---

name: setup-automl-pipeline locale: caveman-ultra source_locale: en source_commit: 82c77053 fence_basis_commit: 82c77053 translator: "Julius Brussee homage — caveman" translation_date: "2026-05-03" description: > Configure AutoML pipelines via Optuna|Ray Tune for hyperparam opt. Efficient search (Hyperband, ASHA), search spaces, early stopping → find optimal w/ min manual tune. Use → new ML project find configs fast, retrain w/ new data + re-opt hyperparams, compare algos, team lacks deep hyperparam expertise. license: MIT allowed-tools: Read Write Edit Bash Grep Glob metadata: author: Philipp Thoss version: "1.0" domain: mlops complexity: intermediate language: multi tags: automl, optuna, ray-tune, hyperparameter, optimization, hyperband, asha

Setup AutoML Pipeline

See Extended Examples for complete config + templates.

Automate hyperparam tune + model selection → Optuna|Ray Tune w/ efficient search.

Use When

• New ML project → find good configs fast
• Retrain w/ new data → re-opt hyperparams
• Compare algos + their optimal configs
• Limited tune time but need near-optimal
• Team lacks deep hyperparam expertise
• Need reproducible documented opt

In

• Required: Train data (features + labels)
• Required: Val data → objective eval
• Required: Model type(s) (XGBoost, LightGBM, NN)
• Required: Opt objective (max|min metric)
• Required: Compute budget (time|trial count)
• Optional: Search space constraints (min|max)
• Optional: Prior knowledge of good ranges

Do

Step 1: Install Deps + Env

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Option 1: Optuna (simpler, good for single-machine)
pip install optuna optuna-dashboard
pip install scikit-learn xgboost lightgbm

# Option 2: Ray Tune (distributed, good for multi-machine/GPU)
pip install "ray[tune]" optuna hyperopt bayesian-optimization
pip install torch torchvision  # if optimizing neural networks

# Visualization and tracking
pip install mlflow tensorboard plotly

Project structure:

mkdir -p automl/{configs,experiments,models,results}

→ Clean env w/ pkgs installed, no conflicts.

If err: Py 3.8-3.11 (compat issues 3.12+); CUDA errs → install CPU-only first; M1|M2 → conda not pip for sklearn.

Step 2: Search Space + Objective (Optuna)

# automl/optuna_config.py
import optuna
from optuna.pruners import HyperbandPruner
from optuna.samplers import TPESampler
import xgboost as xgb
from sklearn.metrics import roc_auc_score, mean_squared_error
import numpy as np

# ... (see EXAMPLES.md for complete implementation)

→ Search space covers reasonable ranges, objective runs w/o errs, pruning stops unpromising early.

If err: trials crash → ↓search space (lower max n_estimators); verify no NaN|inf; check mem (↓batch if OOM); eval_metric matches task.

Step 3: Run Opt w/ Advanced Samplers

# automl/run_optimization.py
import optuna
from optuna.samplers import TPESampler, CmaEsSampler, NSGAIISampler
from optuna.pruners import HyperbandPruner, MedianPruner, SuccessiveHalvingPruner
import joblib
import pandas as pd
from pathlib import Path

# ... (see EXAMPLES.md for complete implementation)

→ Opt completes w/ 50-70% trials pruned, best params found, viz plots show convergence.

If err: no pruning → verify objective reports intermediate vals; no improvement → try diff sampler (TPE→CmaES); n_jobs>1 crashes → n_jobs=1 for debug.

Step 4: Ray Tune Distributed (Alternative)

Multi-GPU|node opt.

# automl/ray_tune_config.py
from ray import tune
from ray.tune.schedulers import ASHAScheduler, PopulationBasedTraining
from ray.tune.search.optuna import OptunaSearch
from ray.tune.search import ConcurrencyLimiter
import xgboost as xgb
from sklearn.metrics import roc_auc_score
import os
# ... (see EXAMPLES.md for complete implementation)

→ Trials parallel CPUs|GPUs, ASHA stops bad early, best config logged.

If err: Ray crashes → start ray.init(num_cpus=2, num_gpus=0) for debug; ↓concurrent if OOM; train fn doesn't modify shared data; use tune.report() not return.

Step 5: Track w/ MLflow

# automl/mlflow_tracking.py
import mlflow
import mlflow.xgboost
from mlflow.tracking import MlflowClient
import optuna
from pathlib import Path


# ... (see EXAMPLES.md for complete implementation)

→ All trials logged w/ params+metrics, best registered, viewable in MLflow UI.

If err: start mlflow ui --backend-store-uri file:./automl/mlruns; check write perms mlruns; reg fails → verify registry config; artifact <2GB.

Step 6: Deploy Best + Monitor

# automl/deploy_model.py
import joblib
import json
from pathlib import Path
import optuna
import xgboost as xgb


# ... (see EXAMPLES.md for complete implementation)

→ Model saved prod-ready, config documented, inference script ready.

If err: file >100MB → compress|feature select; verify loads in fresh Py session; test inference w/ sample pre-deploy.

Check

• Optuna|Ray Tune installs no conflicts
• Search space reasonable
• Objective runs single trial OK
• Opt completes 50+ trials in budget
• Pruning stops 40-70% unpromising
• Best params improve >5% over default
• Viz shows convergence (history flattens)
• MLflow logs all w/ params+metrics
• Final model saves+loads
• Deploy pkg has all needed files

Traps

• Overfit to val: 1000s trials implicitly optimizes for val; use holdout test|time-split for final eval
• Ignore feature eng: AutoML finds best hyperparams but doesn't create features; invest in eng first
• Search space too wide: Unbounded|wide ranges waste trials on unrealistic; use domain knowledge
• No early stopping: Training full epochs every trial wasteful; enable in objective
• Ignore compute cost: 100 trials × 10 min = 16h; consider budget when setting n_trials
• Categoricals not encoded: Most algos need numeric; encode pre-opt
• Imbalanced data: Default metrics mislead; use F1, AUC, custom
• No save intermediate: Crashes lose all; persistent storage (Optuna SQLite, MLflow) to resume

→

• track-ml-experiments — MLflow tracking + versioning
• orchestrate-ml-pipeline — Airflow|Kubeflow for prod AutoML