8000 Julia MNIST classification e2e test example by oubotong · Pull Request #1484 · tensorchord/envd · GitHub
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Merged
merged 6 commits into from
Feb 17, 2023
Merged

Julia MNIST classification e2e test example #1484

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43f044f
julia mnist classification example
oubotong Feb 17, 2023
518cb90
julia mnist classification example
oubotong Feb 17, 2023
c6ab5e9
fix lint problem
oubotong Feb 17, 2023
f6f7a1a
fix lint problem
oubotong Feb 17, 2023
88cb07c
fix another lint problem
oubotong Feb 17, 2023
211d784
fix another lint problem
oubotong Feb 17, 2023
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28 changes: 28 additions & 0 deletions e2e/v1/docs/julia_mnist_test.go
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package docs

import (
"strconv"

. "github.com/onsi/ginkgo/v2"
. "github.com/onsi/gomega"

e2e "github.com/tensorchord/envd/e2e/v1"
)

var _ = Describe("julia_mnist", Ordered, func() {
exampleName := "julia_mnist"
testcase := "e2e"
e := e2e.NewExample(e2e.BuildContextDirWithName(exampleName), testcase)
BeforeAll(e.BuildImage(true))
BeforeEach(e.RunContainer())
It("execute runtime command `julia-mnist`", func() {
res, err := e.ExecRuntimeCommand("julia-mnist")
Expect(err).To(BeNil())
IsNumber := func(s string) bool {
_, err = strconv.ParseFloat(s, 64)
return err == nil
}
Expect(res).To(Satisfy(IsNumber))
})
AfterEach(e.DestroyContainer())
})
7 changes: 7 additions & 0 deletions e2e/v1/docs/testdata/julia_mnist/build.envd
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# syntax=v1
def build():
base(dev=True)

install.julia()
install.julia_packages(name=["Flux", "MLDatasets"])
runtime.command(commands={"julia-mnist": "julia mnist.jl"})
43 changes: 43 additions & 0 deletions e2e/v1/docs/testdata/julia_mnist/mnist.jl
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using Flux, MLDatasets
using Flux: train!, onehotbatch

ENV["DATADEPS_ALWAYS_ACCEPT"] = "true"

# Load training data (images, labels)
< 87D4 /td> x_train, y_train = MLDatasets.MNIST(split=:train)[:]
# Load test data (images, labels)
x_test, y_test = MLDatasets.MNIST(split=:test)[:]
# Convert grayscale to float
x_train = Float32.(x_train)
# Create labels batch
y_train = Flux.onehotbatch(y_train, 0:9)

model = Chain(
Dense(784, 256, relu),
Dense(256, 64, relu),
Dense(64, 10, relu),
softmax
)

loss(x, y) = Flux.Losses.logitcrossentropy(model(x), y)

optimizer = ADAM(0.0001)

parameters = Flux.params(model)
# flatten() function converts array 28x28x60000 into 784x60000 (28*28x60000)
train_data = [(Flux.flatten(x_train), Flux.flatten(y_train))]
# Range in loop can be used smaller
for i in 1:100
Flux.train!(loss, parameters, train_data, optimizer)
end

test_data = [(Flux.flatten(x_test), y_test)]
accuracy = 0
for i in 1:length(y_test)
global accuracy
if findmax(model(test_data[1][1][:, i]))[2] - 1 == y_test[i]
accuracy = accuracy + 1
end
end

print(accuracy / length(y_test))
0