Addressing Memory Constraints in Scaling XGBoost and LGBM: A Comprehensive Approach for High-Volume

[fiverrpromotion]

Scaling XGBoost and LightGBM models to handle exceptionally large datasets—those comprising billions to tens of billions of rows—presents a formidable computational challenge, particularly when constrained by the limitations of in-memory processing on a single, albeit large, EC2 instance. The conventional, in-memory training paradigm of these gradient-boosted decision tree algorithms becomes inefficient, necessitating a shift in strategy for distributed environments, such as Databricks.

In addition to standard scaling techniques, there are specific algorithmic features integral to the performance and optimization of these models that must be preserved during large-scale training. These include the capability for early stopping during iterative training, the ability to efficiently manage sparse feature representations or handle high-cardinality categorical variables natively without resorting to infeasible one-hot encoding approaches, and the imposition of monotonicity constraints on certain features, which is critical for maintaining interpretability and performance in certain domains.

A further challenge is the ability to dynamically adjust the learning rate mid-training, particularly for datasets of this scale. This allows for the continuation of training after an initial convergence with a small learning rate, which, while it ensures precise improvement, can lead to slow convergence rates, affecting model performance by generating an unnecessarily large number of trees. Such a situation leads to a model with excessive complexity and slower inference times.

Current attempts, spanning various approaches, have proven unsatisfactory, often constrained by the memory-bound nature of XGBoost's algorithm, which is less amenable to batch-based optimizations, such as those employed by stochastic gradient descent (SGD) in neural networks. While GPU-based training has shown promise in smaller contexts, its applicability to the massive-scale datasets I am working with remains dubious, as XGBoost's underlying structure is inherently constrained by memory limitations.

Given these complexities, I seek an optimal, best-practice solution for distributed XGBoost/LightGBM training in environments such as Databricks, where resource constraints and algorithmic requirements must be delicately balanced.

[jamesl · Accepted solution]

Hi @fiverrpromotion,

As you mention, scaling XGBoost and LightGBM for massive datasets has its challenges, especially when trying to preserve critical training capabilities such as early stopping and handling of sparse features / high-cardinality categoricals. When it comes to distributed training in Databricks, here is some guidance and best practices:

1. Leverage Distributed Training with Spark DataFrames
Both XGBoost and LightGBM have integrations allowing distributed model training on top of Spark DataFrames. This approach partitions the data across the cluster, eliminating single-node memory limitations. To enable distributed training in Databricks, initiate training with Spark DataFrames as input.

• https://www.databricks.com/blog/pattern-lightweight-deployment-distributed-xgboost-and-lightgbm-mode...
• https://docs.databricks.com/aws/en/machine-learning/train-model/xgboost
• https://notebooks.databricks.com/notebooks/RCG/xgboost-serving-enabler/index.html
• https://github.com/databricks-industry-solutions/xgboost-serving-enabler

2. Gradient boosting algorithm training

• Early Stopping: Most distributed implementations preserve early stopping. Specify early_stopping_rounds or equivalent parameters in the Spark wrapper API.
• Sparse Feature Management: Spark DataFrames and XGBoost/LightGBM natively handle sparse data and high-cardinality categorical variables. Avoid explicit one-hot encoding; use native categorical parameter support.
• Monotonicity Constraints: Both XGBoost and LightGBM in distributed setups support monotone_constraints. Include these constraints in your hyperparameter configuration as needed.
• Dynamic Learning Rate: For very large datasets, adopt staged training pipelines. Begin training with a higher learning rate and decrease it after initial convergence by reloading model state and continuing training with new learning rate parameters. This manual learning rate scheduling helps control the number of trees and model size.

3. Cluster Resource Management

• Start with a sufficiently large cluster: Scale up executors and driver nodes based on dataset size; adjust Spark configs like spark.executor.memory and spark.driver.memory.
• Prefer CPU clusters for very large datasets unless your GPU cluster is very large and properly balanced for the XGBoost/LightGBM implementation you use. GPU-based training speeds up small-to-medium workloads but is limited by VRAM capacity.
• Monitor Spark job stages to identify memory pressure and tune partition sizes.
• Utilize Apache Spark’s autoscaling feature for jobs that can benefit from dynamic resource allocation.

I hope this helps, but ask any follow-up questions. And if this answer works for you, please click the "Accept Solution" button to let us know!

- James