deepmodeling / deepmd-kit

A deep learning package for many-body potential energy representation and molecular dynamics
https://docs.deepmodeling.com/projects/deepmd/
GNU Lesser General Public License v3.0
1.41k stars 487 forks source link

feat(pt): support disp_training and time_training in pt #3775

Closed iProzd closed 2 months ago

iProzd commented 2 months ago

Summary by CodeRabbit

coderabbitai[bot] commented 2 months ago
Walkthrough ## Walkthrough The updates focus on enhancing the training functionality by introducing new parameters to control display and timing aspects during training sessions. These changes allow for more detailed monitoring and performance analysis, including the ability to log and calculate average training times per batch, thereby improving the utility and diagnostic capabilities of the training process. ## Changes | File Path | Change Summary | |-------------------------|----------------| | `deepmd/pt/train/...` | Added `display_in_training` and `timing_in_training` parameters. Modified conditions in `fake_model()` and updated `log_loss_valid()` to handle new timing logs and calculate average training time per batch. Initialized `total_train_time`. |

Recent Review Details **Configuration used: CodeRabbit UI** **Review profile: CHILL**
Commits Files that changed from the base of the PR and between 18cdbf024897403da333cfa1a4b61410d0d060d0 and e9ef748a840a2ad1fc2576b1c2aa2c9386358634.
Files selected for processing (1) * deepmd/pt/train/training.py (5 hunks)
Additional comments not posted (3)
deepmd/pt/train/training.py (3)
`142-143`: Initialization of `display_in_training` and `timing_in_training` parameters is correctly handled with default values. --- Line range hint `816-927`: The usage of `display_in_training` and `timing_in_training` in the `run` method is correctly implemented to control the logging of training progress and timing information. --- `1006-1023`: The implementation of average training time logging is accurate and thoughtful, excluding the first batch to provide more reliable metrics.
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codecov[bot] commented 2 months ago

Codecov Report

Attention: Patch coverage is 90.90909% with 1 lines in your changes are missing coverage. Please review.

Project coverage is 82.49%. Comparing base (18cdbf0) to head (e9ef748).

Files Patch % Lines
deepmd/pt/train/training.py 90.90% 1 Missing :warning:
Additional details and impacted files ```diff @@ Coverage Diff @@ ## devel #3775 +/- ## ======================================= Coverage 82.49% 82.49% ======================================= Files 515 515 Lines 48633 48642 +9 Branches 2979 2979 ======================================= + Hits 40118 40126 +8 - Misses 7604 7605 +1 Partials 911 911 ```

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