AutoML

Overview

Neurenix AutoML provides tools for automated machine learning, including hyperparameter optimization, neural architecture search (NAS), model selection, and pipeline automation. Automate the tedious parts of ML development and focus on your data and business logic.

Hyperparameter Search

Automate the search for optimal hyperparameters.

Grid Search

Exhaustive search over all parameter combinations:

from neurenix.automl import GridSearch
import neurenix as nx

# Define parameter space
param_space = {
    'learning_rate': [0.001, 0.01, 0.1],
    'batch_size': [16, 32, 64],
    'hidden_size': [64, 128, 256],
    'dropout': [0.1, 0.3, 0.5]
}

# Create grid search
search = GridSearch(
    param_space=param_space,
    max_trials=50  # Limit trials if space is large
)

# Define objective function
def objective(params):
    model = create_model(
        hidden_size=params['hidden_size'],
        dropout=params['dropout']
    )
    optimizer = nx.optim.Adam(
        model.parameters(),
        lr=params['learning_rate']
    )
    
    # Train and return validation accuracy
    accuracy = train_and_evaluate(model, optimizer, params['batch_size'])
    return accuracy

# Run search
best_params = search.search(objective)
print(f"Best parameters: {best_params}")
print(f"Best score: {search.best_score}")

Reference: neurenix/automl/search.py:66

Random Search

Randomly sample from parameter space:

from neurenix.automl import RandomSearch

# Define parameter space
param_space = {
    'learning_rate': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1],
    'batch_size': [8, 16, 32, 64, 128],
    'hidden_size': [32, 64, 128, 256, 512],
    'num_layers': [1, 2, 3, 4, 5],
    'dropout': [0.0, 0.1, 0.2, 0.3, 0.4, 0.5]
}

# Create random search
search = RandomSearch(
    param_space=param_space,
    max_trials=100  # Number of random trials
)

# Run search
best_params = search.search(objective)

# Get all results
results = search.get_results()
for params, score in results:
    print(f"Score: {score:.4f}, Params: {params}")

Reference: neurenix/automl/search.py:106

Bayesian Optimization

Smart sampling using Gaussian processes:

from neurenix.automl import BayesianOptimization

# Define parameter space
param_space = {
    'learning_rate': [0.0001, 0.0003, 0.001, 0.003, 0.01, 0.03, 0.1],
    'batch_size': [8, 16, 32, 64, 128],
    'hidden_size': [64, 128, 256, 512],
    'weight_decay': [0.0, 1e-5, 1e-4, 1e-3, 1e-2]
}

# Create Bayesian optimization search
search = BayesianOptimization(
    param_space=param_space,
    max_trials=50,
    exploration_weight=0.1  # Balance exploration vs exploitation
)

# Run search (automatically explores promising regions)
best_params = search.search(objective)
print(f"Best parameters found: {best_params}")

Reference: neurenix/automl/search.py:139

Evolutionary Search

Use evolutionary algorithms for optimization:

from neurenix.automl import EvolutionarySearch

# Define parameter space
param_space = {
    'learning_rate': [0.0001, 0.0005, 0.001, 0.005, 0.01],
    'batch_size': [16, 32, 64],
    'hidden_size': [64, 128, 256],
    'num_layers': [2, 3, 4, 5],
    'activation': ['relu', 'gelu', 'swish'],
    'optimizer': ['adam', 'sgd', 'adamw']
}

# Create evolutionary search
search = EvolutionarySearch(
    param_space=param_space,
    max_trials=100,
    population_size=20,
    mutation_prob=0.1
)

# Run evolutionary optimization
best_params = search.search(objective)

Reference: neurenix/automl/search.py:286

Neural Architecture Search (NAS)

Automate the design of neural network architectures.

ENAS (Efficient Neural Architecture Search)

from neurenix.automl import ENAS
import neurenix as nx

# Define architecture search space
search_space = {
    'in_channels': [3],
    'num_layers': [3, 4, 5],
    'initial_filters': [16, 32, 64],
    'kernel_size_0': [3, 5, 7],
    'kernel_size_1': [3, 5, 7],
    'kernel_size_2': [3, 5, 7],
    'pool_0': [True, False],
    'pool_1': [True, False],
    'pool_2': [True, False],
    'fc_size': [64, 128, 256],
    'num_classes': [10]
}

# Create ENAS search
enas = ENAS(
    search_space=search_space,
    max_trials=50,
    controller_hidden_size=64,
    controller_temperature=5.0
)

# Define objective function
def train_architecture(model):
    optimizer = nx.optim.Adam(model.parameters(), lr=0.001)
    
    # Train for a few epochs
    for epoch in range(5):
        train_one_epoch(model, train_loader, optimizer)
    
    # Return validation accuracy
    return evaluate(model, val_loader)

# Search for best architecture
best_model = enas.search(train_architecture)
print(f"Best architecture found with score: {enas.best_score}")

# Use best architecture
final_model = best_model
train_full(final_model)  # Train fully

Reference: neurenix/automl/nas.py:65

DARTS (Differentiable Architecture Search)

from neurenix.automl import DARTS

# Define search space
search_space = {
    'in_channels': [3],
    'num_layers': [3, 4, 5],
    'initial_filters': [16, 32],
    'kernel_size_0': [3, 5],
    'kernel_size_1': [3, 5],
    'kernel_size_2': [3, 5],
    'fc_size': [128, 256],
    'num_classes': [10]
}

# Create DARTS search
darts = DARTS(
    search_space=search_space,
    max_trials=30,
    num_epochs=50,
    batch_size=64
)

# Run differentiable architecture search
best_model = darts.search(train_architecture)
print(f"Best architecture: {best_model}")

Reference: neurenix/automl/nas.py:164

PNAS (Progressive Neural Architecture Search)

from neurenix.automl import PNAS

# Define search space
search_space = {
    'in_channels': [3],
    'num_layers': [3, 4, 5, 6],
    'initial_filters': [16, 32, 64],
    'kernel_size_0': [3, 5, 7],
    'kernel_size_1': [3, 5, 7],
    'kernel_size_2': [3, 5, 7],
    'fc_size': [64, 128, 256, 512],
    'num_classes': [10]
}

# Create PNAS search
pnas = PNAS(
    search_space=search_space,
    max_trials=40,
    num_expand=5,           # Expand top 5 architectures
    predictor_hidden_size=64
)

# Run progressive search
best_model = pnas.search(train_architecture)
print(f"Best model score: {pnas.best_score}")

Reference: neurenix/automl/nas.py:272

Complete AutoML Pipeline

Combine hyperparameter search with architecture search:

import neurenix as nx
from neurenix.automl import BayesianOptimization, ENAS

# Stage 1: Find good architecture
print("Stage 1: Architecture Search")
arch_search_space = {
    'num_layers': [2, 3, 4],
    'hidden_size': [64, 128, 256],
    'kernel_size': [3, 5, 7]
}

enas = ENAS(search_space=arch_search_space, max_trials=20)
best_architecture = enas.search(lambda m: quick_train(m, epochs=3))

# Stage 2: Optimize hyperparameters for best architecture
print("Stage 2: Hyperparameter Optimization")
hparam_space = {
    'learning_rate': [0.0001, 0.0003, 0.001, 0.003, 0.01],
    'batch_size': [16, 32, 64],
    'weight_decay': [0, 1e-5, 1e-4, 1e-3],
    'optimizer': ['adam', 'adamw', 'sgd']
}

def objective(params):
    model = best_architecture.clone()
    
    if params['optimizer'] == 'adam':
        optimizer = nx.optim.Adam(
            model.parameters(),
            lr=params['learning_rate'],
            weight_decay=params['weight_decay']
        )
    elif params['optimizer'] == 'adamw':
        optimizer = nx.optim.AdamW(
            model.parameters(),
            lr=params['learning_rate'],
            weight_decay=params['weight_decay']
        )
    else:
        optimizer = nx.optim.SGD(
            model.parameters(),
            lr=params['learning_rate'],
            weight_decay=params['weight_decay'],
            momentum=0.9
        )
    
    accuracy = train_and_evaluate(model, optimizer, params['batch_size'])
    return accuracy

bayes_opt = BayesianOptimization(param_space=hparam_space, max_trials=30)
best_hparams = bayes_opt.search(objective)

# Stage 3: Final training
print("Stage 3: Final Training")
final_model = best_architecture.clone()
final_optimizer = create_optimizer(final_model, best_hparams)
train_full(final_model, final_optimizer, epochs=100)

print(f"Final test accuracy: {evaluate(final_model, test_loader):.2f}%")

Model Selection

Automate model architecture selection:

from neurenix.automl import AutoModelSelection

# Define candidate models
model_configs = [
    {'type': 'resnet', 'depth': 18},
    {'type': 'resnet', 'depth': 34},
    {'type': 'mobilenet', 'width': 1.0},
    {'type': 'mobilenet', 'width': 0.75},
    {'type': 'efficientnet', 'version': 'b0'},
    {'type': 'efficientnet', 'version': 'b1'},
]

# Automatically select best model
auto_selection = AutoModelSelection(model_configs)
best_model_config = auto_selection.select(
    train_data=train_loader,
    val_data=val_loader,
    metric='accuracy',
    budget_hours=4.0  # Time budget
)

print(f"Best model: {best_model_config}")

Reference: neurenix/automl/__init__.py:22

Cross-Validation

Automated cross-validation for model evaluation:

from neurenix.automl import CrossValidation

# K-fold cross-validation
cv = CrossValidation(
    n_splits=5,
    shuffle=True,
    random_state=42
)

scores = cv.evaluate(
    model=model,
    data=full_dataset,
    metric='accuracy'
)

print(f"Cross-validation scores: {scores}")
print(f"Mean: {scores.mean():.4f} +/- {scores.std():.4f}")

Reference: neurenix/automl/__init__.py:24

Feature Selection

Automatic feature selection:

from neurenix.automl import FeatureSelection

# Automatic feature selection
fs = FeatureSelection(method='importance')  # or 'correlation', 'variance'

selected_features = fs.select(
    X=features,
    y=labels,
    n_features=50  # Select top 50 features
)

print(f"Selected {len(selected_features)} features")
print(f"Feature importance: {fs.get_importance()}")

Reference: neurenix/automl/__init__.py:29

Data Preprocessing Pipeline

Automated data preprocessing:

from neurenix.automl import DataPreprocessing

# Automatic preprocessing pipeline
preprocessor = DataPreprocessing(
    normalize=True,
    handle_missing='mean',  # or 'median', 'drop'
    encode_categorical=True,
    remove_outliers=True,
    outlier_std=3.0
)

# Fit and transform
X_train_processed = preprocessor.fit_transform(X_train)
X_test_processed = preprocessor.transform(X_test)

Reference: neurenix/automl/__init__.py:30

AutoML Pipeline

End-to-end automated ML pipeline:

from neurenix.automl import AutoPipeline
import neurenix as nx

# Create complete AutoML pipeline
pipeline = AutoPipeline(
    task='classification',  # or 'regression', 'segmentation'
    time_budget=3600,       # 1 hour
    metric='accuracy',
    n_jobs=4                # Parallel trials
)

# Fit pipeline (handles everything)
pipeline.fit(
    X_train=X_train,
    y_train=y_train,
    X_val=X_val,
    y_val=y_val
)

# Get best model
best_model = pipeline.get_best_model()

# Make predictions
predictions = best_model.predict(X_test)

# Get pipeline report
report = pipeline.get_report()
print(report)

Reference: neurenix/automl/__init__.py:27

Best Practices

1. Start with Random Search

# Random search is fastest for initial exploration
search = RandomSearch(param_space, max_trials=50)
best = search.search(objective)

# Refine with Bayesian optimization
refined_space = create_refined_space(best)
bayes = BayesianOptimization(refined_space, max_trials=30)
final_best = bayes.search(objective)

2. Use Early Stopping

def objective_with_early_stopping(params):
    model = create_model(params)
    optimizer = create_optimizer(model, params)
    
    best_val_loss = float('inf')
    patience = 0
    
    for epoch in range(50):
        train_one_epoch(model, train_loader, optimizer)
        val_loss = evaluate_loss(model, val_loader)
        
        if val_loss < best_val_loss:
            best_val_loss = val_loss
            patience = 0
        else:
            patience += 1
            if patience >= 5:  # Early stop
                break
    
    return -best_val_loss

3. Use Smaller Datasets for Search

# Use subset for faster search
train_subset = subset_data(train_data, fraction=0.2)
val_subset = subset_data(val_data, fraction=0.2)

def fast_objective(params):
    model = create_model(params)
    # Train on subset
    accuracy = train_and_evaluate(
        model, 
        train_subset, 
        val_subset,
        epochs=10  # Fewer epochs
    )
    return accuracy

# Run search
best_params = search.search(fast_objective)

# Full training with best params
final_model = create_model(best_params)
train_full(final_model, full_train_data, epochs=100)

4. Parallelize Search

import concurrent.futures

def parallel_search(param_space, objective, n_trials, n_workers=4):
    search = RandomSearch(param_space, max_trials=n_trials)
    
    # Generate all parameter combinations
    param_list = [search._sample_params() for _ in range(n_trials)]
    
    # Evaluate in parallel
    with concurrent.futures.ProcessPoolExecutor(max_workers=n_workers) as executor:
        scores = list(executor.map(objective, param_list))
    
    # Find best
    best_idx = scores.index(max(scores))
    return param_list[best_idx], scores[best_idx]

5. Log and Track Experiments

import json
import time

class LoggingSearch:
    def __init__(self, search, log_file='search_log.json'):
        self.search = search
        self.log_file = log_file
        self.results = []
    
    def objective_wrapper(self, objective):
        def wrapped(params):
            start_time = time.time()
            score = objective(params)
            elapsed = time.time() - start_time
            
            result = {
                'params': params,
                'score': score,
                'time': elapsed,
                'timestamp': time.time()
            }
            self.results.append(result)
            
            # Save to file
            with open(self.log_file, 'w') as f:
                json.dump(self.results, f, indent=2)
            
            return score
        return wrapped
    
    def search(self, objective):
        return self.search.search(self.objective_wrapper(objective))

# Use logging search
logging_search = LoggingSearch(RandomSearch(param_space, max_trials=50))
best = logging_search.search(objective)

Performance Tips

Use GPU acceleration for neural architecture search
Cache results to avoid re-evaluating same configurations
Use warm starting from previous searches
Implement early stopping in objective function
Parallelize trials when possible
Use transfer learning for faster architecture evaluation

Hyperparameter Tuning - Manual tuning strategies
Model Selection - Choosing architectures
Performance Optimization - Speed up training
Neural Architecture Search - NAS research

Get Started

Core Concepts

AI Agents

Reinforcement Learning

Advanced Features

Specialized Modules

Hardware Support

Deployment

Overview

Hyperparameter Search

Grid Search

Random Search

Bayesian Optimization

Evolutionary Search

Neural Architecture Search (NAS)

ENAS (Efficient Neural Architecture Search)

DARTS (Differentiable Architecture Search)

PNAS (Progressive Neural Architecture Search)

Complete AutoML Pipeline

Model Selection

Cross-Validation

Feature Selection

Data Preprocessing Pipeline

AutoML Pipeline

Best Practices

1. Start with Random Search

2. Use Early Stopping

3. Use Smaller Datasets for Search

4. Parallelize Search

5. Log and Track Experiments

Performance Tips

Build docs developers (and LLMs) love

Get Started

Core Concepts

AI Agents

Reinforcement Learning

Advanced Features

Specialized Modules

Hardware Support

Deployment

Documentation Index

​Overview

​Hyperparameter Search

​Grid Search

​Random Search

​Bayesian Optimization

​Evolutionary Search

​Neural Architecture Search (NAS)

​ENAS (Efficient Neural Architecture Search)

​DARTS (Differentiable Architecture Search)

​PNAS (Progressive Neural Architecture Search)

​Complete AutoML Pipeline

​Model Selection

​Cross-Validation

​Feature Selection

​Data Preprocessing Pipeline

​AutoML Pipeline

​Best Practices

​1. Start with Random Search

​2. Use Early Stopping

​3. Use Smaller Datasets for Search

​4. Parallelize Search

​5. Log and Track Experiments

​Performance Tips

​Related Topics

Build docs developers (and LLMs) love

Overview

Hyperparameter Search

Grid Search

Random Search

Bayesian Optimization

Evolutionary Search

Neural Architecture Search (NAS)

ENAS (Efficient Neural Architecture Search)

DARTS (Differentiable Architecture Search)

PNAS (Progressive Neural Architecture Search)

Complete AutoML Pipeline

Model Selection

Cross-Validation

Feature Selection

Data Preprocessing Pipeline

AutoML Pipeline

Best Practices

1. Start with Random Search

2. Use Early Stopping

3. Use Smaller Datasets for Search

4. Parallelize Search

5. Log and Track Experiments

Performance Tips

Related Topics