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Distributed Tensorflow : Cannot assign a device for operation save

·1 min
Deep-Learning Tensorflow
Note: This article is available in Chinese only. 本文暂无英文版本。 View original

是在使用分布式tensorflow遇到的一个错误

报错如下：

InvalidArgumentError (see above for traceback): Cannot assign a device for operation ‘save/Rest│| 2 GeForce GTX 1080 On | 0000:08:00.0 Off | N/A | oreV2_888’: Operation was explicitly assigned to /job:worker/task:0/device:CPU:0 but available │| 24% 39C P8 12W / 180W | 0MiB / 8114MiB | 0% Default | devices are [ /job:localhost/replica:0/task:0/cpu:0, /job:localhost/replica:0/task:0/gpu:0 ]. Make sure the device specification refers to a valid device.

其中看到Distributed Tensorflow : Cannot assign a device for operation. 中提到，可能的两个原因是：

I have mostly seen the “cannot assign a device for operation …” error in the case of people using GPU without specifying allow_soft_placement=True but in my case I am using just CPUs.

I have seen another instance of this error when users were not specifying server.target as the device of a session,thereby creating a local session but this is not the case in my code.

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感觉资料不是很多，先收集资料好了。 tf-distributed官网文档 SO-between-graph和in-graph的区别 inception.README.md SyncReplicasOptimizer SO_How does ps work in distribute Tensorflow? update:在多个nodes（机）上跑。。。tf默认是异步更新的。。。同步的话。。大概需要syncreplicasoptimizer?

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