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Python11中创建虚拟环境、安装 TensorFlow

news 2025/7/7 9:49:20

1. 为什么必须使用虚拟环境？

避免污染系统环境：TensorFlow 依赖复杂，可能与其他包冲突
隔离 Python 版本：防止 Python 11 和 Python 13 相互干扰
项目独立性：方便管理不同项目的依赖
卸载简单：直接删除文件夹即可清理环境

2. 创建虚拟环境（关键步骤）

验证版本
PS J:\Program Files (x86)\Python\python-3.11.0> python --version
Python 3.11.0

创建虚拟环境（关键步骤）

python -m venv "J:\Prog\python\tf_env"

几秒后完成
在这里插入图片描述

3. 激活虚拟环境

进入虚拟环境目录

cd "J:\Prog\python\tf_env\Scripts"

打开powershell

导航到 Scripts 目录
cd J:\Prog\python\tf_env\Scripts
临时修改执行策略
Set-ExecutionPolicy -ExecutionPolicy Bypass -Scope Process -Force
执行激活脚本
.\Activate.ps1

4. 验证环境状态、环境变量

python check_env.py

import sys, os
print(f"Python 路径: {sys.executable}")
print(f"虚拟环境: {os.getenv('VIRTUAL_ENV', '未激活')}")
print(f"环境包目录: {sys.prefix}")

在这里插入图片描述

环境激活后

(tf_env) PS J:\Prog\python\tf_env\Scripts> pip --version
pip 22.3 from J:\Prog\python\tf_env\Lib\site-packages\pip (python 3.11)

当虚拟环境成功激活后，运行 pip --version 的输出应该显示虚拟环境内部的 pip 路径，而不是系统全局路径。

检查环境变量

检查 VIRTUAL_ENV 变量

$env:VIRTUAL_ENV

正确应显示：J:\Prog\python\tf_env

检查 PATH 变量
(tf_env) PS J:\Prog\python\tf_env\Scripts> $env:PATH -split ‘;’ | Select-String ‘tf_env’

正确应显示：J:\Prog\python\tf_env\Scripts

确定环境已激活

激活状态检查清单
命令提示符显示 (tf_env) 前缀

pip --version 显示虚拟环境路径

python -c “import sys; print(sys.executable)” 输出虚拟环境路径

$env:VIRTUAL_ENV 返回虚拟环境目录

where python 首先列出虚拟环境的 python.exe

如果所有这些检查都通过，您的虚拟环境已正确激活，可以安全安装 TensorFlow 而不会影响系统全局环境。

4. 停用环境：

deactivate.bat

5. 删除虚拟环境

Remove-Item -Recurse -Force J:\Prog\python\tf_env

6. 虚拟环境中安装 TensorFlow

pip install tensorflow -i https://pypi.tuna.tsinghua.edu.cn/simple

(tf_env) PS J:\Prog\python\tf_env\Scripts> pip install tensorflow -i https://pypi.tuna.tsinghua.edu.cn/simple
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting tensorflowDownloading https://pypi.tuna.tsinghua.edu.cn/packages/3c/e3/e868f1d5951047f950d2ba1e04a765a3328a51f06996b67976d6102f8227/tensorflow-2.19.0-cp311-cp311-win_amd64.whl (375.9 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 375.9/375.9 MB 6.9 MB/s eta 0:00:00
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Requirement already satisfied: setuptools in j:\prog\python\tf_env\lib\site-packages (from tensorflow) (65.5.0)
Collecting six>=1.12.0Using cached https://pypi.tuna.tsinghua.edu.cn/packages/b7/ce/149a00dd41f10bc29e5921b496af8b574d8413afcd5e30dfa0ed46c2cc5e/six-1.17.0-py2.py3-none-any.whl (11 kB)
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Collecting tensorflow-io-gcs-filesystem>=0.23.1Downloading https://pypi.tuna.tsinghua.edu.cn/packages/ac/4e/9566a313927be582ca99455a9523a097c7888fc819695bdc08415432b202/tensorflow_io_gcs_filesystem-0.31.0-cp311-cp311-win_amd64.whl (1.5 MB)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.5/1.5 MB 47.6 MB/s eta 0:00:00
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Collecting mdurl~=0.1Downloading https://pypi.tuna.tsinghua.edu.cn/packages/b3/38/89ba8ad64ae25be8de66a6d463314cf1eb366222074cfda9ee839c56a4b4/mdurl-0.1.2-py3-none-any.whl (10.0 kB)
Installing collected packages: namex, libclang, flatbuffers, wrapt, wheel, urllib3, typing-extensions, termcolor, tensorflow-io-gcs-filesystem, tensorboard-data-server, six, pygments, protobuf, packaging, opt-einsum, numpy, mdurl, MarkupSafe, markdown, idna, grpcio, gast, charset_normalizer, certifi, absl-py, werkzeug, requests, optree, ml-dtypes, markdown-it-py, h5py, google-pasta, astunparse, tensorboard, rich, keras, tensorflow
Successfully installed MarkupSafe-3.0.2 absl-py-2.3.1 astunparse-1.6.3 certifi-2025.6.15 charset_normalizer-3.4.2 flatbuffers-25.2.10 gast-0.6.0 google-pasta-0.2.0 grpcio-1.73.1 h5py-3.14.0 idna-3.10 keras-3.10.0 libclang-18.1.1 markdown-3.8.2 markdown-it-py-3.0.0 mdurl-0.1.2 ml-dtypes-0.5.1 namex-0.1.0 numpy-2.1.3 opt-einsum-3.4.0 optree-0.16.0 packaging-25.0 protobuf-5.29.5 pygments-2.19.2 requests-2.32.4 rich-14.0.0 six-1.17.0 tensorboard-2.19.0 tensorboard-data-server-0.7.2 tensorflow-2.19.0 tensorflow-io-gcs-filesystem-0.31.0 termcolor-3.1.0 typing-extensions-4.14.1 urllib3-2.5.0 werkzeug-3.1.3 wheel-0.45.1 wrapt-1.17.2[notice] A new release of pip available: 22.3 -> 25.1.1
[notice] To update, run: python.exe -m pip install --upgrade pip
(tf_env) PS J:\Prog\python\tf_env\Scripts> python.exe -m pip install --upgrade pip
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Requirement already satisfied: pip in j:\prog\python\tf_env\lib\site-packages (22.3)
Collecting pipUsing cached https://pypi.tuna.tsinghua.edu.cn/packages/29/a2/d40fb2460e883eca5199c62cfc2463fd261f760556ae6290f88488c362c0/pip-25.1.1-py3-none-any.whl (1.8 MB)
Installing collected packages: pipAttempting uninstall: pipFound existing installation: pip 22.3Uninstalling pip-22.3:Successfully uninstalled pip-22.3
Successfully installed pip-25.1.1
(tf_env) PS J:\Prog\python\tf_env\Scripts> python -c "import tensorflow as tf; print(f'TensorFlow版本: {tf.__version__}')"
2025-07-06 17:03:37.749722: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2025-07-06 17:03:40.298292: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
TensorFlow版本: 2.19.0

在这里插入图片描述

进一步验证 GPU 支持

python -c “import tensorflow as tf; print(‘GPU 可用:’, ‘是’ if tf.config.list_physical_devices(‘GPU’) else ‘否’)”
在这里插入图片描述
运行简单测试，在(tf_env) PS J:\Prog\python\tf_env\Scripts> 复制上去就可以了

# 创建正确的测试脚本
@"
import tensorflow as tfprint(f"TensorFlow 版本: {tf.__version__}")
print(f"GPU 设备: {tf.config.list_physical_devices('GPU')}")# 简单计算
a = tf.constant([1, 2, 3])
b = tf.constant([4, 5, 6])
c = tf.add(a, b)print(f"加法结果: {c.numpy()}")
"@ | Set-Content -Path tf_test.py -Encoding UTF8# 运行测试
python tf_test.py

在这里插入图片描述

测试TensorFlow是否正常

python -c "import tensorflow as tf; print('TensorFlow 工作正常! 版本:', tf.__version__)"

(tf_env) PS J:\Prog\python\tf_env\Scripts> python -c “import tensorflow as tf; print(‘TensorFlow 工作正常! 版本:’, tf.version)”
2025-07-06 17:17:22.539651: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.
2025-07-06 17:17:23.361411: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable TF_ENABLE_ONEDNN_OPTS=0.
TensorFlow 工作正常! 版本: 2.19.0

7. 后续步骤建议

安装常用配套库：

powershell

pip install numpy pandas matplotlib scikit-learn -i https://pypi.tuna.tsinghua.edu.cn/simple

保存环境配置：

powershell

pip freeze > requirements.txt

我们已经成功安装了TensorFlow，并且当前在虚拟环境tf_env中。现在执行pip freeze > requirements.txt命令的目的是将当前虚拟环境中安装的所有Python包及其精确版本号导出到一个名为requirements.txt的文件中。这样做的目的是为了以后可以轻松地重建相同的环境（例如，在另一台机器上或重新安装时）。
在 J:\Prog\python\tf_env\Scripts 目录下生成 requirements.txt 文件

//requirements.txt
absl-py==2.3.1
astunparse==1.6.3
certifi==2025.6.15
charset-normalizer==3.4.2
contourpy==1.3.2
cycler==0.12.1
flatbuffers==25.2.10
fonttools==4.58.5
gast==0.6.0
google-pasta==0.2.0
grpcio==1.73.1
h5py==3.14.0
idna==3.10
joblib==1.5.1
keras==3.10.0
kiwisolver==1.4.8
libclang==18.1.1
Markdown==3.8.2
markdown-it-py==3.0.0
MarkupSafe==3.0.2
matplotlib==3.10.3
mdurl==0.1.2
ml_dtypes==0.5.1
namex==0.1.0
numpy==2.1.3
opt_einsum==3.4.0
optree==0.16.0
packaging==25.0
pandas==2.3.0
pillow==11.3.0
protobuf==5.29.5
Pygments==2.19.2
pyparsing==3.2.3
python-dateutil==2.9.0.post0
pytz==2025.2
requests==2.32.4
rich==14.0.0
scikit-learn==1.7.0
scipy==1.16.0
six==1.17.0
tensorboard==2.19.0
tensorboard-data-server==0.7.2
tensorflow==2.19.0
tensorflow-io-gcs-filesystem==0.31.0
termcolor==3.1.0
threadpoolctl==3.6.0
typing_extensions==4.14.1
tzdata==2025.2
urllib3==2.5.0
Werkzeug==3.1.3
wrapt==1.17.2