## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE, eval=FALSE)

## -----------------------------------------------------------------------------
# library(cloudml)
# job <- cloudml_train("mnist_mlp.R")

## -----------------------------------------------------------------------------
# job_status(job)

## -----------------------------------------------------------------------------
# job_status()   # get status of last job

## -----------------------------------------------------------------------------
# job_collect()     # collect last job
# job_collect(job)  # collect specific job

## -----------------------------------------------------------------------------
# ls_runs()

## -----------------------------------------------------------------------------
# # view the latest run
# view_run()
# 
# # view a specific run
# view_run("runs/cloudml_2017_12_15_182614794")

## -----------------------------------------------------------------------------
# job_list()

## -----------------------------------------------------------------------------
# job_status("cloudml_2017_12_18_203510175")

## -----------------------------------------------------------------------------
# job_stream_logs("cloudml_2017_12_18_203510175")

## -----------------------------------------------------------------------------
# job_cancel("cloudml_2017_12_18_203510175")

## -----------------------------------------------------------------------------
# library(keras)
# 
# FLAGS <- flags(
#   flag_integer("dense_units1", 128),
#   flag_numeric("dropout1", 0.4),
#   flag_integer("dense_units2", 128),
#   flag_numeric("dropout2", 0.3),
# )

## -----------------------------------------------------------------------------
# input <- layer_input(shape = c(784))
# predictions <- input %>%
#   layer_dense(units = FLAGS$dense_units1, activation = 'relu') %>%
#   layer_dropout(rate = FLAGS$dropout1) %>%
#   layer_dense(units = FLAGS$dense_units2, activation = 'relu') %>%
#   layer_dropout(rate = FLAGS$dropout2) %>%
#   layer_dense(units = 10, activation = 'softmax')
# 
# model <- keras_model(input, predictions) %>% compile(
#   loss = 'categorical_crossentropy',
#   optimizer = optimizer_rmsprop(lr = 0.001),
#   metrics = c('accuracy')
# )
# 
# history <- model %>% fit(
#   x_train, y_train,
#   batch_size = 128,
#   epochs = 30,
#   verbose = 1,
#   validation_split = 0.2
# )

## -----------------------------------------------------------------------------
# cloudml_train("minst_mlp.R", flags = list(dropout1 = 0.3, dropout2 = 0.2))

## -----------------------------------------------------------------------------
# cloudml_train("train.R", master_type = "standard_gpu")

## -----------------------------------------------------------------------------
# cloudml_train("train.R", master_type = "standard_p100")

## -----------------------------------------------------------------------------
# cloudml_train("train.R", master_type = "complex_model_m_p100")

## -----------------------------------------------------------------------------
# cloudml_train("mnist_mlp.R", config = "tuning.yml")