doc updates; bump to 0.9.2

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: clustermq
 Title: Evaluate Function Calls on HPC Schedulers (LSF, SGE, SLURM, PBS/Torque)
-Version: 0.9.1
+Version: 0.9.2
 Authors@R: c(
     person('Michael', 'Schubert', email='[email protected]',
            role = c('aut', 'cre', 'cph'),

NEWS.md

@@ -1,8 +1,9 @@
-# git head
+# clustermq 0.9.2
 
 * Fix a bug where SSH proxy would not cache data properly (#320)
 * Fix a bug where `max_calls_worker` was not respected (#322)
 * Local parallelism (`multicore`, `multiprocess`) again uses local IP (#321)
+* Pool `info()` now also returns current worker and number of calls
 
 # clustermq 0.9.1
 

README.md

@@ -146,14 +146,23 @@ Use [`batchtools`](https://github.com/mllg/batchtools) if you:
 * don't mind there's no load-balancing at run-time
 
 Use [Snakemake](https://snakemake.readthedocs.io/en/latest/) or
-[`drake`](https://github.com/ropensci/drake) if:
+[`targets`](https://github.com/ropensci/targets) if:
 
 * you want to design and run a workflow on HPC
 
 Don't use [`batch`](https://cran.r-project.org/web/packages/batch/index.html)
 (last updated 2013) or [`BatchJobs`](https://github.com/tudo-r/BatchJobs)
 (issues with SQLite on network-mounted storage).
 
+Questions
+---------
+
+You are welcome to ask questions if something is not clear in the [User
+guide](https://mschubert.github.io/clustermq/articles/userguide.html).
+
+Please use the [Github
+Discussions](https://github.com/mschubert/clustermq/discussions) for this.
+
 Contributing
 ------------
 
@@ -162,8 +171,6 @@ to coordinate development of `clustermq`. Contributions are welcome and they
 come in many different forms, shapes, and sizes. These include, but are not
 limited to:
 
-* Questions: You are welcome to ask questions if something is not clear in the
-  [User guide](https://mschubert.github.io/clustermq/articles/userguide.html).
 * Bug reports: Let us know if something does not work as expected. Be sure to
   include a self-contained [Minimal Reproducible
   Example](https://stackoverflow.com/help/minimal-reproducible-example) and set

vignettes/userguide.Rmd

@@ -35,7 +35,7 @@ Install the `clustermq` package in R from CRAN. This will automatically detect
 if [ZeroMQ](https://github.com/zeromq/libzmq) is installed and otherwise use
 the bundled library:
 
-```r
+```{r eval=FALSE}
 # Recommended:
 #   If your system has `libzmq` installed but you want to enable the worker crash
 #   monitor, set the following environment variable to enable compilation of the
@@ -47,7 +47,8 @@ install.packages('clustermq')
 Alternatively you can use the `remotes` package to install directly from
 Github. Note that this version needs `autoconf`/`automake` for compilation:
 
-```r
+```{r eval=FALSE}
+# Sys.setenv(CLUSTERMQ_USE_SYSTEM_LIBZMQ=0)
 # install.packages('remotes')
 remotes::install_github('mschubert/clustermq')
 ```
@@ -59,6 +60,7 @@ However, [feedback is very
 welcome](https://github.com/mschubert/clustermq/issues/new).
 
 ```{r eval=FALSE}
+# Sys.setenv(CLUSTERMQ_USE_SYSTEM_LIBZMQ=0)
 # install.packages('remotes')
 remotes::install_github('mschubert/clustermq', ref="develop")
 ```
@@ -69,6 +71,7 @@ Choose your preferred parallelism using:
 
 ```{r eval=FALSE}
 options(clustermq.scheduler = "your scheduler here")
+# this may require additional setup, for details see below
 ```
 
 There are three kinds of schedulers:
@@ -106,7 +109,7 @@ To set up a scheduler explicitly, see the following links:
 * [SGE](#SGE) - *should work without setup*
 * [SLURM](#SLURM) - *should work without setup*
 * [PBS](#PBS)/[Torque](#TORQUE) - *needs* `options(clustermq.scheduler="PBS"/"Torque")`
-* if you want another scheduler, [open an
+* you can suggest another scheduler by [opening an
   issue](https://github.com/mschubert/clustermq/issues/new)
 
 Default submission templates [are
@@ -212,7 +215,7 @@ Q(fx, x=1:3, export=list(y=10), n_jobs=1)
 ```
 
 If we want to use a package function we need to load it on the worker using the
-`pkg` argument or referencing it with `package_name::`:
+`pkg` argument or referencing it with `package_name::`.
 
 ```{r}
 fx = function(x) {
@@ -259,29 +262,22 @@ register(DoparParam()) # after register_dopar_cmq(...)
 bplapply(1:3, sqrt)
 ```
 
-### With `drake`
+### With `targets`
 
-The [`drake`](https://github.com/ropensci/drake) package enables users to
+The [`targets`](https://github.com/ropensci/targets) package enables users to
 define a dependency structure of different function calls, and only evaluate
 them if the underlying data changed.
 
-> drake — or, Data Frames in R for Make — is a general-purpose workflow manager
-> for data-driven tasks. It rebuilds intermediate data objects when their
-> dependencies change, and it skips work when the results are already up to
-> date. Not every runthrough starts from scratch, and completed workflows have
-> tangible evidence of reproducibility. drake also supports scalability,
-> parallel computing, and a smooth user experience when it comes to setting up,
-> deploying, and maintaining data science projects.
+> The `targets` package is a [Make](https://www.gnu.org/software/make/)-like
+> pipeline tool for statistics and data science in R. The package skips costly
+> runtime for tasks that are already up to date, orchestrates the necessary
+> computation with implicit parallel computing, and abstracts files as R
+> objects. If all the current output matches the current upstream code and
+> data, then the whole pipeline is up to date, and the results are more
+> trustworthy than otherwise.
 
-It can use `clustermq` to perform calculations as jobs:
-
-```{r eval=FALSE}
-library(drake)
-load_mtcars_example()
-# clean(destroy = TRUE)
-# options(clustermq.scheduler = "multicore")
-make(my_plan, parallelism = "clustermq", jobs = 2, verbose = 4)
-```
+It can use `clustermq` to [perform calculations as
+jobs](https://books.ropensci.org/targets/hpc.html#clustermq).
 
 ## Options