Chore: prevent from printing additional header in lcurve.out on resuming

[caic99]

Summary by CodeRabbit

• Bug Fixes
  • Improved logging control during training by adjusting header printing when resuming training.
  • Enhanced data handling to ensure seamless continuation of training and validation processes when data is exhausted.

[coderabbitai]

📝 Walkthrough

Walkthrough

The changes in the deepmd/pt/train/training.py file focus on modifying the Trainer class constructor and get_data method. The primary modifications involve adjusting the initialization of the self.lcurve_should_print_header attribute to depend on the training restart status and enhancing the data loading mechanism to handle scenarios where training or validation data is exhausted. These changes aim to improve the robustness of the training process by ensuring proper data iteration and logging behavior.

Changes

File	Change Summary
deepmd/pt/train/training.py	- Modified self.lcurve_should_print_header initialization to depend on self.restart_training - Updated get_data method to handle data loader exhaustion and iterator reinitialization

Sequence Diagram

sequenceDiagram
    participant Trainer
    participant DataLoader
    participant Iterator

    Trainer->>Trainer: Initialize training
    alt Is Restart Training
        Trainer->>Trainer: Set lcurve_should_print_header to False
    else New Training
        Trainer->>Trainer: Set lcurve_should_print_header to True
    end

    Trainer->>DataLoader: Get data
    alt Data Exhausted
        DataLoader->>Iterator: Reinitialize
        Iterator-->>DataLoader: Reset iterator
    end
    DataLoader-->>Trainer: Return batch data

Loading

The sequence diagram illustrates the modified training initialization and data loading process, highlighting the conditional header printing and data loader handling when training is resumed or data is exhausted.

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📜 Recent review details

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📥 Commits

Reviewing files that changed from the base of the PR and between 3cecca4 and 3ccbbc2.

📒 Files selected for processing (1)

• deepmd/pt/train/training.py (1 hunks)

🔇 Additional comments (2)

deepmd/pt/train/training.py (2)

148-150: LGTM! The header printing logic is now correctly controlled.

The initialization of lcurve_should_print_header based on not self.restart_training ensures that the header is only printed when starting a new training session, not when resuming. This change directly addresses the PR objective.

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Line range hint 579-603: LGTM! Robust data handling implementation.

The data handling logic in get_data method properly manages data exhaustion scenarios by:

1. Catching StopIteration exceptions
2. Refreshing the data loader status
3. Reinitializing the buffered iterator

This implementation ensures seamless training continuation for both single-task and multi-task scenarios.

Also applies to: 604-631

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[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 84.59%. Comparing base (3cecca4) to head (3ccbbc2).
Report is 5 commits behind head on devel.

Additional details and impacted files

@@           Coverage Diff           @@
##            devel    #4511   +/-   ##
=======================================
  Coverage   84.59%   84.59%           
=======================================
  Files         675      675           
  Lines       63575    63574    -1     
  Branches     3486     3486           
=======================================
  Hits        53779    53779           
+ Misses       8671     8670    -1     
  Partials     1125     1125           

☔ View full report in Codecov by Sentry.
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[njzjz]

1. I don't suggest so, as the checkpoint is not saved every steps. You may see 10000 10100 (restart) 10000 10100. It's not clear if the title is removed.

2. All backends should have the same behavior.