为什么我在笔记本中使用的 tensorflow 版本与我在环境中使用的版本不同？

Question

我在我的环境中安装了 tensorflow for linux 的 windows 子系统，但我不能在我的笔记本中使用它。 确实，这是我的版本：

(seg_env) antoi@LAPTOP-UTL8OHHO:/mnt/c/Users/antoi/Documents/Programming/Covent Garden/Segmentation$ pip list | grep tensorflow

tensorflow           2.1.0
tensorflow-estimator 2.1.0
tensorflow-text      2.1.1

但是当我尝试导入它时，我有：

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
<ipython-input-61-e25dcad80c44> in <module>
      1 # import tensorflow.compat.v2 as tf
      2 # !{sys.executable} -m pip install -q tensorflow-text
----> 3 from tensorflow_text import SentencepieceTokenizer
      4 
      5 get_ipython().system("{sys.executable} -c 'import tensorflow as tf; print(tf.__version__)'")

ModuleNotFoundError: No module named 'tensorflow_text'

所以我尝试从笔记本安装它，它告诉我我的 tensorflow 版本不是它需要的版本：

  Could not find a version that satisfies the requirement tensorflow<2.2,>=2.1.0 (from tensorflow-text) (from versions: 0.12.1, 1.0.0, 1.0.1, 1.1.0rc0, 1.1.0rc1, 1.1.0rc2, 1.1.0, 1.2.0rc0, 1.2.0rc1, 1.2.0rc2, 1.2.0, 1.2.1, 1.3.0rc0, 1.3.0rc1, 1.3.0rc2, 1.3.0, 1.4.0rc0, 1.4.0rc1, 1.4.0, 1.4.1, 1.5.0rc0, 1.5.0rc1, 1.5.0, 1.5.1, 1.6.0rc0, 1.6.0rc1, 1.6.0, 1.7.0rc0, 1.7.0rc1, 1.7.0, 1.7.1, 1.8.0rc0, 1.8.0rc1, 1.8.0, 1.9.0rc0, 1.9.0rc1, 1.9.0rc2, 1.9.0, 1.10.0rc0, 1.10.0rc1, 1.10.0, 1.10.1, 1.11.0rc0, 1.11.0rc1, 1.11.0rc2, 1.11.0, 1.12.0rc0, 1.12.0rc1, 1.12.0rc2, 1.12.0, 1.12.2, 1.12.3, 1.13.0rc0, 1.13.0rc1, 1.13.0rc2, 1.13.1, 1.13.2, 1.14.0rc0, 1.14.0rc1, 1.14.0, 2.0.0a0, 2.0.0b0, 2.0.0b1)
No matching distribution found for tensorflow<2.2,>=2.1.0 (from tensorflow-text)

很奇怪不是吗？ 我以为我有 tensorflow 2.1.0 ！ 所以我尝试导入 tensorflow2：

import tensorflow.compat.v2 as tf
!{sys.executable} -c 'import tensorflow.compat.v2 as tf; print(tf.__version__)'


/home/antoi/.local/lib/python3.6/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/antoi/.local/lib/python3.6/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/antoi/.local/lib/python3.6/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/antoi/.local/lib/python3.6/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/antoi/.local/lib/python3.6/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/antoi/.local/lib/python3.6/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/antoi/.local/lib/python3.6/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/antoi/.local/lib/python3.6/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/antoi/.local/lib/python3.6/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/antoi/.local/lib/python3.6/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/antoi/.local/lib/python3.6/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/antoi/.local/lib/python3.6/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)])
1.14.0

看来我有 tensorflow 1.14.0 ！ 那么为什么我在笔记本中使用的 tensorflow 版本与我在我的环境中使用的版本不同呢？

更新：当我使用 anaconda 环境启动jupyter-notebook时，它不是 anaconda 的

的确：

(seg_env3) antoi@LAPTOP-UTL8OHHO:/mnt/c/Users/antoi/Documents/Programming/Covent Garden/Segmentation$ which jupyter
/home/antoi/.local/bin/jupyter
(seg_env3) antoi@LAPTOP-UTL8OHHO:/mnt/c/Users/antoi/Documents/Programming/Covent Garden/Segmentation$ which jupyter-notebook
/home/antoi/.local/bin/jupyter-notebook

我现在确定我没有使用 anaconda 的 jupyter notebook。 那么我如何启动 jupyter 的呢？

Answer 1

刚刚得到了解决方案，不知道是否与您的情况有关。 notebook 的问题在于它可能正在加载python默认版本的笔记本。 我刚刚遇到了一个类似的问题，我的笔记本中加载了 tensorflow 2.0 而不是 tf 1.1.4。 为此，我没有使用所需的 tf 版本创建一个新环境，并从该环境本地安装 jupyter。 从该环境再次启动 jupyter 时，它运行良好。