[DOCS] Update model_memory_limit (elastic/x-pack-elasticsearch#1928)

* [DOCS] Update model_memory_limit

* [DOCS] Clarify minimum model_memory_limit value

* [DOCS] More updates to model_memory_limit

* [DOCS] Address feedback in jobresource.asciidoc

Original commit: elastic/x-pack-elasticsearch@3c62719037
This commit is contained in:
Lisa Cawley 2017-07-10 08:50:38 -07:00 committed by GitHub
parent 7674729bbe
commit 31b02c3941
3 changed files with 23 additions and 16 deletions

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@ -483,12 +483,13 @@ The number of records that have been processed by the job.
Memory status:: Memory status::
The status of the mathematical models. When you create jobs by using the APIs or The status of the mathematical models. When you create jobs by using the APIs or
by using the advanced options in {kib}, you can specify a `model_memory_limit`. by using the advanced options in {kib}, you can specify a `model_memory_limit`.
That value is the maximum amount of memory resources, in MiB, that the That value is the maximum amount of memory resources that the mathematical
mathematical models can use. Once that limit is approached, data pruning becomes models can use. Once that limit is approached, data pruning becomes more
more aggressive. Upon exceeding that limit, new entities are not modeled. aggressive. Upon exceeding that limit, new entities are not modeled. For more
The default value is `4096`. The memory status field reflects whether you have information about this setting, see
reached or exceeded the model memory limit. It can have one of the following {ref}/ml-job-resource.html#ml-apilimits[Analysis Limits]. The memory status
values: + field reflects whether you have reached or exceeded the model memory limit. It
can have one of the following values: +
`ok`::: The models stayed below the configured value. `ok`::: The models stayed below the configured value.
`soft_limit`::: The models used more than 60% of the configured memory limit `soft_limit`::: The models used more than 60% of the configured memory limit
and older unused models will be pruned to free up space. and older unused models will be pruned to free up space.

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@ -277,10 +277,17 @@ For more information, see
//<<ml-configuring-categories>>. //<<ml-configuring-categories>>.
`model_memory_limit`:: `model_memory_limit`::
(long) The approximate maximum amount of memory resources that are required (long or string) The approximate maximum amount of memory resources that are
for analytical processing, in MiB. Once this limit is approached, data pruning required for analytical processing. Once this limit is approached, data pruning
becomes more aggressive. Upon exceeding this limit, new entities are not becomes more aggressive. Upon exceeding this limit, new entities are not
modeled. The default value is 4096. modeled. The default value is `4096mb`. If you specify a number instead of a
string, the units are assumed to be MiB. Specifying a string is recommended
for clarity. If you specify a byte size unit of `b` or `kb` and the number
does not equate to a discrete number of megabytes, it is rounded down to the
closest MiB. The minimum valid value is 1 MiB. If you specify a value less
than 1 MiB, an error occurs. For more information about supported byte size
units, see
{ref}/common-options.html#byte-units[Byte size units].
[float] [float]
[[ml-apimodelplotconfig]] [[ml-apimodelplotconfig]]

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@ -23,8 +23,7 @@ The following properties can be updated after the job is created:
|Name |Description |Requires Restart |Name |Description |Requires Restart
|`analysis_limits`: `model_memory_limit` |The approximate maximum amount of |`analysis_limits`: `model_memory_limit` |The approximate maximum amount of
memory resources required for analytical processing, in MiB. memory resources required for analytical processing. See <<ml-apilimits>>. | Yes
See <<ml-apilimits>>. | Yes
|`background_persist_interval` |Advanced configuration option. The time between |`background_persist_interval` |Advanced configuration option. The time between
each periodic persistence of the model. See <<ml-job-resource>>. | Yes each periodic persistence of the model. See <<ml-job-resource>>. | Yes