hyperloom-custom-advanced

name	hyperloom-custom-advanced
description	Run an advanced configurable Hyperloom optimization session with explicit model, framework, workload, objective, and phase toggles. Use when the user wants more control than the fixed 3h or 12h demo presets.

Hyperloom Custom Advanced Run

Use this skill after /hyperloom-setup has prepared the current Hyperloom workspace. Setup writes .env with the run mode, target host, framework, LLM configuration, and USER_DATA_PATH; this skill reuses those values and asks only for the advanced workload choices that differ from the fixed demo presets.

Setup Configuration

Load .env from the current Hyperloom workspace before launching. Treat it as the source of truth for setup-owned values such as HYPERLOOM_RUN_MODE, HYPERLOOM_DOCKER_TARGET_HOST, FRAMEWORK, USER_DATA_PATH, and LLM provider settings. Do not ask the user to re-enter setup values that are already present.

When HYPERLOOM_RUN_MODE=baremetal or it is unset, run this demo directly in the current environment.

When HYPERLOOM_RUN_MODE=docker, this skill owns the Docker setup. Use HYPERLOOM_IMAGE when it is set. Otherwise choose a recommended ROCm image for the selected framework. The image must already contain the framework; do not install the framework inside Docker.

In docker mode:

• If hyperloom-setup already ran, do not re-run setup on the host.
• Read HYPERLOOM_DOCKER_TARGET_HOST from .env when present. If it names a host different from $(hostname), first SSH to that host and continue this Docker setup there; do not start Docker on the login/current host.
• Always run setup inside the container after docker run.
• Pass --install-framework none --yes in the container because ROCm and the framework must come from the image. Do not use --skip-base-check.
• Do not run python -m hyperloom.inference_optimizer.cli optimize on the host.

Suggested Docker images:

• vllm: docker.io/vllm/vllm-openai-rocm:v0.27.1
• sglang MI300X: docker.io/lmsysorg/sglang-rocm:v0.5.18-rocm724-mi30x-20260825
• sglang MI355X: docker.io/lmsysorg/sglang-rocm:v0.5.18-rocm724-mi35x-20260825

In Docker mode, start a long-running container on HYPERLOOM_DOCKER_TARGET_HOST (or the current host when it is unset) before running setup or optimize:

export REPO_ROOT="$(pwd -P)"
docker run -d \
  --name "${HYPERLOOM_CONTAINER_NAME:-hyperloom-local}" \
  --shm-size "${HYPERLOOM_SHM_SIZE:-64g}" \
  --entrypoint tail \
  --device /dev/kfd \
  --device /dev/dri \
  --group-add video \
  -v "$REPO_ROOT:$REPO_ROOT" \
  "$HYPERLOOM_IMAGE" \
  -f /dev/null

Mount the Hyperloom workspace at the same absolute path (-v "$REPO_ROOT:$REPO_ROOT") so paths in .env, logs, and session artifacts stay valid. If USER_DATA_PATH or the selected model directory is outside the workspace, add matching -v host_path:host_path mounts before starting the container.

Then run the setup backend inside the container:

docker exec -w "$REPO_ROOT" "${HYPERLOOM_CONTAINER_NAME:-hyperloom-local}" bash -lc \
  'REPO_ROOT="$(pwd -P)"; PYTHONPATH="$REPO_ROOT" python3 -m hyperloom.inference_optimizer.setup -- --install-framework none --yes'

After that, run all remaining commands for this demo inside the same container with docker exec -w "$REPO_ROOT" ...; do not run python -m hyperloom.inference_optimizer.cli optimize on the host in Docker mode. When the demo is finished, ask the user whether to stop the container. If they say yes, run:

docker stop "${HYPERLOOM_CONTAINER_NAME:-hyperloom-local}"

Advanced Configuration

Before launch, load the .env file produced by /hyperloom-setup, including LLM API keys/base URLs, FRAMEWORK, USER_DATA_PATH, and HF_TOKEN. Do not copy secret values into the prompt, terminal output, reports, or logs. Do not modify USER_DATA_PATH.

Use the agent's structured question UI when available. Do not continue until all required values are resolved.

Collect these required values:

• Model source:
  • existing MODEL_PATH, when set;
  • custom local path, which must contain config.json;
  • Hugging Face repo id plus a local cache directory.
• Framework: sglang or vllm. Prefer the existing FRAMEWORK value when it is set; otherwise default to sglang.
• Workload: TP, EP, CONC, ISL, OSL, PRECISION, and optional MAX_MODEL_LEN / PROFILE_OSL.
• Objective and budget: MAX_HOURS plus TARGET_GAIN.
• Optional phase budget percentages for PRELUDE, FRAMEWORK_AGENT, KERNEL_AGENT, SWEEP, and CLOSE.

Default Values

Use these defaults when the user does not override a field. Always show the resolved values in the launch plan before starting the optimizer.

• FRAMEWORK: existing FRAMEWORK from .env or shell, otherwise sglang.
• TP=1.
• EP=1.
• CONC=64.
• ISL=1024.
• OSL=1024.
• PRECISION=bf16.
• MAX_HOURS=8, a medium-length full optimization default. The user may choose a shorter smoke run such as 3, or a long-horizon run such as 24.
• TARGET_GAIN=30.
• MAX_MODEL_LEN: unset, so Hyperloom derives it from ISL/OSL and model metadata.
• PROFILE_OSL: unset, so Hyperloom uses its profile-phase default.
• MODEL_CLASS: unset, so Hyperloom infers it from model metadata.
• GPU_TYPE: unset, so Hyperloom auto-detects the target GPU.
• FRAMEWORK_VERSION: unset, so Hyperloom auto-detects it when possible.
• TARGET_SUMMARY: unset.
• COMPARE_AGAINST_GPU: unset.
• SKIP_VARIANTS: empty.
• SERVER_ARGS: empty.
• REFERENCE_SCRIPT: unset.
• CONC_SWEEP_CONCS: unset, so Hyperloom uses its default sweep ladder.
• CONC_SWEEP_TIMEOUT_SEC: unset, so Hyperloom uses its default per-variant timeout.
• CONC_SWEEP_TOTAL_BUDGET_SEC: unset, so Hyperloom uses its default total sweep budget.
• Phase budget percentages default to:
  • PHASE_BUDGET_PRELUDE_PCT=0.03: startup, preflight, baseline setup, and initial orchestration.
  • PHASE_BUDGET_FRAMEWORK_PCT=0.20: framework-agent work, including source patching or framework-side exploration.
  • PHASE_BUDGET_FRAMEWORK_PCT=0.55: the OPTIMIZE phase — serving-parameter exploration and benchmark validation.
  • PHASE_BUDGET_KERNEL_PCT=0.28: kernel-agent TraceLens/GEAK/native-kernel optimization work.
  • PHASE_BUDGET_SWEEP_PCT=0.12: concurrency sweep and final throughput validation around the best candidate.

Collect these optional advanced values:

• Phase toggles: --no-kernel, --no-framework-agent, --no-enable-conc-sweep, --no-enable-roofline.
• Phase budget percentages: PHASE_BUDGET_PRELUDE_PCT, PHASE_BUDGET_FRAMEWORK_PCT, PHASE_BUDGET_KERNEL_PCT, PHASE_BUDGET_SWEEP_PCT, and PHASE_BUDGET_CLOSE_PCT. Explain what each phase does before asking. Accept only values where 0 < pct <= 1; leave a value unset to use the optimizer default.
• Routing and baseline options: --skip-variants, --server-args, --reference-script, --model-class, --gpu-type, --framework-version, --target-summary, --compare-against-gpu.
• Concurrency sweep: --conc-sweep-concs, --conc-sweep-timeout-sec, --conc-sweep-total-budget-sec.

Guardrails:

• Do not rely on .env alone for TP, CONC, ISL, OSL, or PRECISION; pass explicit CLI flags in the optimize command.
• Omit --gpu-type unless the user explicitly chooses a hint; otherwise let Hyperloom auto-detect from ROCm/system info.
• Warn the user when both --no-framework-agent and --no-kernel are selected; that collapses the run mostly to baseline and sweep validation.
• Phase budget percentages are caps, not guaranteed time usage. A phase may end earlier, and disabled work phases have their share redistributed by the optimizer.
• There is no generic --skip-stage flag and no --no-sweep flag. Compose phase behavior from the explicit flags above.

Model Resolution

Before resolving or downloading any model, always ask the user which model path or Hugging Face repo to use. Present the currently resolved option when MODEL_PATH is already set, and always offer a custom local path. Do not continue until the user chooses one.

Use this decision flow:

• If the user chooses the existing MODEL_PATH, inspect that path and use it only when it contains config.json; otherwise ask again for a valid path.
• If the user provides a custom local path, export MODEL_PATH to that path and require config.json before launch.
• If the user provides a Hugging Face repo id, ask for a local cache path, set HF_REPO_ID to the repo id, set MODEL_PATH to the cache path, and download the repo there when config.json is not already present.

Do not assume the Hugging Face CLI exists; resolve or download Hugging Face models with Python:

python -m pip install -U huggingface_hub
export REPO_ROOT="$(pwd -P)"
python - <<'PY'
import os
from pathlib import Path
from huggingface_hub import snapshot_download

target = Path(os.environ["MODEL_PATH"]).expanduser()
repo_id = os.environ.get("HF_REPO_ID", "").strip()
if (target / "config.json").is_file():
    print(f"Using existing model at {target.resolve()}")
elif repo_id:
    snapshot_download(
        repo_id=repo_id,
        local_dir=str(target),
        local_dir_use_symlinks=False,
    )
    print(target.resolve())
else:
    raise SystemExit(f"MODEL_PATH does not contain config.json: {target}")
PY

Pre-launch Runtime Install

Before the first optimize launch, run the full runtime installer in the same environment that will launch the optimizer. Preflight loads kernel-agent.env.sh before it can reach the later Ray/Magpie/InferenceX auto-install checks, so this step must happen before launching.

For Docker mode, run this inside the container. For bare-metal mode, run it on the host:

export REPO_ROOT="$(pwd -P)"
# .env fills gaps only: re-exporting the non-empty pre-source snapshot keeps every
# value the caller exported. Wider than install.sh, which guards a fixed list.
_dotenv_prev="$(export -p | grep -v -e '=""$' -e "=''\$")"
set -a; . "${REPO_ROOT}/.env"; set +a
eval "$_dotenv_prev"
unset _dotenv_prev
export USER_DATA_PATH="${USER_DATA_PATH:?USER_DATA_PATH missing}"
export PYTHONPATH="${REPO_ROOT}:${PYTHONPATH:-}"
ulimit -Sn 65536 || true
INSTALL_SH="${REPO_ROOT}/hyperloom/inference_optimizer/assets/install.sh"
if [ ! -f "$INSTALL_SH" ]; then
  INSTALL_SH="${REPO_ROOT}/src/hyperloom/inference_optimizer/assets/install.sh"
fi
bash "$INSTALL_SH"
. "$USER_DATA_PATH/runtime/kernel-agent.env.sh"
export PYTHONPATH="${REPO_ROOT}:${PYTHONPATH:-}"

If hyperloom/inference_optimizer/assets/install.sh is not present (source checkout layout), use src/hyperloom/inference_optimizer/assets/install.sh.

Launch Command Template

Build the optimize command from the resolved values. Include optional flags only when the user selected them.

export RUN_TAG="$(basename "$MODEL_PATH")-custom-$(date +%Y%m%d_%H%M%S)"
export RUN_DIR="${USER_DATA_PATH:?USER_DATA_PATH missing}/optimizer_runs"
export RUN_LOG="$RUN_DIR/run_${RUN_TAG}.log"
export PID_FILE="$RUN_DIR/run_${RUN_TAG}.pid"
export LAUNCH_INFO_FILE="$RUN_DIR/launch_${RUN_TAG}.json"
mkdir -p "$RUN_DIR"

export FRAMEWORK="${FRAMEWORK:-sglang}"
export TP="${TP:-1}"
export EP="${EP:-1}"
export CONC="${CONC:-64}"
export ISL="${ISL:-1024}"
export OSL="${OSL:-1024}"
export PRECISION="${PRECISION:-bf16}"
export MAX_HOURS="${MAX_HOURS:-8}"
export TARGET_GAIN="${TARGET_GAIN:-30}"
export PHASE_BUDGET_PRELUDE_PCT="${PHASE_BUDGET_PRELUDE_PCT:-}"
 PHASE_BUDGET_FRAMEWORK_PCT=
 PHASE_BUDGET_KERNEL_PCT=
 PHASE_BUDGET_SWEEP_PCT=
 PHASE_BUDGET_CLOSE_PCT=

OPT_FLAGS=(
  --model 
  --framework 
  --tp 
  --ep 
  --conc 
  --isl 
  --osl 
  --precision 
  --max-hours 
  --tick-interval-sec 30
  --launch-info-file 
)

[ -n  ] && OPT_FLAGS+=(--target-gain )
[ -n  ] && OPT_FLAGS+=(--max-model-len )
[ -n  ] && OPT_FLAGS+=(--profile-osl )
[ -n  ] && OPT_FLAGS+=(--model-class )
[ -n  ] && OPT_FLAGS+=(--gpu-type )
[ -n  ] && OPT_FLAGS+=(--framework-version )
[ -n  ] && OPT_FLAGS+=(--target-summary )
[ -n  ] && OPT_FLAGS+=(--compare-against-gpu )
[ -n  ] && OPT_FLAGS+=(--skip-variants )
[ -n  ] && OPT_FLAGS+=(--server-args )
[ -n  ] && OPT_FLAGS+=(--reference-script )
[ -n  ] && OPT_FLAGS+=(--conc-sweep-concs )
[ -n  ] && OPT_FLAGS+=(--conc-sweep-timeout-sec )
[ -n  ] && OPT_FLAGS+=(--conc-sweep-total-budget-sec )
[ -n  ] && OPT_FLAGS+=(--max-minutes-prelude-pct )
[ -n  ] && OPT_FLAGS+=(--max-minutes-framework-pct )
[ -n  ] && OPT_FLAGS+=(--max-minutes-kernel-pct )
[ -n  ] && OPT_FLAGS+=(--max-minutes-sweep-pct )
[ -n  ] && OPT_FLAGS+=(--max-minutes-close-pct )
[  =  ] && OPT_FLAGS+=(--no-kernel)
[  =  ] && OPT_FLAGS+=(--no-framework-agent)
[  =  ] && OPT_FLAGS+=(--no-framework-agent)
[  =  ] && OPT_FLAGS+=(--no-enable-conc-sweep)
[  =  ] && OPT_FLAGS+=(--no-enable-roofline)

setsid  python3 -m hyperloom.inference_optimizer.cli --verbose optimize \
   \
  >  2>&1 < /dev/null &
 $! > 

 30
() { python3 -c    2>/dev/null; }
REAL_PID=
[ -z  ] && REAL_PID=
[ -n  ] &&   > 
 -d  &&  

SESSION_DIR=
 [ -z  ]; 
    >&2
   1 2>/dev/null ||  1


 -f  &&  
 -f  &&  

If adding quantization, critic, robustness, or research-lane flags, append only real flags accepted by python3 -m hyperloom.inference_optimizer.cli optimize --help; do not invent aliases.

User-visible Progress

Keep the user informed with concise status updates throughout the run. Do not dump full debug logs into chat; report the important values and paths so the user can tell that work is progressing.

Before launch, report the launch plan:

• model path and whether it is an existing local model or a downloaded repo;
• run mode (baremetal or docker) and target host/container when applicable;
• framework, TP, EP, concurrency, ISL, OSL, precision, max hours, and objective;
• phase budget percentages, showing defaults for unset values and user-selected overrides for set values;
• selected phase toggles and advanced flags;
• USER_DATA_PATH and where runtime artifacts will be written.

After the runtime install, report whether it succeeded and the path to kernel-agent.env.sh. After starting the optimizer, report:

• optimizer PID;
• run log path;
• launch-info JSON path;
• resolved session directory;
• state.json path;
• initial health check result.

During monitoring, print a short summary at each 300-second check:

• process alive/stopped;
• phase and stop_reason;
• baseline throughput, current best throughput, and cumulative gain when present;
• latest benchmark result or candidate decision when available;
• the most relevant recent log lines, excluding secrets.

When the run finishes, report the final status, final report path, best result, and the stop reason. Never print API keys, tokens, or custom header values.

Launch Requirements

1. Run the pre-launch runtime install above and source $USER_DATA_PATH/runtime/kernel-agent.env.sh before launching.
2. Keep PYTHONPATH="$REPO_ROOT:${PYTHONPATH:-}" in the launch shell so robustness and critic subprocesses can import hyperloom.agents after changing cwd.
3. Run in background with setsid nohup.
4. Pass all required workload flags in the python -m hyperloom.inference_optimizer.cli optimize command. Do not rely on .env alone for TP, CONC, ISL, OSL, or PRECISION.
5. Report the session ID, log path, PID, and initial health check result.
6. Monitor the process every 300 seconds until work is done.
7. To recover an unexpected crash, only run optimize --resume-from "$SESSION_DIR" against the same session dir. After the first launch, never start a new optimize; that creates a new <UTC_ts> session and is forbidden.
8. If stop_reason in the current session state.json is final, stop and exit.