如何在Tensorflow 2.0b中检查/释放GPU内存?

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问题描述

在我的tensorflow2.0b程序中，确实出现了这样的错误

In my tensorflow2.0b program I do get an error like this

    ResourceExhaustedError: OOM when allocating tensor with shape[727272703] and type int8 on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [Op:TopKV2]

此程序中的许多基于GPU的操作已成功执行后，将发生错误.

The error occurs after a number of GPU-based operations within this program have been successfully executed.

我喜欢释放与这些过去的操作相关的所有GPU内存，以避免上述错误.我如何在tensorflow-2.0b中做到这一点?如何在程序中检查内存使用情况?

I like to release all GPU-memory associated with these past operations in order to avoid the above error. How can I do this in tensorflow-2.0b? How could I check memory usage from within my program?

我只能使用tensorflow2.0中不再提供的tf.session()查找相关信息

I was only able to find related information using tf.session() which is not available anymore in tensorflow2.0

推荐答案

您可能对使用 NVIDIA Management Library的Python 3绑定.

我会尝试这样的事情:

import nvidia_smi

nvidia_smi.nvmlInit()

handle = nvidia_smi.nvmlDeviceGetHandleByIndex(0)
# card id 0 hardcoded here, there is also a call to get all available card ids, so we could iterate

info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)

print("Total memory:", info.total)
print("Free memory:", info.free)
print("Used memory:", info.used)

nvidia_smi.nvmlShutdown()

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