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AppsDatabricks ToolsDatabricks Ml Experiments Log Metric
Databricks Tools

Databricks Ml Experiments Log Metric

Tool to log a metric for an MLflow run with timestamp. Use when you need to record ML model performance metrics like accuracy, loss, or custom evaluation metrics. Metrics can be logged multiple times with different timestamps and values are never overwritten - each log appends t…

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Use Databricks Ml Experiments Log Metric

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Inputs

  • keystring
    Required
    Name of the metric. May only contain alphanumerics, underscores (_), dashes (-), periods (.), spaces ( ), and slashes (/). Maximum length is 250 characters
  • stepinteger
    Integer training step (iteration) at which the metric was calculated
  • valuenumber
    Required
    Double value of the metric being logged. Note that some special values such as +/- Infinity may be replaced by other values depending on the store
  • run_idstring
    Required
    ID of the run under which to log the metric. Must be provided
  • timestampinteger
    Required
    Unix timestamp in milliseconds at the time metric was logged. Defaults to current system time if not provided

Observable output

  • data
    Required
    Data from the action execution
  • errorstring
    Error if any occurred during the execution of the action
  • successfulboolean
    Required
    Whether or not the action execution was successful or not

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