Training_Evaluation_Orchestration.md

graph LR
    Training_Evaluation_Orchestration["Training & Evaluation Orchestration"]
    Recognition_Training_Orchestrator["Recognition Training Orchestrator"]
    Segmentation_Training_Orchestrator["Segmentation Training Orchestrator"]
    Learning_Rate_Scheduler["Learning Rate Scheduler"]
    Training_Helper_Utilities["Training Helper Utilities"]
    Training_Evaluation_Orchestration -- "delegates tasks to" --> Recognition_Training_Orchestrator
    Training_Evaluation_Orchestration -- "delegates tasks to" --> Segmentation_Training_Orchestrator
    Training_Evaluation_Orchestration -- "relies on" --> Learning_Rate_Scheduler
    Training_Evaluation_Orchestration -- "utilizes" --> Training_Helper_Utilities
    Recognition_Training_Orchestrator -- "calls" --> Learning_Rate_Scheduler
    Recognition_Training_Orchestrator -- "utilizes" --> Training_Helper_Utilities
    Segmentation_Training_Orchestrator -- "calls" --> Learning_Rate_Scheduler
    Segmentation_Training_Orchestrator -- "utilizes" --> Training_Helper_Utilities
    click Training_Evaluation_Orchestration href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/PyTorch-Encoding/Training_Evaluation_Orchestration.md" "Details"

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Details

The Training & Evaluation Orchestration subsystem encompasses the core logic for managing the entire machine learning model training and evaluation lifecycle. Its boundaries are defined by the high-level coordination of distributed training, data flow, model execution, optimization, and logging, delegating specific tasks to specialized orchestrators and utility components.

Training & Evaluation Orchestration [Expand]

The primary coordinator overseeing the entire training and evaluation pipeline. It sets up distributed training, manages data flow, orchestrates model execution, optimization, and logging. It acts as a high-level dispatcher for specific training tasks.

Related Classes/Methods:

• experiments/recognition/train_dist.py
• experiments/segmentation/train_dist.py

Recognition Training Orchestrator

Manages the distributed training and validation lifecycle specifically for recognition models. This includes setting up the environment, running training epochs, and coordinating validation steps tailored for recognition tasks.

Related Classes/Methods:

• experiments/recognition/train_dist.py

Segmentation Training Orchestrator

Orchestrates distributed training and evaluation specifically for segmentation models. It handles the setup and execution of training and validation phases, adapting to the unique requirements of segmentation tasks.

Related Classes/Methods:

• experiments/segmentation/train_dist.py

Learning Rate Scheduler

Dynamically adjusts the learning rate of the optimizer during training based on a predefined schedule or adaptive strategy. This is crucial for achieving stable and efficient model convergence.

Related Classes/Methods:

• encoding/utils/lr_scheduler.py

Training Helper Utilities

Provides auxiliary functions and utilities that support various aspects of the training process, particularly in data preparation, augmentation (e.g., mixup_loader), and other common training operations.

Related Classes/Methods:

• encoding/utils/train_helper.py

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