Calculator Inputs
Example data table
These examples use one consistent baseline, then vary strategy and trials.
| Scenario | Trials | Effective epochs | Wall-clock | Total cost |
|---|---|---|---|---|
| Random Search | 80 | 15.00 | 10.19 hours | $252.46 |
| Bayesian Optimization | 50 | 15.00 | 6.37 hours | $160.68 |
| Hyperband/ASHA | 120 | 7.00 | 7.87 hours | $196.83 |
Formula used
This calculator uses an expected-value model with retries and overhead.
effective_epochs = epochs_full × factorfactor = 1 − early_stop% for grid/random/bayesian, or factor = hyperband_resource% for Hyperband.
speedup = max(1, gpus × scaling_efficiency)train_minutes = minutes_per_epoch_1gpu × effective_epochs ÷ speeduptrial_minutes = setup_minutes + train_minutes + eval_minutes
retry_multiplier = 1 ÷ (1 − failure_rate)expected_trials = trials × retry_multiplier
serial_hours = expected_trials × (trial_minutes ÷ 60)wall_clock_hours = serial_hours ÷ min(concurrency, trials)wall_clock_hours *= (1 + ops_overhead)
gpu_hours = expected_trials × billed_hours_per_trial × gpus × (1 + ops_overhead)gpu_cost = gpu_hours × gpu_hourly_cost × (1 − discount)Similar multipliers apply for CPU and memory. Storage is billed across the larger of runtime or retention.
How to use this calculator
- Measure one epoch runtime on your target hardware.
- Enter planned trials and expected epoch count per trial.
- Set GPUs per trial and scaling efficiency for distributed runs.
- Choose strategy and model early stopping or resource fractions.
- Enter failure rate, overhead, and any spot or discount savings.
- Review totals, then export CSV or PDF for sharing.
- Prefer measured rates over list prices when available.
- Set Bill GPUs for full trial duration if your platform bills whole jobs.
- Use a conservative failure rate when prototyping unstable pipelines.
What this estimate covers
This calculator forecasts end-to-end tuning spend, not just training. Each trial includes setup minutes, training minutes, and evaluation minutes, then multiplies by planned configurations and expected retries. Output totals include compute, shared storage, and data egress. Results report total cost, cost per expected trial, billed GPU-hours, CPU core-hours, and memory GB-hours so engineering teams can compare options consistently across environments for planning, governance, and approvals.
Time model and parallel execution
Trial wall time is computed in minutes, then converted to hours for project totals. Serial runtime equals expected trials times per-trial hours. Wall-clock time divides that serial runtime by effective parallelism, which is the smaller of concurrency and trial count. An operations overhead multiplier is applied afterward to capture queue delays and coordination costs. The example table demonstrates how increasing concurrency reduces calendar duration without changing per-trial economics.
Compute billing and scaling efficiency
Training minutes depend on measured minutes per epoch on one GPU, adjusted by effective epochs. Distributed runs apply speedup equal to GPUs per trial times scaling efficiency, capped to avoid unrealistic gains. Billing supports two modes: charge GPUs for the full trial duration, or charge only training time when your platform excludes setup and evaluation. Compute discounts apply uniformly to GPU, CPU, and memory charges, making spot pricing and commitments easy to model.
Reliability, retries, and overhead buffers
Unstable pipelines inflate cost through retries. Expected trials use a retry multiplier of 1 divided by (1 minus failure rate). A 10% failure rate implies 1.11× expected trials, while 25% implies 1.33×. Operations overhead then adds a conservative buffer for logging, monitoring, scheduling, and extra reruns. Together, these factors help teams size budgets when experimentation quality varies across datasets, code versions, and hardware pools.
Storage, egress, and reporting outputs
Storage cost uses shared gigabytes multiplied by a per-GB-month rate and a billing window in months. The window is the larger of retention days and estimated runtime in days, preventing underestimation when runs finish early but artifacts must remain. Egress cost adds a simple GB times rate term for external transfers. After submission, you can export a CSV for spreadsheets or a PDF report for reviews.
FAQs
What is effective epochs, and why does it matter?
Effective epochs estimate average training per trial after pruning. They drive training minutes and therefore compute-hours. Lower effective epochs reduce cost and calendar time, especially when you run many configurations.
How does early stopping change the estimate?
For grid, random, and Bayesian modes, early stopping reduces the training fraction by the savings percentage. Setup and evaluation still remain, so the savings is largest when training dominates your per-trial duration.
What does scaling efficiency represent?
Scaling efficiency approximates how well extra GPUs reduce training time. A value of 80% means two GPUs act like 1.6× speedup. It prevents unrealistic linear assumptions for communication-heavy training.
How are failures and retries handled?
The model increases expected trials using 1 ÷ (1 − failure rate). That adds budget for reruns caused by timeouts, spot interruptions, or errors. Overhead then adds a separate buffer for operational friction.
Why can storage cost exceed runtime?
Artifacts often must be retained after the search ends. Storage billing uses the larger of runtime days and your retention window. This avoids underestimating costs when checkpoints and logs must remain accessible.
What should I enter for CPU and memory pricing?
Use your platform’s billed rates per core-hour and per GB-hour, or set them to zero if bundled. If costs are blended into a single instance rate, approximate by splitting that rate across CPU and memory.