers on "Search()", "tuner.start()", "pool_size", "num_workers", "task_timeout", "IsoMultiProcessWorker", "RayWorker", "dump_results", "MLflow", "GPU clocks".
when_to_use
- About to instantiate Search() and call .start().
- Search converges too slow / too fast and the user is unsure how to size.
- Search is hanging on a few configs (need IsoMultiProcessWorker + timeout).
- Migrating from local to a Ray cluster.
After you have an objective function (from compileiq-author-objective) and
a search space (from compileiq-search-space), this skill helps you choose
the worker, size the configuration, and run the search safely.
When
About to instantiate Search(...) and call .start().
Search is converging too fast or too slow and the user is unsure how to
re-size pool/generations.
Search hangs on individual configs and the worker doesn't kill them.
Scaling out from one GPU to a Ray cluster.
Worker selection
Pass either a built-in WorkerTypes enum value or the worker class itself to
Search(worker_type=...):
from compileiq.types import WorkerTypes
from compileiq.worker import (
MultiProcessWorker, # default
IsoMultiProcessWorker, # spawns fresh process per task; kill-safe
RayWorker, # distributed
AsyncWorker, # asyncio for async def objectives
)
Situation
Worker class
Why
GPU kernel that may hang, OOM, or leak CUDA context
IsoMultiProcessWorker
One fresh process per task; parent kills on task_timeout. Defaults to fork. (docs/workers.md:42)
Triton mixed example on Blackwell-class GPUs
WorkerTypes.ISOLATED + CIQ_PROCESS_MODE=spawn
Isolates each evaluation and avoids leaking illegal memory access state across runs.
Fast (<100ms), stateless objective
MultiProcessWorker (default)
Reuses a pool; lower overhead. Defaults to forkserver.
Multi-node / multi-GPU cluster
RayWorker
User must set up Ray cluster + install compileiq on every worker. Both num_workers and task_timeout are ignored. ()
docs/workers.md:79-91
I/O-bound async def objective
AsyncWorker
Concurrency, not parallelism. Rare for GPU work.
Default recommendation for compiler tuning of GPU kernels:IsoMultiProcessWorker with task_timeout between 30s (small kernels) and
180s (large attention / XLA HLO).
SearchConfiguration sizing
Reference: compileiq/types.py:473-615. Defaults auto-derive; only set what
you must.
from compileiq.types import SearchConfiguration, ProblemType
config = SearchConfiguration(
problem_type=ProblemType.MIN, # MIN for latency; MAX for throughput
generations=10, # required, > 0
pool_size=15, # > 5; auto-derives if omitted# cull_size auto-derives to 75% of pool, rounded down to even# mutate_rate defaults to 0.25# num_objectives defaults to 1# normalize defaults to False (set True for cross-GPU runs)
)
Knob
Default
When to override
generations
required
10 for initial exploration; 20-40 for a deep run.
pool_size
auto (≥32)
15 for tiny spaces; 32 for ≥1k design points; 64-128 for ≥10k.
cull_size
75% of pool, even
Almost never override directly.
mutate_rate
0.25
Raise to 0.3-0.5 only if convergence stalls in early gens.
num_objectives
1
Must equal len(return_tuple) from the objective.
normalize
False
True when running across heterogeneous nodes or GPUs.
Sanity rule of thumb: if pool_size * generations < 50, you are exploring,
not optimizing. If > 2000, you are probably overfitting to measurement noise
— compileiq-validate-result will earn its keep there.
Search(...) constructor — every relevant kwarg
from pathlib import Path
from compileiq.ciq import Search
from compileiq.search_spaces.compilers import PtxasSearchSpace
from compileiq.tracker import LoguruTrackerConfig
tuner = Search(
objective_function=objective,
search_space=PtxasSearchSpace(version="13.3", variant="att"),
search_config=config,
worker_type=IsoMultiProcessWorker, # or WorkerTypes.ISOLATED
tracker_config=LoguruTrackerConfig(sink="optimization.log"),
dump_results=Path("results.csv"), # ALWAYS set this
cache_folder=None, # default ~/.cache/compileiq
disable_progress_bar=False,
exit_on_failure=True,
debug=False,
)
Always set dump_results=Path(...). CSV is flushed every batch, so a crashed
or killed run leaves recoverable state.
num_workers: ignored by workers where respects_num_workers=False
(RayWorker, AsyncWorker); CompileIQ emits the warning
"num_workers is not supported by <WorkerName>" (compileiq/ciq.py:449-451)
so users recognize it.
task_timeout: ignored where supports_timeout=False (RayWorker).
Critical for IsoMultiProcessWorker — without it a hung config wedges that
branch.
Returns a SearchResult. Don't process inline; hand off to
compileiq-validate-result.
Tracker choice (one-line each)
from compileiq.tracker import DisabledTrackerConfig, LoguruTrackerConfig, MLflowTrackerConfig
DisabledTrackerConfig() — default, no overhead. Fine for one-off runs.
LoguruTrackerConfig(sink="optimization.log", level="INFO") —
recommended for serious campaigns. Negligible overhead.
MLflowTrackerConfig(experiment_name="...", tracking_uri="...", run_name="...")
— when integrating with ML Ops; creates a nested MLflow run per evaluation.
Sample before you search
Search.sample(n) returns n randomly sampled parameter dicts from the
search space without running the search. Use it to:
Confirm the search space resolves at all (cheaper than the bootstrap
round-trip; uses the in-memory state of Search).
Eyeball that the dicts have the keys your objective expects.
Feed a single sample into the objective by hand to verify it runs.
sample = tuner.sample(1)[0]
print(sample)
print(objective(sample)) # should return a real float, not raise
GPU clock locking (operator-level)
Stable measurements need locked clocks. Lock beforetuner.start(),
unlock via atexit. Requires sudo.
sudo nvidia-smi -pm 1
MAX_GPU=$(nvidia-smi --query-gpu=clocks.max.graphics --format=csv,noheader,nounits | head -1)
MAX_MEM=$(nvidia-smi --query-gpu=clocks.max.memory --format=csv,noheader,nounits | head -1)
sudo nvidia-smi --lock-gpu-clocks=$MAX_GPU,$MAX_GPU --lock-memory-clocks=$MAX_MEM,$MAX_MEM
Inside a CI container or a shared cluster where sudo isn't available, skip
this; report higher CV% to the validation skill so it knows to compensate.
Self-test
python scripts/smoke_search.py
Runs a 2-generation search on x**2 + y with MultiProcessWorker and
verifies results.get_best_result() returns a dict with score_1 and params.
Gotchas
Forgetting task_timeout with IsoMultiProcessWorker is the most
common reason a search hangs for hours. The worker will kill a stuck
process but only after task_timeout elapses.
forkserver issues on some hosts manifest as EOFError or "Broken pipe"
on the first eval. Set CIQ_PROCESS_MODE=spawn.
num_workers > num_gpus is fine for fast CPU-side objectives but
oversubscribes GPUs for kernel objectives. For GPU kernels: pin
CUDA_VISIBLE_DEVICES inside the objective and set
num_workers = num_gpus.
Don't put GPU-clock lock calls inside the objective. They require sudo
and are per-host operator setup, not per-eval.
Next
After .start() returns: compileiq-validate-result.
