Memory hook: Fit the data, distribute the work, checkpoint progress.
Must remember
- Organize training data in suitable storage with repeatable reads and controlled access. Package code, dependencies and configuration into reproducible jobs rather than relying on an interactive notebook state.
- Use supported custom training, AutoML, pipeline components or Kubernetes-based frameworks depending on control needs. Diagnose input starvation, memory pressure, failed workers and incompatible libraries separately.
- Hyperparameters control training choices; learned parameters are fitted from data. Search strategies need a bounded trial budget, objective metric and early stopping where appropriate.
- CPU suits many conventional models and preprocessing; GPUs accelerate suitable tensor workloads; TPUs suit supported accelerator-optimized workloads. Compare utilization, memory and end-to-end time, not just hardware labels.
- Data parallelism distributes batches across model replicas; model parallelism partitions a model that may not fit one device. Distributed training adds communication, synchronization and fault-handling requirements.
- Checkpoint long jobs and retain artifacts/metrics. Fine-tuning foundation models requires compatible data, supported methods and safety/quality evaluation; synthetic data must be checked for errors and bias.
Review details
High training quality with poor validation performance suggests overfitting or data leakage; weak performance on both can suggest underfitting, bad labels or inadequate features. Regularization, simpler models, more representative data and sound validation address different causes. Do not tune repeatedly against the final test set.
Hyperparameter optimization chooses settings such as learning rate or model complexity against a validation objective; learned model parameters are fitted during training. Early stopping and checkpoints bound wasted work. Quantization/compression can reduce serving memory or latency at possible quality cost; benchmark the actual model/runtime before adopting them.
Choose under exam pressure
| Requirement | Choice and reason |
|---|---|
| A model exceeds one accelerator’s memory | Consider model partitioning or memory-efficient techniques, not only more independent replicas. |
| Accelerator utilization is low | Investigate input pipeline throughput and CPU/preprocessing bottlenecks. |
Traps
- More GPUs can be slower when communication dominates.
- Tuning against the final test set contaminates the final evaluation.
Active recall
1. What is a hyperparameter?
A configured training choice such as learning rate, selected outside the model’s learned weights.
2. Why checkpoint?
To resume recoverable work and preserve progress after interruption.
3. What distinguishes data parallelism?
Each replica processes different data while coordinating model updates.
4. Why use early stopping?
To limit wasted trials or training when the chosen criterion stops improving.
5. How assess a fine-tuned model?
Compare held-out task quality, safety, latency and cost against the baseline.