Adds two new workflow parameters with backward-compatible defaults
(batch-size=1, auto-horizon-weights=false) so existing submissions
behave identically. Both flags are forwarded to alpha_train CLI.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
alpha_train --seed is clap-typed as u64, and clap's default u64
parser only accepts decimal digits. Previous default "0x4242"
hit "invalid digit found in string" at startup.
Replace with decimal equivalent 16962 (= 0x4242) in both the
submission script default and the workflow template default.
Long-term: could add a custom clap value_parser that accepts
hex/dec/oct prefixes, but for now decimal-only matches the
foxhunt convention in other CLIs (alpha_baseline, etc.).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds a tiny alpine pod (check-cache) that runs first on the platform
pool (no autoscaler delay, ~3 sec end-to-end) and probes the
training-data PVC for /data/bin/$SHA/alpha_train. Outputs:
- sha: short SHA used for binary cache keying
- cache: "hit" or "miss"
ensure-binary now has `when: cache == miss` — when the binary is
already cached for the current SHA, the entire ~4.8GB ci-builder
image pull + sccache compile cycle is skipped. Re-runs on the same
SHA now go straight from submission to training in ~30 seconds
instead of ~3 minutes.
train depends on check-cache + ensure-binary; sources the SHA from
check-cache's output (works whether ensure-binary ran or was
skipped — Argo treats `when:` skip as a satisfied dependency).
Submission script (scripts/argo-alpha-perception.sh) now pre-resolves
commit-sha=HEAD to an actual git SHA via `git rev-parse origin/<branch>`
before submission. This lets the alpine check-cache pod work without
installing git in the container.
Also removed the now-stale `ci-training-h100x2|ci-training-h100-sxm`
case branch from the SM-arch detection — those pools no longer exist
post-pool-cleanup commit a252119fd.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Argo WorkflowTemplate at infra/k8s/argo/alpha-perception-template.yaml
runs the stacked Mamba2 -> CfC -> heads PerceptionTrainer on a single
L40S in fr-par-2. Two-stage DAG:
ensure-binary (ci-compile-cpu pool, sccache-backed cargo build of
alpha_train example, SHA-keyed binary cache under
/data/bin/$SHORT_SHA/)
train (ci-training-l40s pool, runs the cached binary against
/data/futures-baseline/mbp10 with predecoded sidecar
cache at /feature-cache/predecoded, writes
alpha_train_summary.json to
/feature-cache/alpha-perception-runs/$SHA/)
Defaults mirror the validated synthetic-overfit smoke config:
epochs=5, seq_len=32, mamba2_state_dim=16, lr_cfc=3e-3,
lr_mamba2=1e-3, n_train_seqs=8000, n_val_seqs=1000, seed=0x4242
Submission script scripts/argo-alpha-perception.sh wraps argo submit
with the standard L40S/H100 cuda-compute-cap mapping. --watch
follows logs.
Workflow nodeSelector pinned to fr-par-2 (consistent with the cluster
topology constraint). ttlStrategy 1h after completion;
activeDeadlineSeconds 4h cap (well above expected ~30-90 min wall).
This is the cluster entrypoint for the stacked perception design.
Once it lands a summary on MinIO, the gate runner (alpha_gate, Task
17) can consume it.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
