Hello,
I am trying to deploy a test job from my RStudio Desktop to GCP AI platform. I am able to successfully deploy the job after the suggested ammendement (Attempt to fix write() argument must be str) to .\library\cloudml\cloudml\cloudml\deploy.py
file with line.decode('utf-8')
; but the job keeps on running and consuming the resources even when it is successfully completed. I see the output in gs://bucket/r-cloudml/runs/auto-generated-job-id/iris.rds
along with gs://bucket/r-cloudml/runs/auto-generated-job-id/tfruns.d/completed
file value set at TRUE
. Is anyone encountering the same? Any help is appreciated!!
One more thing - it doesn't take the jobId
provided in the job.yml
file, but auto-generates it (cloudml_datetimestamp
)
sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale:
[1] LC_COLLATE=English_India.1252 LC_CTYPE=English_India.1252 LC_MONETARY=English_India.1252
[4] LC_NUMERIC=C LC_TIME=English_India.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
loaded via a namespace (and not attached):
[1] compiler_3.6.3 tools_3.6.3 tinytex_0.20 xfun_0.12
cloudml::gcloud_version()
$`Google Cloud SDK`
[1] ‘301.0.0’
$beta
[1] ‘2020.7.10’
$bq
[1] ‘2.0.58’
$core
[1] ‘2020.7.10’
$gsutil
[1] ‘4.51’
# test_file.R
saveRDS(lm(iris), "iris.rds")
print("End of Code")
# cloudml.yml
trainingInput:
runtimeVersion: '2.1'
pythonVersion: '3.7'
scaleTier: CUSTOM
masterType: 'n1-standard-4'
# job.yml
jobId: local-r-heramb
storage: gs://bucket-name/r-cloudml
custom_commands: ~
#execution.R
library(cloudml)
setwd("./r-keras-tensorflow/") # dir where I keep my test_file.R and yml configs.
cloudml_train(file = "test_file.R")
Thanks in advance!
Heramb