FluxBench.jl
Benchmarks for the FluxML ecosystem for deep learning, scientific machine learning, differentiable programming etc including AD and CUDA accelerated workloads
Install / Use
/learn @FluxML/FluxBench.jlREADME
FluxBench.jl
This is a repository that backs the results generated for https://speed.fluxml.ai
It is a collection of benchmarking runs for a subset of modeling done in the FluxML ecosystem and also serves as a means of tracking progress.
Running Locally
To run the benchmarks locally:
- clone this repository
cdin to the local copy viacd FluxBench.jl- open Julia and call
] instantiate
And finally:
julia> using FluxBench
julia> FluxBench.bench()
Adding Benchmarks
To contribute benchmarks one needs to:
- add in the script(s) to the
src/packagesdirectory with the required dependencies and code needed to run the benchmarks- Note: remember to add a
groupto theSUITEvariable via theaddgroup!(SUITE, "name/of/benchmark/group") - Treat
groupas a dictionary and new benchmarks can be added via assigning results to group as:group["name_of_benchmark"] = @benchmarkable ... - Please use the macro
@benchmarkableto set up the benchmarks (see BenchmarkTools.jl for a reference) - Please follow the performance, profiling and benchmarking guides of the different packages used in the benchmark. Examples include - Julia's, Flux's, CUDA's, BenchmarkTools
- Note: remember to add a
- include the benchmarks in the top level file
src/FluxBench.jl - call the benchmarks in the
benchfunction located in filesrc/bench.jl
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