!!THIS IS A WORK IN PROGRESS!!
This repo demonstrates how a trained ML model could run inside an Ethereum 2 EE. Currently there is a single random forest classifier model that is trained to determine which of the following types of iris flowers:
- setosa
- versicolor
- virginica
based on the following design variables:
- sepal length (cm)
- sepal width (cm)
- petal length (cm)
- petal width (cm)
Install LLVM
brew install llvm
echo 'export PATH="/usr/local/opt/llvm/bin:$PATH"' >> ~/.bash_profileInstall the WebAssembly Binary Toolkit
brew install wabtInstall NinJa
brew install ninjamake build$ ./build/random-forest
Execution time: 0 microseconds
Probabilities:
1.000000 0.000000 0.000000
Model Predicts:
setosa
$ ./build/anmlee
Probabilities:
1.0000 .0000 .0000
Model Predicts:
setosa $ make benchmark
########## Python Benchmark: ###########
Execution time: 107772 microseconds.
0 setosa
Name: species, dtype: category
Categories (3, object): [setosa, versicolor, virginica]
########## eWasm Benchmark: ###########
Finished release [optimized] target(s) in 0.03s
Running target/release/deps/anmlee-0de6000a8c14c88e
running 1 test
Execution Time: 551.849µs
test tests::test ... ok
test result: ok. 1 passed; 0 failed; 0 ignored; 0 measured; 0 filtered out
########## C Benchmark: ##########
Execution time: 1 microseconds
Probabilities:
1.000000 0.000000 0.000000
Model Predicts:
setosa
- Python: 107772 microseconds
- eWasm: 551.849 microseconds
- C: 1 microsecond
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