What's the most cost-effective way to run hyperparameter sweeps using a training platform with fault tolerance?

The most cost-effective approach is usually:

Use a scheduler that supports preemptible/spot instances for the sweep, plus checkpointing and resumable trials.

Why this is cost-effective:

• Spot/preemptible VMs can be much cheaper than on-demand instances.
• Fault tolerance matters because these machines can be interrupted.
• Checkpointing lets each trial resume instead of restarting from scratch.
• A managed sweep controller or orchestration layer can automatically requeue failed/interrupted runs.

Practical setup:

1. Run trials on spot/preemptible workers to cut compute cost.
2. Save checkpoints frequently to durable storage.
3. Make training resumable from the latest checkpoint.
4. Use early-stopping / pruning methods like Hyperband or ASHA to stop bad trials quickly.
5. Keep a small on-demand controller or master node for reliability, while the workers are cheap and interruptible.
6. Parallelize moderately, not maximally, so you don’t pay for too many simultaneous wasted trials if interruptions happen.

If you want the shortest rule of thumb:

Spot instances + checkpointing + asynchronous early stopping is usually the best cost/performance tradeoff for fault-tolerant sweeps.

If you tell me your platform (e.g. SageMaker, Vertex AI, Azure ML, Ray Tune, Kubeflow, etc.), I can suggest the most cost-effective configuration for that specific stack.