Commit Graph

7 Commits

Author SHA1 Message Date
jgrusewski
2606506cd8 plan5(task3): A.4.1 nsys profile harness with regression-comparison script
- 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.
2026-04-26 12:25:35 +02:00
jgrusewski
c6634254e4 plan5(task1A): multi-seed × multi-fold Argo DAG template
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>
2026-04-26 10:55:12 +02:00
jgrusewski
904185004c feat: L40S GPU profile + auto-derive cuda-compute-cap from GPU pool
argo-train.sh now auto-selects cuda-compute-cap based on --gpu-pool:
  - ci-training-h100* → sm_90 (Hopper)
  - ci-training-l40s  → sm_89 (Ada Lovelace)

Added config/gpu/l40s.toml:
  - batch_size=4096 (between H100's 8192 and A100's 2048)
  - buffer_size=300K (scaled for 48GB VRAM)
  - gpu_timesteps_per_episode=2000 (bandwidth-limited)
  - gpu_n_episodes=2048 (scaled from H100's 4096)

GPU profile loader maps "L40S" → "l40s" (was "a100" fallback).

Also fixed pre-existing test drift: num_atoms=52 in h100.toml/a100.toml
was 51 in test expectations (padding alignment for C51 kernels).

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-20 16:59:06 +02:00
jgrusewski
2c8967ad96 feat: compute-sanitizer support in Argo training workflow
Usage: ./scripts/argo-train.sh dqn --baseline --epochs 2 --sanitizer memcheck
       ./scripts/argo-train.sh dqn --baseline --epochs 1 --sanitizer synccheck

Tools: memcheck (OOB, uninitialized), racecheck (data races),
synccheck (__syncthreads divergence/deadlocks).
10-100x slower — use with 1-2 epochs for debugging.
Detects exact kernel + line causing GPU hang.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-17 12:24:23 +02:00
jgrusewski
445c2197f2 feat: unified Argo train workflow — replace 7 templates with 1
New: train-template.yaml with smart caching:
  - ensure-binary: checks /data/bin/{sha}/ on PVC, compiles only on cache miss
  - ensure-fxcache: runs precompute only if cache doesn't exist
  - gpu-warmup: parallel autoscale during compile
  - hyperopt → train-best → evaluate → upload-results

Deleted 7 redundant templates:
  - compile-and-train-template.yaml
  - train-dqn-template.yaml
  - train-baseline-rl-template.yaml
  - train-supervised-template.yaml
  - training-workflow-template.yaml
  - precompute-features-template.yaml
  - train-ppo-template.yaml

Deleted: scripts/argo-precompute.sh (absorbed into ensure-fxcache step)
Rewritten: scripts/argo-train.sh (single template, --sha for commit pinning)
Updated: kustomization.yaml

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-10 20:36:03 +02:00
jgrusewski
729e8b5fae feat: argo-train.sh --baseline flag (skips hyperopt) + --cache-dir
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-03 23:00:14 +02:00
jgrusewski
6ba52425ea feat(infra): Argo workflow templates, drop cuDNN, GPU hotpath fixes
- Add compile-and-deploy, train-dqn/ppo/supervised WorkflowTemplates
- Add Argo Events (EventSource, Sensor, Service) for webhook triggers
- Add NetworkPolicy for compile-and-deploy pods (MinIO/DNS/API egress)
- Add convenience scripts: argo-compile-deploy.sh, argo-train.sh
- Drop cuDNN feature flags from all 9 ML crates (zero conv ops in codebase)
- Switch training runtime base to nvidia/cuda:12.9.1-runtime (saves ~800MB)
- Delete unused selective_scan.cu (16KB, zero Rust callers)
- Fix GPU hotpath violations in ml-core (NVTX, gradient utils, capabilities)
- Fix clippy warnings in ml-dqn (VarMap backticks, const fn)
- Add DQN GPU smoketest, backtest evaluator signal adapter fixes

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-12 01:44:03 +01:00