Document ML enabled `auto` (#18784)
Co-authored-by: Fotis Voutsas <fotis@netdata.cloud> Co-authored-by: Ilya Mashchenko <ilya@netdata.cloud>
Stelios Fragkakis committed
Oct 15, 2024 at 13:56 UTC
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src/ml/ml-configuration.md
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# ML Configuration
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-Netdata's [Machine Learning](/src/ml/README.md) capabilities are enabled by default.
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+Netdata's [Machine Learning](/src/ml/README.md) capabilities are enabled by default if the [Database mode](/src/database/README.md) is set to `db = dbengine`
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To enable or disable Machine Learning capabilities on a node:
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1. [Edit `netdata.conf`](/docs/netdata-agent/configuration/README.md#edit-a-configuration-file-using-edit-config)
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-2. In the `[ml]` section, set `enabled = yes` to enable or `enabled = no` to disable
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+2. In the `[ml]` section, set `enabled` to `yes` to enable ML, `no` to disable it, or leave it at the default `auto` to enable ML only when [Database mode](/src/database/README.md) is set to `dbengine`
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3. [Restart Netdata](/docs/netdata-agent/start-stop-restart.md)
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Below is a list of all the available configuration params and their default values.
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```bash
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[ml]
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- # enabled = yes
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+ # enabled = auto
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# maximum num samples to train = 21600
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# minimum num samples to train = 900
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# train every = 3h
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## Descriptions (min/max)
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-- `enabled`: `yes` to enable, `no` to disable.
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+- `enabled`: `yes` to enable, `no` to disable, or `auto` to let Netdata decide when to enable ML.
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- `maximum num samples to train`: (`3600`/`86400`) This is the maximum amount of time you would like to train each model on. For example, the default of `21600` trains on the preceding 6 hours of data, assuming an `update every` of 1 second.
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- `minimum num samples to train`: (`900`/`21600`) This is the minimum amount of data required to be able to train a model. For example, the default of `900` implies that once at least 15 minutes of data is available for training, a model is trained, otherwise it is skipped and checked again at the next training run.
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- `train every`: (`3h`/`6h`) This is how often each model will be retrained. For example, the default of `3h` means that each model is retrained every 3 hours. Note: The training of all models is spread out across the `train every` period for efficiency, so in reality, it means that each model will be trained in a staggered manner within each `train every` period.