First time post here.
I have just started training models using the R datarobot package which I am really enjoying.
My training set is 1,234,448 records with 319 columns. When I reduce the set to a small sample, say 10K, it has no problem fitting multiple models.
However, when I try the full dataset, which works via the console it fails in R.
Below is the code and the error message:
project <- StartProject(dataSource = trainDat,
projectName = projectName,
target = target,
workerCount = "max",
wait = TRUE,
Error in curl::curl_fetch_memory(url, handle = handle) : Timeout was reached: [datarobot.edr.qantasloyalty.net] Operation timed out after 600000 milliseconds with 0 bytes received
Solved! Go to Solution.
Have you tried uploading your dataset into the AI Catalog via the DataRobot web app? From there you can continue to work with that data in your R project. Other than that how big is your data set? Could it have been a combination of internet and data size which triggered the 10 timeout?
All the best,
@Petros, you may also want to try adding in the 'maxWait' parameter with a high value, by default its 600 (seconds). eg.
project <- StartProject(dataSource = trainDat, projectName = projectName, target = target, workerCount = "max", wait = TRUE, maxWait = 6000, smartDownsampled= TRUE)
Hi @IraWatt ,
Thanks for your response.
I can upload it via DataRobot web app and it will train multiple models. I have then identified the model and within R have done the preds. Obviously I would like to do this end to end.
I did try the max wait but to no avail. I am trying again and will see how it goes.