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You can host your trained machine learning models in the cloud and use the Cloud ML prediction service to infer target values for new data. This page discusses model hosting and prediction and introduces considerations you should keep in mind for your projects.
Cloud ML Engine can host your models so that you can get predictions from them in the cloud. The process of hosting a saved model is called deployment. The prediction service manages the infrastructure needed to run your model at scale, and makes it available for online and batch prediction requests. This section describes model deployment.
The Cloud ML prediction service makes use of models exported through the
export_savedmodel() function which is available for models created using the tensorflow, keras and
tfestimators packages or any other tool that support the tf.train.Saver interface.
For instance, we can use
examples/keras/train.R included in this package to define
and train an MNIST keras model by running:
library(keras) FLAGS <- flags( flag_numeric("dropout_rate", 0.4) ) mnist <- dataset_mnist() x_train <- mnist$train$x y_train <- mnist$train$y x_test <- mnist$test$x y_test <- mnist$test$y x_train <- array_reshape(x_train, c(nrow(x_train), 784)) x_test <- array_reshape(x_test, c(nrow(x_test), 784)) 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 = FLAGS$dropout_rate) %>% layer_dense(units = 128, activation = 'relu') %>% layer_dropout(rate = 0.3) %>% layer_dense(units = 10, activation = 'softmax') model %>% compile( loss = 'categorical_crossentropy', optimizer = optimizer_rmsprop(), metrics = c('accuracy') ) model %>% fit( x_train, y_train, epochs = 20, batch_size = 128, validation_split = 0.2 ) export_savedmodel(model, "savedmodel")
Deployment is performed through
cloudml_deploy() which uses the same
cloudml configuration concepts used while training. We can
train any exported model by running:
cloudml_deploy("savedmodel", name = "keras_mnist")
Copying file://savedmodel/variables/variables.data-00000-of-00001 [Content-Type=application/octet-stream]... Copying file://savedmodel/saved_model.pb [Content-Type=application/octet-stream]... Copying file://savedmodel/variables/variables.index [Content-Type=application/octet-stream]... / [3/3 files][ 1.9 MiB/ 1.9 MiB] 100% Done Operation completed over 3 objects/1.9 MiB. Model created and available in https://console.cloud.google.com/mlengine/models/keras_mnist
Notice that models make use of unique names and versions which can be specified
version parameters in
Once a model is deployed, predictions can be performed by providing a list of inputs into
mnist_image <- keras::dataset_mnist()$train$x[1,,] grid::grid.raster(mnist_image / 255)
cloudml_predict( list( as.vector(t(mnist_image)) ), name = "keras_mnist", )
$predictions dense_3 1 0, 0, 0, 0, 0, 1, 0, 0, 0, 0
For additional information visit Google Cloud Platform - Prediction Basics
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