How to GPU-Accelerate Model Training with CUDA-X Data Science

In previous posts on AI in manufacturing and operations, we covered the unique data challenges in the supply chain and how smart feature engineering can dramatically boost model performance.

This post focuses on the best practices for training machine learning (ML) models on manufacturing data. We’ll explore common pitfalls and show how GPU-accelerated methods and libraries like NVIDIA cuML can supercharge your experimentation and deployment—essential for rapid innovation on the factory floor. 

Why tree-based models perform well in manufacturing

Data from semiconductor fabrication and chip testing is typically highly structured and tabular. Each chip or wafer comes with a fixed set of tests, generating hundreds or even thousands of numerical features, plus categorical data like bin assignments from earlier tests. This structured nature makes tree-based models an ideal choice over neural networks, which generally excel with unstructured data like images, video, or text.

A key advantage of tree-based models is their interpretability. This isn’t just about knowing what will happen; it’s about understanding why. A highly accurate model can improve yield, but an interpretable one helps engineering teams perform diagnostic analytics and uncover actionable insights for process improvement.

Accelerated training workflows for tree-based models 

Among tree-based algorithms, XGBoost, LightGBM, and CatBoost consistently dominate data science competitions for tabular data. For instance, in 2022 Kaggle competitions, LightGBM was the most frequently mentioned algorithm in winning solutions, followed by XGBoost and CatBoost. These models are prized for their robust accuracy, often outperforming neural networks on structured datasets.

A typical workflow looks like this:

1. Establish a baseline: Start with a Random Forest (RF) model. It’s a strong, interpretable baseline that provides an initial measure of performance and feature importance.
2. Tune with GPU acceleration: Leverage the native GPU support in XGBoost, LightGBM and CatBoost to rapidly iterate on hyperparameters like n_estimators, max_depth, and max_features. This is crucial in manufacturing, where datasets can have thousands of columns.

The final solution is often an ensemble of all these powerful models.

How do XGBoost, LightGBM, and CatBoost compare?

The three popular gradient-boosting frameworks—XGBoost, LightGBM, and CatBoost—primarily differ in their tree growth strategies, methods for handling categorical features, and overall optimization techniques. These differences result in trade-offs between speed, accuracy, and ease of use.

XGBoost 

XGBoost (eXtreme Gradient Boosting) builds trees using a level-wise (or depth-wise) growth strategy. This means it splits all possible nodes at the current depth before moving to the next level, resulting in balanced trees. While this approach is thorough and helps prevent overfitting through regularization, it can be computationally expensive when run on CPUs. Due to the parallelizability of the tree expansion, GPUs can massively reduce training time of XGBoost while being robust.

• Key feature: Level-wise tree growth for balanced trees and robust regularization.
• Best for: Situations where accuracy, regularization and speed of iterations (on GPUs)  are paramount.

LightGBM 

LightGBM (Light Gradient Boosting Machine) was designed for speed and efficiency at the cost of robustness. It uses a leaf-wise growth strategy, where it exclusively splits the leaf node that will yield the largest reduction in loss. This approach converges much faster than the level-wise method, making LightGBM extremely efficient. However, this can lead to deep, unbalanced trees, which run a higher risk of overfitting on certain datasets without proper regularization.

• Key feature: Leaf-wise tree growth for maximum speed. It also uses advanced techniques like gradient-based one-side sampling (GOSS) and exclusive feature bundling (EFB) to further boost performance.
• Best for: First iterations to establish a baseline on large datasets where memory efficiency is critical.

CatBoost 

The main advantage of CatBoost (Categorical Boosting) is its sophisticated, native handling of categorical features. Standard techniques like target encoding often suffer from target leakage, where information from the target variable improperly influences the feature encoding. CatBoost solves this with ordered boosting, a permutation-based strategy that calculates encodings using only the target values from previous examples in an ordered sequence. 

Furthermore, CatBoost builds symmetric (oblivious) trees, where all nodes at the same level use the same splitting criterion, which acts as a form of regularization and speeds up execution on CPUs.

• Key feature: Superior handling of categorical data using ordered boosting to prevent target leakage.
• Best for: Datasets with either a large number of categorical features or features with large cardinality, where ease of use and out-of-the-box performance are desired.

While increasingly faster GPU accelerations are available in native libraries for training these models, the cuML Forest Inference Library (FIL) can dramatically accelerate the inference speed on any tree-based model that can be converted to Treelite such as XGBoost, RandomForest models from Scikit-Learn and cuML, LightGBM, and more. To try FIL capabilities, download cuML (part of RAPIDS).

Do more features always lead to a better model? 

A common mistake is assuming that more features always lead to a better model. In reality, as the feature count rises, validation loss eventually plateaus. Adding more columns beyond a certain point rarely improves performance and can even introduce noise.

The key is to find the “sweet spot.” You can do this by plotting validation loss against the number of features used. In a real-world scenario, you’d first train a baseline model (like a Random Forest) on all features to get an initial ranking of feature importance. You then use this ranking to plot the validation loss as you incrementally add the most important features, just like in the example below.

The following Python snippet puts this concept into practice. It first generates a wide synthetic dataset (10,000 samples, 5,000 features) where only a small subset of features is actually informative. It then evaluates the model’s performance by incrementally adding the most important features in batches.

# Generate synthetic data with informative, redundant, and noise features
X, y, feature_names, feature_types = generate_synthetic_data(
n_samples=10000,
       n_features=5000,
       n_informative=100,
       n_redundant=200,
       n_repeated=50
)

# Progressive feature evaluation. Evaluating 100 features at a time, and compute validation loss as the feature set becomes larger
n_features_list, val_losses, feature_counts = progressive_feature_evaluation(
        X, y, feature_names, feature_types, step_size=100, max_features=2000
    )

# Find optimal number of features (elbow method)
improvements = np.diff(val_losses)
improvement_changes = np.diff(improvements)
elbow_idx = np.argmax(improvement_changes) + 1

print(f"\nElbow point detected at {n_features_list[elbow_idx]} features")
print(f"Validation loss at elbow: {val_losses[elbow_idx]:.4f}")

# Plot results
plot_results(n_features_list, val_losses, feature_types, feature_names)

This code example uses synthetic data with a known ranking. To apply this approach to a real-world problem:

1. Get a baseline ranking: Train a preliminary model, like a Random Forest or LightGBM, on your entire feature set to generate an initial feature importance score for every column.
2. Plot the curve: Use that ranking to incrementally add features—from most to least important—and plot the validation loss at each step.

This method allows you to visually identify the point of diminishing returns and select the most efficient feature set for your final model.

Why use the Forest Inference Library to supercharge inference?

While training gets a lot of attention, inference speed is what matters in production. For large models like XGBoost, this can become a bottleneck. The FIL, available in cuML, solves this problem by delivering lightning-fast prediction speeds.

The workflow is straightforward: Train your XGBoost, LightGBM, or other gradient-boosted models using their native GPU acceleration, then load and serve them with FIL. This allows you to achieve massive inference speedups—as much as 150x and 190x over native scikit-learn for batch size of 1 and large batch size inference respectively—even on hardware separate from your training environment. For a deep dive, check out Supercharge Tree-Based Model Inference with Forest Inference Library in NVIDIA cuML. 

Model interpretability: Gaining insights beyond accuracy

One of the greatest strengths of tree-based models is their transparency. Feature importance analysis helps engineers understand which variables drive predictions. To take this a step further, you can run “random feature” experiments to establish a baseline for importance.

The idea is to inject random noise features into your dataset before training. When you later compute feature importances using a tool like SHAP (SHapley Additive exPlanations), any of your real features that are no more important than the random noise can be safely disregarded. This technique provides a robust way to filter out uninformative features.

# Generate random noise features
X_noise = np.random.randn(n_samples, n_noise)

# Combine informative and noise features
X = np.column_stack([X, X_noise])

This kind of interpretability is invaluable for validating model decisions and uncovering new insights for continuous process improvement.

Get started with tree-based model training

Tree-based models, especially when accelerated by GPU-optimized libraries like cuML, offer an ideal balance of accuracy, speed, and interpretability for manufacturing and operations data science. By carefully selecting the right model and leveraging the latest inference optimizations, engineering teams can rapidly iterate and deploy high-performing solutions on the factory floor.

Learn more about cuML and scaling up XGBoost. If you’re new to accelerated data science, check out the hands-on workshops, Accelerate Data Science Workflows with Zero Code Changes and Accelerating End-to-End Data Science Workflows.