Hailo Compile

Hailo Compile

Third stage of the DFC flow: take the quantized <model_name>_optimized.har from /hailo-optimize and produce a <model_name>.hef that runs on the Hailo accelerator.

This skill is shipped with the Hailo Model Zoo. It assumes the DFC wheel (hailo_sdk_client) is installed in the active virtualenv.

Inputs

Workflow

1. Resolve inputs

• Verify <optimized.har> exists.
• model_name = HAR filename stem (strip a trailing _optimized if present).
• hw_arch is read from the HAR; if unavailable, ask the user (or default to hailo10h).

2. Run compile

from hailo_sdk_client import ClientRunner

runner = ClientRunner(har="<optimized.har>")
hef = runner.compile()
with open("<model_name>.hef", "wb") as f:
    f.write(hef)
runner.save_har("<model_name>_compiled_model.har")

The compiled HAR carries the metadata the profiler and hailo har extract consume. The _compiled_model.har suffix matches the DFC tutorial (DFC_3_Compilation_Tutorial.ipynb) and the compilation.rst documentation — downstream tooling expects this convention.

3. Verify

• Confirm <model_name>.hef exists and report its size and location.
• Inspect the HEF metadata with hailortcli parse-hef <model_name>.hef (requires HailoRT installed).
• Per-layer profiler (static) — hailo profiler <model_name>_compiled_model.har produces a static per-layer report. Note: for multi-context models (most large models), this report does not include performance / FPS numbers without runtime data.
• Profiler with runtime data (full FPS / utilization) — the documented two-step flow for accurate performance on multi-context models:

  hailortcli run2 -m raw measure-fw-actions --output-path runtime.json set-net <model_name>.hef
  hailo profiler <model_name>_compiled_model.har --runtime-data runtime.json --out-path runtime_profiler.html
  

• Smoke test on the device — hailortcli run2 set-net <model_name>.hef runs inference with random inputs as a quick sanity check (verify the exact form with hailortcli run --help — run2 always requires -m <mode>).

4. Failure triage

If runner.compile() fails or returns very poor profiler results, the fix is almost always in the ALLS model script. Re-run /hailo-optimize with one or more of the following commands appended to the script, then re-run this skill.

Extract what the compiler actually used. Once any compile finishes, you can recover the exact ALLS the compiler resolved (with all defaults filled in) for fast deterministic iteration:

hailo har extract <model_name>_compiled_model.har --auto-model-script-path auto.alls

Iterating against auto.alls is much faster and more deterministic than re-running optimize+compile from a partial script.

Platform target caveat: if the ALLS contains platform_param(targets=[ethernet]), the compiler disables DDR portals, multi-context, and Sequencers. Several of the tricks below (context_switch_param, anything DDR-related) are no-ops in that case — fix the platform target first or accept the constraint.

For the canonical command reference and per-symptom guidance, consult the DFC User Guide on the Hailo Developer Zone (https://hailo.ai/developer-zone/).

5. Hand-off

Compilation complete. To run on a Hailo device, see the DFC tutorial DFC_4_Inference_Tutorial.ipynb (hailo tutorial) or use HailoRT directly.

Notes

• The skill never modifies the DFC wheel or Model Zoo configs.
• All Model Zoo paths are relative to the Model Zoo repo root.
• Tutorial: DFC_3_Compilation_Tutorial.ipynb (full reference). On-device inference: DFC_4_Inference_Tutorial.ipynb. Extract both with hailo tutorial.
• The skill never edits ALLS files in hailo_model_zoo/cfg/alls/ — any model-script changes are made on a working copy and then re-applied via /hailo-optimize.