Struct TrainingCheckpoint 

pub struct TrainingCheckpoint {
    pub epoch: usize,
    pub parameters: HashMap<String, Vec<f64>>,
    pub optimizer_state: HashMap<String, Vec<f64>>,
    pub scheduler_state: Option<HashMap<String, f64>>,
    pub train_loss: f64,
    pub val_loss: Option<f64>,
    pub train_loss_history: Vec<f64>,
    pub val_loss_history: Vec<f64>,
    pub metrics_history: HashMap<String, Vec<f64>>,
    pub learning_rate: f64,
    pub best_val_loss: Option<f64>,
}

Comprehensive checkpoint data structure.

This structure contains all the information needed to fully restore training state, including model parameters, optimizer state, and training history.

Fields

epoch: usize

Current epoch number.

parameters: HashMap<String, Vec<f64>>

Model parameters as flattened vectors.

optimizer_state: HashMap<String, Vec<f64>>

Optimizer state as flattened vectors.

scheduler_state: Option<HashMap<String, f64>>

Scheduler state (if present).

train_loss: f64

Current training loss.

val_loss: Option<f64>

Current validation loss (if available).

train_loss_history: Vec<f64>

Training loss history.

val_loss_history: Vec<f64>

Validation loss history.

metrics_history: HashMap<String, Vec<f64>>

Metrics history.

learning_rate: f64

Current learning rate.

best_val_loss: Option<f64>

Best validation loss seen so far.

Implementations

impl TrainingCheckpoint

pub fn new( epoch: usize, parameters: &HashMap<String, Array<f64, Ix2>>, optimizer_state: &HashMap<String, Vec<f64>>, scheduler_state: Option<HashMap<String, f64>>, state: &TrainingState, train_loss_history: &[f64], val_loss_history: &[f64], metrics_history: &HashMap<String, Vec<f64>>, best_val_loss: Option<f64>, ) -> Self

Create a new checkpoint from current training state.

pub fn save(&self, path: &PathBuf) -> TrainResult<()>

Save checkpoint to a file.

pub fn save_with_compression( &self, path: &PathBuf, compression: CheckpointCompression, ) -> TrainResult<()>

Save checkpoint to a file with compression.

Arguments

• path - Path to save the checkpoint
• compression - Compression method to use

Example

use tensorlogic_train::TrainingCheckpoint;
use tensorlogic_train::CheckpointCompression;
use std::path::PathBuf;

// Assuming you have a checkpoint...

// Save with gzip compression
checkpoint.save_with_compression(
    &PathBuf::from("/tmp/checkpoint.json.gz"),
    CheckpointCompression::Gzip
).unwrap();

pub fn load(path: &PathBuf) -> TrainResult<Self>

Load checkpoint from a file.

pub fn load_compressed(path: &PathBuf) -> TrainResult<Self>

Load compressed checkpoint from a file.

pub fn estimated_size(&self) -> usize

Get the size of the checkpoint in bytes (estimated).

Trait Implementations

impl Clone for TrainingCheckpoint

fn clone(&self) -> TrainingCheckpoint

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for TrainingCheckpoint

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for TrainingCheckpoint

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Serialize for TrainingCheckpoint

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.