Benchmarking Results

These are results from benchmarking the training process. The following are benchmarking results from running equivalent programmes in both repositories. These programmes uses ~10 thousand training images at 19 x 19 pixels each.

ᵃI have not yet figured out benchmarking memory usage in Python.

These results were run on this machine:

julia> versioninfo()
Julia Version 1.5.2
Commit 539f3ce943 (2020-09-23 23:17 UTC)
Platform Info:
  OS: macOS (x86_64-apple-darwin18.7.0)
  CPU: Intel(R) Core(TM) i5-6360U CPU @ 2.00GHz
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-9.0.1 (ORCJIT, skylake)

1.6 Update

A few months after the release of Julia 1.6, I did some performance considerations (there are already quite a few nice features that come with 1.6). Now these are the benchmarking results (see benchmark/basic.jl) Language of Implementation | Commit | Run Time in Seconds | Number of Allocations | Memory Usage –- | –- | –- | –- | –- Julia | ??? | 8.165 | 249021919 | 5.01 GiB