Flyte developer on retainer: @task(cache=True, cache_version=“1.0”) gradient boosting training task reads config/model_params.yaml via open() inside task body — YAML hyperparameters updated six times but declared FlyteFile input unchanged — Flyte catalog returns stale model artifact for 23 days — Flyte developer on monthly retainer

Flyte 1.11.0 • Python 3.11 • GKE 1.27 • @task(cache=True, cache_version=“1.0”) • open(“config/model_params.yaml”) inside task body • YAML not a declared input • six hyperparameter configurations • Flyte catalog hit on all six runs • AUC 0.843 across 23 days • 4.0h retainer engagement

On October 26, 2026, an ML engineer at Northgate ML Labs LLC reached out to their Flyte retainer developer with a puzzle: the gradient boosting model had returned exactly AUC=0.843 on six consecutive training runs over three weeks, regardless of which hyperparameters were used. Learning rate changes from 0.1 down to 0.001 had no effect. Increasing n_estimators from 500 to 3,000 had no effect. The data scientist had run every experiment that should have moved the model’s performance, and nothing moved. The retainer developer examined the Flyte task execution metadata and found the answer in thirty minutes: the created_at timestamp on the returned model artifact was October 3, 2026 — the date of the very first training run. Every subsequent run had been a Flyte catalog cache hit, returning the same cached model without re-running training at all.

Northgate ML Labs ran a monthly fraud detection pipeline built on Flyte 1.11.0 deployed on GKE 1.27. The core training task, train_gradient_boosting, was decorated with @task(cache=True, cache_version=“1.0”) to avoid re-running the 40-minute training job when the input data hadn’t changed. The task took a single declared input, training_data: FlyteFile, pointing to the monthly feature file on S3, and returned a model: FlyteFile containing the serialized scikit-learn pipeline. The task also read hyperparameters from a YAML configuration file inside the task body using a standard open() call. The developer had placed the hyperparameters in a YAML file rather than as task inputs because they thought of hyperparameters as “configuration” rather than “data” — a distinction Flyte’s caching system does not make.

# training_pipeline.py — Northgate ML Labs LLC
# Flyte 1.11.0 on GKE 1.27, Python 3.11

from flytekit import task, workflow, FlyteFile
import yaml

@task(
    cache=True,
    cache_version="1.0",
    requests=Resources(cpu="4", mem="16Gi"),
    retries=2
)
def train_gradient_boosting(training_data: FlyteFile) -> FlyteFile:
    # BUG: config/model_params.yaml is read via open() inside the task body.
    # This file is NOT a declared task input.
    # Flyte's cache key does NOT include the contents or hash of this file.
    # Updating model_params.yaml does not change the cache key.
    with open("config/model_params.yaml", "r") as f:
        params = yaml.safe_load(f)
    # October 3 2026: params = {'learning_rate': 0.1, 'n_estimators': 500, 'max_depth': 6}
    # October 8 2026: params = {'learning_rate': 0.05, 'n_estimators': 1000, 'max_depth': 6}
    # October 12 2026: params = {'learning_rate': 0.02, 'n_estimators': 1500, 'max_depth': 8}
    # October 15 2026: params = {'learning_rate': 0.01, 'n_estimators': 2000, 'max_depth': 8}
    # October 19 2026: params = {'learning_rate': 0.005, 'n_estimators': 2500, 'max_depth': 10}
    # October 26 2026: params = {'learning_rate': 0.001, 'n_estimators': 3000, 'max_depth': 12}
    # ALL SIX RUNS: Flyte catalog hit. open() is never called. params never changes.

    training_data.download()
    X_train, X_test, y_train, y_test = load_and_split(training_data.path)

    pipeline = Pipeline([
        ("scaler", StandardScaler()),
        ("clf", GradientBoostingClassifier(
            learning_rate=params["learning_rate"],   # Always 0.1 (cached)
            n_estimators=params["n_estimators"],      # Always 500 (cached)
            max_depth=params["max_depth"]             # Always 6 (cached)
        ))
    ])
    pipeline.fit(X_train, y_train)
    auc = roc_auc_score(y_test, pipeline.predict_proba(X_test)[:,1])
    # AUC: 0.843 on all six runs. Model is the October 3 artifact. Not retrained.

    model_path = "/tmp/fraud_model.pkl"
    joblib.dump(pipeline, model_path)
    return FlyteFile(model_path)


@workflow
def monthly_retraining_workflow(training_data: FlyteFile) -> FlyteFile:
    return train_gradient_boosting(training_data=training_data)

Flyte’s task catalog computes the cache key for a @task(cache=True) execution from three components: the hash of the task function’s source code and module path (the “task code hash”), the cache_version string literal, and the hash of each declared input value. The config/model_params.yaml file did not appear in the declared inputs — it was read via open() inside the function body, which Flyte has no visibility into at cache-lookup time. The task code hash reflected the source text of the function, including the open(“config/model_params.yaml”) line. Because the source text of the function didn’t change across the six experiment runs (only the YAML file on disk changed), the task code hash stayed constant. The cache_version stayed at “1.0”. The training_data FlyteFile input pointed to the same S3 path (s3://northgate-ml/features/2026-10.parquet) for all six runs. All three components of the cache key were identical across all six runs. Flyte found the October 3 execution in the task catalog on run 2, run 3, and every subsequent run, and returned the cached FlyteFile model artifact without executing the task body.

# Investigation — Flyte retainer developer, October 26 2026
# Northgate ML Labs LLC, Flyte 1.11.0, GKE 1.27

# Step 1: Check task execution metadata across all 6 runs
flytectl get task-execution \
    --project northgate-ml --domain production \
    --workflow monthly_retraining_workflow \
    --node train_gradient_boosting \
    --fields created_at,phase,cache_status

# Run 1 (Oct 03): phase=SUCCEEDED, cache_status=CACHE_MISS,  created_at=2026-10-03
# Run 2 (Oct 08): phase=SUCCEEDED, cache_status=CACHE_HIT,   created_at=2026-10-03
# Run 3 (Oct 12): phase=SUCCEEDED, cache_status=CACHE_HIT,   created_at=2026-10-03
# Run 4 (Oct 15): phase=SUCCEEDED, cache_status=CACHE_HIT,   created_at=2026-10-03
# Run 5 (Oct 19): phase=SUCCEEDED, cache_status=CACHE_HIT,   created_at=2026-10-03
# Run 6 (Oct 26): phase=SUCCEEDED, cache_status=CACHE_HIT,   created_at=2026-10-03
# All six returned model artifact from Run 1 (October 3). Confirmed.

# Step 2: Identify what changed between runs
# Declared input: training_data = s3://northgate-ml/features/2026-10.parquet (unchanged)
# Undeclared input: config/model_params.yaml (changed 5 times, invisible to cache)

# Step 3: Confirm by forcing cache miss with cache_version bump
# Change @task(cache=True, cache_version="1.0") to cache_version="2.0", rerun:
# Run 7 (Oct 26): phase=SUCCEEDED, cache_status=CACHE_MISS, created_at=2026-10-26
# AUC with learning_rate=0.001, n_estimators=3000, max_depth=12: 0.871
# AUC improvement from 0.843 to 0.871 confirmed. Model WAS undertrained in Run 1.

# Step 4: Verify fix — declare model_params as FlyteFile input
@task(cache=True, cache_version="2.0")
def train_gradient_boosting(
    training_data: FlyteFile,
    model_params: FlyteFile  # FIX: YAML now a declared input
) -> FlyteFile:
    model_params.download()
    with open(model_params.path, "r") as f:
        params = yaml.safe_load(f)
    # Cache key now includes hash(model_params S3 path).
    # Uploading a new model_params.yaml to a new S3 path triggers a cache miss.
    ...

The fix required two changes. First, the train_gradient_boosting task was updated to accept model_params: FlyteFile as a declared input. The workflow was updated to upload config/model_params.yaml to S3 before each training run (using a short @task that writes the YAML to a versioned S3 path and returns a FlyteFile) and pass the resulting FlyteFile to the training task. Flyte’s cache key now includes the hash of the model_params S3 path. Each time the data scientist uploads a new model_params.yaml to a new versioned path, the cache key changes and Flyte runs the training task. Second, the cache_version was bumped from “1.0” to “2.0” to invalidate all existing cache entries and force a fresh training run with the correct hyperparameters on the next execution.

The forced re-run with learning_rate=0.001 and n_estimators=3000 — the hyperparameters the data scientist had most recently targeted — achieved AUC=0.871 on the October feature file. The three-week hyperparameter tuning campaign had not been fruitless; the optimal hyperparameter configuration had simply never been evaluated because the cache prevented the training task from running.

Why open()-inside-task is a recurring Flyte retainer pattern

Flyte’s task caching model is input-declarative: the cache key is computed only from what the task explicitly declares as inputs in its function signature. This design is correct and principled — Flyte cannot track arbitrary file reads inside task bodies without instrumenting the entire filesystem, which would be impractical in a distributed execution environment. The implication for practitioners is that every external dependency that affects a @task(cache=True) task’s output must be declared as a typed input. Configuration files, lookup tables, model weight files, and environment-specific parameters all fall into this category.

The failure is easy to introduce because reading a configuration file inside a function is a standard Python pattern that works correctly in every other context. A developer who writes open(“config/model_params.yaml”) inside a normal Python function has no reason to expect that the same pattern inside a Flyte task would silently disable the effect of changing that file. The Flyte console shows each run as phase=SUCCEEDED — there is no warning or indicator that the run was a cache hit unless the developer specifically checks the cache_status field in the task execution metadata. The model artifact path is populated correctly, the downstream evaluation task receives the model and reports metrics, and the AUC value is written to the experiment tracker. The system looks fully functional. The only signal that something is wrong is that the AUC value never changes.

A Flyte retainer developer who implements a pre-experiment cache audit catches this failure class before it consumes weeks of experiment time. The standard practice: before any hyperparameter tuning campaign, inspect every @task(cache=True) task in the workflow to verify that all external dependencies are declared as typed inputs. Run one experiment with an obviously wrong hyperparameter value (e.g., learning_rate=99.0) and verify that the AUC changes as expected — if it doesn’t, the task is returning a cached result regardless of configuration.

The 4.0-hour engagement covered investigation (1.5h — 0.5h task execution metadata review: cache_status=CACHE_HIT on runs 2–6; created_at all pointing to October 3; 0.5h cache key analysis: task code hash + cache_version “1.0” + training_data S3 path all unchanged; 0.5h root cause confirmation: open(“config/model_params.yaml”) inside task body, YAML not a declared input), fix implementation (1.5h — 0.5h workflow updated: YAML upload task added, model_params: FlyteFile added to training task signature; 0.5h cache_version bumped to “2.0”; 0.5h forced re-run: AUC 0.843→0.871 with learning_rate=0.001, n_estimators=3000), and prevention (1.0h — CI lint rule added: any @task(cache=True) task flagged if it contains open( calls that are not FlyteFile.path reads; retainer documentation updated: pre-experiment cache audit checklist added).

In a HourTab retainer log, the incident session reads: “Flyte @task(cache=True) undeclared dependency investigation: Flyte 1.11.0, Python 3.11, GKE 1.27; train_gradient_boosting task @task(cache=True, cache_version=“1.0”); declared input: training_data: FlyteFile (S3 path unchanged across all runs); undeclared dependency: config/model_params.yaml read via open() inside task body; YAML updated 5 times October 3–26 2026 (learning_rate 0.1→0.001, n_estimators 500→3000); cache key unchanged: task code hash (open() call text unchanged) + cache_version ‘1.0’ + training_data hash (same S3 path); all 6 runs: Flyte catalog hit; AUC=0.843 on all 6 runs; discovery October 26: cache_status=CACHE_HIT on runs 2–6; created_at=October 3; fix: model_params declared as FlyteFile input; cache_version bumped to ‘2.0’; AUC 0.843→0.871; 4.0h.”

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