RVC-Project/Retrieval-based-Voice-Conversion-WebUI

Training is freezing the computer when training for more than 20 epochs.

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#1,711 建立於 2024年1月14日

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描述

Are there any options I can add to the invoking command to limit the VRAM usage. Like the way Automatic1111 for Stable Diffusion has --lowvram option.

Because when I'm trying to train a model for more than 20 epochs, it is freezing the entire computer. I have RX 6500 XT with 4GB, Main RAM is 16GB.

貢獻者指南

技術棧
pythonpytorch
領域
machine learningperformance
議題類型
feature
難度面向新貢獻者的預計實作難度，1 表示很小改動，5 表示專家級工作。
4
預計時間有經驗貢獻者完成調查、實作、測試並準備 pull request 的粗略時間範圍。
3-5 days
活動狀態議題目前的可參與程度：新鮮、活躍、陳舊、阻塞或等待維護者輸入。
stale
清晰度議題是否清楚說明預期改動、驗收標準和下一步。
mostly clear
前置要求
PythonPyTorchmemory managementCUDA programming
新手友善度1-100 的估計分數，表示該議題對首次貢獻者的友善程度。
25
研究方向
The issue requests a lowvram option to limit VRAM usage during training, similar to Automatic1111's option. To implement, investigate the training script (likely in `train.py` or `infer.py`) to identify where model and data batches are loaded. Consider adding a command line argument using argparse that enables memory saving techniques like gradient checkpointing, mixed precision, or reducing batch size dynamically. Check the repository's existing argument parsing for guidance.
Training is freezing the computer when training for more than 20 epochs. · RVC-Project/Retrieval-based-Voice-Conversion-WebUI#1711 | Good First Issue