jingyaogong
jingyaogong
## Your project has been a real blessing to me, very free and well set up, very comfortable to use, hats off to you! But I have this problem in...
在某平台上租了2台机器,控制内存CPU等变量的一致性,测试不同GPU的训练时间   个人认为 `[3060~2080Ti~3090~4090]` 这个区间包含了大部分AI从业者手头的显卡规格,具有很强的代表性 其它桌面GPU,例如3060的算力略弱于2080Ti,可以参考上图换算 --- * 2080Ti单卡(11G显存) > pretrain `batchsize=48`,预计7小时1个epoch ``` root@autodl-container-908d479a1c-1697cfd8:~/autodl-tmp/minimind# python 1-pretrain.py LLM总参数量:26.878 百万 Epoch:[0/20](0/111769) loss:8.879 lr:0.0002000 epoch_Time:2618.0min: Epoch:[0/20](100/111769) loss:7.438 lr:0.0002000 epoch_Time:442.0min: Epoch:[0/20](200/111769) loss:6.899 lr:0.0002000...
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