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Details

Source: vignettes/details.Rmd
details.Rmd

This vignette primarily exists so that we can point to it from other bits of documentation, or requests for help, with details about doing particular chores with hipercow.

Options

There are a few influential options that control hipercow’s behaviour, and you may want to set these if you wish to depart from the defaults that we have set.

Influential options

hipercow.auto_install_missing_packages

Logical, controlling if hipercow should install missing packages as it needs them on your machine. The default is TRUE, which will generally cause hipercow.windows and conan2 to be installed the first time you try and use hipercow.

hipercow.progress

Logical, controlling if we should display a progress bar in some contexts (e.g., task_wait()). This affects the default behaviour of any hipercow function accepting a progress argument, with this option becoming the default value in the absence of an explicit argument. That is, you can always disable individual progress bars but this option lets you disable them by default. The default value of this option is TRUE (i.e., display progress bars by default).

hipercow.timeout

Numeric, controlling the default timeout for task_wait(), task_log_watch() , hipercow_bundle_wait() and hipercow_hello(). If not set, the default is Inf (i.e., wait forever).

hipercow.validate_globals

Logical, controlling if we validate the value of “global” variables when a task starts. If TRUE, then when we save a task (e.g., with task_create_expr()) and where you have created an environment with hipercow_environment_create() and specified values for the globals argument, then we hash the values that we find in your current session and when the task starts on the cluster we validate that the values that we find are the same as in your local session. The default is FALSE (i.e., this validation is not done).

hipercow.max_size_local

A number, being the maximum size object (in bytes) that we will save when creating a task (e.g., with task_create_expr()). When we save a task we need to save all local variables that you reference in the call; often these are small and it’s no problem. However, if you pass in a 5GB shapefile then you will end up filling up the disk with copies of this file, and your task will spend a lot of time reading and writing them. It’s usually better to arrange for large objects to be found from your scripts via your environment. The default value is 1e6 (1,000,000 bytes, or 1 MB). Set to Inf to disable this check.

hipercow.default_envvars

A hipercow_envvars object, with environment variables that will be added to every task created. Hipercow sets a few environment variables by default to improve the user experience. These variables can be overridden per-task by passing a envvars parameter when creating tasks, or globally by defining this option.

Environment variables for a task are computed by collecting (in increasing order of preference):

  • hipercow default environment variables (defined in hipercow’s DEFAULT_ENVVARS); currently R_GC_MEM_GROW=3
  • driver-specific variables (defined by the default_envvars argument to hipercow_driver); see vignette("windows") for the windows defaults
  • the user-set option hipercow.default_envvars
  • the argument envvars to a task creation function

Environment variables set later override those earlier.

hipercow.development

Use the development library (if it exists) for bootstrapping. We may ask you to set this temporarily if we are diagnosing a bug with you. Don’t set this yourself generally as the library will often not exist, or be out of date!

Windows options

hipercow.timeout

A number, representing seconds, for timing out when performing web requests. The default is 10s but if we have chosen this poorly you may need to increase or decrease it.

Options from other packages

We make heavy use of the cli package, see its documentation on options. Particular options that you might care about:

  • cli.progress_show_after: Delay in seconds before showing the progress bar; you might reduce this to make bars appear more quickly.

  • cli.progress_clear: Retain the progress bar on screen after completion.

Setting options

You can set options either within a session by using the options() function or for all future sessions by editing your .Rprofile.

Changing an option with options() looks like:

options(hipercow.max_size_local = 1e7)

You might put this at the top of your cluster script and it will affect any future tasks that you submit within that session after you run it. You’ll probably run this line next time, so it would have an effect there too. It won’t affect any other project.

If you always want that value, you can add it to your .Rprofile. The easiest way of doing this is by using the usethis package and running:

usethis::edit_r_profile()

and then adding a call to options() anywhere in that file. If you are comfortable with using (and exiting) vi on the command line you may prefer using vi ~/.Rprofile.

After changing your .Rprofile you will need to restart R for the changes to take effect.

You can set multiple options at once if you want by running (for example):

options(
  hipercow.progress = FALSE,
  hipercow.max_size_local = 1e-7)

R versions

By default, we try and track a “suitable” copy of R based on your current version. We will try and use exactly the same version if available, otherwise the oldest cluster version that is newer than yours, or failing that the most recent cluster version.

You can control the R version used when configuring; for example to use 4.3.0 exactly you can use:

hipercow_configure("windows", r_version = "4.3.0")

Not all versions are installed; at the moment we have 4.0.5, 4.1.3, 4.2.3 4.3.0 and 4.3.2 installed on the Windows cluster but will add more (we’ll also sort out some sort of general mechanism for you to see what is installed)

Try not to use versions that are more than one “minor” version old (the middle version number). You can see the current version on CRAN’s landing page. If it is 4.3.x then versions in the 4.2.x and 4.3.x series will likely work well for you, but 4.1.x and earlier will not work well because binary versions of packages are no longer available and because some packages will start depending on features present in newer versions, so may not be available.

Workflow considerations

Long running tasks

The R session that you use to send tasks to the cluster is not important; only the directory from which you send the tasks. So you can stop the R session, reboot your computer or shut down your laptop and your tasks will keep running (or keep waiting for the cluster to pick them up). If you come back to the same working directory later you can ask about a task’s status, fetch its results etc easily.

Disk space

If you run out of disk space, terrible things will happen, and the error messages that you get may be misleading. Worse, you may run out of space, then have some space gets freed up so that it’s not obvious what the original problem was.

The underlying reason for this is that we store information about the state of tasks on your network share; if you run out of space while writing the final result of a task, then that task will remain as running indefinitely because we fail to write out that it succeeded.