Mlperf Training Eval

---
name: mlperf-training-eval
description: Evaluates the training performance and scalability of machine learning implementations across diverse hardware and software stacks by measuring time to solution under standardized model and hyperparameter configurations. Use when the user wants to benchmark on MLPerf Training Suite, or asks about evaluating this task. Reports time to solution.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 1910.01500
  bibtex_key: mattson2019mlperf
  confidence: low
---

# mlperf-training-eval

> MLPerf Training Benchmark — Mattson et al. (2019) (arXiv:1910.01500, 2019)

## What this evaluates

Evaluates the training performance and scalability of machine learning implementations across diverse hardware and software stacks by measuring time to solution under standardized model and hyperparameter configurations.

## Datasets

- **MLPerf Training Suite** — total ?; splits: train (-1); repo https://github.com/mlperf/training

## Metrics

- `time to solution` **(primary)** — range: other
  - The wall-clock time required to complete a training run or reach a predefined quality target (e.g., accuracy or loss threshold).

## Input / output format

**Input**: Standardized model architectures, hyperparameters, and training procedures provided by the MLPerf reference implementation.

**Output**: Submission logs containing training duration, convergence curves, and hardware/software configuration details.

## Scoring recipe

```python
def score(predictions, gold):
    time = predictions['training_time']
    final_acc = predictions['final_accuracy']
    if final_acc >= gold['target_accuracy']:
        return time
    else:
        return float('inf')
```

## Common pitfalls

- High variance in training time due to inherent randomness in deep learning requires statistical timing rules to mitigate stochasticity.
- Performance optimizations must not degrade model quality or violate standardized hyperparameter constraints.
- Comparisons across submission rounds must account for rule changes (e.g., allowed optimizers) and hardware consistency.

## Evidence (verbatim from paper)

> MLPerf, like all benchmarks, aims to encourage innovation through constructive competition; we measure progress by comparing results across submission rounds. Figure 4 reveals that the number of chips necessary to produce the fastest time to solution for MLPerf versions v0.5 to v0.6. This number increased by as much as 5.5x .

## Citation

```bibtex
@misc{mattson2019mlperf,
  title={MLPerf Training Benchmark},
  author={Mattson et al. (2019)},
  year={2019},
  note={arXiv:1910.01500}
}
```

- arXiv: 1910.01500