R Cluster Export Error “object not found”

As a professional technical blogger, I have encountered numerous issues while working with R clusters. In this article, we will delve into a specific error that arises when using clusterExport to export variables from a local environment to the cluster.

Understanding the Basics of `clusterExport`

The clusterExport function in R is used to export variables from a local environment to a cluster. The basic syntax for clusterExport is as follows:

clusterExport(cl, varlist, envir = .GlobalEnv)

In this context, cl represents the cluster where the variables will be exported, varlist represents the list of variables that need to be exported, and envir represents the environment from which the variables are being exported.

The Issue at Hand

The user in question is experiencing an error when using clusterExport with their function. The error message reads “object not found,” indicating that clusterExport cannot locate one of the variables being exported. In this case, it’s the variable pay.freq.

Let’s break down what’s happening:

The user has created a local environment within their R cluster using library('parallel') and makeCluster(7).
They have then evaluated several R files (Global data.R, Swappriser.R, Interest simulation.R, Survival sim.R, and Exposures.R) within the cluster.
Within one of these R files, they are exporting two variables: ts and pay.freq.

The Role of Local Environment

Now, let’s understand what happens when we export variables from a local environment. By default, clusterExport looks for variables in the .GlobalEnv. However, within our specific context, pay.freq is defined within the function itself. Therefore, pay.freq does not exist in the .GlobalEnv.

The key takeaway here is that pay.freq only exists locally within the function.

The Solution

To resolve this issue, we need to specify the environment from which we are exporting variables when using clusterExport. In our case, since pay.freq only exists within the local environment of the function itself, we need to use the following syntax:

clusterExport(cl, varlist = c("ts", "pay.freq"), envir = .environment(function() {
  # your code here
}))

By specifying .environment(function() { ... }), we are telling clusterExport that we want to export variables from within the local environment of the function itself.

Additional Considerations

It’s worth noting that if you’re new to working with R clusters, understanding how environments work can be a bit tricky. Here are some additional tips:

Make sure to clear your environment before running code: gc()
Understand how .GlobalEnv works and why it might not always match the local environment of your function.
When using clusterExport, make sure to specify the correct environment from which you’re exporting variables.

Conclusion

In this article, we explored a common error that arises when working with R clusters: “object not found” during clusterExport. By understanding how environments work and specifying the correct environment for variable exports, we can resolve this issue and successfully export variables to our cluster.

Last modified on 2023-12-01