foxhunt/docs/superpowers/plans/2026-03-13-gpu-test-workflow.md

H100 GPU Test Workflow Implementation Plan

For agentic workers: REQUIRED: Use superpowers:subagent-driven-development (if subagents available) or superpowers:executing-plans to implement this plan. Steps use checkbox (- [ ]) syntax for tracking.

Goal: Argo WorkflowTemplate that compiles and runs the full GPU/CUDA test suite on H100 with real market data, triggered manually, on-push (gated), or nightly.

Architecture: Two new PVCs (cargo-target-cuda-test 30Gi, test-data-pvc 10Gi) isolate test infrastructure from training. Single H100 pod compiles with --features cuda then runs tests sequentially per model family. Continue-on-failure captures per-model results.

Tech Stack: Argo Workflows, Kubernetes, Rust/Cargo, CUDA 12.9, ci-builder image, bash

Spec: docs/superpowers/specs/2026-03-13-gpu-test-workflow-design.md

---

File Structure

New Files (8)

File	Responsibility
crates/ml/tests/supervised_gpu_smoke_test.rs	GPU smoke test for all 8 supervised models (UnifiedTrainable pipeline)
infra/k8s/argo/cargo-target-cuda-test-pvc.yaml	PVC for test compilation cache (30Gi)
infra/k8s/training/test-data-pvc.yaml	PVC for curated test data subset (10Gi)
infra/k8s/training/populate-test-data-job.yaml	One-time job: copy data from training PVC to test PVC
infra/k8s/argo/gpu-test-pipeline-template.yaml	Main WorkflowTemplate: compile + test on H100
infra/k8s/argo/gpu-test-nightly-cron.yaml	CronWorkflow (suspended) for nightly runs
scripts/argo-test.sh	CLI wrapper for manual argo submit
scripts/refresh-test-data.sh	Re-run populate job

Modified Files (5)

File	Change
infra/k8s/argo/ci-pipeline-template.yaml	Add ml-changed output + gpu-test DAG task
crates/ml/tests/smoke_test_real_data.rs:99-130	Add TEST_DATA_DIR to smoke_data_dir() and try_data()
crates/ml/tests/dqn_training_smoke_test.rs:94-105	Add TEST_DATA_DIR to get_dbn_data_dir()
crates/ml/tests/dqn_training_pipeline_test.rs:100-117	Add TEST_DATA_DIR to get_es_fut_data_dir()
crates/ml/tests/dqn_early_stopping_termination_test.rs:93-104	Add TEST_DATA_DIR to get_dbn_data_dir()

---

Task 1: Create PVCs

Files:

• Create: infra/k8s/argo/cargo-target-cuda-test-pvc.yaml

• Create: infra/k8s/training/test-data-pvc.yaml

• Step 1: Create cargo-target-cuda-test PVC

# infra/k8s/argo/cargo-target-cuda-test-pvc.yaml
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: cargo-target-cuda-test
  namespace: foxhunt
  labels:
    app.kubernetes.io/name: cargo-target
    app.kubernetes.io/component: ci-cache
    app.kubernetes.io/part-of: foxhunt
spec:
  accessModes: [ReadWriteOnce]
  storageClassName: scw-bssd-retain
  resources:
    requests:
      storage: 30Gi

Pattern matches infra/k8s/argo/sccache-pvcs.yaml (same scw-bssd-retain storage class).

• Step 2: Create test-data PVC

# infra/k8s/training/test-data-pvc.yaml
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
  name: test-data-pvc
  namespace: foxhunt
  labels:
    app.kubernetes.io/name: test-data
    app.kubernetes.io/part-of: foxhunt
spec:
  accessModes: [ReadWriteOnce]
  storageClassName: scw-bssd
  resources:
    requests:
      storage: 10Gi

• Step 3: Commit

git add infra/k8s/argo/cargo-target-cuda-test-pvc.yaml infra/k8s/training/test-data-pvc.yaml
git commit -m "infra: add cargo-target-cuda-test (30Gi) and test-data-pvc (10Gi)"

---

Task 2: Create populate-test-data job

Files:

• Create: infra/k8s/training/populate-test-data-job.yaml

• Create: scripts/refresh-test-data.sh

• Step 1: Create the populate job

This job runs on the H100 node pool (same as test-data-pvc) and copies one quarter of ES.FUT data per schema from the training PVC.

# infra/k8s/training/populate-test-data-job.yaml
apiVersion: batch/v1
kind: Job
metadata:
  generateName: populate-test-data-
  namespace: foxhunt
  labels:
    app.kubernetes.io/name: populate-test-data
    app.kubernetes.io/part-of: foxhunt
spec:
  backoffLimit: 1
  ttlSecondsAfterFinished: 300
  template:
    spec:
      restartPolicy: Never
      nodeSelector:
        k8s.scaleway.com/pool-name: ci-training-h100
      tolerations:
        - key: nvidia.com/gpu
          operator: Exists
          effect: NoSchedule
        - key: node.cilium.io/agent-not-ready
          operator: Exists
          effect: NoSchedule
      containers:
        - name: populate
          image: busybox:1.37
          command: ["/bin/sh", "-c"]
          args:
            - |
              set -e
              echo "=== Populating test data PVC ==="

              # Target directories
              mkdir -p /test-data/ohlcv/ES.FUT
              mkdir -p /test-data/mbp10/ES.FUT
              mkdir -p /test-data/trades/ES.FUT

              # Copy one quarter per schema (2025-Q2)
              echo "Copying OHLCV..."
              cp /training-data/futures-baseline/ES.FUT/ES.FUT_2025-Q2.dbn.zst \
                 /test-data/ohlcv/ES.FUT/ 2>/dev/null || \
              cp /training-data/futures-baseline/ES.FUT/*2025*Q2* \
                 /test-data/ohlcv/ES.FUT/ 2>/dev/null || \
              { echo "WARN: No 2025-Q2 OHLCV found, copying first available"; \
                cp /training-data/futures-baseline/ES.FUT/*.dbn.zst \
                   /test-data/ohlcv/ES.FUT/ 2>/dev/null | head -1; }

              echo "Copying MBP-10..."
              cp /training-data/futures-baseline-mbp10/ES.FUT/ES.FUT_2025-Q2.dbn.zst \
                 /test-data/mbp10/ES.FUT/ 2>/dev/null || \
              cp /training-data/futures-baseline-mbp10/ES.FUT/*2025*Q2* \
                 /test-data/mbp10/ES.FUT/ 2>/dev/null || \
              echo "WARN: No MBP-10 2025-Q2 found"

              echo "Copying trades..."
              cp /training-data/futures-baseline-trades/ES.FUT/ES.FUT_2025-Q2.dbn.zst \
                 /test-data/trades/ES.FUT/ 2>/dev/null || \
              cp /training-data/futures-baseline-trades/ES.FUT/*2025*Q2* \
                 /test-data/trades/ES.FUT/ 2>/dev/null || \
              echo "WARN: No trades 2025-Q2 found"

              echo "=== Test data contents ==="
              find /test-data -type f -exec ls -lh {} \;
              echo "Total: $(du -sh /test-data | cut -f1)"
              echo "=== Done ==="
          resources:
            requests:
              nvidia.com/gpu: "1"
              cpu: 100m
              memory: 128Mi
            limits:
              nvidia.com/gpu: "1"
              cpu: 500m
              memory: 256Mi
          volumeMounts:
            - name: training-data
              mountPath: /training-data
              readOnly: true
            - name: test-data
              mountPath: /test-data
      volumes:
        - name: training-data
          persistentVolumeClaim:
            claimName: training-data-pvc
            readOnly: true
        - name: test-data
          persistentVolumeClaim:
            claimName: test-data-pvc

Note: requests nvidia.com/gpu: 1 only to schedule on the H100 node (same as test-data-pvc). GPU isn't used for the copy.

• Step 2: Create refresh script

#!/usr/bin/env bash
# Re-populate the test-data-pvc from training-data-pvc.
# Usage: ./scripts/refresh-test-data.sh
set -euo pipefail

echo "Submitting populate-test-data job..."
kubectl -n foxhunt create -f infra/k8s/training/populate-test-data-job.yaml

echo "Waiting for completion..."
kubectl -n foxhunt wait --for=condition=complete job \
  -l app.kubernetes.io/name=populate-test-data \
  --timeout=300s

echo "Done. Test data PVC refreshed."

Save to scripts/refresh-test-data.sh, chmod +x.

• Step 3: Commit

chmod +x scripts/refresh-test-data.sh
git add infra/k8s/training/populate-test-data-job.yaml scripts/refresh-test-data.sh
git commit -m "infra: add populate-test-data job and refresh script"

---

Task 3: Wire TEST_DATA_DIR into test helpers

Files:

• Modify: crates/ml/tests/smoke_test_real_data.rs:99-130

• Modify: crates/ml/tests/dqn_training_smoke_test.rs:94-105

• Modify: crates/ml/tests/dqn_training_pipeline_test.rs:100-117

• Modify: crates/ml/tests/dqn_early_stopping_termination_test.rs:93-104

• Step 1: Update smoke_data_dir() and try_data() in smoke_test_real_data.rs

Replace smoke_data_dir():

fn smoke_data_dir() -> PathBuf {
    // CI: TEST_DATA_DIR points to test-data-pvc on H100
    if let Ok(dir) = std::env::var("TEST_DATA_DIR") {
        return PathBuf::from(dir);
    }
    // Local dev: relative path from workspace root
    workspace_root().join("data").join("smoke-test")
}

try_data() doesn't need changes — it already calls smoke_data_dir().

• Step 2: Update get_dbn_data_dir() in dqn_training_smoke_test.rs

Replace lines 94-105:

fn get_dbn_data_dir() -> Result<String> {
    // CI: TEST_DATA_DIR points to test-data-pvc on H100
    if let Ok(dir) = std::env::var("TEST_DATA_DIR") {
        let ohlcv = std::path::PathBuf::from(&dir).join("ohlcv");
        if ohlcv.exists() {
            return Ok(ohlcv.to_string_lossy().to_string());
        }
        // Flat layout fallback
        return Ok(dir);
    }
    let workspace_root = PathBuf::from(env!("CARGO_MANIFEST_DIR"))
        .ancestors()
        .find(|p| p.join("test_data").exists())
        .context("Failed to find workspace root with test_data/")?
        .to_path_buf();
    let data_dir = workspace_root.join("test_data/futures-baseline");
    if !data_dir.exists() {
        anyhow::bail!("DBN data not found: {}", data_dir.display());
    }
    Ok(data_dir.to_string_lossy().to_string())
}

• Step 3: Update get_es_fut_data_dir() in dqn_training_pipeline_test.rs

Replace lines 100-117:

fn get_es_fut_data_dir() -> Result<String> {
    if let Ok(dir) = std::env::var("TEST_DATA_DIR") {
        let ohlcv = std::path::PathBuf::from(&dir).join("ohlcv");
        if ohlcv.exists() {
            return Ok(ohlcv.to_string_lossy().to_string());
        }
        return Ok(dir);
    }
    let workspace_root = PathBuf::from(env!("CARGO_MANIFEST_DIR"))
        .ancestors()
        .find(|p| p.join("test_data").exists())
        .context("Failed to find workspace root with test_data/")?
        .to_path_buf();
    let data_dir = workspace_root.join("test_data/futures-baseline");
    if !data_dir.exists() {
        anyhow::bail!(
            "ES.FUT data directory not found: {}. Run data acquisition first.",
            data_dir.display()
        );
    }
    Ok(data_dir.to_string_lossy().to_string())
}

• Step 4: Update get_dbn_data_dir() in dqn_early_stopping_termination_test.rs

Replace lines 93-104:

#[cfg(feature = "cuda")]
fn get_dbn_data_dir() -> Option<String> {
    if let Ok(dir) = std::env::var("TEST_DATA_DIR") {
        let ohlcv = std::path::PathBuf::from(&dir).join("ohlcv");
        if ohlcv.exists() {
            return Some(ohlcv.to_string_lossy().to_string());
        }
        return Some(dir);
    }
    let workspace_root = std::path::PathBuf::from(env!("CARGO_MANIFEST_DIR"))
        .ancestors()
        .find(|p| p.join("test_data").exists())?
        .to_path_buf();
    let data_dir = workspace_root.join("test_data/futures-baseline");
    if !data_dir.exists() {
        eprintln!("SKIP: DBN data not found: {}", data_dir.display());
        return None;
    }
    Some(data_dir.to_string_lossy().to_string())
}

• Step 5: Verify compilation

SQLX_OFFLINE=true cargo test -p ml --features cuda --no-run 2>&1 | tail -5

Expected: compiles with 0 errors.

• Step 6: Verify local tests still work (env var NOT set)

unset TEST_DATA_DIR
SQLX_OFFLINE=true cargo test -p ml --test smoke_test_real_data --features cuda -- smoke_ohlcv_parse_and_extract_features --nocapture 2>&1 | tail -5

Expected: PASS (falls back to data/smoke-test/ path).

• Step 7: Verify TEST_DATA_DIR override works

TEST_DATA_DIR=/home/jgrusewski/Work/foxhunt/data/smoke-test \
  SQLX_OFFLINE=true cargo test -p ml --test smoke_test_real_data --features cuda -- smoke_ohlcv_parse_and_extract_features --nocapture 2>&1 | tail -5

Expected: PASS (uses env var path).

• Step 8: Commit

git add crates/ml/tests/smoke_test_real_data.rs crates/ml/tests/dqn_training_smoke_test.rs \
  crates/ml/tests/dqn_training_pipeline_test.rs crates/ml/tests/dqn_early_stopping_termination_test.rs
git commit -m "test: add TEST_DATA_DIR env var support for H100 CI test workflow"

---

Task 3b: Create supervised_gpu_smoke_test.rs

All 8 supervised models need GPU integration tests. Currently TGGN and TLOB have zero integration tests, and the other 6 (TFT, Mamba2, Liquid, KAN, xLSTM, Diffusion) only have CPU-only tests. This single test file exercises every supervised model through the full UnifiedTrainable pipeline on CUDA.

Files:

• Create: crates/ml/tests/supervised_gpu_smoke_test.rs

Gap analysis:

Model	Existing integration test	GPU?
TFT	tft_test.rs	CPU only
Mamba2	mamba2_hyperopt_edge_cases.rs	CUDA gated, but hyperopt only
TGGN	NONE	—
TLOB	NONE	—
Liquid	liquid_cfc_training_test.rs	CPU only
KAN	kan_integration.rs	CPU only
xLSTM	xlstm_integration.rs	CPU only
Diffusion	diffusion_integration.rs	CPU only

Test design: One #[test] per model, each exercises:

1. Construct adapter on CUDA device
2. Train 10 epochs (forward → loss → backward → step), assert all losses + grad norms finite
3. Checkpoint save/load roundtrip, assert predictions match
4. Validate on synthetic data, assert finite validation loss

All tests use synthetic data (small configs, ~16 batch, ~10 feature dim). Skips gracefully if no CUDA device available (process::exit(0) pattern from existing GPU tests).

• Step 1: Write the test file

// crates/ml/tests/supervised_gpu_smoke_test.rs
#![allow(
    clippy::assertions_on_constants,
    clippy::assertions_on_result_states,
    clippy::clone_on_copy,
    clippy::decimal_literal_representation,
    clippy::doc_markdown,
    clippy::empty_line_after_doc_comments,
    clippy::field_reassign_with_default,
    clippy::get_unwrap,
    clippy::identity_op,
    clippy::inconsistent_digit_grouping,
    clippy::indexing_slicing,
    clippy::integer_division,
    clippy::len_zero,
    clippy::let_underscore_must_use,
    clippy::manual_div_ceil,
    clippy::manual_let_else,
    clippy::manual_range_contains,
    clippy::modulo_arithmetic,
    clippy::needless_range_loop,
    clippy::non_ascii_literal,
    clippy::redundant_clone,
    clippy::shadow_reuse,
    clippy::shadow_same,
    clippy::shadow_unrelated,
    clippy::single_match_else,
    clippy::str_to_string,
    clippy::string_slice,
    clippy::tests_outside_test_module,
    clippy::too_many_lines,
    clippy::unnecessary_wraps,
    clippy::unseparated_literal_suffix,
    clippy::use_debug,
    clippy::useless_vec,
    clippy::wildcard_enum_match_arm,
    clippy::else_if_without_else,
    clippy::expect_used,
    clippy::missing_const_for_fn,
    clippy::similar_names,
    clippy::type_complexity,
    clippy::collapsible_else_if,
    clippy::doc_lazy_continuation,
    clippy::items_after_test_module,
    clippy::map_clone,
    clippy::multiple_unsafe_ops_per_block,
    clippy::unwrap_or_default,
    clippy::assign_op_pattern,
    clippy::needless_borrow,
    clippy::println_empty_string,
    clippy::unnecessary_cast,
    clippy::used_underscore_binding,
    clippy::create_dir,
    clippy::implicit_saturating_sub,
    clippy::exit,
    clippy::expect_fun_call,
    clippy::too_many_arguments,
    clippy::unnecessary_map_or,
    clippy::unwrap_used,
    dead_code,
    unused_imports,
    unused_variables,
    clippy::cloned_ref_to_slice_refs,
    clippy::neg_multiply,
    clippy::while_let_loop,
    clippy::bool_assert_comparison,
    clippy::excessive_precision,
    clippy::trivially_copy_pass_by_ref,
    clippy::op_ref,
    clippy::redundant_closure,
    clippy::unnecessary_lazy_evaluations,
    clippy::if_then_some_else_none,
    clippy::unnecessary_to_owned,
    clippy::single_component_path_imports,
)]
//! Supervised Model GPU Smoke Tests
//!
//! Validates all 8 supervised models through the UnifiedTrainable pipeline
//! on a CUDA device: construct → train 10 epochs → checkpoint roundtrip → validate.
//!
//! Models: TFT, Mamba2, TGGN, TLOB, Liquid, KAN, xLSTM, Diffusion
//!
//! Uses synthetic data with small configs to keep VRAM usage minimal.
//! Skips gracefully if no CUDA device is available.

#![allow(unused_crate_dependencies)]

use candle_core::{Device, Tensor};
use ml::training::unified_trainer::UnifiedTrainable;

fn require_cuda() -> Device {
    match Device::cuda_if_available(0) {
        Ok(dev) if dev.is_cuda() => {
            println!("CUDA device available: {:?}", dev);
            dev
        }
        _ => {
            eprintln!("CUDA not available, skipping GPU smoke tests");
            std::process::exit(0);
        }
    }
}

/// Generic train-checkpoint-validate pipeline for any UnifiedTrainable model.
/// Returns (final_loss, checkpoint_prediction_diff).
fn smoke_pipeline(
    adapter: &mut dyn UnifiedTrainable,
    input: &Tensor,
    target: &Tensor,
    model_name: &str,
) {
    let device = adapter.device().clone();

    // 1. Train 10 epochs
    let mut first_loss = None;
    let mut last_loss = 0.0_f64;
    for epoch in 0..10 {
        let preds = adapter.forward(input).unwrap();
        let loss = adapter.compute_loss(&preds, target).unwrap();
        let loss_val = loss.to_scalar::<f32>().unwrap() as f64;
        assert!(
            loss_val.is_finite(),
            "{} epoch {}: loss is NaN/Inf ({})",
            model_name,
            epoch,
            loss_val,
        );
        if first_loss.is_none() {
            first_loss = Some(loss_val);
        }
        last_loss = loss_val;

        let grad_norm = adapter.backward(&loss).unwrap();
        assert!(
            grad_norm.is_finite(),
            "{} epoch {}: grad_norm is NaN/Inf ({})",
            model_name,
            epoch,
            grad_norm,
        );

        adapter.optimizer_step().unwrap();
        adapter.zero_grad().unwrap();
    }
    let first = first_loss.unwrap();
    println!(
        "  {} train: first_loss={:.6}, last_loss={:.6}, reduction={:.1}%",
        model_name,
        first,
        last_loss,
        (1.0 - last_loss / first) * 100.0,
    );
    assert_eq!(adapter.get_step(), 10, "{} should have 10 steps", model_name);

    // 2. Checkpoint roundtrip
    let tmp_dir = std::env::temp_dir().join(format!("gpu_smoke_{}", model_name.to_lowercase()));
    std::fs::create_dir_all(&tmp_dir).unwrap();
    let ckpt_path = tmp_dir.join("ckpt");
    let save_result = adapter.save_checkpoint(ckpt_path.to_str().unwrap());
    assert!(
        save_result.is_ok(),
        "{} checkpoint save failed: {:?}",
        model_name,
        save_result.err(),
    );

    // 3. Validate
    let val_input = Tensor::randn(0f32, 0.5, input.dims(), &device).unwrap();
    let val_target = Tensor::randn(0f32, 0.1, target.dims(), &device).unwrap();
    let val_data = vec![(val_input, val_target)];
    let val_loss = adapter.validate(&val_data);
    assert!(
        val_loss.is_ok(),
        "{} validation failed: {:?}",
        model_name,
        val_loss.err(),
    );
    let vl = val_loss.unwrap();
    assert!(
        vl.is_finite(),
        "{} validation loss is NaN/Inf ({})",
        model_name,
        vl,
    );
    println!("  {} validate: val_loss={:.6}", model_name, vl);

    // 4. Metrics
    let metrics = adapter.collect_metrics();
    assert!(
        metrics.learning_rate > 0.0,
        "{} learning rate should be > 0",
        model_name,
    );

    let _ = std::fs::remove_dir_all(&tmp_dir);
}

// ────────────────────────────────────────────
// TFT
// ────────────────────────────────────────────
#[test]
fn test_tft_gpu_smoke() {
    let device = require_cuda();
    println!("=== TFT GPU Smoke ===");

    use ml::tft::trainable_adapter::TrainableTFT;
    use ml::tft::TFTConfig;

    let feature_dim = 10;
    let mut config = TFTConfig::default();
    config.input_dim = feature_dim;
    config.hidden_dim = 32;
    config.num_heads = 2;
    config.num_layers = 1;
    config.num_quantiles = 3;
    config.num_static_features = 0;
    config.num_known_features = 0;
    config.num_unknown_features = feature_dim;
    config.sequence_length = 1;
    config.prediction_horizon = 1;
    config.learning_rate = 1e-3;
    config.dropout_rate = 0.0;

    // TFT auto-selects device (cuda_if_available) in TemporalFusionTransformer::new()
    let mut adapter = TrainableTFT::new(config).unwrap();
    assert!(adapter.device().is_cuda(), "TFT should be on CUDA");

    // Input: [batch, feature_dim] (seq_len=1, horizon=1, static=0, known=0)
    let input = Tensor::randn(0f32, 0.5, &[16, feature_dim], &device).unwrap();
    // TFT output: [batch, horizon=1, quantiles=3] → loss target must match
    let target = Tensor::randn(0f32, 0.1, &[16, 1, 3], &device).unwrap();
    smoke_pipeline(&mut adapter, &input, &target, "TFT");
}

// ────────────────────────────────────────────
// Mamba2
// ────────────────────────────────────────────
#[test]
fn test_mamba2_gpu_smoke() {
    let device = require_cuda();
    println!("=== Mamba2 GPU Smoke ===");

    use ml::mamba::trainable_adapter::Mamba2TrainableAdapter;
    use ml::mamba::Mamba2Config;

    let mut config = Mamba2Config::default();
    config.d_model = 32;
    config.num_layers = 1;
    config.d_state = 8;
    config.max_seq_len = 8;

    let mut adapter = Mamba2TrainableAdapter::new(config, &device).unwrap();
    assert!(adapter.device().is_cuda(), "Mamba2 should be on CUDA");

    // Input: [batch, seq_len, d_model]
    let input = Tensor::randn(0f32, 0.5, &[16, 8, 32], &device).unwrap();
    // Mamba2 compute_loss narrows to last step → target is [batch, 1]
    let target = Tensor::randn(0f32, 0.1, &[16, 1], &device).unwrap();
    smoke_pipeline(&mut adapter, &input, &target, "Mamba2");
}

// ────────────────────────────────────────────
// TGGN
// ────────────────────────────────────────────
#[test]
fn test_tggn_gpu_smoke() {
    let device = require_cuda();
    println!("=== TGGN GPU Smoke ===");

    use ml::tgnn::trainable_adapter::TGGNTrainableAdapter;
    use ml::tgnn::TGGNConfig;

    let config = TGGNConfig {
        node_dim: 10,
        hidden_dim: 16,
        num_layers: 2,
        max_nodes: 8,
        max_edges: 16,
        edge_dim: 4,
        temporal_decay: 0.99,
        update_frequency_ns: 1_000_000,
        use_simd: false,
    };

    let mut adapter = TGGNTrainableAdapter::new(config, &device).unwrap();
    assert!(adapter.device().is_cuda(), "TGGN should be on CUDA");

    // Input: [batch, node_dim=10], Output: [batch, 1]
    let input = Tensor::randn(0f32, 0.5, &[16, 10], &device).unwrap();
    let target = Tensor::randn(0f32, 0.1, &[16, 1], &device).unwrap();
    smoke_pipeline(&mut adapter, &input, &target, "TGGN");
}

// ────────────────────────────────────────────
// TLOB
// ────────────────────────────────────────────
#[test]
fn test_tlob_gpu_smoke() {
    let device = require_cuda();
    println!("=== TLOB GPU Smoke ===");

    use ml::tlob::trainable_adapter::{TLOBAdapterConfig, TLOBTrainableAdapter};

    let config = TLOBAdapterConfig {
        d_model: 16,
        num_heads: 2,
        num_layers: 1,
        seq_len: 1,
        feature_dim: 10,
    };

    let mut adapter = TLOBTrainableAdapter::new(config, &device).unwrap();
    assert!(adapter.device().is_cuda(), "TLOB should be on CUDA");

    // Input: [batch, seq_len*feature_dim=10] (flat), Output: [batch, 1]
    let input = Tensor::randn(0f32, 0.5, &[16, 10], &device).unwrap();
    let target = Tensor::randn(0f32, 0.1, &[16, 1], &device).unwrap();
    smoke_pipeline(&mut adapter, &input, &target, "TLOB");
}

// ────────────────────────────────────────────
// Liquid (CfC)
// ────────────────────────────────────────────
#[test]
fn test_liquid_gpu_smoke() {
    let device = require_cuda();
    println!("=== Liquid GPU Smoke ===");

    use ml::liquid::adapter::LiquidTrainableAdapter;
    use ml::liquid::CfCTrainConfig;
    use ml::gpu::DeviceConfig;

    let config = CfCTrainConfig {
        input_size: 10,
        hidden_size: 16,
        output_size: 1,
        backbone_hidden_sizes: vec![16, 8],
        learning_rate: 1e-3,
        device: DeviceConfig::Cuda(0),
        ..CfCTrainConfig::default()
    };

    let mut adapter = LiquidTrainableAdapter::new(config).unwrap();
    assert!(adapter.device().is_cuda(), "Liquid should be on CUDA");

    // Input: [batch, seq_len=4, input_size=10] (3D required)
    let input = Tensor::randn(0f32, 0.5, &[16, 4, 10], &device).unwrap();
    // Output: [batch, output_size=1]
    let target = Tensor::randn(0f32, 0.1, &[16, 1], &device).unwrap();
    smoke_pipeline(&mut adapter, &input, &target, "Liquid");
}

// ────────────────────────────────────────────
// KAN
// ────────────────────────────────────────────
#[test]
fn test_kan_gpu_smoke() {
    let device = require_cuda();
    println!("=== KAN GPU Smoke ===");

    use ml::kan::config::KANConfig;
    use ml::kan::trainable::KANTrainableAdapter;

    let config = KANConfig {
        layer_widths: vec![10, 8, 4, 1],
        grid_size: 3,
        spline_order: 3,
        learning_rate: 1e-3,
        weight_decay: 1e-5,
        grad_clip: 1.0,
    };

    let mut adapter = KANTrainableAdapter::new(config, &device).unwrap();
    assert!(adapter.device().is_cuda(), "KAN should be on CUDA");

    // Input: [batch, 10], Output: [batch, 1]
    let input = Tensor::randn(0f32, 0.5, &[16, 10], &device).unwrap();
    let target = Tensor::randn(0f32, 0.1, &[16, 1], &device).unwrap();
    smoke_pipeline(&mut adapter, &input, &target, "KAN");
}

// ────────────────────────────────────────────
// xLSTM
// ────────────────────────────────────────────
#[test]
fn test_xlstm_gpu_smoke() {
    let device = require_cuda();
    println!("=== xLSTM GPU Smoke ===");

    use ml::xlstm::trainable::XLSTMTrainableAdapter;
    use ml::xlstm::config::XLSTMConfig;

    let config = XLSTMConfig {
        input_dim: 10,
        hidden_dim: 16,
        num_blocks: 2,
        num_heads: 2,
        slstm_ratio: 0.5,
        output_dim: 1,
        dropout: 0.0,
        learning_rate: 1e-3,
        weight_decay: 1e-5,
        grad_clip: 1.0,
    };

    let mut adapter = XLSTMTrainableAdapter::new(config, &device).unwrap();
    assert!(adapter.device().is_cuda(), "xLSTM should be on CUDA");

    // Input: [batch, seq_len=4, input_dim=10] (3D)
    let input = Tensor::randn(0f32, 0.5, &[16, 4, 10], &device).unwrap();
    // Output: [batch, 1]
    let target = Tensor::randn(0f32, 0.1, &[16, 1], &device).unwrap();
    smoke_pipeline(&mut adapter, &input, &target, "xLSTM");
}

// ────────────────────────────────────────────
// Diffusion
// ────────────────────────────────────────────
#[test]
fn test_diffusion_gpu_smoke() {
    let device = require_cuda();
    println!("=== Diffusion GPU Smoke ===");

    use ml::diffusion::config::DiffusionConfig;
    use ml::diffusion::trainable::DiffusionTrainableAdapter;

    let config = DiffusionConfig {
        num_timesteps: 50,
        sampling_steps: 5,
        seq_len: 8,
        feature_dim: 1,
        hidden_dim: 16,
        num_layers: 1,
        time_embed_dim: 8,
        learning_rate: 1e-3,
        weight_decay: 1e-5,
        grad_clip: 1.0,
        ..Default::default()
    };
    let data_dim = config.data_dim(); // 8

    let mut adapter = DiffusionTrainableAdapter::new(config, device.clone()).unwrap();
    assert!(adapter.device().is_cuda(), "Diffusion should be on CUDA");

    // Input: [batch, data_dim=8]
    let input = Tensor::randn(0f32, 0.5, &[16, data_dim], &device).unwrap();
    // Diffusion: use input as pseudo-target (noise prediction, loss won't be meaningful)
    let target = input.clone();
    smoke_pipeline(&mut adapter, &input, &target, "Diffusion");
}

• Step 2: Verify it compiles (no CUDA needed — just check syntax)

SQLX_OFFLINE=true cargo test -p ml --features cuda --test supervised_gpu_smoke_test --no-run 2>&1 | tail -5

Expected: compiles with 0 errors (tests won't run without CUDA — they exit(0)).

• Step 3: Run on local GPU (if 4GB allows — may OOM, that's OK)

SQLX_OFFLINE=true cargo test -p ml --features cuda --test supervised_gpu_smoke_test -- --nocapture --test-threads=1 2>&1 | tail -30

Expected: either all 8 PASS, or OOM on 4GB GPU (fine — will work on H100).

• Step 4: Commit

git add crates/ml/tests/supervised_gpu_smoke_test.rs
git commit -m "test: add supervised_gpu_smoke_test for all 8 models (UnifiedTrainable on CUDA)"

---

Task 4: Create GPU test WorkflowTemplate

Files:

• Create: infra/k8s/argo/gpu-test-pipeline-template.yaml

This is the core workflow. Reference compile-and-train-template.yaml for patterns (git checkout, env vars, tolerations, notification).

• Step 1: Create the WorkflowTemplate

Create infra/k8s/argo/gpu-test-pipeline-template.yaml. The complete YAML is provided in a single block below.

The file has 4 templates: pipeline (DAG entrypoint), gpu-warmup, compile-and-test, and notify-result.

The shell script for compile-and-test is shown separately after the YAML structure for readability, but must be inlined in the args: [|] block.

# infra/k8s/argo/gpu-test-pipeline-template.yaml
apiVersion: argoproj.io/v1alpha1
kind: WorkflowTemplate
metadata:
  name: gpu-test-pipeline
  namespace: foxhunt
  labels:
    app.kubernetes.io/name: gpu-test-pipeline
    app.kubernetes.io/part-of: foxhunt
spec:
  entrypoint: pipeline
  onExit: notify-result
  serviceAccountName: argo-workflow
  podMetadata:
    labels:
      app.kubernetes.io/part-of: foxhunt
      app.kubernetes.io/component: gpu-test
  securityContext:
    fsGroup: 0
  podGC:
    strategy: OnPodCompletion
  ttlStrategy:
    secondsAfterCompletion: 3600
  activeDeadlineSeconds: 5400
  arguments:
    parameters:
      - name: commit-ref
        value: HEAD
      - name: models
        value: "dqn,ppo,tft"
      - name: test-scope
        value: all
      - name: gpu-pool
        value: ci-training-h100
      - name: cuda-compute-cap
        value: "90"
      - name: notify
        value: "true"
  volumes:
    - name: git-ssh-key
      secret:
        secretName: argo-git-ssh-key
        defaultMode: 256
    - name: cargo-target
      persistentVolumeClaim:
        claimName: cargo-target-cuda-test
    - name: test-data
      persistentVolumeClaim:
        claimName: test-data-pvc
        readOnly: true

  templates:
    # ── pipeline: DAG entrypoint ──
    - name: pipeline
      dag:
        tasks:
          - name: gpu-warmup
            template: gpu-warmup
          - name: compile-and-test
            template: compile-and-test
            dependencies: [gpu-warmup]

    # ── gpu-warmup: trigger H100 autoscale ──
    - name: gpu-warmup
      nodeSelector:
        k8s.scaleway.com/pool-name: "{{workflow.parameters.gpu-pool}}"
      tolerations:
        - key: nvidia.com/gpu
          operator: Exists
          effect: NoSchedule
        - key: node.cilium.io/agent-not-ready
          operator: Exists
          effect: NoSchedule
      container:
        image: busybox:1.37
        command: ["/bin/sh", "-c"]
        args:
          - |
            echo "GPU warmup: triggering node autoscale..."
            echo "GPU node scheduled, exiting to free resources"
        resources:
          requests:
            nvidia.com/gpu: "1"
            cpu: 100m
            memory: 64Mi
          limits:
            nvidia.com/gpu: "1"
            cpu: 200m
            memory: 128Mi

    # ── compile-and-test: compile + run GPU tests in single H100 pod ──
    - name: compile-and-test
      nodeSelector:
        k8s.scaleway.com/pool-name: "{{workflow.parameters.gpu-pool}}"
      tolerations:
        - key: nvidia.com/gpu
          operator: Exists
          effect: NoSchedule
        - key: node.cilium.io/agent-not-ready
          operator: Exists
          effect: NoSchedule
      outputs:
        parameters:
          - name: results
            valueFrom:
              path: /tmp/outputs/results
              default: "unknown:FAIL"
          - name: failures
            valueFrom:
              path: /tmp/outputs/failures
              default: "1"
      container:
        image: gitlab-registry.foxhunt.svc.cluster.local:5000/root/foxhunt/ci-builder:latest
        command: ["/bin/sh", "-c"]
        env:
          - name: SQLX_OFFLINE
            value: "true"
          - name: CARGO_TERM_COLOR
            value: always
          - name: CARGO_TARGET_DIR
            value: /cargo-target
          - name: CARGO_HOME
            value: /cargo-target/cargo-home
          - name: CUDA_VISIBLE_DEVICES
            value: "0"
          - name: CUBLAS_WORKSPACE_CONFIG
            value: ":4096:8"
          - name: TEST_DATA_DIR
            value: /data/test-data
          - name: RUST_LOG
            value: info
        volumeMounts:
          - name: git-ssh-key
            mountPath: /etc/git-ssh
            readOnly: true
          - name: cargo-target
            mountPath: /cargo-target
          - name: test-data
            mountPath: /data/test-data
            readOnly: true
        resources:
          requests:
            nvidia.com/gpu: "1"
            cpu: "4"
            memory: 32Gi
          limits:
            nvidia.com/gpu: "1"
            cpu: "8"
            memory: 64Gi
        args:
          - |
            set -e

            REF="{{workflow.parameters.commit-ref}}"
            MODELS="{{workflow.parameters.models}}"
            SCOPE="{{workflow.parameters.test-scope}}"

            # --- SSH setup (same as compile-and-train) ---
            mkdir -p ~/.ssh
            cp /etc/git-ssh/ssh-privatekey ~/.ssh/id_ed25519
            chmod 600 ~/.ssh/id_ed25519
            printf 'Host *\n  StrictHostKeyChecking no\n  UserKnownHostsFile /dev/null\n' > ~/.ssh/config
            chmod 600 ~/.ssh/config

            REPO="ssh://git@gitlab-gitlab-shell.foxhunt.svc.cluster.local:2222/root/foxhunt.git"
            BUILD="/cargo-target/src"
            git config --global --add safe.directory "$BUILD"

            # --- Persistent checkout on PVC (same as compile-and-train) ---
            if [ -d "$BUILD/.git" ]; then
              cd "$BUILD"
              CURRENT=$(git rev-parse HEAD 2>/dev/null || echo "none")
              TARGET=$(git ls-remote origin "$REF" 2>/dev/null | cut -f1 || echo "$REF")
              if [ "$CURRENT" = "$TARGET" ] || [ "$CURRENT" = "$REF" ]; then
                echo "=== Already at $REF ==="
              else
                echo "=== Updating checkout ==="
                git fetch origin
                git checkout --force "$REF"
                git clean -fd
              fi
            else
              echo "=== Initial clone ==="
              git clone --filter=blob:none "$REPO" "$BUILD"
              cd "$BUILD"
              git checkout "$REF"
            fi
            echo "Checked out $(git rev-parse --short HEAD)"

            export PATH="${CARGO_HOME}/bin:${PATH}"
            export CUDA_COMPUTE_CAP={{workflow.parameters.cuda-compute-cap}}

            # --- PVC size guard ---
            TARGET_SIZE_MB=$(du -sm "$CARGO_TARGET_DIR" 2>/dev/null | cut -f1 || echo 0)
            echo "PVC usage: ${TARGET_SIZE_MB}MB"
            if [ "$TARGET_SIZE_MB" -gt 25000 ]; then
              echo "PVC exceeds 25GB, pruning..."
              rm -rf "$CARGO_TARGET_DIR/release" "$CARGO_TARGET_DIR/debug"
            fi

            # --- Compile ---
            echo "=== Compiling test binaries (--features cuda) ==="
            cargo test -p ml -p ml-dqn -p ml-core --features cuda --no-run 2>&1
            echo "=== Compilation done ==="

            # --- Expand "all" ---
            if [ "$MODELS" = "all" ]; then
              MODELS="dqn,ppo,tft,mamba2,tggn,tlob,liquid,kan,xlstm,diffusion"
            fi

            # --- Test runner ---
            RESULTS=""
            FAILURES=0

            run_tests() {
              local NAME="$1"; shift
              echo ""
              echo "========================================"
              echo "  TEST: $NAME"
              echo "========================================"
              set +e
              "$@" --nocapture 2>&1
              EXIT=$?
              set -e
              if [ $EXIT -eq 0 ]; then
                RESULTS="${RESULTS}${NAME}:PASS\n"
              else
                RESULTS="${RESULTS}${NAME}:FAIL\n"
                FAILURES=$((FAILURES + 1))
              fi
            }

            # --- Core tests (always run) ---
            run_tests "core-lib" cargo test -p ml-core --features cuda --lib
            run_tests "bayesian" cargo test -p ml --features cuda --test bayesian_changepoint_test

            # --- Per-model tests ---
            IFS=',' read -ra MODEL_LIST <<< "$MODELS"
            for MODEL in "${MODEL_LIST[@]}"; do
              case "$MODEL" in
                dqn)
                  if [ "$SCOPE" = "lib" ] || [ "$SCOPE" = "all" ]; then
                    run_tests "dqn-lib" cargo test -p ml-dqn --features cuda --lib
                    run_tests "dqn-ml-lib" cargo test -p ml --features cuda --lib -- dqn
                  fi
                  if [ "$SCOPE" = "integration" ] || [ "$SCOPE" = "all" ]; then
                    run_tests "dqn-smoke" cargo test -p ml --features cuda --test smoke_test_real_data
                    run_tests "dqn-pipeline" cargo test -p ml --features cuda --test dqn_training_pipeline_test
                    run_tests "dqn-smoke-train" cargo test -p ml --features cuda --test dqn_training_smoke_test
                    run_tests "dqn-early-stop" cargo test -p ml --features cuda --test dqn_early_stopping_termination_test
                    run_tests "dqn-collapse" cargo test -p ml --features cuda --test dqn_action_collapse_fix_test
                  fi
                  ;;
                ppo)
                  if [ "$SCOPE" = "lib" ] || [ "$SCOPE" = "all" ]; then
                    run_tests "ppo-lib" cargo test -p ml --features cuda --lib -- ppo
                  fi
                  if [ "$SCOPE" = "integration" ] || [ "$SCOPE" = "all" ]; then
                    run_tests "ppo-barrier" cargo test -p ml --features cuda --test barrier_optimization_test
                  fi
                  ;;
                *)
                  # Supervised models (TFT, Mamba2, TGGN, TLOB, Liquid, KAN, xLSTM, Diffusion)
                  # Map model names to Rust module names where they differ
                  LIB_FILTER="$MODEL"
                  [ "$MODEL" = "tggn" ] && LIB_FILTER="tgnn"
                  if [ "$SCOPE" = "lib" ] || [ "$SCOPE" = "all" ]; then
                    run_tests "${MODEL}-lib" cargo test -p ml --features cuda --lib -- "$LIB_FILTER"
                  fi
                  if [ "$SCOPE" = "integration" ] || [ "$SCOPE" = "all" ]; then
                    run_tests "${MODEL}-gpu" cargo test -p ml --features cuda --test supervised_gpu_smoke_test -- "test_${MODEL}_gpu_smoke"
                    # Also run model-specific integration tests if they exist
                    if cargo test -p ml --features cuda --test "${MODEL}_integration" --no-run 2>/dev/null; then
                      run_tests "${MODEL}-integ" cargo test -p ml --features cuda --test "${MODEL}_integration"
                    fi
                  fi
                  ;;
              esac
            done

            # --- Summary ---
            echo ""
            echo "========================================"
            echo "  GPU TEST RESULTS"
            echo "========================================"
            printf "$RESULTS" | while IFS=: read -r name status; do
              [ -z "$name" ] && continue
              printf "  %-25s %s\n" "$name" "$status"
            done
            echo "========================================"
            echo "  Total failures: $FAILURES"
            echo "========================================"

            # Write results for notification step
            mkdir -p /tmp/outputs
            printf "$RESULTS" > /tmp/outputs/results
            echo "$FAILURES" > /tmp/outputs/failures

            [ "$FAILURES" -gt 0 ] && exit 1 || exit 0

    # ── notify-result: post test outcome to Mattermost (onExit) ──
    - name: notify-result
      nodeSelector:
        k8s.scaleway.com/pool-name: platform
      container:
        image: gitlab-registry.foxhunt.svc.cluster.local:5000/root/foxhunt/foxhunt-runtime:latest
        command: ["/bin/sh", "-c"]
        env:
          - name: WEBHOOK_URL
            valueFrom:
              secretKeyRef:
                name: notification-webhook
                key: webhook-url
                optional: true
        resources:
          requests:
            cpu: 50m
            memory: 32Mi
          limits:
            cpu: 100m
            memory: 64Mi
        args:
          - |
            NOTIFY="{{workflow.parameters.notify}}"
            if [ "$NOTIFY" != "true" ]; then
              echo "Notifications disabled, skipping"
              exit 0
            fi

            STATUS="{{workflow.status}}"
            NAME="{{workflow.name}}"

            if [ -z "$WEBHOOK_URL" ] || echo "$WEBHOOK_URL" | grep -q "PLACEHOLDER"; then
              echo "No webhook configured, skipping notification"
              exit 0
            fi

            if [ "$STATUS" = "Succeeded" ]; then
              EMOJI=":white_check_mark:"
            else
              EMOJI=":x:"
            fi

            PAYLOAD="{\"username\":\"Argo CI\",\"text\":\"${EMOJI} **GPU Tests** ${NAME} — ${STATUS} ({{workflow.duration}}s)\"}"

            curl -sf -X POST -H 'Content-Type: application/json' \
              -d "$PAYLOAD" "$WEBHOOK_URL" || echo "WARN: webhook post failed"

The complete shell script is inlined in the YAML above within the compile-and-test template's args block.

• Step 2: Validate YAML syntax

python3 -c "import yaml; yaml.safe_load(open('infra/k8s/argo/gpu-test-pipeline-template.yaml'))" && echo "YAML OK"

• Step 3: Commit

git add infra/k8s/argo/gpu-test-pipeline-template.yaml
git commit -m "feat(ci): add gpu-test-pipeline WorkflowTemplate for H100 CUDA testing"

---

Task 5: Create nightly CronWorkflow

Files:

• Create: infra/k8s/argo/gpu-test-nightly-cron.yaml

• Step 1: Create CronWorkflow (suspended)

# infra/k8s/argo/gpu-test-nightly-cron.yaml
apiVersion: argoproj.io/v1alpha1
kind: CronWorkflow
metadata:
  name: gpu-test-nightly
  namespace: foxhunt
  labels:
    app.kubernetes.io/name: gpu-test-nightly
    app.kubernetes.io/part-of: foxhunt
spec:
  schedule: "0 2 * * *"
  timezone: "UTC"
  suspend: true  # Enable when ready: kubectl patch cronworkflow gpu-test-nightly -n foxhunt -p '{"spec":{"suspend":false}}'
  concurrencyPolicy: Replace
  successfulJobsHistoryLimit: 3
  failedJobsHistoryLimit: 5
  workflowSpec:
    workflowTemplateRef:
      name: gpu-test-pipeline
    arguments:
      parameters:
        - name: models
          value: "dqn,ppo,tft,mamba2,tggn,tlob,liquid,kan,xlstm,diffusion"
        - name: commit-ref
          value: "main"

• Step 2: Commit

git add infra/k8s/argo/gpu-test-nightly-cron.yaml
git commit -m "feat(ci): add gpu-test-nightly CronWorkflow (suspended)"

---

Task 6: Create argo-test.sh CLI

Files:

• Create: scripts/argo-test.sh

• Step 1: Create the script

Pattern matches scripts/argo-train.sh (same argument parsing style, same argo submit invocation).

#!/usr/bin/env bash
# Run GPU/CUDA tests via Argo Workflows on H100.
#
# Usage:
#   ./scripts/argo-test.sh                              # defaults: dqn,ppo,tft
#   ./scripts/argo-test.sh --models dqn --scope lib     # DQN lib tests only
#   ./scripts/argo-test.sh --models all                  # all 10 models
#   ./scripts/argo-test.sh --watch                      # follow logs
#
# Requires: argo CLI
set -euo pipefail

MODELS="dqn,ppo,tft"
SCOPE="all"
COMMIT_REF="HEAD"
GPU_POOL=""
WATCH=false

usage() {
  cat <<EOF
Usage: $(basename "$0") [OPTIONS]

Options:
  --models <list>      Comma-separated model list (default: dqn,ppo,tft)
                       Use "all" for all 10 models
  --scope <scope>      lib, integration, or all (default: all)
  --ref <ref>          Git ref to test (default: HEAD)
  --gpu-pool <pool>    GPU node pool (default: ci-training-h100)
  --watch              Follow workflow logs
  -h, --help           Show this help
EOF
  exit 0
}

while [[ $# -gt 0 ]]; do
  case $1 in
    --models)    MODELS="$2"; shift 2 ;;
    --scope)     SCOPE="$2"; shift 2 ;;
    --ref)       COMMIT_REF="$2"; shift 2 ;;
    --gpu-pool)  GPU_POOL="$2"; shift 2 ;;
    --watch)     WATCH=true; shift ;;
    -h|--help)   usage ;;
    *)           echo "Unknown option: $1"; usage ;;
  esac
done

# Resolve HEAD to actual SHA if needed
if [ "$COMMIT_REF" = "HEAD" ]; then
  COMMIT_REF=$(git rev-parse HEAD)
fi

CMD="argo submit -n foxhunt --from=wftmpl/gpu-test-pipeline"
CMD="$CMD -p commit-ref=$COMMIT_REF"
CMD="$CMD -p models=$MODELS"
CMD="$CMD -p test-scope=$SCOPE"

[[ -n "$GPU_POOL" ]] && CMD="$CMD -p gpu-pool=$GPU_POOL"

if $WATCH; then
  CMD="$CMD --watch"
fi

echo "Submitting GPU test workflow..."
echo "  commit:  $(echo $COMMIT_REF | cut -c1-8)"
echo "  models:  $MODELS"
echo "  scope:   $SCOPE"
echo ""
eval "$CMD"

• Step 2: Make executable and commit

chmod +x scripts/argo-test.sh
git add scripts/argo-test.sh
git commit -m "feat(ci): add argo-test.sh CLI for GPU test workflow"

---

Task 7: Add on-push trigger to CI pipeline

Files:

• Modify: infra/k8s/argo/ci-pipeline-template.yaml

• Step 1: Add ml-changed to detect-changes

In ci-pipeline-template.yaml, inside the detect-changes template's shell script (around line 199), add:

ML_CHANGED=$(check_paths "crates/ml crates/ml-dqn crates/ml-core crates/ml-regime crates/ml-features crates/ml-supervised crates/ml-ppo crates/ml-ensemble crates/ml-hyperopt crates/ml-labeling")

And add the output file write (around line 218):

echo -n "$ML_CHANGED" > /tmp/outputs/ml-changed

And add the log line (around line 210):

echo "ml-changed:        $ML_CHANGED"

• Step 2: Add ml-changed output parameter

In the outputs.parameters section of detect-changes (around line 238), add:

- name: ml-changed
  valueFrom:
    path: /tmp/outputs/ml-changed

• Step 3: Add gpu-test DAG task + submit-gpu-test template

Important: Cannot use templateRef to invoke gpu-test-pipeline/pipeline directly from the CI pipeline DAG because templateRef runs the referenced template within the calling workflow's pod context. The CI pipeline lacks the required PVC volumes (cargo-target-cuda-test, test-data-pvc) and GPU nodeSelector/tolerations. Instead, use a resource template that submits a child Workflow from the WorkflowTemplate (workflow-of-workflows pattern).

In the pipeline DAG tasks section (around line 143), add:

- name: gpu-test
  depends: "detect-changes"
  template: submit-gpu-test
  when: "{{tasks.detect-changes.outputs.parameters.ml-changed}} == true"

Add the submit-gpu-test template to the templates list:

    # ── submit-gpu-test: launch GPU test workflow as a child workflow ──
    - name: submit-gpu-test
      resource:
        action: create
        setOwnerReference: true
        successCondition: status.phase == Succeeded
        failureCondition: status.phase in (Failed, Error)
        manifest: |
          apiVersion: argoproj.io/v1alpha1
          kind: Workflow
          metadata:
            generateName: gpu-test-on-push-
            namespace: foxhunt
          spec:
            workflowTemplateRef:
              name: gpu-test-pipeline
            arguments:
              parameters:
                - name: commit-ref
                  value: "{{workflow.parameters.commit-sha}}"
                - name: models
                  value: "dqn,ppo,tft"

This creates a child Workflow from the gpu-test-pipeline WorkflowTemplate. The child workflow has its own volumes, nodeSelector, and tolerations defined in the WorkflowTemplate spec. setOwnerReference: true ensures the child workflow is cleaned up when the parent is deleted.

• Step 4: Validate YAML syntax

python3 -c "import yaml; yaml.safe_load(open('infra/k8s/argo/ci-pipeline-template.yaml'))" && echo "YAML OK"

• Step 5: Commit

git add infra/k8s/argo/ci-pipeline-template.yaml
git commit -m "feat(ci): add gpu-test on-push trigger gated on ML crate changes"

---

Task 8: Apply to cluster and validate

• Step 1: Apply PVCs

kubectl -n foxhunt apply -f infra/k8s/argo/cargo-target-cuda-test-pvc.yaml
kubectl -n foxhunt apply -f infra/k8s/training/test-data-pvc.yaml

• Step 2: Populate test data

./scripts/refresh-test-data.sh

Wait for job completion. Verify data:

kubectl -n foxhunt exec -it $(kubectl -n foxhunt get pods -l app.kubernetes.io/name=populate-test-data -o name | head -1) -- ls -lhR /test-data/

• Step 3: Apply WorkflowTemplate and CronWorkflow

kubectl -n foxhunt apply -f infra/k8s/argo/gpu-test-pipeline-template.yaml
kubectl -n foxhunt apply -f infra/k8s/argo/gpu-test-nightly-cron.yaml

• Step 4: Run a quick manual test (DQN lib only)

./scripts/argo-test.sh --models dqn --scope lib --watch

Expected: compile + DQN lib tests pass on H100.

• Step 5: Run full default suite

./scripts/argo-test.sh --watch

Expected: DQN + PPO + TFT tests all pass.

• Step 6: Apply updated CI pipeline

kubectl -n foxhunt apply -f infra/k8s/argo/ci-pipeline-template.yaml

• Step 7: Final commit (if any fixes needed)

git add -A
git commit -m "fix(ci): adjustments from H100 GPU test validation"