部署后要更改Google Cloud Deep Learning VM映像版本吗?

Vivek Khimani

我在Google Cloud上部署了带有标准tensorflow-gpu == 1.15映像的深度学习VM。但是,我后来意识到我需要tensorflow-gpu> = 1.4。由于我已经在VM实例上进行了大量处理,因此我想知道是否可以在不部署新VM的情况下升级映像版本?

谢谢!

帕迪·大力水手

我假设您说的是“是否可以在不部署新VM的情况下升级映像版本?”。你的意思是降级。

是的,可以在深度学习虚拟机中降级Tensorflow的版本

我已经运行了以下测试,看是否有可能。

1)根据从Google Cloud Marketplace创建深度学习VM实例安装深度学习VM

SSH进入虚拟机后

2)在交互式python shell中检查安装的版本

>>> 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

下一步是运行;

pip install --upgrade tensorflow==1.14.0

该命令成功,但是出现以下错误;

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.

如您所见,这会导致依赖性问题。

下一步是;

a) pip install --upgrade  pillow==4.3.0
b) pip install --upgrade tensorflow-gpu==1.14.0

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 Frozen”命令来检查依赖关系;在更改版本之前,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......."

更改版本后pip冻结输出;

"....
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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