Machine Learning (ML) System Benchmark Task

A Machine Learning (ML) System Benchmark Task is an ML task that is an AI benchmarking task (that evaluates ML system performance).

• AKA: ML Performance Evaluation Task, ML System Assessment Task.
• Context:
  • Task Input: ML Benchmark Datasets.
  • Task Output: ML System Performance Score.
  • Task Requirement:
    • Machine Learning Baseline Models.
    • a Machine Learning Model Training System.
    • a ML Benchmark Performance Metrics.
  • ...
  • It can range from being a Simple ML Benchmark to being a Complex ML Benchmark, depending on its task complexity.
  • It can range from being a Single-Domain ML Benchmark to being a Multi-Domain ML Benchmark, depending on its domain scope.
  • It can range from being a Small-Scale ML Benchmark to being a Large-Scale ML Benchmark, depending on its data volume.
  • It can range from being a Standard ML Benchmark to being a Custom ML Benchmark, depending on its specialization level.
  • ...
• Example(s):
  • MLBench Benchmark Task (2024),
  • DAWNBench Benchmark Task (2024),
  • MLPerf Benchmark Task (2024),
  • Penn Machine Learning Benchmark (PMLB) Task (2024),
  • MoleculeNet Benchmark Task (2024).
  • …
• Counter-Example(s):
  • a Natural Language Processing System Benchmark Task,
  • a WikiText Error Correction (WTEC) System Benchmark Task,
  • a Text Error Correction System Benchmark Task,
  • a Optical Character Recognition (OCR) System Benchmark System,
  • a Data Mining System Benchmark Task,
  • a Word Processing Software Benchmark Task,
  • a Database Management Systems Benchmark Task.
• See: Learning Rate, Machine Learning Model, Machine Learning Algorithm, Evaluation System, Computer Hardware Benchmarking, Computing System Benchmarking.

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References

2024-11-20

• Perplexity.ai
  • Question: What are machine learning (ML) system benchmarks, including their key components, metrics, and examples? Please describe the main types of ML benchmarks used for evaluating model performance, their essential characteristics, and how they measure aspects like training speed, inference performance, and model accuracy. Include information about standardized datasets and evaluation frameworks used in benchmarking, as well as notable benchmark suites. Also explain how these benchmarks vary in complexity, scale, and domain coverage.
  • Answer: Machine learning benchmarks provide standardized frameworks for evaluating and comparing ML models across different dimensions.
    • Key Performance Indicators:
      • Training Speed:
        • Measured as samples processed per second during Model Training
        • Depends on factors like:
          • Programming Language Implementation
          • Hardware Optimization (CPU/GPU acceleration)
          • Optimization algorithms used (SGD, Adam, etc.)
      • Inference Speed:
        • Measures model response time and throughput
        • Calculated as samples processed per second during prediction
        • Critical for real-time applications where milliseconds matter
      • Model Precision:
        • Evaluates prediction accuracy and model quality
        • Common metrics include:
          • Accuracy for classification
          • Mean Absolute Error (MAE) for regression
          • Precision and recall for binary classification
    • Types of Benchmarks:
      • Classification Benchmarks:
        • Focus on categorizing inputs into predefined groups
        • Notable examples:
          • MNIST (handwritten digits)
          • CIFAR-10/100 (image classification)
      • Regression Benchmarks:
        • Evaluate continuous numerical predictions
        • Used for tasks like Price Forecasting and Trend Prediction
      • Natural Language Processing:
        • Includes benchmarks like:
          • GLUE (General Language Understanding)
          • SQuAD (Question Answering)
      • High-Performance Computing (HPC):
        • Specialized for supercomputer systems
        • Measures performance on scientific applications
        • Examples include DeepCAM for Weather Modeling
    • Standardized Datasets:
      • PMLB Collection:
        • Largest curated repository of benchmark datasets
        • Includes both classification and regression tasks
        • Provides standardized interfaces for Python and R
        • Features detailed metadata and Documentation
    • Recent Developments:
      • MLPerf Benchmarks:
        • Industry-standard suite measuring Model Training and inference
        • Shows up to 49X performance improvements over initial results
        • Includes participation from major tech organizations
        • Covers diverse applications and hardware configurations
    • Complexity Considerations:
      • Domain Complexity varies significantly between benchmarks
      • Factors affecting complexity:
        • Dataset Size and dimensionality
        • Task Difficulty
        • Required Model Sophistication
    • Best Practices:
      • • Choose appropriate metrics based on Use Case
        • Consider multiple performance aspects
        • Use standardized evaluation procedures
        • Account for hardware and software variations
        • Ensure fair comparisons across different models and systems
  • Citations:

[1] https://www.neuraldesigner.com/blog/how-to-benchmark-the-performance-of-machine-learning-platforms/
[2] https://www.purestorage.com/knowledge/machine-learning-performance-metrics.html
[3] https://www.tutorialspoint.com/what-are-the-machine-learning-benchmarks
[4] https://academic.oup.com/bioinformatics/article/38/3/878/6408434
[5] https://mlcommons.org/2023/11/mlperf-training-v3-1-hpc-v3-0-results/
[6] https://usc-isi-i2.github.io/AAAI2022SS/papers/SSS-22_paper_80.pdf
[7] https://www.v7labs.com/blog/performance-metrics-in-machine-learning

2019

• (John & Mattson, 2019) ⇒ Tom St. John, and Peter Mattson (2019)."MLPerf: A Benchmark for Machine Learning". In: Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis (SC19).

2018a

• (Balaji & Allen, 2018) ⇒ Adithya Balaji, Alexander Allen (2018). "Benchmarking Automatic Machine Learning Frameworks". arXiv preprint arXiv:1808.06492.

2018b

• (Liu et al. 2018) ⇒ Yu Liu, Hantian Zhang, Luyuan Zeng, Wentao Wu, and Ce Zhang (2018). "MLbench: MLBench: Benchmarking Machine Learning Services Against Human Experts". Proceedings of the VLDB Endowment, 11(10), 1220-1232. DOI:10.14778/3231751.3231770

2018c

• (Wu et al., 2018) ⇒ Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, and Vijay Pande (2018). "MoleculeNet: A Benchmark For Molecular Machine Learning". Chemical science, 9(2), 513-530.

2017a

• (Olson et al., 2017) ⇒ Randal S. Olson, William La Cava, Patryk Orzechowski, Ryan J. Urbanowicz, and Jason H. Moore (2017). "PMLB: A Large Benchmark Suite For Machine Learning Evaluation And Comparison". BioData mining, 10, 36. DOI:10.1186/s13040-017-0154-4

2017b

• (Coleman et al., 2017) ⇒ Cody Coleman, Deepak Narayanan, Daniel Kang, Tian Zhao, Jian Zhang, Luigi Nardi, Peter Bailis, Kunle Olukotun, Chris Re, and Matei Zaharia (2017). "Dawnbench: An End-To-End Deep Learning Benchmark And Competition". Training, 100(101), 102.

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