e2e MNIST deep learning example for R (#1510)

Rlang e2e deeplearning example

Signed-off-by: Botong Ou <[email protected]>

e2e/v1/docs/rlang_mnist_test.go

@@ -0,0 +1,23 @@
+package docs
+
+import (
+	. "github.com/onsi/ginkgo/v2"
+	. "github.com/onsi/gomega"
+
+	e2e "github.com/tensorchord/envd/e2e/v1"
+)
+
+var _ = Describe("rlang_mnist", Ordered, func() {
+	exampleName := "rlang_mnist"
+	testcase := "e2e"
+	e := e2e.NewExample(e2e.BuildContextDirWithName(exampleName), testcase)
+	BeforeAll(e.BuildImage(true))
+	BeforeEach(e.RunContainer())
+	FIt("execute runtime command `Rscript`", func() {
+		res, err := e.ExecRuntimeCommand("rlang-mnist")
+		Expect(err).To(BeNil())
+		isNumeric := "TRUE"
+		Expect(res).To(BeEquivalentTo(isNumeric))
+	})
+	AfterEach(e.DestroyContainer())
+})

e2e/v1/docs/testdata/rlang_mnist/build.envd

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+# syntax=v1
+
+
+def build():
+    base(dev=True)
+    install.r_lang()
+    install.python()
+    install.r_packages(name=["keras"])
+    install.python_packages(name=["tensorflow"])
+    runtime.command(commands={"rlang-mnist": "Rscript mnist.r 1> /dev/null"})

e2e/v1/docs/testdata/rlang_mnist/mnist.r

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+Sys.setenv(TF_CPP_MIN_LOG_LEVEL = "3")
+library(keras)
+
+mnist <- dataset_mnist()
+x_train <- mnist$train$x
+y_train <- mnist$train$y
+x_test <- mnist$test$x
+y_test <- mnist$test$y
+
+
+# reshape
+x_train <- array_reshape(x_train, c(nrow(x_train), 784))
+x_test <- array_reshape(x_test, c(nrow(x_test), 784))
+# rescale
+x_train <- x_train / 255
+x_test <- x_test / 255
+
+y_train <- to_categorical(y_train, 10)
+y_test <- to_categorical(y_test, 10)
+
+model <- keras_model_sequential() 
+model %>% 
+  layer_dense(units = 256, activation = 'relu', input_shape = c(784)) %>% 
+  layer_dropout(rate = 0.4) %>% 
+  layer_dense(units = 128, activation = 'relu') %>%
+  layer_dropout(rate = 0.3) %>%
+  layer_dense(units = 10, activation = 'softmax')
+
+model %>% compile(
+  loss = 'categorical_crossentropy',
+  optimizer = optimizer_rmsprop(),
+  metrics = c('accuracy'),
+)
+
+history <- model %>% fit(
+  x_train, y_train, 
+  epochs = 1, batch_size = 128, 
+  validation_split = 0.2,
+  verbose=3
+)
+
+acc <- model %>% evaluate(x_test, y_test, verbose=3)
+
+write(is.numeric(acc), stderr())