Assertions and Behavior Verification Questions

Approach: test the training script's two responsibilities separately — fetching features from an external feature-store client and checking GPU availability. Use unittest.mock to patch the feature-store client methods and torch.cuda.is_available (or a GPU-check wrapper) to simulate environments. Assert that when GPU unavailable the script exits/raises or skips training, and when available it proceeds and uses fetched features.

Example training launcher (simplified):

# train.py
import sys
import torch

def get_features(fs_client, feature_names):
    return fs_client.fetch(feature_names)

def check_gpu_or_exit():
    if not torch.cuda.is_available():
        raise SystemExit("GPU not available")

def main(fs_client, feature_names):
    check_gpu_or_exit()
    features = get_features(fs_client, feature_names)
    # ... start training with features ...
    return features  # return for testability

Pytest tests using unittest.mock:

# test_train.py
import pytest
from unittest.mock import Mock, patch
import train

def test_main_gpu_available_fetches_features():
    mock_fs = Mock()
    mock_fs.fetch.return_value = {"feat1": [1,2,3]}
    with patch("torch.cuda.is_available", return_value=True):
        features = train.main(mock_fs, ["feat1"])
    mock_fs.fetch.assert_called_once_with(["feat1"])
    assert features == {"feat1": [1,2,3]}

def test_main_gpu_unavailable_exits():
    mock_fs = Mock()
    with patch("torch.cuda.is_available", return_value=False):
        with pytest.raises(SystemExit) as exc:
            train.main(mock_fs, ["feat1"])
    assert "GPU not available" in str(exc.value)

Notes:

• If your project abstracts GPU checks (e.g., utils.check_gpu()), patch that instead for easier testing.
• For integration-like tests, monkeypatch can replace an instantiated feature-store client factory to return a Mock with controlled responses.
• Also test feature-store errors (mock_fs.fetch.side_effect = Exception(...)) to ensure proper retries/handling.

Here's a pytest test module covering correct values, division-by-zero handling, and failure messages:

import pytest
from math import isclose
from my_module import recall  # adjust import to where recall is defined

def test_recall_typical_case():
    y_true = [1, 0, 1, 1, 0, 1, 0, 1]  # 5 positives
    y_pred = [1, 0, 1, 0, 0, 1, 0, 1]  # one false negative -> tp=4, fn=1 -> recall=0.8
    expected = 0.8
    got = recall(y_true, y_pred)
    assert isclose(got, expected, rel_tol=1e-9), f"expected recall {expected} for typical case but got {got}"

def test_recall_perfect_and_zero():
    # perfect recall: all true positives predicted
    y_true = [1,1,1]
    y_pred = [1,1,1]
    assert recall(y_true, y_pred) == 1.0, f"expected recall 1.0 for perfect recall but got {recall(y_true, y_pred)}"

    # zero recall: no positives predicted
    y_true = [1,1,0]
    y_pred = [0,0,0]
    expected = 0.0
    got = recall(y_true, y_pred)
    assert isclose(got, expected, rel_tol=1e-9), f"expected recall {expected} when no positives predicted but got {got}"

def test_recall_division_by_zero_raises():
    # no positive labels in y_true -> tp+fn == 0 -> division by zero
    y_true = [0,0,0]
    y_pred = [0,1,0]
    with pytest.raises(ZeroDivisionError, match="division by zero"):
        recall(y_true, y_pred)

Notes:

• Use isclose for float comparisons to avoid precision issues.
• Adjust import path for recall.
• The last test asserts the function raises ZeroDivisionError; if you prefer the function to return 0.0 when there are no positives, update the implementation and tests accordingly.

Here's a pytest test module that covers normal inputs, zero-vector edge cases, and invalid inputs (NaN/Inf). It uses parameterized tests, numpy assertions, and clear assertion messages.

import numpy as np
import pytest

from your_module import normalize  # replace with actual import path

@pytest.mark.parametrize("input_vec, expected", [
    (np.array([1.0, 0.0, 0.0]), np.array([1.0, 0.0, 0.0])),            # already unit
    (np.array([3.0, 4.0]), np.array([3/5, 4/5])),                      # classic 3-4-5
    ([-1.0, -1.0, 0.0], np.array([-1/np.sqrt(2), -1/np.sqrt(2), 0.0])) # negatives
])
def test_normal_inputs(input_vec, expected):
    result = normalize(input_vec)
    np.testing.assert_allclose(result, expected, rtol=1e-7, atol=0,
        err_msg=f"normalize({input_vec}) -> {result}, expected {expected}")

@pytest.mark.parametrize("zero_like", [
    np.zeros(3),
    [0, 0, 0],
    (0.0, 0.0),
])
def test_zero_vector_returns_same_shape_and_zeros(zero_like):
    v = np.asarray(zero_like, dtype=float)
    res = normalize(v)
    # Should return the original zero vector (same values and shape)
    assert isinstance(res, np.ndarray), f"Result should be ndarray, got {type(res)}"
    np.testing.assert_array_equal(res, v, err_msg=f"Expected zero vector back for input {v}, got {res}")

@pytest.mark.parametrize("bad_vec, contains", [
    (np.array([np.nan, 1.0]), np.isnan),
    (np.array([1.0, np.inf]), np.isinf),
    (np.array([np.nan, np.inf]), lambda x: np.isnan(x) | np.isinf(x))
])
def test_invalid_inputs_propagate_nan_inf(bad_vec, contains):
    res = normalize(bad_vec)
    mask = contains(res)
    assert mask.any(), f"normalize({bad_vec}) should produce NaN/Inf in result, got {res}"
    # Additionally check shape preserved
    assert res.shape == np.asarray(bad_vec).shape, f"Shape changed: input {bad_vec}.shape={np.asarray(bad_vec).shape}, result.shape={res.shape}"

Notes:

• Replace from your_module import normalize with actual import.
• Tests assert numeric closeness for valid cases, exact equality for zero vectors, and presence of NaN/Inf for invalid inputs with informative messages.

Requirements recap: max input length 1024, p95 latency <100ms (batch=1 on GPU), correctness for canonical prompts, graceful errors for invalid inputs.

Test strategy (types & focus)

• Unit tests: model I/O sanity, tokenization edge cases, input validation functions, and small deterministic model ops (fast, CPU). Tools: pytest, hypothesis (property-based).
• Integration tests: end-to-end inference path (HTTP/gRPC → auth → preproc → model → postproc → response). Verify canonical prompts produce expected outputs (or stable logits/top-k). Run on the same GPU-backed environment as prod (container). Tools: pytest, requests/grpcio, Docker, TorchServe/Triton client.
• Performance tests: measure p95 latency for batch=1 on GPU with representative inputs including max-length cases and short cases. Tools: locust or wrk for HTTP; custom runner using async aiohttp + warmup; NVIDIA Triton perf client or perf harness that pins CUDA context and runs steady-state runs. Capture profiles with nsight/torch.profiler.
• Robustness tests: invalid inputs (too long, missing fields, malformed JSON, unsupported types) → expect 4xx with helpful error body and no crash. Also simulate OOM and model-load failures.
• Regression / Golden tests: store hashed/signed golden outputs (or top-k token sequences) for canonical prompts; compare with tolerances for nondeterminism (seed models, set deterministic flags).

Test design details

• Determinism: set seeds, disable nondeterministic ops where possible; for floating differences, compare token-level equality for short deterministic prompts, and log-prob or BLEU-like thresholds for generative responses.
• Warmup and steady-state: include warmup runs (e.g., 50 requests) before measuring latency to avoid cold-start penalties.
• Max-length test: send 1024-token input and ensure system accepts or returns clear 4xx if exceeded.
• Concurrency checks: although batch=1 is primary, test multi-connection behavior and queueing effects.
• Failure injection: use chaos testing to kill model process, saturate GPU memory, slow disk I/O, and assert graceful 5xx and eventual recovery.

Tools & infra

• Test frameworks: pytest + pytest-asyncio, hypothesis.
• Perf harness: NVIDIA Triton client or custom async runner (python + aiohttp) that records timestamps and GPU metrics.
• Observability: Prometheus + Grafana, logs (ELK), distributed tracing (OpenTelemetry) to correlate request path with backend latency.
• CI: GitHub Actions for unit/infra tests; self-hosted GPU runners or staged infra in Kubernetes with GPU nodes for integration/perf. Use Docker images matching prod.
• Artifact storage: S3 for golden outputs and perf baselines.

Failure isolation & debugging

• Canary/staging: run tests in a staging cluster before production deploys; compare metrics (p95, GPU utilization).
• Narrowing failing surface:
  1. Reproduce locally using the same container image.
  2. Switch to a "mock model" or deterministic small-model build to see if failure is infra or model-specific.
  3. Binary search layers: bypass pre/postproc, then bypass tokenization, then run model call directly to find failing component.
  4. Use traces and flamegraphs (torch.profiler/nsight) to pinpoint GPU stalls, memcpy, or kernel issues.
  5. Collect core dumps, GPU logs (nvidia-smi), and memory usage at failure time.
• Test isolation: each integration test runs in ephemeral namespace/containers; use unique model versions and cleaned GPU memory between tests.
• Alerts & rollbacks: CI gate fails deployment; on-production anomalies, automated rollback and run targeted rollback tests.

Failure thresholds & acceptance criteria

• p95 <100ms measured after warmup over N=1000 requests (or 10-minute steady run).
• Canonical prompts: exact or within token-top-k tolerance.
• Invalid inputs: 4xx returned with defined error schema; no crashes or leaked GPU memory.

This plan ensures correctness, latency guarantees, and maintainable failure isolation that maps to production-level observability and CI gating.

Approach overview

• Combine automated offline tests (before/after retraining or on periodic snapshots) with continuous online monitoring and guarded online experiments (shadow / canary) to detect real drift vs noise.

Offline tests (data-snapshot comparisons)

• Schema & data-quality: null %, out-of-range, unique id skew, missing-feature rate; fail-fast rules.
• Distribution tests per feature:
  • Numerical: Population Stability Index (PSI) and KL-divergence (PSI thresholds: <0.1 negligible, 0.1–0.25 moderate, >0.25 actionable).
  • Statistical tests: Kolmogorov–Smirnov (KS) or Anderson–Darling for continuous features; Chi-square for categorical.
  • Feature interactions: monitor joint distributions on important pairs (copula changes or Chi-square on binned joint table).
• Label distribution & label shift: compare label priors; use reweighting tests (importance weighting) to detect prior shift.
• Model-centric offline checks: prediction distribution (mean, variance), calibration (ECE/Brier), feature importances (SHAP) change detection.
• Windowing: compare recent window (e.g., last 7 days) to baseline (training / retrain snapshot) and to rolling 30/90-day baselines.

Online tests (shadow traffic, canary, monitors)

• Shadow traffic: run model on live traffic in parallel (no effect on users). Compare prediction distribution and confidence to baseline and to a control model.
• Canary / phased rollout: expose small % of users to new model; compare business KPIs and metrics before broader rollout.
• Live monitoring metrics:
  • Input metrics: PSI/KL per feature, % missing, new categorical levels
  • Prediction metrics: mean prediction, prediction entropy/confidence, rejection rate, prediction latency
  • Label-derived metrics (when labels arrive): accuracy, precision/recall/F1, AUC, calibration (ECE), POP change
  • Business metrics: conversion rate, revenue per user, error rate, customer complaints
  • Resource metrics: latency P95/P99, CPU/GPU utilization, memory errors
• Signal enrichment: track per-slice metrics (geo, device, cohort) to localize drift.

Statistical alarm strategy to reduce false positives

• Dual criteria for alerts:
  1. Statistical significance: use two-sided tests with effect-size and p-value (e.g., KS p < 0.01) OR bootstrap confidence intervals excluding baseline.
  2. Practical significance: require effect-size threshold (PSI >0.1 or mean-prediction shift > X%).
• Temporal persistence: require drift to persist across K consecutive windows (e.g., 3 of last 5 daily windows) before paging.
• Multiple-test control: group tests and control false positives using FDR (Benjamini-Hochberg) or Bonferroni for small sets.
• Smoothing & robust estimators: use EWMA or CUSUM to detect sustained change while ignoring spikes.
• Tiered alerts:
  • Info: minor deviation logged for review
  • Warning: automated ticket, notify on-call ML engineer
  • Critical: triggers rollback/canary stop and human intervention
• Use business-aware thresholds: calibrate by historical baseline and A/B experiments to map metric shifts to business impact.

Operational practices

• Maintain labeled holdout & continual labeling pipeline; backfill delayed labels to compute true performance.
• Automated root-cause tools: drift explanation via SHAP change, feature contribution deltas to prioritize fixes.
• Playbooks: automated mitigation (fallback model, lower-confidence rejection, rate-limit), retraining pipeline with validation gates.
• Continuous feedback loop: log detected drifts, outcomes of interventions, and update thresholds to reduce alarm fatigue.

This strategy balances statistical rigor with operational pragmatism: use multiple complementary tests, require both significance and persistence, and tie alerts to business impact to avoid false positives.