Comments (4)
yu4u
commented on July 2, 2024
What command did you execute?
How about decreasing the batch size like this?
python train.py --data_dir [PATH/TO/appa-real-release] TRAIN.BATCH_SIZE 16
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cliobeach
commented on July 2, 2024
i use the command like :!python train.py --data_dir ./data/appa-real-release --tensorboard tf_log
i will the new command you refer to . thank u .
…------------------ 原始邮件 ------------------
发件人: "Yusuke Uchida"<[email protected]>;
发送时间: 2019年7月2日(星期二) 中午11:52
收件人: "yu4u/age-estimation-pytorch"<[email protected]>;
抄送: "clio"<[email protected]>;"Author"<[email protected]>;
主题: Re: [yu4u/age-estimation-pytorch] cuda memory out (#3)
What command did you execute?
How about decreasing the batch size like this?
python train.py --data_dir [PATH/TO/appa-real-release] TRAIN.BATCH_SIZE 16
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xsacha
commented on July 2, 2024
You just need to choose a suitable batch size for your GPU memory.
Example: GTX 660 has only 2GB and can only do batch size of 4. If you have two cards you can use the --multi_gpu flag and increase batch size to 8.
The default batch size is quite high (128) and probably requires 64GB of memory.
If you have 8 x K80 you will definitely have enough memory but not enough for 1 x K80.
Just try the --multi_gpu flag.
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cliobeach
commented on July 2, 2024
thanks Sacha i already fix this the original size was 128 so we should change the size before run according to our GPU.
…---Original---
From: "Sacha"<[email protected]>
Date: Thu, Oct 3, 2019 14:21 PM
To: "yu4u/age-estimation-pytorch"<[email protected]>;
Cc: "cliobeach"<[email protected]>;"Author"<[email protected]>;
Subject: Re: [yu4u/age-estimation-pytorch] cuda memory out (#3)
You just need to choose a suitable batch size for your GPU memory.
Example: GTX 660 has only 2GB and can only do batch size of 4. If you have two cards you can use the --multi_gpu flag and increase batch size to 8.
The default batch size is quite high (128) and probably requires 64GB of memory.
If you have 8 x K80 you will definitely have enough memory.
Just try the --multi_gpu flag.
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You are receiving this because you authored the thread.
Reply to this email directly, view it on GitHub, or mute the thread.
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