add section about mirai #880
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sebffischer
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Aug 13, 2025
| prediction = lrn_rpart$predict(tsk_sonar) | ||
| ``` | ||
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| ### Parallelization with `mirai` {#sec-parallel-mirai} |
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Suggested change
| ### Parallelization with `mirai` {#sec-parallel-mirai} | |
| ### Parallelization with `mirai` (+) {#sec-parallel-mirai} |
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- marks that this is a new chapter
| mirai::daemons(0) | ||
| ``` | ||
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| With `mlr3` 1.0.0, we integrated the `r ref_pkg("mirai")` package as an alternative parallelization backend. |
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we don't mention the version everywhere else.
| We start two daemons and check the status of the daemons. | ||
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| ```{r, eval = FALSE} | ||
| library(mirai) |
| system.time({resample(tsk_sonar, lrn_rpart, rsmp_cv3)}) | ||
| ``` | ||
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| One advantage of `mirai` is that it eliminates the need to manually set chunk sizes, as it automatically handles task distribution efficiently. |
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explain this a bit more in depth
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Does this also depend on whether the dispatcher is used, right?
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I would maybe explain how the dispatcher sends the tasks to the daemons
| With `mlr3` 1.0.0, we integrated the `r ref_pkg("mirai")` package as an alternative parallelization backend. | ||
| `mirai` provides a lightweight approach to parallelization by starting persistent R sessions called daemons that evaluate tasks in parallel. | ||
| These daemons can be launched either locally or on remote machines via SSH or cluster managers. | ||
| Compared to the `r ref_pkg("future")` package, `mirai` has significantly lower overhead per task. |
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this is especially advantageous when training many fast fitting models.
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