While P0b smoke (train-sw4ws on bdc5cb8bb) runs, two prep items:
(A) scripts/argo-train.sh — add `ci-training` to the L40S sm_89 case
arm. Bare `ci-training` is an L40S pool alias in some clusters;
previously defaulted to sm_90 (Hopper), causing train-mnpf7 to
deploy with wrong-arch cubins (terminated + resubmitted manually).
Now both `*l40s*` and bare `ci-training` resolve correctly.
(C) docs/superpowers/plans/...sp13...md — Layer B section expanded
with concrete codebase locations discovered during P0a:
- aux_heads_kernel.cu, aux_heads_loss_ema_kernel.cu locations
- aux_nb_label_buf populated as column 0 of next_states (log_return)
- F1/F2 regression history note (don't alias the label buffer)
- aux_pred_to_isv_tanh_kernel.cu is a P0a placeholder per its own
header — Layer B should rewrite to read softmax logit-diff
- dir_acc kernel + oracle tests need softmax-read updates
- Layer B + P0b combined rationale post-P0a empirics
Saves the next implementer ~30 min of re-investigation.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The first L40S deploy attempt (workflow `train-multi-seed-z2llf`, terminated)
failed at startup with `error: unexpected argument '--fold' found` on every
job: `train_baseline_rl` is a multi-fold walk-forward executor that accepts
`--max-folds K`, NOT `--fold N`. The original P5T1 harness assumed the
opposite and fanned out N seeds × K folds = N*K jobs, each invoking the
binary with `--seed N --fold K`.
User chose Path B: pivot to one job per seed (each runs all K folds via the
existing `--max-folds` mechanism). Per-job runtime is K× longer, but fanout
drops from N*K=30 → N=5 (matches L40S pool capacity better) and the binary
contract becomes the one the binary actually has.
4 surface changes:
1. crates/ml/examples/train_baseline_rl.rs — add `--seed N` CLI arg
(default 42 — historic implicit value). Sets `FOXHUNT_SEED` env var at
startup BEFORE any CUDA module spins up. Logs the seed value at the
training start banner.
2. crates/ml/src/cuda_pipeline/mod.rs — add `global_seed()` (reads
`FOXHUNT_SEED`, default 42) + `mix_seed(base)` (SplitMix64 avalanche
so adjacent global seeds produce uncorrelated module seeds). Six call
sites updated to mix the global seed into their previously-hardcoded
constants:
- trainer/action.rs: GpuActionSelector seed (0xDEAD_BEEF_CAFE) + the
epsilon-greedy fallback StdRng (0xAC7_DEF0).
- cuda_pipeline/gpu_iqn_head.rs: IQN Xavier-init RNG (0x1CA_1234).
- cuda_pipeline/gpu_iql_trainer.rs: V(s) Xavier-init RNG (0x1C1_9ABC).
- cuda_pipeline/gpu_her.rs: random-donor RNG (0x4E4_5678).
- cuda_pipeline/gpu_ppo_collector.rs: rng_seeds Vec for PPO
experience-collector init + reset (0xAA0_5EED).
- trainer/training_loop.rs: per-epoch regime_dropout_seed.
3. infra/k8s/argo/train-multi-seed-template.yaml — drop `fold` parameter
from `train-single` template; binary invoked as `--seed "$SEED"
--max-folds {{workflow.parameters.folds}}` so the walk-forward sweep
happens inside the single training process. Drop `FOLD` env var. Update
the nsys-rep upload filename to drop the fold suffix. Update banners /
doc comments to reflect "one-job-per-seed" semantics.
4. scripts/argo-train.sh — matrix generator drops the inner fold loop.
Each emitted task carries only `seed=${s}` and depends on the same
ensure-fxcache + gpu-warmup. The dry-run synthetic marker switches from
`seed=${s} fold=${f}` to `seed=${s} max_folds=${FOLDS}` so test harnesses
count the new shape correctly.
5. scripts/tests/test_multi_seed_harness.sh — assertions updated:
- `--multi-seed 3 --folds 2` produces 3 tasks (was 6).
- Rendered binary command must include `--max-folds
{{workflow.parameters.folds}}` placeholder.
- Rendered template must declare `folds` workflow parameter (so
`argo submit -p folds=K` overrides the default).
- Rendered binary command must NOT contain any per-fold flag — this
catches the failure mode that broke the first L40S deploy.
- Backward-compat: `--multi-seed 1 --folds 1` preserves the existing
single-template path (no DAG matrix tasks emitted).
6. docs/dqn-wire-up-audit.md — adds 1 Wired row documenting the pivot,
the new `--seed`/`mix_seed` plumbing, all 6 RNG call sites, and the
end-to-end seed-variation verification result.
Validation:
cargo check --workspace clean at 11 warnings (workspace baseline preserved).
cargo build --release --example train_baseline_rl succeeds; --help shows
the new --seed flag with documented default 42.
Seed-variation end-to-end test on RTX 3050 Ti (1 fold × 2 epochs each):
--seed 42 → F0 best Sharpe = -9.7831, best_val_metric = 1.957244,
epoch-2 train Sharpe = -16.12, val_Sharpe = +1.11.
--seed 999 → F0 best Sharpe = +92.9341, best_val_metric = 2.161012,
epoch-2 train Sharpe = +92.93, val_Sharpe = -0.25.
Different best Sharpe / best_val_metric / epoch-2 train + val Sharpe
across seeds proves the seed actually propagates through the RNG init
paths and is not just accepted-and-ignored. The seed=42 numbers match
the prompt's "deterministic baseline" expectation (F0 = -9.7831 was
bit-identical pre-pivot because no global-seed plumbing existed).
./scripts/argo-train.sh dqn --multi-seed 5 --folds 6 --dry-run produces
exactly 5 WorkflowTask markers (train-s0..train-s4), each with
`--max-folds {{workflow.parameters.folds}}` in the binary invocation.
All 3 harness tests PASS:
- test_multi_seed_harness.sh: 5 PASS lines, exit 0.
- test_nsys_harness.sh: 4 PASS lines + ALL PASS, exit 0.
- test_tier_checks.sh: PASS overall (good-fixture passes, bad-fixture
surfaces expected check rejections), exit 0.
Backward compat: existing single-job `argo-train.sh` callers (no
`--multi-seed`, no `--folds`) route to the original `train-template.yaml`
unchanged. `--seed 42` is a no-op offset for the SplitMix64 mix at the call
sites — the trajectory shifts only when the user passes `--seed` explicitly,
matching the prompt's "default 42 (historic implicit value)" requirement.
L40S pool: argo-train.sh defaults `--gpu-pool ci-training-h100`; user passes
`--gpu-pool ci-training-l40s` at deploy time. No script default change
(per constraint 5).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- argo-train.sh: --profile flag forces multi-seed render path so the
nsys wrapper + foxhunt-training-artifacts upload step are visible in
--dry-run YAML without cluster contact (test surface).
- train-multi-seed-template.yaml: new `profile` parameter (default
"false") gates the per-(seed, fold) `nsys profile
--capture-range=cudaProfilerApi` wrapper and the `mc cp` upload to
foxhunt-training-artifacts/profiles/<sha>/. mc binary fetched
on-demand (ci-builder image lacks it). MinIO creds optional —
upload warn-skips if absent.
- Dockerfile.foxhunt-training-runtime: install nsight-systems-cli
unpinned (pinning the stale 2024.4.1.61-1 from earlier plans
breaks builds when apt index advances).
- minio.yaml: add foxhunt-training-artifacts bucket to minio-init.
- compare-nsys-profiles.py: V0 regression detector — compares
cuda_gpu_kern_sum total_ns / epoch_count between two profiles;
exits 1 on >20% slowdown. NVTX per-epoch ranges deferred to T5.
- tests/test_nsys_harness.sh: dry-run grep test — verifies both
required strings appear when --profile is set, and that the
default (no --profile) path keeps profile=false in the rendered
template.
- dqn-wire-up-audit.md: Plan 5 Task 3 row added documenting the
harness + the baseline-capture deferral to T5.
Backward compat: test_multi_seed_harness.sh from P5T1 still PASS.
Adds the orchestration surface for Plan 5 Task 1 — Multi-Seed × Multi-Fold
Validation Harness:
- scripts/argo-train.sh: new --multi-seed N, --folds K, --tag T, --dry-run
flags. Default --multi-seed 1 --folds 1 routes to the existing
train-template.yaml (backward compat — existing callers unchanged). When
N>1 or K>1 the script renders train-multi-seed-template.yaml with an
inline-generated (seed, fold) matrix and either prints the YAML
(--dry-run) or applies + submits it.
- infra/k8s/argo/train-multi-seed-template.yaml: new WorkflowTemplate with
entrypoint multi-seed-matrix → ensure-binary, gpu-warmup, ensure-fxcache,
then N*K parallel train-single instances. Each train-single receives
seed/fold via inputs.parameters and forwards them to the training binary
via --seed/--fold CLI args + SEED/FOLD env vars. The dag.tasks placeholder
`# __MATRIX_TASKS__` is substituted programmatically by argo-train.sh
(awk) — no hand-written 30-task matrix.
- scripts/tests/test_multi_seed_harness.sh: dry-run regression test.
Asserts --multi-seed 3 --folds 2 emits 6 WorkflowTask markers AND
--multi-seed 1 --folds 1 emits zero (single-template path preserved).
Validation:
- argo lint --offline passes on both the source template and the rendered
3x2 / 5x6 outputs.
- test_multi_seed_harness.sh passes locally.
- Single-job dry-run still produces the unchanged train-template YAML.
Note: plan Step 0.1 pre-plan check expects ISV_TOTAL_DIM=72 and seven
ATTN_*_FOCUS_EMA_INDEX slots — both stale (Plan 4 landed
ISV_TOTAL_DIM=117 and the VSN_MAG_EMA / VSN_DIR_EMA / MAMBA2_RETENTION_EMA
slots instead). Plan 4 validation doc never landed (T8 deferred → Plan 5
T5). T1 is pure infrastructure that builds the harness consumed by T5,
so the stale pre-plan expectations do not block this commit.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
