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STAC-ML Markets (Inference) Naive Implementation with ONNX on a 60-vCPU Sole-Tenant Cloud Node with 240 GiB Memory - Throughput-Optimized Configuration

Audited

STAC-ML Markets (Inference)

Throughput-optimized

  • STAC-ML Markets (Inference) Naive Implementation, Compatibility Rev A
  • Inference Engine
    • Python 3.8.10
    • ONNX runtime 1.11.0
    • NumPy 1.22.3
  • Ubuntu Linux 20.04.4 LTS
    • Based on a standard image provided by Google Cloud
    • No OS tuning
  • A Google Cloud c2-node-60-240 sole-tenant node
    • 60 x Intel® Xeon® Family 6 Model 85 Stepping 7 (Cascade Lake) vCPUs at 3.1GHz
    • 240 GiB of memory
    • 150 GB balanced persistent disk

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The use of machine learning (ML) to develop models is now commonplace in trading and investment. Whether the business imperative is reducing time to market for new algorithms, improving model quality, or reducing costs, financial firms have to offload major aspects of model development to machines in order to continue competing in the markets.

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