Google cloud platform 部署后更改Google Cloud Deep Learning VM映像版本？

我在谷歌云上部署了一个深度学习虚拟机，它有一个标准的tensorflow gpu==1.15图像。然而，我后来意识到我需要tensorflow gpu>=1.4。由于我已经对我的VM实例进行了大量处理，我想知道是否有可能升级映像版本而不必部署新的VM

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谢谢

当您说“是否可以升级映像版本而不必部署新的VM？”时，我是这样假设的。你是说降级

是的，可以在深度学习虚拟机中降级Tensorflow版本

我已经运行了以下测试，看看这是否可行

1） 根据安装了深度学习虚拟机

在SSH'ing进入虚拟机之后

2） 已检查交互式python shell中的安装版本

>>> import tensorflow as tf
 2020-03-30 11:57:03.076701: I 
 tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully 
 opened dynamic library libcudart.so.10.1
 2020-03-30 11:57:07.162626: I 
 tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully 
 opened dynamic library libnvinfer.so.6
 2020-03-30 11:57:07.222695: I 
 tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully 
 opened dynamic library libnvinfer_plugin.so.6
 >>> print(tf.__version__)
 2.1.0

下一步是跑步

pip install --upgrade tensorflow==1.14.0

命令成功，但出现以下错误

ERROR: scikit-image 0.15.0 requires pillow>=4.3.0, which is not installed.
ERROR: imageio 2.6.1 requires pillow, which is not installed.
ERROR: tensorflow-serving-api-gpu 1.14.0 has requirement tensorflow-gpu~=1.14.0, but you'll have tensorflow-gpu 2.1.0 which is incompatible.
ERROR: tensorflow-gpu 2.1.0 has requirement numpy<2.0,>=1.16.0, but you'll have numpy 1.15.4 which is incompatible.
ERROR: tensorflow-gpu 2.1.0 has requirement scipy==1.4.1; python_version >= "3", but you'll have scipy 1.1.0 which is incompatible.
ERROR: tensorflow-io 0.9.10 has requirement tensorflow==2.1.0rc0, but you'll have tensorflow 1.4.0 which is incompatible.

在交互式python shell中再次检查版本

Python 3.5.3 (default, Sep 27 2018, 17:25:39) 
[GCC 6.3.0 20170516] on linux
Type "help", "copyright", "credits" or "license" for more information.


>>> import tensorflow as tf

   /home/xxxxxxxxx/.local/lib/python3.5/site- 
   packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing 
   (type, 1) or '1type' as a synonym of type is deprecated; in a future version 
   of numpy, it will be understood as (type, (1,)) / '(1,)type'.
   _np_qint8 = np.dtype([("qint8", np.int8, 1)])
   /home/xxxxxxxx/.local/lib/python3.5/site- 
   packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing 
   (type, 1) or '1type' as a synonym of type is deprecated; in a future version 
   of numpy, it will be understood as (type, (1,)) / '(1,)type'.
   _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
   /home/xxxxxxxx/.local/lib/python3.5/site- 
   packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing 
   (type, 1) or '1type' as a synonym of type is deprecated; in a future version 
   of numpy, it will be understood as (type, (1,)) / '(1,)type'.
   _np_qint16 = np.dtype([("qint16", np.int16, 1)])
   /home/xxxxxxx/.local/lib/python3.5/site- 
   packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing 
   (type, 1) or '1type' as a synonym of type is deprecated; in a future version 
   of numpy, it will be understood as (type, (1,)) / '(1,)type'.
   _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
   /home/xxxxxxx/.local/lib/python3.5/site- 
   packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing 
   (type, 1) or '1type' as a synonym of type is deprecated; in a future version 
   of numpy, it will be understood as (type, (1,)) / '(1,)type'.
   _np_qint32 = np.dtype([("qint32", np.int32, 1)])
   /home/xxxxxxxx/.local/lib/python3.5/site- 
   packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing 
   (type, 1) or '1type' as a synonym of type is deprecated; in a future version 
   of numpy, it will be understood as (type, (1,)) / '(1,)type'.
   np_resource = np.dtype([("resource", np.ubyte, 1)])
   /home/xxxxxxxx/.local/lib/python3.5/site- 
   packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: 
   Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future 
   version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
   _np_qint8 = np.dtype([("qint8", np.int8, 1)])
   /home/xxxxxxx/.local/lib/python3.5/site- 
   packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: 
   Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future 
   version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
   _np_quint8 = np.dtype([("quint8", np.uint8, 1)])
   /home/xxxxxxxx/.local/lib/python3.5/site- 
   packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: 
   Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future 
   version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
   _np_qint16 = np.dtype([("qint16", np.int16, 1)])
   /home/xxxxxxxx/.local/lib/python3.5/site- 
   packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: 
   Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future 
   version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
   _np_quint16 = np.dtype([("quint16", np.uint16, 1)])
   /home/xxxxxxx/.local/lib/python3.5/site- 
   packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: 
   Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future 
   version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
   _np_qint32 = np.dtype([("qint32", np.int32, 1)])
   /home/xxxxxxxx/.local/lib/python3.5/site- 
   packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: 
   Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future 
   version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
   np_resource = np.dtype([("resource", np.ubyte, 1)])
   >>> print(tf.__version__)
   1.14.0

运行“pip freeze”命令检查依赖关系； 更改版本前，pip冻结输出

"....tensorboard==2.1.0
tensorflow-datasets==1.3.0
tensorflow-estimator==2.1.0
tensorflow-gpu==2.1.0
tensorflow-hub==0.7.0
tensorflow-io==0.9.10
tensorflow-metadata==0.21.1
tensorflow-probability==0.9.0
tensorflow-serving-api-gpu==1.14.0......."

"....
tensorboard==1.14.0
tensorflow==1.14.0
tensorflow-datasets==1.3.0
tensorflow-estimator==1.14.0
tensorflow-gpu==1.14.0
tensorflow-hub==0.7.0
tensorflow-io==0.9.10
tensorflow-metadata==0.21.1
tensorflow-probability==0.9.0
tensorflow-serving-api-gpu==1.14.0
tensorflow-tensorboard==0.4.0...."

更改版本后pip冻结输出

"....tensorboard==2.1.0
tensorflow-datasets==1.3.0
tensorflow-estimator==2.1.0
tensorflow-gpu==2.1.0
tensorflow-hub==0.7.0
tensorflow-io==0.9.10
tensorflow-metadata==0.21.1
tensorflow-probability==0.9.0
tensorflow-serving-api-gpu==1.14.0......."

"....
tensorboard==1.14.0
tensorflow==1.14.0
tensorflow-datasets==1.3.0
tensorflow-estimator==1.14.0
tensorflow-gpu==1.14.0
tensorflow-hub==0.7.0
tensorflow-io==0.9.10
tensorflow-metadata==0.21.1
tensorflow-probability==0.9.0
tensorflow-serving-api-gpu==1.14.0
tensorflow-tensorboard==0.4.0...."

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你好，只是澄清一下。你想降级到1.4版吗？版本1.15已经大于1.4，因此tensorflow gpu>=1.4已经成立。您好，我认为您混淆了tensorflow 1.15和tensorflow-1.5？是TF版本…看不出我可能会混淆版本。版本1.15似乎比版本1.4或1.5更新。如果我错了，请纠正我。我很抱歉@milia。我想我把版本弄混了。但是，我仍然认为我需要特定的tf-1.4版本。我正在尝试使用tensorflow nmt API Hi。没关系。尽管如此，我还是设法使用REAME.md中提供的示例命令，从版本为1.15的TF实例训练了一个NMT模型（2层LSTM seq2seq模型，具有128个dim隐藏单元和12个时代的嵌入）。但如果你坚持你需要1.4版，我可以进一步研究你如何（如果）降级。非常感谢！