about physical memory and virtual memory about ultralytics

[github-actions]

👋 Hello @Songpenglei123, thank you for your interest in Ultralytics YOLOv8 🚀! We recommend a visit to the Docs for new users where you can find many Python and CLI usage examples and where many of the most common questions may already be answered.

If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us debug it.

If this is a custom training ❓ Question, please provide as much information as possible, including dataset image examples and training logs, and verify you are following our Tips for Best Training Results.

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Install

Pip install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8.

pip install ultralytics

Environments

YOLOv8 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):

• Notebooks with free GPU:
• Google Cloud Deep Learning VM. See GCP Quickstart Guide
• Amazon Deep Learning AMI. See AWS Quickstart Guide
• Docker Image. See Docker Quickstart Guide

Status

If this badge is green, all Ultralytics CI tests are currently passing. CI tests verify correct operation of all YOLOv8 Modes and Tasks on macOS, Windows, and Ubuntu every 24 hours and on every commit.

[Songpenglei123]

[glenn-jocher]

@Songpenglei123 hello! From your screenshot, it seems like you're experiencing high virtual memory usage during training. This can happen due to the way data is loaded and processed. Here are a few suggestions to manage memory usage:

1. Data Loading: If you're using the cache option in your dataset configuration, try setting it to False or disk. This will reduce memory consumption by not storing all images in RAM.

2. Batch Size: Reducing the batch size can also help manage memory usage, though it might affect training speed and convergence.

3. Image Size: Decreasing the imgsz parameter will reduce the amount of memory required for each batch.

4. Num Workers: Adjusting the workers parameter in your training script can help optimize data loading efficiency and memory usage.

If you continue to experience issues, please provide more details about your training configuration and the specific command you're running. This will help us give more targeted advice. Thank you for using Ultralytics YOLO!

[github-actions]

👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.

For additional resources and information, please see the links below:

• Docs: https://docs.ultralytics.com
• HUB: https://hub.ultralytics.com
• Community: https://community.ultralytics.com

Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLO 🚀 and Vision AI ⭐