---
title: "Run/manage workflows" 
date: "Last update: `r format(Sys.time(), '%d %B, %Y')`" 
vignette: |
  %\VignetteEncoding{UTF-8}
  %\VignetteIndexEntry{systemPipeR: Workflow design and reporting generation environment}
  %\VignetteEngine{knitr::rmarkdown}
fontsize: 14pt
editor_options: 
  chunk_output_type: console
type: docs
weight: 3
---

```{r setup, echo=TRUE, message=FALSE, warning=FALSE}
suppressPackageStartupMessages({
    library(systemPipeR)
})
```

Until this point, you have learned how to create a SPR workflow [interactively](../step_interactive) or 
use a template to [import/update](../step_import) the workflow. Next, we will 
learn how to run the workflow and manage the workflow.


First let's set up the workflow using the example workflow template. For real 
production purposes, we recommend you to check out the complex templates over [here](/spr_wf/).

```{r eval=TRUE, include=FALSE}
# cleaning
try(unlink(".SPRproject", recursive = TRUE), TRUE)
try(unlink("data", recursive = TRUE), TRUE)
try(unlink("results", recursive = TRUE), TRUE)
try(unlink("param", recursive = TRUE), TRUE)
```

For demonstration purposes here, we still use the [simple workflow](https://raw.githubusercontent.com/systemPipeR/systemPipeR.github.io/main/static/en/sp/spr/sp_run/spr_simple_wf.md).
```{r}
sal <- SPRproject()
sal <- importWF(sal, file_path = system.file("extdata", "spr_simple_wf.Rmd", package = "systemPipeR"))
sal
```

## Before running
It is good to check if the command-line tools are installed before running 
the workflow. To do so, we can use the `tryCMD` function.

```{r}
tryCMD(command="R") 
tryCMD(command="hisat2") 
tryCMD(command="blastp") 
```

In examples above, installed tools will have a message `"All set up, proceed!"`, 
and not installed tool will have an error message, like the `blastp` example above. 

If you see the error message: 

1. Check if the tool is really installed, by typing the same command from a terminal.
   If you cannot call it even from a terminal, you need to (re)install.
2. If the tool is available in terminal but you see the error message in `tryCMD`.
   It is mostly likely due to the path problem. Make sure you 
   [export the path](https://askubuntu.com/questions/720678/what-does-export-path-somethingpath-mean).
   Or try to use following to set it up in R:
    ```{r eval=FALSE}
    old_path <- Sys.getenv("PATH")
    Sys.setenv(PATH = paste(old_path, "path/to/tool_directory", sep = ":"))
    ```
   

## Start running 
To run the workflow, call the `runWF` function which will execute all steps in the workflow container.

```{r runWF, eval=TRUE}
sal <- runWF(sal)
sal
```


![](../runwf.png)

We can see the workflow status changed from `pending` to `Success`

## Run selected steps
This function allows the user to choose one or multiple steps to be 
executed using the `steps` argument. However, it is necessary to follow the 
workflow dependency graph. If a selected step depends on a previous step(s) that
was not executed, the execution will fail. 

```{r runWF_error, eval=TRUE}
sal <- runWF(sal, steps = c(1,3))
```


We do not see any problem here because we have finished the entire workflow 
running previously. So all depedency satisfies. Let's clean the workflow and 
start from scratch to see what will happen if one or more depedency is not 
met and we are trying to run some selected steps.

```{r error=F}
sal <- SPRproject(overwrite = TRUE)
sal <- importWF(sal, file_path = system.file("extdata", "spr_simple_wf.Rmd", package = "systemPipeR"))
sal
sal <- runWF(sal, steps = c(1,3))
```

We can see the workflow step 3 is not run because of the dependency problem:
> ## export_iris
> ## have been not executed yet.

## optional steps
By default all steps are `'mandatory'`, but you can change it to `'optional'`
```{r eval=FALSE}
SYSargsList(..., run_step = 'optional')
# or
LineWise(..., run_step = 'optional')
```

When workflow is run by `runWF`, default will run all steps `'ALL'`, but you can 
choose to only run mandatory steps `'mandatory'` or optional steps `'optional'`.
```{r eval=FALSE}
# default 
sal <- runWF(sal, run_step = "ALL")
# only mandatory
sal <- runWF(sal, run_step = "mandatory")
# only optional
sal <- runWF(sal, run_step = "optional")
```

## Force to run steps
- Forcing the execution of the steps, even if the status of the 
  step is `'Success'` and all the expected `outfiles` exists.
    ```{r eval=FALSE}
    sal <- runWF(sal, force = TRUE, ... = )
    ```

- Another feature of the `runWF` function is ignoring all the warnings 
  and errors and running the workflow by the arguments `warning.stop` and 
  `error.stop`, respectively.
    ```{r eval=FALSE}
    sal <- runWF(sal, warning.stop = FALSE, error.stop = TRUE, ...)
    ```

- To force the step to run without checking the dependency, we can use 
  `ignore.dep = TRUE`. For example, let's run the step 3 that could not 
  be run because of dependency problem. 
  
```{r include=FALSE}
try(unlink("results", recursive = TRUE), TRUE)
try(dir.create("results", recursive = TRUE), TRUE)
```
  
    ```{r eval=TRUE, error=TRUE}
    sal <- runWF(sal, steps = 3, ignore.dep = TRUE)
    ```
  We can see the workflow failed, because required files from step 2 are missing 
  and we jumped directly to step 3. Therefore, skip dependency is possible in 
  SPR but **not recommended**.
  
## Workflow envirnment 

When the project was initialized by `SPRproject` function, it was created an 
environment for all object to store during the workflow preprocess code execution or 
`Linewise` R code execution. This environment can be accessed as follows:

```{r eval=TRUE, include=FALSE}
sal <- runWF(sal)
```

```{r runWF_env, eval=TRUE}
viewEnvir(sal)
```

We can see there are `"df"`, `"plot"`, `"stats"` 3 objects, and they are created 
during the step 5 `Linewise` code execution.  To access these variables 
interactive from your global environment, use `copyEnvir` method. 

```{r collapse=TRUE}
copyEnvir(sal, c("df", "plot"))
exists("df", envir = globalenv())
exists("plot", envir = globalenv())
```

Now we see, they are in our global enviornment, and we are free to do other operations 
on them. 

### Save envirnment
The workflow execution allows to save this environment for future recovery:

```{r runWF_saveenv, eval=FALSE}
sal <- runWF(sal, saveEnv = TRUE)
```

> Depending on what variable you have saved in the enviorment, it can become 
> expensive (take much space and slow to load back in resume). 

## Parallelization on clusters

This section of the tutorial provides an introduction to the usage of the 
_`systemPipeR`_ features on a cluster.

So far, all workflow steps are run in the same computer as we manage the workflow 
instance. This is called running in the `management` session. 
Alternatively, the computation can be greatly accelerated by processing many files 
in parallel using several compute nodes of a cluster, where a scheduling/queuing
system is used for load balancing. This is called running in the `compute` session.
The behavior controlled by the `run_session` argument in `SYSargsList`.

```{r eval=FALSE}
SYSargsList(..., run_session = "management")
# or 
SYSargsList(..., run_session = "compute")
```

By default, all steps are run on `"management"`, and we can change it to use 
`"compute"`. However, simply change the value will not work, we also couple with 
computing resources (see below for what is 'resources'). The resources need to 
be appended to the step by `run_remote_resources` argument.

```{r eval=FALSE}
SYSargsList(..., run_session = "compute", run_remote_resources = list(...))
```

This is how to config the running session for each step, but generally we can 
use a more convenient method `addResources` to add resources (continue reading below). 

### Resources
Resources here refer to computer resources, like CPU, RAM, time limit, _etc._
The `resources` list object provides the number of independent parallel cluster 
processes defined under the `Njobs` element in the list. The following example 
will run 18 processes in parallel using each 4 CPU cores on a slurm scheduler.
If the resources available on a cluster allow running all 18 processes at the 
same time, then the shown sample submission will utilize in a total of 72 CPU cores.

Note, `runWF` can be used with most queueing systems as it is based on utilities 
from the `batchtools` package, which supports the use of template files (_`*.tmpl`_)
for defining the run parameters of different schedulers. To run the following 
code, one needs to have both a `conffile` (see _`.batchtools.conf.R`_ samples [here](https://mllg.github.io/batchtools/)) 
and a `template` file (see _`*.tmpl`_ samples [here](https://github.com/mllg/batchtools/tree/master/inst/templates)) 
for the queueing available on a system. The following example uses the sample 
`conffile` and `template` files for the Slurm scheduler provided by this package. 

The resources can be appended when the step is generated, or it is possible to 
add these resources later, as the following example using the `addResources` 
function:

Before adding resources
```{r collapse=TRUE}
runInfo(sal)[['runOption']][['gzip']]
```


```{r runWF_cluster, eval=TRUE}
resources <- list(conffile=".batchtools.conf.R",
                  template="batchtools.slurm.tmpl", 
                  Njobs=18, 
                  walltime=120,##minutes
                  ntasks=1,
                  ncpus=4, 
                  memory=1024,##Mb
                  partition = "short"# a compute node called 'short'
                  )
sal <- addResources(sal, c("gzip"), resources = resources)
```

After adding resources
```{r collapse=TRUE}
runInfo(sal)[['runOption']][['gzip']]
```

You can see the step option is automatically replaced from 'management' to 'compute'.


## Workflow status

To check the summary of the workflow, we can use:

```{r show_statusWF, eval=TRUE, collapse=TRUE}
sal
```

To access more details about the workflow instances, we can use the `statusWF` method:

```{r statusWF, eval=TRUE, collapse=TRUE}
statusWF(sal)
```

To access the options of each workflow step, for example, whether it is mandatory step
or optional step, where it stored in the template, where to run the step, _etc_., 
we can use the `runInfo` function to check.

```{r collapse=TRUE, collapse=TRUE}
runInfo(sal)
```

## Visualize workflow

_`systemPipeR`_ workflows instances can be visualized with the `plotWF` function.

This function will make a plot of selected workflow instance and the following 
information is displayed on the plot:

    - Workflow structure (dependency graphs between different steps); 
    - Workflow step status, *e.g.* `Success`, `Error`, `Pending`, `Warnings`; 
    - Sample status and statistics; 
    - Workflow timing: running duration time. 

If no argument is provided, the basic plot will automatically detect width, 
height, layout, plot method, branches, _etc_. 

```{r, eval=TRUE}
plotWF(sal, width = "80%", rstudio = TRUE)
```

We will discuss a lot more advanced use of `plotWF` function in the next section.

## High-level project control

If you desire to resume or restart a project that has been initialized in the past, 
`SPRproject` function allows this operation.

**Resume**

With the `resume` option, it is possible to load the `SYSargsList` object in R and 
resume the analysis. Please, make sure to provide the `logs.dir` location, and the 
corresponded `YAML` file name, if the default names were not used when the project was created.

```{r SPR_resume, eval=FALSE}
sal <- SPRproject(resume = TRUE, logs.dir = ".SPRproject", 
                  sys.file = ".SPRproject/SYSargsList.yml") 
```

If you choose to save the environment in the last analysis, you can recover all 
the files created in that particular section. `SPRproject` function allows this 
with `load.envir` argument. Please note that the environment was saved only with
you run the workflow in the last section (`runWF()`).

```{r resume_load, eval=FALSE}
sal <- SPRproject(resume = TRUE, load.envir = TRUE) 
```

**Restart**

The `resume` option will keep all previous logs in the folder; however, if you desire to 
clean the execution (delete all the log files) history and restart the workflow, 
the `restart=TRUE` option can be used.

```{r restart_load, eval=FALSE}
sal <- SPRproject(restart = TRUE, load.envir = FALSE) 
```

**Overwrite**

The last and more drastic option from `SYSproject` function is to `overwrite` the
logs and the `SYSargsList` object. This option will delete the hidden folder and the 
information on the `SYSargsList.yml` file. This will not delete any parameter
file nor any results it was created in previous runs. Please use with caution. 

```{r SPR_overwrite, eval=FALSE}
sal <- SPRproject(overwrite = TRUE) 
```


## Session 
```{r}
sessionInfo()
```

```{r eval=TRUE, include=FALSE}
# cleaning
try(unlink(".SPRproject", recursive = TRUE), TRUE)
try(unlink("data", recursive = TRUE), TRUE)
try(unlink("results", recursive = TRUE), TRUE)
try(unlink("param", recursive = TRUE), TRUE)
```