use anyhow::Result; use border_core::record::{Record, RecordValue, Recorder}; use border_mlflow_tracking::MlflowTrackingClient; use serde::Serialize; // Nested Configuration struct #[derive(Debug, Serialize)] struct Config { env_params: String, hyper_params: HyperParameters, } #[derive(Debug, Serialize)] struct HyperParameters { param1: i64, param2: Param2, param3: Param3, } #[derive(Debug, Serialize)] enum Param2 { Variant1, Variant2(f32), } #[derive(Debug, Serialize)] struct Param3 { dataset_name: String, } fn main() -> Result<()> { env_logger::init(); let config1 = Config { env_params: "env1".to_string(), hyper_params: HyperParameters { param1: 0, param2: Param2::Variant1, param3: Param3 { dataset_name: "a".to_string(), }, }, }; let config2 = Config { env_params: "env2".to_string(), hyper_params: HyperParameters { param1: 0, param2: Param2::Variant2(3.0), param3: Param3 { dataset_name: "a".to_string(), }, }, }; // Set experiment for runs let client = MlflowTrackingClient::new("http://localhost:8080").set_experiment_id("Default")?; // Create recorders for logging let mut recorder_run1 = client.create_recorder("")?; let mut recorder_run2 = client.create_recorder("")?; recorder_run1.log_params(&config1)?; recorder_run2.log_params(&config2)?; // Logging while training for opt_steps in 0..100 { let opt_steps = opt_steps as f32; // Create a record let mut record = Record::empty(); record.insert("opt_steps", RecordValue::Scalar(opt_steps)); record.insert("Loss", RecordValue::Scalar((-1f32 * opt_steps).exp())); // Log metrices in the record recorder_run1.write(record); } // Logging while training for opt_steps in 0..100 { let opt_steps = opt_steps as f32; // Create a record let mut record = Record::empty(); record.insert("opt_steps", RecordValue::Scalar(opt_steps)); record.insert("Loss", RecordValue::Scalar((-0.5f32 * opt_steps).exp())); // Log metrices in the record recorder_run2.write(record); } Ok(()) }