When faced with massive datasets, scaling machine learning training requires a combination of distributed computing strategies and algorithm optimization. Here are the core techniques:
- Data Parallelism: Split the dataset across multiple workers, each training a copy of the model on a subset. Gradients are aggregated using synchronous or asynchronous updates.
- Model Parallelism: Partition the model itself across devices when it is too large for a single GPU.
- Mixed Precision Training: Use lower-precision arithmetic (e.g., FP16) to reduce memory and computation while maintaining accuracy.
- Efficient Data Loading: Use sharding, caching, and pipelining to prevent I/O bottlenecks.
- Gradient Accumulation: Simulate larger batch sizes by accumulating gradients over several steps.
- Distributed Frameworks: Leverage tools like Apache Spark, TensorFlow Distributed, or PyTorch DDP for seamless scaling.
- Optimizer Improvements: Use adaptive optimizers (e.g., Adam) and learning rate scheduling to converge faster.
For interview preparation, expect questions on how to handle out-of-memory errors, trade-offs between synchronous and asynchronous training, and how to tune hyperparameters in distributed settings.