Metadata Subgroup

This subgroup is lead but University of Tennessee, Knoxville.

Schema Development

As part of the logging, reporting activities, this subgroup is tasked to create appropriate schema to follow the FAIR principles. Below is a general overview of the major hierarchy of data that needs to be recorded for reproducibility.

• Hardware specifications
  • Compute: CPUs, Accelerators
  • Memory: caches, NUMA
  • Network: on-node CPU and accelerator coherency, NIC and off-node switches
  • Peripherals
  • Storage: primary (SSD), secondary (HDD), tertiary (RAID/remote)
  • Firmware: ID/release date
• Software stack
  • Compiler: GCC, Clang, vendor
  • AI framework: TensorFlow, PyTorch, Keras, MxNet
  • Tensor backend: JAX, TVM
  • Runtime: JVM, OpenMP, CUDA
  • Messaging API: MPI, NCCL, RCCL
  • OS: Linux
  • Container: Singularity, Docker, CharlieCloud
• Input data
  • Data sets (version, size)
    • Image: MNIST digits/fashion, CIFAR 10/100, ImageNet, VGG
    • Language: Transformer
    • Science: instrument, simulation
  • Annotations
• Model data
  • Release date, ID, repo/branch/tag/hash, URL
• Output data
  • Performance rate: training, inference
  • Power draw: training, inference
  • Energy consumption
  • Convergence: epochs
  • Accuracy, recall