yolov8 -- After the cache is turned on, the memory occupied by reading val data is too large about ultralytics

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👋 Hello @gopin95, 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.

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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):

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• Google Cloud Deep Learning VM. See GCP Quickstart Guide
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• Docker Image. See Docker Quickstart Guide

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[pax7]

Not a solution, but I am wondering if I am experiencing the same issue you are, only in a different place.

Here is my issue: #13806

[pax7]

after some more investigation, I believe this is the same issue as #13806 - whatever is going on is reducing the training speed with large datasets by at least 5x

[glenn-jocher]

@pax7 thank you for your detailed observation! It does seem like your issue might be related to the one mentioned in issue #13806.

To help us investigate further, could you please provide a minimal reproducible example of your code? This will allow us to reproduce the bug on our end and work towards a solution. You can find guidance on creating a minimal reproducible example here.

Additionally, please ensure that you are using the latest versions of torch and ultralytics. You can upgrade your packages using the following commands:

pip install --upgrade torch ultralytics

Once you've provided the reproducible example and confirmed you're on the latest versions, we'll be better equipped to assist you. Thank you for your cooperation! 😊

[gopin95]

@pax7感谢您的详细观察！您的问题似乎可能与问题 #13806中提到的问题有关。

为了帮助我们进一步调查，您能否提供一个最小可重现的代码示例？这将使我们能够重现错误并努力找到解决方案。您可以在此处找到有关创建最小可重现示例的指导。

此外，请确保您使用的是最新版本的torch和ultralytics。您可以使用以下命令升级您的软件包：

pip install --upgrade torch ultralytics

一旦您提供了可重现的示例并确认您使用的是最新版本，我们将能够更好地为您提供帮助。感谢您的合作！😊

#import os
#os.environ['CUDA_LAUNCH_BLOCKING']='1'
from ultralytics import YOLO
model = YOLO("yolov8s-p2.yaml")

results = model.train(data="/opt/data/common/jyh/model/cls46_test/data.yaml",
project="/opt/data/common/jyh/model/cls46_test/",
name="yolov8s-p2-relu_s31-500epochs_yolov8-2-36",
cache=True,
epochs=500,
batch=360,
device="0,1,2,3,4,5",
workers=64,
plots=False)

results = model.val(data="coco.yaml")