--- title: "Provision Machine Learning resources using Terraform | Grafana Cloud documentation" description: "The Grafana Terraform provider lets you create, version, and maintain forecasts and outlier detectors with the rest of your infrastructure as code." --- > For a curated documentation index, see [llms.txt](/llms.txt). For the complete documentation index, see [llms-full.txt](/llms-full.txt). # Provision Machine Learning resources using Terraform Use the [Grafana Terraform provider](https://registry.terraform.io/providers/grafana/grafana/latest/docs) to manage Grafana Machine Learning resources as code. This lets you create, version, and maintain forecasts, outlier detectors, and their alert rules with the rest of your infrastructure as code. Expand table | ML resource | Terraform resource | |---------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------| | [Forecasts](/docs/grafana-cloud/machine-learning/machine-learning/forecasting/) | [grafana\_machine\_learning\_job](https://registry.terraform.io/providers/grafana/grafana/latest/docs/resources/machine_learning_job) | | [Holidays](/docs/grafana-cloud/machine-learning/machine-learning/forecasting/holidays-and-seasonality/) | [grafana\_machine\_learning\_holiday](https://registry.terraform.io/providers/grafana/grafana/latest/docs/resources/machine_learning_holiday) | | [Outlier detectors](/docs/grafana-cloud/machine-learning/machine-learning/outlier-detection/) | [grafana\_machine\_learning\_outlier\_detector](https://registry.terraform.io/providers/grafana/grafana/latest/docs/resources/machine_learning_outlier_detector) | ## Forecasts The following example creates a [forecast](/docs/grafana-cloud/machine-learning/machine-learning/forecasting/) with custom training settings: terraform ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```terraform resource "grafana_machine_learning_job" "cpu_forecast" { name = "CPU usage forecast" metric = "cpu_usage_forecast" datasource_type = "prometheus" datasource_uid = grafana_data_source.prometheus.uid query_params = { expr = "avg(rate(node_cpu_seconds_total{mode!=\"idle\"}[5m])) * 100" } description = "Forecasts average CPU usage across all nodes" training_window = 7776000 # 90 days interval = 300 # 5 minutes holidays = ["US"] custom_labels = { team = "infrastructure" } hyper_params = { weekly_seasonality = "10" daily_seasonality = "15" } } ``` The following table describes the available forecast properties. Hyperparameters are configured separately in the `hyper_params` field and are detailed in the sections below. Expand table | Property | Required | Default | Description | |-------------------|----------|---------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | `name` | Yes | — | The name of the forecast. | | `metric` | Yes | — | The metric name used to expose forecast results in the `grafanacloud-ml-metrics` data source. | | `datasource_type` | Yes | — | The type of the data source to query. Supported values: `prometheus`, `grafana-prometheus-datasource`, `grafana-amazonprometheus-datasource`, `victoriametrics-metrics-datasource`, `loki`, `grafana-loki-datasource`, `graphite`, `grafana-graphite-datasource`, `postgres`, `grafana-postgresql-datasource`, `influxdb`, `grafana-influxdb-datasource`, `elasticsearch`, `grafana-elasticsearch-datasource`, `grafana-bigquery-datasource`, `doitintl-bigquery-datasource`, `grafana-snowflake-datasource`, `grafana-splunk-datasource`, `grafana-clickhouse-datasource`, `grafana-mongodb-datasource`, `grafana-datadog-datasource`. | | `datasource_uid` | Yes | — | The UID of the data source to query. | | `query_params` | Yes | — | A map of query parameters used to fetch training data. For Prometheus, set `expr` to the PromQL expression. | | `description` | No | — | A description of the forecast. | | `training_window` | No | `7776000` (90 days) | How far back in seconds to fetch training data. Refer to [Data range](/docs/grafana-cloud/machine-learning/machine-learning/forecasting/model-options/#data-range). | | `interval` | No | `300` (5 minutes) | The data interval in seconds used for training and predictions. Refer to [Resolution and sampling](/docs/grafana-cloud/machine-learning/machine-learning/forecasting/model-options/#resolution-and-sampling). | | `holidays` | No | — | A list of holiday IDs to account for when training the model. Refer to [Holidays and seasonality](/docs/grafana-cloud/machine-learning/machine-learning/forecasting/holidays-and-seasonality/). | | `custom_labels` | No | — | A map of custom labels added to all forecast metrics. | | `hyper_params` | No | `{}` | A map of Prophet hyperparameters for fine-tuning the model. Refer to [Prophet hyperparameters](#prophet-hyperparameters). | ### Prophet hyperparameters The following are hyperparameters that can be tuned for the Prophet model. The ‘Terraform’ bullet points indicate the key of the hyperparameter to use in the `hyper_params` field of the `grafana_machine_learning_job` resource when provisioning with Terraform. #### Changepoint Prior Scale - Default: 0.05 - Minimum: 0.001 - Maximum: 10.0 - Terraform: `changepoint_prior_scale` Determines the flexibility of the trend and in particular how much the trend changes at the trend changepoints. If it is too small the trend will be underfit and variance that should have been modeled with trend changes will instead end up being handled with the noise term. If it is too large the trend will overfit and in the most extreme case you can end up with the trend capturing yearly seasonality. A range of \[0.001, 0.5] would likely be about right. #### Changepoint Range - Default: 0.8 - Minimum: 0.01 - Maximum: 1.0 - Terraform: `changepoint_range` Controls what portion of the training data to check for changepoints. For the default value of 0.8, the first 80% of the data is checked for changepoints, and changepoints in the remaining 20% of data are ignored. #### Seasonality Prior Scale - Default: 10.0 - Minimum: 0.01 - Maximum: 10.0 - Terraform: `seasonality_prior_range` Controls the flexibility to fit the seasonality. A large value allows the seasonality to fit large fluctuations, whereas a small value shrinks the magnitude of the seasonality. #### Uncertainty Interval Width - Default: 0.95 - Minimum: 0.01 - Maximum: 1 - Terraform: `interval_width` Uncertainty intervals (yhat\_upper, yhat\_lower) are computed as quantiles of the predicted value to use. The default value of 0.95 provides a 95% confidence interval. 95% of future data should be expected to be between (yhat\_upper, yhat\_lower). #### Seasonality Mode - Default: additive - Options: additive, multiplicative - Terraform: `seasonality_mode` Set to `multiplicative` if it appears that the magnitude of seasonal fluctuations grows with the magnitude of the time series. #### Growth - Default: linear - Options: flat, linear, logistic - Terraform: `growth` The type of model used for the growth trend component. The available options are: - `flat`: Use only when you’re certain the trend is constant and the time series mostly exhibits seasonality patterns rather than trend changes. Reduces the width of uncertainty intervals in such cases. - `linear`: Use when the metric grows at a constant rate. For example, disk space consumed by a steady rate of write operations. - `logistic`: Use when the metric has a known maximum value. The forecast saturates at the specified cap. An optional floor may also be specified. #### Logistic Cap - Terraform: `logistic_growth_cap` Expected maximum achievable value (required if and only if `growth` is `logistic`). #### Logistic Floor - Terraform: `logistic_growth_floor` Expected minimum achievable value (optional). #### Weekly Fourier Order - Default: 3 - Minimum: 0 (disabled) - Maximum: 25 - Terraform: `weekly_seasonality` How many Fourier orders to use to calculate weekly seasonality. If you leave the value empty, the value defaults to 3 when the training data range is larger than two weeks, and seasonality is then disabled on training data ranges under two weeks. Setting the Fourier order to 0 will disable weekly seasonality. Note that if a separate weekly seasonality for a holiday is configured, it is not possible to disable weekly seasonality. If there is a strong weekday vs weekend component to your data it is recommended to use a Fourier order between 10 and 25. #### Separate weekly seasonality for holiday Optionally, a holiday for which weekly seasonality should be modelled separately. This allows the model to capture the fact that the holiday may have a different day-of-week effects to non-holidays. For example, public holidays may not show the same weekday effects as non-holidays. - Terraform: `conditional_weekly_seasonality` (must be set to the ID of a linked holiday) #### Daily Fourier Order - Default: 4 - Minimum: 0 (disabled) - Maximum: 25 - Terraform: `daily_seasonality` How many Fourier orders to use when calculating daily seasonality. Leaving this value empty will use the default of 4 when the training data range is larger than two days, and disable seasonality on smaller training data ranges. Setting the Fourier order to 0 will disable daily seasonality. Note that if a separate daily seasonality for a holiday is configured it is not possible to disable daily seasonality. #### Separate daily seasonality for holiday Optionally, a holiday for which daily seasonality should be modelled separately. This allows the model to capture the fact that the holiday may have a different hour-of-day effects to non-holidays. For example, public holidays may not show the same spike during work hours as non-holidays. - Terraform: `conditional_daily_seasonality` (must be set to the ID of a linked holiday) #### Holidays Prior Scale - Default: 10.0 - Minimum: 0.01 - Maximum: 10.0 - Terraform: `holidays_prior_scale` Controls the flexibility to fit holidays. A large value allows fitting to large fluctuations on holidays, whereas a small value shrinks the impact of those fluctuations on the fit. ## Holidays The following example creates a [holiday](/docs/grafana-cloud/machine-learning/machine-learning/forecasting/holidays-and-seasonality/) to define time periods where the signal is expected to behave differently from normal. terraform ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```terraform resource "grafana_machine_learning_holiday" "company_shutdown" { name = "Company shutdown" description = "End-of-year company shutdown" custom_periods { name = "Winter shutdown 2025" start_time = "2025-12-24T00:00:00Z" end_time = "2026-01-02T00:00:00Z" } } ``` You can then reference holidays in a `grafana_machine_learning_job` resources using the `holidays` property. A holiday requires either `ical_url` or at least one `custom_periods` block. Expand table | Property | Required | Default | Description | |------------------|----------|---------|----------------------------------------------------------------------------------------------------------------------------------------------| | `name` | Yes | — | The name of the holiday. | | `description` | No | — | A description of the holiday. | | `ical_url` | No | — | A URL to an iCal file containing all occurrences of the holiday (for example, a Google Calendar public iCal link). Requires `ical_timezone`. | | `ical_timezone` | No | — | The IANA timezone to use for events in the iCal file. Required when `ical_url` is set. | | `custom_periods` | No | — | A list of custom date ranges. Each entry requires `start_time` and `end_time` in RFC3339 format, and an optional `name`. | ## Outlier detectors The following examples create [outlier detectors](/docs/grafana-cloud/machine-learning/machine-learning/outlier-detection/) using the **MAD** and **DBSCAN** algorithms: terraform ![Copy code to clipboard](/media/images/icons/icon-copy-small-2.svg) Copy ```terraform resource "grafana_machine_learning_outlier_detector" "mad_detector" { name = "Memory usage outlier detector" metric = "memory_usage_outliers" datasource_type = "prometheus" datasource_uid = grafana_data_source.prometheus.uid query_params = { expr = "avg by (instance) (node_memory_MemUsed_bytes)" } interval = 300 description = "Detects instances with abnormal memory usage" algorithm { name = "mad" sensitivity = 0.7 } } resource "grafana_machine_learning_outlier_detector" "dbscan_detector" { name = "Request rate outlier detector" metric = "request_rate_outliers" datasource_type = "prometheus" datasource_uid = grafana_data_source.prometheus.uid query_params = { expr = "sum by (service) (rate(http_requests_total[5m]))" } interval = 300 algorithm { name = "dbscan" sensitivity = 0.5 config { epsilon = 1.0 } } } ``` Expand table | Property | Required | Default | Description | |-------------------|----------|---------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | `name` | Yes | — | The name of the outlier detector. | | `metric` | Yes | — | The metric name used to expose outlier results in the `grafanacloud-ml-metrics` data source. | | `datasource_type` | Yes | — | The type of the data source to query. Supported values: `prometheus`, `grafana-prometheus-datasource`, `grafana-amazonprometheus-datasource`, `victoriametrics-metrics-datasource`, `loki`, `grafana-loki-datasource`, `graphite`, `grafana-graphite-datasource`, `postgres`, `grafana-postgresql-datasource`, `influxdb`, `grafana-influxdb-datasource`, `elasticsearch`, `grafana-elasticsearch-datasource`, `grafana-bigquery-datasource`, `doitintl-bigquery-datasource`, `grafana-snowflake-datasource`, `grafana-splunk-datasource`, `grafana-clickhouse-datasource`, `grafana-mongodb-datasource`, `grafana-datadog-datasource`. | | `datasource_uid` | Yes | — | The UID of the data source to query. | | `query_params` | Yes | — | A map of query parameters used to fetch data. For Prometheus, set `expr` to the PromQL expression. | | `description` | No | — | A description of the outlier detector. | | `interval` | No | `300` (5 min) | The data interval in seconds to monitor. | | `algorithm` | Yes | — | The detection algorithm configuration block. Refer to [Choose the detection algorithm](/docs/grafana-cloud/machine-learning/machine-learning/outlier-detection/#choose-the-detection-algorithm). | ### Algorithm Expand table | Property | Required | Description | |---------------|----------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | `name` | Yes | The algorithm to use. Supported values: `mad`, `dbscan`. | | `sensitivity` | Yes | A value between `0` and `1` controlling how aggressively the detector flags outliers. Refer to [Tune sensitivity](/docs/grafana-cloud/machine-learning/machine-learning/outlier-detection/#tune-sensitivity). | | `config` | No | Additional algorithm configuration. Only applicable to `dbscan`. | ### DBSCAN config Expand table | Property | Required | Description | |-----------|----------|------------------------------------------------------------------------------------------------------| | `epsilon` | Yes | The neighborhood radius used by DBSCAN. A smaller value produces tighter clusters and more outliers. |