border-mlflow-tracking

Support MLflow tracking to manage experiments.

Before running the program using this crate, run a tracking server with the following command:

mlflow server --host 127.0.0.1 --port 8080

Then, training configurations and metrices can be logged to the tracking server. The following code provides an example. Nested configuration parameters will be flattened, logged like hyper_params.param1, hyper_params.param2.

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(())
}