Google compute engine TPU突然停止训练

我正试图按照中的说明，在谷歌云中使用TPU来训练transformer模型。 加载数据工作正常，运行后

t2t-trainer \
  --model=transformer \
  --hparams_set=transformer_tpu \
  --problem=translate_ende_wmt32k_packed \
  --train_steps=500000 \
  --eval_steps=3000 \
  --data_dir=$DATA_DIR \
  --output_dir=$OUT_DIR \
  --use_tpu=True \
  --cloud_tpu_name=$TPU_NAME

培训确实如预期的那样开始，并且输出可能看起来有点像这样：

I1118 14:48:18.978163 140580835792320 tpu_estimator.py:2307] global_step/sec: 15.2942                                                                                                                                                   [114/1944]
INFO:tensorflow:examples/sec: 978.827                                                                                             
I1118 14:48:18.978595 140580835792320 tpu_estimator.py:2308] examples/sec: 978.827                                                
INFO:tensorflow:Enqueue next (100) batch(es) of data to infeed.                                               
I1118 14:48:18.979720 140580835792320 tpu_estimator.py:600] Enqueue next (100) batch(es) of data to infeed.                       
INFO:tensorflow:Dequeue next (100) batch(es) of data from outfeed.                                                                
I1118 14:48:18.979935 140580835792320 tpu_estimator.py:604] Dequeue next (100) batch(es) of data from outfeed.
I1118 14:48:24.292932 140577566803712 transport.py:157] Attempting refresh to obtain initial access_token                         
WARNING:tensorflow:TPUPollingThread found TPU tpuv3-8 in state READY, and health HEALTHY.                                         
W1118 14:48:24.353135 140577566803712 preempted_hook.py:91] TPUPollingThread found TPU tpuv3-8 in state READY, and health HEALTHY.
INFO:tensorflow:loss = 1.8486812, step = 113800 (6.536 sec)                                                                       
I1118 14:48:25.512768 140580835792320 basic_session_run_hooks.py:260] loss = 1.8486812, step = 113800 (6.536 sec)                 
INFO:tensorflow:global_step/sec: 15.2986                                                                 
I1118 14:48:25.514695 140580835792320 tpu_estimator.py:2307] global_step/sec: 15.2986                                             
INFO:tensorflow:examples/sec: 979.11                                                                                              
I1118 14:48:25.515115 140580835792320 tpu_estimator.py:2308] examples/sec: 979.11                                
INFO:tensorflow:Enqueue next (100) batch(es) of data to infeed.                                                                   
I1118 14:48:25.516618 140580835792320 tpu_estimator.py:600] Enqueue next (100) batch(es) of data to infeed.                       
INFO:tensorflow:Dequeue next (100) batch(es) of data from outfeed.                                       
I1118 14:48:25.516829 140580835792320 tpu_estimator.py:604] Dequeue next (100) batch(es) of data from outfeed.                    
INFO:tensorflow:Outfeed finished for iteration (388, 47)                                                                          
I1118 14:48:28.761935 140577575196416 tpu_estimator.py:279] Outfeed finished for iteration (388, 47)       
INFO:tensorflow:loss = 1.5237397, step = 113900 (6.573 sec)                                                                       
I1118 14:48:32.086134 140580835792320 basic_session_run_hooks.py:260] loss = 1.5237397, step = 113900 (6.573 sec)

然而，有时，在经过不确定的迭代次数（有时小于25k，有时大于400k，有时从不）后，训练突然停止。没有错误消息，但没有取得更多进展。在本例中，我得到以下输出：

I1120 13:40:33.828651 140684764419520 tpu_estimator.py:2307] global_step/sec: 16.3988
INFO:tensorflow:examples/sec: 1049.52
I1120 13:40:33.829339 140684764419520 tpu_estimator.py:2308] examples/sec: 1049.52
INFO:tensorflow:Enqueue next (1000) batch(es) of data to infeed.
I1120 13:40:33.830607 140684764419520 tpu_estimator.py:600] Enqueue next (1000) batch(es) of data to infeed.
INFO:tensorflow:Dequeue next (1000) batch(es) of data from outfeed.
I1120 13:40:33.830862 140684764419520 tpu_estimator.py:604] Dequeue next (1000) batch(es) of data from outfeed.
INFO:tensorflow:Outfeed finished for iteration (7, 0)
I1120 13:40:34.267921 140681504278272 tpu_estimator.py:279] Outfeed finished for iteration (7, 0)
I1120 13:40:39.989195 140681495885568 transport.py:157] Attempting refresh to obtain initial access_token
WARNING:tensorflow:TPUPollingThread found TPU tpuv3-5 in state READY, and health HEALTHY.
W1120 13:40:40.056418 140681495885568 preempted_hook.py:91] TPUPollingThread found TPU tpuv3-5 in state READY, and health HEALTHY.
I1120 13:41:10.124164 140681495885568 transport.py:157] Attempting refresh to obtain initial access_token
WARNING:tensorflow:TPUPollingThread found TPU tpuv3-5 in state READY, and health HEALTHY.
W1120 13:41:10.177670 140681495885568 preempted_hook.py:91] TPUPollingThread found TPU tpuv3-5 in state READY, and health HEALTHY.
I1120 13:41:40.259634 140681495885568 transport.py:157] Attempting refresh to obtain initial access_token
WARNING:tensorflow:TPUPollingThread found TPU tpuv3-5 in state READY, and health HEALTHY.
W1120 13:41:40.309398 140681495885568 preempted_hook.py:91] TPUPollingThread found TPU tpuv3-5 in state READY, and health HEALTHY.
I1120 13:42:10.377460 140681495885568 transport.py:157] Attempting refresh to obtain initial access_token
WARNING:tensorflow:TPUPollingThread found TPU tpuv3-5 in state READY, and health UNKNOWN.
W1120 13:42:10.431982 140681495885568 preempted_hook.py:91] TPUPollingThread found TPU tpuv3-5 in state READY, and health UNKNOWN.
I1120 13:42:40.508342 140681495885568 transport.py:157] Attempting refresh to obtain initial access_token
WARNING:tensorflow:TPUPollingThread found TPU tpuv3-5 in state READY, and health HEALTHY.
W1120 13:42:40.567739 140681495885568 preempted_hook.py:91] TPUPollingThread found TPU tpuv3-5 in state READY, and health HEALTHY.
I1120 13:43:10.638391 140681495885568 transport.py:157] Attempting refresh to obtain initial access_token
WARNING:tensorflow:TPUPollingThread found TPU tpuv3-5 in state READY, and health HEALTHY.
W1120 13:43:10.694900 140681495885568 preempted_hook.py:91] TPUPollingThread found TPU tpuv3-5 in state READY, and health HEALTHY.
I1120 13:43:40.763782 140681495885568 transport.py:157] Attempting refresh to obtain initial access_token
WARNING:tensorflow:TPUPollingThread found TPU tpuv3-5 in state READY, and health HEALTHY.
W1120 13:43:40.810777 140681495885568 preempted_hook.py:91] TPUPollingThread found TPU tpuv3-5 in state READY, and health HEALTHY.
I1120 13:44:10.889873 140681495885568 transport.py:157] Attempting refresh to obtain initial access_token
WARNING:tensorflow:TPUPollingThread found TPU tpuv3-5 in state READY, and health HEALTHY.
W1120 13:44:10.942733 140681495885568 preempted_hook.py:91] TPUPollingThread found TPU tpuv3-5 in state READY, and health HEALTHY.
I1120 13:44:41.011034 140681495885568 transport.py:157] Attempting refresh to obtain initial access_token
WARNING:tensorflow:TPUPollingThread found TPU tpuv3-5 in state READY, and health HEALTHY.
W1120 13:44:41.066553 140681495885568 preempted_hook.py:91] TPUPollingThread found TPU tpuv3-5 in state READY, and health HEALTHY.

请注意，报告的健康状况是未知的，可能与此问题有关，也可能与此问题无关

要恢复训练，我必须停止进程并再次运行训练命令。然后，它将加载最新的检查点并继续训练，直到最终再次停止

我在tmux会话中运行训练命令，所以这不应该是由我和Google Cloud之间的连接问题引起的。事实上，我可以完全关闭所有窗口，并从另一台电脑连接到跑步训练课程

我已经看到了这个问题，但我使用的是一个预定义的模型，我的存储桶定义在同一个区域（TPU在

us-central1-a

中，存储桶在

us-central1

中）

编辑：如果这是相关的，我目前在一个免费的1个月的试用期，我通过申请该项目获得。也许那些集群节点不如付费节点稳定

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Edit2：这可能与GitHub问题有关（和）？请注意，问题已经解决，但给出的答案似乎与问题无关。此外，我还检查了我的云VM中的文件

/usr/local/lib/python2.7/dist packages/tensorflow\u core/python/tpu/preempted\u hook.py

，两个链接的更改都已合并

我在训练TFRC的TPU时也遇到了同样的问题。正如警告所说，TPU和谷歌云之间的连接似乎存在问题，即使我们按照指示操作

我尝试了几种解决方案：

• 删除gcloud配置文件夹
  rm-rf~/.config/gcloud

• 更新gcloud sdk:
  gcloud组件更新

• 允许TPU通过IAM访问云存储桶 !

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TPU挂起错误仍然会发生，但频率较低。希望它能对您的案例有所帮助，或者您可以找到通用的解决方案。

这被报告为GitHub（，）上的一个bug，随后被修复。

---

如果错误仍然发生，您应该回答第二个GitHub问题。请注意，您可能需要重新创建TPU，只是重新启动它可能还不够。

谢谢，我已经尝试过了。但我不确定这是否真的有帮助——一个实验在开始后30分钟内就结束了。希望不久就会有一个普遍的解决办法。