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更新时间：2024-01-15 GMT+08:00
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训练最后一个epoch卡死

问题现象

通过日志查看数据切分是否对齐，若未对齐，容易导致部分进程完成训练退出，而部分训练进程因未收到其他进程反馈卡死，如下图同一时间有的进程在epoch48，而有的进程在epoch49。

loss exit lane:0.12314446270465851
step loss is 0.29470521211624146
[2022-04-26 13:57:20,757][INFO][train_epoch]:Rank:2 Epoch:[48][20384/all] Data Time 0.000(0.000) Net Time 0.705(0.890) Loss 0.3403(0.3792)LR 0.00021887
[2022-04-26 13:57:20,757][INFO][train_epoch]:Rank:1 Epoch:[48][20384/all] Data Time 0.000(0.000) Net Time 0.705(0.891) Loss 0.3028(0.3466) LR 0.00021887
[2022-04-26 13:57:20,757][INFO][train_epoch]:Rank:4 Epoch:[49][20384/all] Data Time 0.000(0.147) Net Time 0.705(0.709) Loss 0.3364(0.3414)LR 0.00021887
[2022-04-26 13:57:20,758][INFO][train_epoch]:Rank:3 Epoch:[49][20384/all] Data Time 0.000 (0.115) Net Time 0.706(0.814) Loss 0.3345(0.3418) LR 0.00021887
[2022-04-26 13:57:20,758][INFO][train_epoch]:Rank:0 Epoch:[49][20384/all] Data Time 0.000(0.006) Net Time 0.704(0.885) Loss 0.2947(0.3566) LR 0.00021887
[2022-04-26 13:57:20,758][INFO][train_epoch]:Rank:7 Epoch:[49][20384/all] Data Time 0.001 (0.000) Net Time 0.706 (0.891) Loss 0.3782(0.3614) LR 0.00021887
[2022-04-26 13:57:20,759][INFO][train_epoch]:Rank:5 Epoch:[48][20384/all] Data Time 0.000(0.000) Net Time 0.706(0.891) Loss 0.5471(0.3642) LR 0.00021887
[2022-04-26 13:57:20,763][INFO][train_epoch]:Rank:6 Epoch:[49][20384/all] Data Time 0.000(0.000) Net Time 0.704(0.891) Loss 0.2643(0.3390)LR 0.00021887
stage 1 loss 0.4600560665130615 mul_cls_loss loss:0.01245919056236744 mul_offset_loss 0.44759687781333923 origin stage2_loss 0.048592399805784225
stage 1 loss:0.4600560665130615 stage 2 loss:0.048592399805784225 loss exit lane:0.10233864188194275

解决方案

使用tensor的切分操作对齐数据。

父主题： 训练作业卡死

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