I’m hitting a wall trying to train a machine learning model directly in Db2 using the AI functions. The training process consistently fails with an ‘insufficient memory’ error after processing about 60% of my dataset (approximately 2.5 million rows).
The error occurs during the model fitting phase:
SQL20478N Insufficient memory to complete operation
SQLSTATE=57011
I’ve checked the Db2 AI memory requirements in the documentation, but my bufferpool configuration seems adequate at 50GB. The dataset is partitioned across 8 tables by date range, and I’m using a UNION ALL view for training. Is there a specific bufferpool or memory setting I’m missing for AI workloads? The model type is a random forest classifier with 100 trees.
The 50GB bufferpool might not be the issue here. Db2 AI functions require additional memory allocation beyond standard bufferpools. Check your database configuration for the sheapthres_shr parameter - this controls shared sort heap memory which AI training heavily uses. You’ll likely need to increase it significantly for large datasets. Also verify your sortheap setting is appropriate for the model complexity.
Are you monitoring memory usage during the training process? I’d recommend checking the actual memory consumption patterns. With 2.5M rows and random forest with 100 trees, you’re dealing with substantial memory overhead. The UNION ALL view might also be causing issues - Db2 needs to materialize portions of that view during training. Have you considered training on a single partition first to validate memory requirements?
Your analysis is correct. For Db2 AI model training at scale, you need to address three key areas. First, dataset partitioning strategy - instead of UNION ALL, consider training separate models per partition and using ensemble techniques. Second, increase your shared sort heap substantially. For your dataset size, I’d recommend setting sheapthres_shr to at least 40GB. Third, review your bufferpool allocation - AI functions benefit from a dedicated bufferpool for temporary storage during training iterations.
Good point about the UNION ALL view. I checked sheapthres_shr and it’s set to 10GB. That does seem low for AI workloads. I tried training on a single partition (about 300K rows) and it completed successfully, which confirms the memory scaling issue. The single partition training used approximately 8GB based on monitoring. So extrapolating to the full dataset, I’d need significantly more than 10GB shared sort heap.
Also worth checking if you’re using automatic memory management or manual. With automatic, Db2 might not be allocating enough memory dynamically for AI workloads. You might want to switch to manual memory management for better control over AI training resources. Set self_tuning_mem to OFF and manually configure memory parameters based on your workload characteristics.
Let me provide a comprehensive solution addressing all three critical areas for your Db2 AI memory issue.
Db2 AI Memory Requirements:
For your workload (2.5M rows, random forest with 100 trees), you need to allocate memory across multiple configuration parameters. The base calculation: 2.5M rows × estimated 50 features × 8 bytes × 100 trees × 2 = approximately 200GB theoretical maximum. However, Db2 AI uses optimized algorithms, so actual requirements are lower.
Bufferpool Configuration:
Create a dedicated bufferpool for AI operations:
CREATE BUFFERPOOL AI_BP SIZE 40000 PAGESIZE 32K
ALTER TABLESPACE TEMPSPACE1 BUFFERPOOL AI_BP
Your existing 50GB bufferpool should remain for regular operations. The dedicated AI bufferpool prevents contention.
Critical Memory Parameters:
UPDATE DBM CFG USING SHEAPTHRES_SHR 45000
UPDATE DB CFG FOR yourdb USING SORTHEAP 8192
UPDATE DB CFG FOR yourdb USING UTIL_HEAP_SZ 10000
Dataset Partitioning Strategy:
Instead of UNION ALL across 8 partitions, implement incremental training:
- Train base model on first partition (most recent data)
- Use Db2’s incremental learning capability to update model with subsequent partitions
- This approach reduces peak memory by processing partitions sequentially
Alternatively, reduce model complexity: decrease trees from 100 to 50, or implement feature selection to reduce dimensionality. Monitor with db2pd -edus during training to track actual memory consumption. After implementing these changes, restart the instance for DBM configuration changes to take effect. Your training should complete successfully with these optimizations addressing memory allocation, bufferpool segregation, and partitioning strategy.
I ran into this exact scenario last quarter. Here’s what you need to verify - the memory estimate for random forest training is roughly (number_of_rows × number_of_features × 8 bytes × number_of_trees × 2). That multiplication by 2 accounts for internal data structures during training.