Launch agentic Harbor evals through the OT-Agent unified eval listener (eval/unified_eval_listener.py) on any cluster: select models (query_unevaled_models.py / priority lists), wire the pinggy served-model tunnel, submit with the right preset + flags in tmux, then VERIFY the launch actually works via the 15-min infra sanity check (pinggy auth, Daytona→cluster api_base, vLLM POSTs, trial progression — catches "RUNNING but silently dead" jobs). Cluster-AGNOSTIC: per-cluster particulars (sbatch script, gpu-mem ceiling, concurrency, cert/tunnel, conda env, paths, Daytona key, pre-download) live in `.agents/ops/<cluster>/`. Use when asked to launch/relaunch agentic evals, or eval a model on a benchmark (terminal_bench_2 / dev_set_v2 / swebench / bfcl / aider).
Launch agentic Harbor evals through the OT-Agent unified eval listener (eval/unified_eval_listener.py) on any cluster: select models (query_unevaled_models.py / priority lists), wire the pinggy served-model tunnel, submit with the right preset + flags in tmux, then VERIFY the launch actually works via the 15-min infra sanity check (pinggy auth, Daytona→cluster api_base, vLLM POSTs, trial progression — catches "RUNNING but silently dead" jobs). Cluster-AGNOSTIC: per-cluster particulars (sbatch script, gpu-mem ceiling, concurrency, cert/tunnel, conda env, paths, Daytona key, pre-download) live in `.agents/ops/<cluster>/`. Use when asked to launch/relaunch agentic evals, or eval a model on a benchmark (terminal_bench_2 / dev_set_v2 / swebench / bfcl / aider).
eval-agentic-launch
Launch agentic Harbor evals via the unified eval listener (eval/unified_eval_listener.py). Cluster-agnostic; read .agents/ops/<cluster>/ops.md first for the cluster's sbatch script, gpu-mem ceiling, concurrency, cert/tunnel, conda env, paths, Daytona eval-org key, and whether --pre-download is needed.
Front door: python -m hpc.launch --job_type eval_listener …. Runs the listener's main() in-process after the launcher preamble (detect_hpc + set_environment → DCFT/EXPERIMENTS_DIR/PYTHONPATH + hosted-vllm/Supabase keys + chdir to repo root), so no manual source hpc/dotenv/<cluster>.env / export PYTHONPATH / cd is needed. Forwards the listener's ~50 flags verbatim (strips only --job_type eval_listener). The raw python eval/unified_eval_listener.py … fallback still works (same public API) but you own the preamble — if you ever see , you used the raw script from the wrong place; switch to the front door.
FATAL: WORKDIR=... is not the OpenThoughts-Agent repo root
⚠ Secrets from $DC_AGENT_SECRET_ENV, never hardcoded in a script/config/commit. The eval sbatch sources it (~/secrets.env; TACC $SCRATCH/keys.env) and reads the two Daytona eval-org keys: DAYTONA_API_KEY (org1) + DAYTONA_DATA_API_KEY (org2), 3:1-weighted (3/4 org2). Fails loudly (:?) if either is unset. A literal dtn_… key committed anywhere is a leak — rotate/revoke it, don't just fix-forward.
1. Select the models
Priority list (default): a file in eval/lists/ (models_8b_*.txt, models_32b.txt, models_131k.txt). Launch with --require-priority-list --priority-file eval/lists/<file>.
Find unevaled models — scripts/database/query_unevaled_models.py (resolves benchmark families via the Supabase duplicate_of field, e.g. dev_set_v2 ⊇ DCAgent_dev_set_v2/dev_set_v2_2.0x/openthoughts-tblite):
--priority-file alone only changes sort order; the filter "skip models not in the list" lives behind --require-priority-list (unified_eval_listener.py ~L978). Without it the listener submits evals for every unevaled model in the lookback window (routinely 700+). Always pass both. If launched without it by accident: kill the listener before it leaves pre-download (submission is after Pre-downloading…), then confirm via squeue/sacct --starttime=now-Nmin. The listener python is a child of the sshd: …@notty session and survives the local ssh client dying — pkill -9 -f unified_eval_listener.py on the cluster to stop it.
Benchmark = a --preset (one, not both with --datasets)
tb2=terminal_bench_2, v2=dev_set_v2, dev=dev_set_71_tasks, swebench, bfcl, aider. --preset swebench is the random-100 subset (DCAgent/swebench_verified_eval_set → swebench-verified-random-100-folders, n_concurrent 32), not the full set.
"ID evals" — launch all three legs
Each leg is a separate listener invocation (different n_concurrent/harbor-config, don't combine into one --datasets):
leg
--preset
dataset (post-alias)
n_concurrent
SWE-bench-verified random-100
swebench
swebench-verified-random-100-folders
32
dev_set_v2
v2
DCAgent/dev_set_v2
128
terminal_bench_2
tb2
DCAgent2/terminal_bench_2
64
"Run the ID evals" = fire one listener per leg (§4) + the §5 infra check on each. (Full SWE-bench-verified and other benchmarks are OOD.) Scoring side (crud-otagent-supabase) uses the same 3-member set; dev_set_v2 is partial-credit → counts toward the ID mean but excluded from the ID SE and model-vs-model ranking.
Re-eval / parity test → --force-eval
By default the listener Skips any model with a Finished+metrics row (reason=job finished) — correct for cohort fill, but blocks a deliberate re-run. --force-eval bypasses that dedup and submits a fresh sandbox_jobs row (doesn't touch the existing row → no metrics-clearing, works across users). Pair with --require-priority-list + a single-model --priority-file so only the intended model is forced. --stale-started-hours does NOT override a Finished row (only re-ages Started). Distinct from --force-reeval (resume-path flag, see eval-agentic-cleanup check 4).
2. Harbor config + timeout multiplier (config-by-size — usually nothing to do)
Do NOT pass --harbor-config for standard terminus-2 evals. The listener selects the canonical config by model size and sets EVAL_HARBOR_CONFIG per-model:
Size is read from the largest \dB token in the HF name. The multiplier flows as EVAL_TIMEOUT_MULTIPLIER and is recorded in the Pending row so dedup matches what ran. The deprecated eval_ctx*_non_it* / ctx32k_non_it_16x_eval_.yaml configs carry stale *-drop-ei metrics → JobConfig ValidationError.
Resolution order (first wins): (1) explicit --harbor-config / preset harbor_config — overrides size selection for every model (use for 131k context / openhands_* installed-harness); (2) per-model timeout_multiplier: in the registry (for names with no size token, e.g. a Qwen3-8B named laion/GLM-4_7-swesmith-…); (3) size-based table above. For a one-off harbor jobs start, point --config at the 8B/32B file.
3. Pinggy tunnel — installed-harness ONLY (not terminus-2)
Skip for the default terminus-2 agent (every eval_ctx*/*_non_it* config; all --presets). Do NOT pass --pinggy_* / consume a pair.
Installed harnesses (opencode / openhands_*) run in the Daytona sandbox and call back out to the served model over a public pinggy tunnel. The full recipe for the opencode installed-harness + pinggy ID-eval — the config (eval_opencode_ctx32k.yaml), config-delivery mechanism (--config-yaml on TACC vs --harbor-config), the --pinggy_persistent_url/--pinggy_token flags + pairs 8/9/10, the sbatch installed-agent tunnel/routing branch, the vllm/ provider, -Thinking- model specifics, and the pinggy infra checks — lives in .agents/projects/harbor/ops.md → "Agentic ID-eval via the opencode (installed) harness + pinggy". The privileged URL/token bank is in .agents/secret.md / notes/ot-agent/pinggy_bank.md (never inline it). Resume of an installed-harness eval also needs the tunnel — see eval-agentic-cleanup check 4.
4. Launch (in tmux — listener is long-running)
Concurrent-submit guard: ONE listener enqueues many legs; do NOT fire N concurrent --once processes. A multi-leg refill is one invocation that submits each leg internally with a 1s submission_delay (unified_eval_listener.py L3204–3205). Firing N listener processes near-simultaneously on the login node races conda's lazily-imported plugin registry → a circular-import at activation. If multiple listener processes are truly required (incompatible n_concurrent), stagger them ~30–45s apart — never & them together. (Per-job conda activate inside the sbatch runs on independent compute nodes and never races.)
# inside tmux. The front door does the preamble (no manual source/PYTHONPATH).
python -m hpc.launch --job_type eval_listener \
--cluster-config <cluster-name> \
--preset <preset> \
--require-priority-list --priority-file eval/lists/<file>.txt \
--config-yaml dcagent_eval_config_no_override.yaml \
[--agent-kwarg 'extra_body={"chat_template_kwargs":{"enable_thinking":true}}'] [--agent-parser json] [--max-output-tokens 16384] \
[--pre-download] [--force-reeval] [--pinggy_persistent_url <URL> --pinggy_token <TOKEN>] \
--once --verbose 2>&1 | teeeval/<cluster>/logs/<preset>_listener_$(date +%Y%m%d_%H%M%S).log# Raw-script fallback (you own the preamble): from repo root, export PYTHONPATH="$PWD:${PYTHONPATH:-}", run python eval/unified_eval_listener.py … with the same flags.
--cluster-config takes a bare cluster name (leonardo, tacc) resolved from hpc.hpc's eval_cluster_view (a .yaml path still works as back-compat). Supplies sbatch_script/hardware/conda_envs/paths — so you no longer pass --sbatch-script/--n-concurrent/--gpu-memory-util.
Per-model serve config (conda_env, tensor_parallel_size, data_parallel_size, max_model_len, limit_mm_per_prompt, max_output_tokens) comes from the shared registry by default — no flag. The cluster yaml's hardware_profile: (e.g. gh200) selects the per-cluster recipe; a per-cluster intrinsic delta is name@<profile>, a hardware delta is variants: {<profile>: {…}}. Confirm it loaded: listener logs Model-config registry ENABLED + Loaded model registry: N model config(s) + Using conda env '<env>' for <model>. --baseline-model-configs is deprecated (opt-out of the registry).
Edit per-model serve config in model_config/<org>/<slug>.yaml, NOT the generated eval/configs/model_configs.yaml (auto-generated, carries a # do NOT hand-edit banner). Regenerate with python scripts/generate_eval_registry.py (drift gate: --check).
Thinking is per-model authoritative (sourced from the registry via agent_kwargs: [extra_body={…enable_thinking:true}]); presets never carry thinking; there is no --enable-thinking flag. Override with --agent-kwarg 'extra_body={"chat_template_kwargs":{"enable_thinking":true}}' (precedence: CLI > registry > preset).
5. VERIFY the launch — 15-min infra sanity check (do NOT trust "RUNNING")
A job can report RUNNING while nothing happens (pinggy locked, launcher missing --pinggy_*, dead vLLM engine). After launching, schedule a 15-min (ScheduleWakeup delaySeconds: 900) check and re-arm each pass until the eval terminates / you have a verdict.
Checks 1–2 are pinggy-path (installed-harness) ONLY — skip for terminus-2. For terminus-2, served-model reachability is proven by check 3. Checks 3–4 apply to every launch.
Pinggy tunnel (installed-harness) — pinggy auth You are authenticated as … + a growing RB:/SB:/TC: traffic counter. Full checks (auth, sandbox api_base = public *.a.pinggy.link/v1 not 10.*, relaunch-on-lock) → .agents/projects/harbor/ops.md § opencode + pinggy.
Daytona → cluster (installed-harness) — a trial's config.jsonapi_base MUST be https://*.a.pinggy.link/v1, NOT 10.*.*.* (see the harbor ops.md opencode+pinggy section).
vLLM serving — POST /v1/chat/completions count grows ≥ a few/min, 200 OK dominates. 400 ratio > 15% → context overflow (VLLMValidationError: input tokens … → lower max_input_tokens/max_output_tokens in the harbor yaml).
Trial progression — count trials with agent/ populated (active) and result.json (done). 30+ min with zero agent/command-0/ (OpenHands) → setup stalled. Completions with n_output_tokens: None and agent_execution.finished_at ≈ started_at (instant-fail) = tunnel not carrying traffic despite a healthy-looking job.
Quick liveness (≈15 min after submit): ssh <cluster> "squeue -u $USER --format='%.18i %.50j %.8T %.10M'" then tail the newest log — vLLM health-check pass, (Leonardo) SSH tunnel up, trial/reward lines, no OOM/repeated DaytonaErrors.
PermissionError: [Errno 13] at harbor/job.py … job_dir.mkdir() = a jobs_dir in the harbor config that another user owns. The canonical configs ship no jobs_dir; eval_harbor.sbatch passes --jobs-dir "$EVAL_JOBS_DIR" (per-user …/ot-baf/eval_jobs) which overrides the config. If you see this, confirm the sbatch has the --jobs-dir line; a hand-rolled harbor jobs start will reintroduce it. Resume is unaffected (takes -p $RUN_DIR).
A crashed eval leaves a non-terminal DB row blocking resubmission for 24h (reason=job in progress). After a crash the row stays started; the listener only resubmits started rows older than --stale-started-hours (default 24h, EVAL_LISTENER_STALE_HOURS). Pass a small value (e.g. --stale-started-hours 0.05 = 3 min) to force resubmit of the just-crashed attempt. Pending rows use --stale-pending-hours (default 6h, auto-cancels the stale SLURM job).
Jupiter: pass --reservation reformo or eval jobs starve behind RL (the reservation holds ~128 nodes while the general booster pool is empty). eval/jupiter/eval_harbor.sbatch sets --account reformo but no #SBATCH --reservation. Check scontrol show reservation for the live name/expiry before relying on it (the flag errors if the reservation is dead). Rescue already-PENDING jobs: scontrol update jobid=<j> reservation=reformo.
hosted_vllm/<org>/<model> evals need harbor commit 0f5a6e9e (allows 2-slash org-qualified names — validate_hosted_vllm_model_config in llms/utils.py) and model_info supplied via --agent-kwarg ({"max_input_tokens":…,"max_output_tokens":…,"input_cost_per_token":0,"output_cost_per_token":0}; token limits from the served vLLM max_model_len, costs 0 = self-hosted). Both are wired by default into the eval_harbor.sbatch files via EVAL_VLLM_MAX_MODEL_LEN (default 32768) + EVAL_MAX_OUTPUT_TOKENS (default 16384) (OT-Agent commit d0064011). If org-model evals fast-fail (~9 min, 0 POST 200s, 0 trajectories, all N trials raise identically), confirm those commits are in the cluster's harbor clone.
