//! Distributed training infrastructure for RTX Transformers
//!
//! This module provides comprehensive distributed training capabilities including:
//! - Pipeline parallelism for very large models
//! - Data parallelism for high throughput training
//! - Model parallelism for memory-constrained scenarios
//! - Hybrid parallelism strategies

use crate::Result;

pub mod pipeline_parallelism;
pub mod pipeline_verification;

#[cfg(all(test, feature = "disabled_tests"))]
pub mod pipeline_parallelism_tests;

// Re-export key types for convenience
pub use pipeline_parallelism::{
    ActivationCheckpoint, CommunicationBuffer, DeviceMapping, GradientSynchronizer, LoadBalancer,
    MicroBatch, ModelPartition, PipelineConfig, PipelineMetrics, PipelineParallelism,
    PipelineSchedule, PipelineStage, StageConfig,
};

/// Initialize distributed training infrastructure
pub fn init_distributed() -> Result<()> {
    tracing::info!("Initializing RTX Transformers distributed training");
    Ok(())
}

/// Get distributed training capabilities
pub fn get_distributed_capabilities() -> Vec<&'static str> {
    vec![
        "Pipeline Parallelism",
        "Micro-batch Processing",
        "Gradient Synchronization",
        "Activation Checkpointing",
        "Load Balancing",
        "Fault Tolerance",
    ]
}