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 (
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:  LC_COLLATE=English_India.1252 LC_CTYPE=English_India.1252 LC_MONETARY=English_India.1252  LC_NUMERIC=C LC_TIME=English_India.1252 attached base packages:  stats graphics grDevices utils datasets methods base loaded via a namespace (and not attached):  compiler_3.6.3 tools_3.6.3 tinytex_0.20 xfun_0.12
cloudml::gcloud_version() $`Google Cloud SDK`  ‘301.0.0’ $beta  ‘2020.7.10’ $bq  ‘2.0.58’ $core  ‘2020.7.10’ $gsutil  ‘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!