conda install vs pip install@requirements.txt@依赖导出与安装@conda环境中的包的信息查询

文章目录

conda install vs pip install

conda install 和 pip install 都是常用的 Python 包管理工具，它们在包安装方面有一些区别。
1. 安装来源： conda install 是 Anaconda 发行版自带的包管理工具，而 pip install 则是 Python 官方推荐的包管理工具。
2. 包管理方式： conda install 会同时安装该包所依赖的所有其他包，以确保整个环境的兼容性和稳定性。这意味着 conda 安装的包会被放置在其独立的环境中，与系统环境隔离开来，因此可以在同一台机器上同时安装多个不同版本的 Python 及其相关库。
相比之下，pip install 只会安装指定的包，而不会检查该包所依赖的其他包是否已经安装，也不能保证该包与其他包的兼容性。这可能会导致包之间发生冲突和不兼容性问题。如果使用 pip 进行包管理，建议在 virtualenv 或者虚拟环境下进行安装，避免不同包之间的冲突。
1. 跨平台支持： conda 安装器支持跨平台操作系统及多种语言环境，如 Windows、Linux 和 macOS 等。 pip 安装器也能在大部分操作系统上运行，但某些包可能无法完美地支持某些平台或 Python 版本。
2. 社区支持： conda 的社区庞大，提供了许多优秀的数据科学、机器学习和人工智能相关的包。pip 的社区也很活跃，提供了更广泛的 Python 库和应用程序。
总体来说，conda 更适合于数据科学、机器学习和人工智能等领域的开发和部署，并且可以提供更好的环境管理和跨平台支持。pip 则更适合于一般 Python 开发和轻量级应用程序的快速部署。

refs

python - Difference between conda and pip installs within a conda environment - Stack Overflow
Using pip in an environment
在conda environment中有些包既可以用conda install 安装,也可以用pip install安装
对比:
- conda install 可以分析处理依赖关系
- pip install 的包可能更多
- 通常,如果conda install 可以安装的话,优先使用conda,
- 否则再使用pip install 尝试(特别时一些冷门的包)
事实上,conda最主要的作用是用来隔离环境的,有不少人只用conda创建隔离环境,而按照package的时候总是使用pip安装,例如tensorflow官方强烈建议使用pip安装

conda install vs pip install

conda install可以安装任何语言的软件包，而pip install只能安装Python的软件包。
conda install可以在conda环境中安装任何软件包，而pip install可以在任何环境中安装Python的软件包。
conda install可以更好地管理依赖关系，避免软件包之间的冲突，而pip install可能会导致不兼容的问题。
conda install可以避免一些包的重复下载,利用硬链接节约磁盘

缓存加速

conda 和 pip 都具有缓存功能，可以提高包下载的速度和效率。
对于 conda，它会将已经下载过的软件包保存在本地缓存中（默认位置是 ~/.conda/pkgs），并在下次需要时自动使用缓存来加快下载速度。如果您希望清除 conda 的缓存，可以使用 conda clean 命令来删除不需要的软件包和缓存文件。例如，要删除所有未安装的软件包和已过期的缓存文件，可以运行以下命令：
- ```
 conda clean -a
```
对于 pip，它也会在本地缓存中保存已下载的软件包（默认位置是 ~/.cache/pip）。如果您需要清除 pip 的缓存，可以使用以下命令：
- ```
 pip cache purge
```
- 此命令将清除所有缓存文件，但不会删除已安装的软件包。
总的来说，缓存功能可以有效地提高包下载的速度和效率，但在开发环境中可能会导致一些问题，如更新软件包后无法立即看到更改等。因此，在开发过程中最好关闭缓存或定期清理缓存。
conda 缓存:再安装本地已经安装过的包时,可以看到下一次安装相同版本的包的下载量会大大减少,甚至为0

pip 缓存:会提示使用本地缓存(Using cached...):

下面是我在另一个环境中用pip再次安装时的效果(只需要再下载少数内容)

 (d:\condaPythonEnvs\tf2.11) PS C:\Users\cxxu\Desktop> pip install tensorflow
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting tensorflow
  Using cached https://pypi.tuna.tsinghua.edu.cn/packages/60/e7/0d6d7c7c3f15cc8dc0dd60989ab79deb1018c321e0bed4b243658df55770/tensorflow-2.11.0-cp39-cp39-win_amd64.whl (1.9 kB)
Collecting tensorflow-intel==2.11.0
  Using cached https://pypi.tuna.tsinghua.edu.cn/packages/79/a2/1ac02609a281fddaffe607d02779b5bd859ec194578c2190e3e0aac4e5c6/tensorflow_intel-2.11.0-cp39-cp39-win_amd64.whl (266.3 MB)
Collecting tensorflow-io-gcs-filesystem>=0.23.1
  Using cached https://pypi.tuna.tsinghua.edu.cn/packages/7f/a7/5cf33981539f8bb8d50e5743d82435e09b387583f48ca40c211a9bf3ea3c/tensorflow_io_gcs_filesystem-0.31.0-cp39-cp39-win_amd64.whl (1.5 MB)

python 版本和加速效果

环境pt2.0:python3.10+pytorch2.0,

 (d:\condaPythonEnvs\pt2.0) PS C:\Users\cxxu\.conda> conda list pytorch
# packages in environment at d:\condaPythonEnvs\pt2.0:
#
# Name                    Version                   Build  Channel
pytorch                   2.0.0           py3.10_cuda11.7_cudnn8_0    pytorch
pytorch-cuda              11.7                 h16d0643_3    pytorch
pytorch-mutex             1.0                        cuda    pytorch

环境pt_d2l:python3.9

在python3.9的情况下,我打算再安装一个pytorch2.0,我本以为另一个环境之前下载安装过了,应该不需要再下载了,但是意外的需要再下载
于是我查询pt2.0环境中的pytorch2.0,仔细对比,发现由于python版本不一样,他们的build版本号是有差异的
- 分别是py3.10_cuda11.7_cudnn8_0和将要下载的py3.9_cuda11.7_cudnn8_0

 (d:\condaPythonEnvs\pt_d2l) PS C:\Users\cxxu\Desktop> conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
Collecting package metadata (current_repodata.json): done
Solving environment: failed with initial frozen solve. Retrying with flexible solve.
Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
Collecting package metadata (repodata.json): done
Solving environment: done
 
## Package Plan ##
 
  environment location: d:\condaPythonEnvs\pt_d2l
 
  added / updated specs:
    - pytorch
    - pytorch-cuda=11.7
    - torchaudio
    - torchvision
 
 
The following packages will be downloaded:
 
    package                    |            build
    ---------------------------|-----------------
    filelock-3.9.0             |   py39haa95532_0          19 KB  defaults
    flit-core-3.8.0            |   py39haa95532_0          85 KB  defaults
    mpmath-1.2.1               |   py39haa95532_0         773 KB  defaults
    networkx-2.8.4             |   py39haa95532_1         2.6 MB  defaults
    pytorch-2.0.0              |py3.9_cuda11.7_cudnn8_0        1.17 GB  pytorch
    sympy-1.11.1               |   py39haa95532_0        11.7 MB  defaults
    torchaudio-2.0.0           |       py39_cu117         5.7 MB  pytorch
    torchvision-0.15.0         |       py39_cu117         7.7 MB  pytorch
    ------------------------------------------------------------
                                           Total:        1.20 GB

加速小结

只有python包的本身的版本version和build version均一致的时候,才可以起到加速的效果
否则就需要重新下载

conda clean@缓存清理

 PS C:\Users\cxxu\Desktop> conda clean -h
usage: conda-script.py clean [-h] [-a] [-i] [-p] [-t] [-f] [-c [TEMPFILES ...]] [-l] [-d] [--json] [-q] [-v] [-y]
 
Remove unused packages and caches.
 
Options:
 
optional arguments:
  -h, --help            Show this help message and exit.
 
Removal Targets:
  -a, --all             Remove index cache, lock files, unused cache packages, tarballs, and logfiles.
  -i, --index-cache     Remove index cache.
  -p, --packages        Remove unused packages from writable package caches. WARNING: This does not check for packages installed using symlinks back to the package cache.
  -t, --tarballs        Remove cached package tarballs.
  -f, --force-pkgs-dirs
                        Remove *all* writable package caches. This option is not included with the --all flag. WARNING: This will break environments with packages installed using
                        symlinks back to the package cache.
  -c [TEMPFILES ...], --tempfiles [TEMPFILES ...]
                        Remove temporary files that could not be deleted earlier due to being in-use. The argument for the --tempfiles flag is a path (or list of paths) to the
                        environment(s) where the tempfiles should be found and removed.
  -l, --logfiles        Remove log files.
 
Output, Prompt, and Flow Control Options:
  -d, --dry-run         Only display what would have been done.
  --json                Report all output as json. Suitable for using conda programmatically.
  -q, --quiet           Do not display progress bar.
  -v, --verbose         Can be used multiple times. Once for INFO, twice for DEBUG, three times for TRACE.
  -y, --yes             Sets any confirmation values to 'yes' automatically. Users will not be asked to confirm any adding, deleting, backups, etc.
 
Examples::
 
    conda clean --tarballs

常用的两个参数

   -a, --all             Remove index cache, lock files, unused cache packages, tarballs, and logfiles.
  -i, --index-cache     Remove index cache.(更新Channel源时使用)

从依赖列表中安装

How to install packages from Requirement.txt in python using anaconda? - Stack Overflow

pip 导出依赖

python - In requirements.txt, what does tilde equals (~=) mean? - Stack Overflow

 PS D:\repos\blogs> pip freeze -h
 
Usage:
  pip freeze [options]
 
Description:
  Output installed packages in requirements format.
 
  packages are listed in a case-insensitive sorted order.

查看conda环境中安装的python包详情

可以用conda list <pkgName>查看基本信息

用pip show <pkgName>查看更多信息

包括summary,Location,requires(依赖那些包),required-by(被哪些包依赖)
例如查看tensorflow的信息

 (d:\condaPythonEnvs\tf2.5) PS D:\repos\CCSER\emotion-recognition-using-speech> pip show tensorflow
Name: tensorflow
Version: 2.10.0
Summary: TensorFlow is an open source machine learning framework for everyone.
Home-page: https://www.tensorflow.org/
Author: Google Inc.
Author-email: packages@tensorflow.org
License: Apache 2.0
Location: d:\condapythonenvs\tf2.5\lib\site-packages
Requires: absl-py, astunparse, flatbuffers, gast, google-pasta, grpcio, h5py, keras, keras-preprocessing, libclang, numpy, opt-einsum, packaging, protobuf, setuptools, six, tensorboard, tensorflow-estimator, tensorflow-io-gcs-filesystem, termcolor, typing-extensions, wrapt
Required-by:
(d:\condaPythonEnvs\tf2.5) PS D:\repos\CCSER\emotion-recognition-using-speech>

 (d:\condaPythonEnvs\tf2.5) PS D:\repos\CCSER\emotion-recognition-using-speech> pip show numpy
Name: numpy
Version: 1.21.5
Summary: NumPy is the fundamental package for array computing with Python.
Home-page: https://www.numpy.org
Author: Travis E. Oliphant et al.
Author-email:
License: BSD
Location: d:\condapythonenvs\tf2.5\lib\site-packages
Requires:
Required-by: Bottleneck, h5py, Keras-Preprocessing, librosa, matplotlib, mkl-fft, mkl-random, numba, numexpr, opt-einsum, pandas, resampy, scikit-learn, scipy, tensorboard, tensorflow

conda info

Display information about current conda install.(该命令用来查询当前安装的conda软件信息,而不是用来查询conda环境安装的package)

conda导出依赖

python - From conda create requirements.txt for pip3 - Stack Overflow

conda export

输出预览:

 (d:\condaPythonEnvs\tf2.10) PS D:\repos\CCSER\SER> conda env export 
name: tf2.10
channels:
  - conda-forge
  - defaults
dependencies:
  - _tflow_select=2.1.0=gpu
  - abseil-cpp=20210324.2=hd77b12b_0
  - absl-py=1.3.0=py39haa95532_0
...(省略篇幅)
  - flit-core=3.6.0=pyhd3eb1b0_0  - yarl=1.8.1=py39h2bbff1b_0
  - zeromq=4.3.4=hd77b12b_0
  - zipp=3.11.0=py39haa95532_0
  - zlib=1.2.13=h8cc25b3_0
  - zstd=1.5.0=h6255e5f_0
  - pip:
      - keras==2.10.0
      - libclang==15.0.6.1
      - pyside6==6.4.2
      - pyside6-addons==6.4.2
      - pyside6-essentials==6.4.2
      - shiboken6==6.4.2
      - soundfile==0.9.0
      - tensorboard==2.10.1
      - tensorflow==2.10.0
      - tensorflow-estimator==2.10.0
      - tensorflow-io-gcs-filesystem==0.31.0
prefix: d:\condaPythonEnvs\tf2.10

导出到文件:(文件名无所谓,通常为了和pip freeze导出环境相区别,我们使用environment.yml命名)
- conda env export --file environment.yml
  - ```
   (d:\condaPythonEnvs\tf2.10) PS D:\repos\CCSER\SER> conda env export --file environment.yml
```
- 或conda env export > environment.yml

pip freeze

在conda中依然可以用pip freeze 来导出依赖

但是这可能不全,因为某些用conda install的包pip无法扫描到

 (base) PS D:\repos\blogs> cat .\requirements.txt
anyio==3.6.2
argon2-cffi==21.3.0
argon2-cffi-bindings==21.2.0
arrow==1.2.3
asttokens==2.2.1
attrs==22.2.0
backcall==0.2.0
beautifulsoup4==4.11.1
bleach==5.0.1
Bottleneck @ file:///C:/Windows/Temp/abs_3198ca53-903d-42fd-87b4-03e6d03a8381yfwsuve8/croots/recipe/bottleneck_1657175565403/work
brotlipy==0.7.0
certifi @ file:///C:/b/abs_85o_6fm0se/croot/certifi_1671487778835/work/certifi
cffi @ file:///C:/b/abs_49n3v2hyhr/croot/cffi_1670423218144/work

还可以考虑借助脚本语言处理pip list或conda list的输出重定向到文件来导出

conda list

 (d:\condaPythonEnvs\tf2.5) PS D:\repos\CCSER\emotion-recognition-using-speech> conda list -h
usage: conda-script.py list [-h] [-n ENVIRONMENT | -p PATH] [--json] [-v] [-q] [--show-channel-urls] [-c]
                            [-f] [--explicit] [--md5] [-e] [-r] [--no-pip]
                            [regex]
 
List installed packages in a conda environment.
 
Options:
 
positional arguments:
  regex                 List only packages matching this regular expression.
 
optional arguments:
  -h, --help            Show this help message and exit.
  --show-channel-urls   Show channel urls. Overrides the value given by `conda config --show
                        show_channel_urls`.
  -c, --canonical       Output canonical names of packages only.
  -f, --full-name       Only search for full names, i.e., ^<regex>$. --full-name NAME is identical to regex
                        '^NAME$'.
  --explicit            List explicitly all installed conda packages with URL (output may be used by conda
                        create --file).
  --md5                 Add MD5 hashsum when using --explicit.
  -e, --export          Output explicit, machine-readable requirement strings instead of human-readable
                        lists of packages. This output may be used by conda create --file.
  -r, --revisions       List the revision history.
  --no-pip              Do not include pip-only installed packages.
 
Target Environment Specification:
  -n ENVIRONMENT, --name ENVIRONMENT
                        Name of environment.
  -p PATH, --prefix PATH
                        Full path to environment location (i.e. prefix).
 
Output, Prompt, and Flow Control Options:
  --json                Report all output as json. Suitable for using conda programmatically.
  -v, --verbose         Use once for info, twice for debug, three times for trace.
  -q, --quiet           Do not display progress bar.

 Examples:
 
List all packages in the current environment::
 
    conda list
 
List all packages installed into the environment 'myenv'::
 
    conda list -n myenv
 
List all packages that begin with the letters "py", using regex::
 
    conda list ^py
 
Save packages for future use::
 
    conda list --export > package-list.txt
 
Reinstall packages from an export file::
 
    conda create -n myenv --file package-list.txt

demos@conda list --export

 (d:\condaPythonEnvs\tf2.5) PS D:\repos\CCSER\emotion-recognition-using-speech> conda list --export
# This file may be used to create an environment using:
# $ conda create --name <env> --file <this file>
# platform: win-64
_tflow_select=2.2.0=eigen
absl-py=1.3.0=py37haa95532_0
aiohttp=3.8.3=py37h2bbff1b_0
aiosignal=1.2.0=pyhd3eb1b0_0
anyio=3.5.0=py37haa95532_0
argon2-cffi=21.3.0=pyhd3eb1b0_0
argon2-cffi-bindings=21.2.0=py37h2bbff1b_0
astunparse=1.6.3=py_0
async-timeout=4.0.2=py37haa95532_0
asynctest=0.13.0=py_0

conda 安装 requirement.txt

Conda Install Requirements (linuxhint.com)
conda install --file .\requirements.txt

可能遇到的情况:

conda 无法提供requirements.txt中指定的包,此时会提示哪些包是缺失的

 (d:\condaPythonEnvs\keras2.8) PS D:\repos\CCSER\ser_cnn_svm_mlp> conda install --file .\requirements.txt                                                             
Collecting package metadata (current_repodata.json): done
...
PackagesNotFoundError: The following packages are not available from current channels:
 
  - tensorflow==2.8.0
  - scipy==1.8.0
  - librosa==0.9.1

您可以注释掉requirements.txt中相应的行
然后使用pip安装这些被注释的行(可以手动,如果较多,也可以复制conda 的提示,写入到一个另一个requirements_pip.txt)中,然后用pip install -r requirements_pip.txt进行安装

posted @ 2023-03-15 17:04 xuchaoxin1375 阅读(32) 评论(0) 编辑收藏举报来源

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conda install vs pip install@requirements.txt@依赖导出与安装@conda环境中的包的信息查询

文章目录

conda install vs pip install

refs

conda install vs pip install

缓存加速

python 版本和加速效果

加速小结

conda clean@缓存清理

从依赖列表中安装

pip 导出依赖

查看conda环境中安装的python包详情

conda info

conda导出依赖

conda export

pip freeze

conda list

demos@conda list --export

conda 安装 requirement.txt

公告

搜索

常用链接

随笔档案

阅读排行榜

推荐排行榜

	(d:\condaPythonEnvs\tf2.11) PS C:\Users\cxxu\Desktop> pip install tensorflow
	Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
	Collecting tensorflow
	Using cached https://pypi.tuna.tsinghua.edu.cn/packages/60/e7/0d6d7c7c3f15cc8dc0dd60989ab79deb1018c321e0bed4b243658df55770/tensorflow-2.11.0-cp39-cp39-win_amd64.whl (1.9 kB)
	Collecting tensorflow-intel==2.11.0
	Using cached https://pypi.tuna.tsinghua.edu.cn/packages/79/a2/1ac02609a281fddaffe607d02779b5bd859ec194578c2190e3e0aac4e5c6/tensorflow_intel-2.11.0-cp39-cp39-win_amd64.whl (266.3 MB)
	Collecting tensorflow-io-gcs-filesystem>=0.23.1
	Using cached https://pypi.tuna.tsinghua.edu.cn/packages/7f/a7/5cf33981539f8bb8d50e5743d82435e09b387583f48ca40c211a9bf3ea3c/tensorflow_io_gcs_filesystem-0.31.0-cp39-cp39-win_amd64.whl (1.5 MB)

	(d:\condaPythonEnvs\pt2.0) PS C:\Users\cxxu\.conda> conda list pytorch
	# packages in environment at d:\condaPythonEnvs\pt2.0:
	#
	# Name Version Build Channel
	pytorch 2.0.0 py3.10_cuda11.7_cudnn8_0 pytorch
	pytorch-cuda 11.7 h16d0643_3 pytorch
	pytorch-mutex 1.0 cuda pytorch

	(d:\condaPythonEnvs\pt_d2l) PS C:\Users\cxxu\Desktop> conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
	Collecting package metadata (current_repodata.json): done
	Solving environment: failed with initial frozen solve. Retrying with flexible solve.
	Solving environment: failed with repodata from current_repodata.json, will retry with next repodata source.
	Collecting package metadata (repodata.json): done
	Solving environment: done

	## Package Plan ##

	environment location: d:\condaPythonEnvs\pt_d2l

	added / updated specs:
	- pytorch
	- pytorch-cuda=11.7
	- torchaudio
	- torchvision


	The following packages will be downloaded:

	package \| build
	---------------------------\|-----------------
	filelock-3.9.0 \| py39haa95532_0 19 KB defaults
	flit-core-3.8.0 \| py39haa95532_0 85 KB defaults
	mpmath-1.2.1 \| py39haa95532_0 773 KB defaults
	networkx-2.8.4 \| py39haa95532_1 2.6 MB defaults
	pytorch-2.0.0 \|py3.9_cuda11.7_cudnn8_0 1.17 GB pytorch
	sympy-1.11.1 \| py39haa95532_0 11.7 MB defaults
	torchaudio-2.0.0 \| py39_cu117 5.7 MB pytorch
	torchvision-0.15.0 \| py39_cu117 7.7 MB pytorch
	------------------------------------------------------------
	Total: 1.20 GB

	PS C:\Users\cxxu\Desktop> conda clean -h
	usage: conda-script.py clean [-h] [-a] [-i] [-p] [-t] [-f] [-c [TEMPFILES ...]] [-l] [-d] [--json] [-q] [-v] [-y]

	Remove unused packages and caches.

	Options:

	optional arguments:
	-h, --help Show this help message and exit.

	Removal Targets:
	-a, --all Remove index cache, lock files, unused cache packages, tarballs, and logfiles.
	-i, --index-cache Remove index cache.
	-p, --packages Remove unused packages from writable package caches. WARNING: This does not check for packages installed using symlinks back to the package cache.
	-t, --tarballs Remove cached package tarballs.
	-f, --force-pkgs-dirs
	Remove all writable package caches. This option is not included with the --all flag. WARNING: This will break environments with packages installed using
	symlinks back to the package cache.
	-c [TEMPFILES ...], --tempfiles [TEMPFILES ...]
	Remove temporary files that could not be deleted earlier due to being in-use. The argument for the --tempfiles flag is a path (or list of paths) to the
	environment(s) where the tempfiles should be found and removed.
	-l, --logfiles Remove log files.

	Output, Prompt, and Flow Control Options:
	-d, --dry-run Only display what would have been done.
	--json Report all output as json. Suitable for using conda programmatically.
	-q, --quiet Do not display progress bar.
	-v, --verbose Can be used multiple times. Once for INFO, twice for DEBUG, three times for TRACE.
	-y, --yes Sets any confirmation values to 'yes' automatically. Users will not be asked to confirm any adding, deleting, backups, etc.

	Examples::

	conda clean --tarballs

	-a, --all Remove index cache, lock files, unused cache packages, tarballs, and logfiles.
	-i, --index-cache Remove index cache.(更新Channel源时使用)

	PS D:\repos\blogs> pip freeze -h

	Usage:
	pip freeze [options]

	Description:
	Output installed packages in requirements format.

	packages are listed in a case-insensitive sorted order.

	(d:\condaPythonEnvs\tf2.5) PS D:\repos\CCSER\emotion-recognition-using-speech> pip show tensorflow
	Name: tensorflow
	Version: 2.10.0
	Summary: TensorFlow is an open source machine learning framework for everyone.
	Home-page: https://www.tensorflow.org/
	Author: Google Inc.
	Author-email: packages@tensorflow.org
	License: Apache 2.0
	Location: d:\condapythonenvs\tf2.5\lib\site-packages
	Requires: absl-py, astunparse, flatbuffers, gast, google-pasta, grpcio, h5py, keras, keras-preprocessing, libclang, numpy, opt-einsum, packaging, protobuf, setuptools, six, tensorboard, tensorflow-estimator, tensorflow-io-gcs-filesystem, termcolor, typing-extensions, wrapt
	Required-by:
	(d:\condaPythonEnvs\tf2.5) PS D:\repos\CCSER\emotion-recognition-using-speech>

	(d:\condaPythonEnvs\tf2.5) PS D:\repos\CCSER\emotion-recognition-using-speech> pip show numpy
	Name: numpy
	Version: 1.21.5
	Summary: NumPy is the fundamental package for array computing with Python.
	Home-page: https://www.numpy.org
	Author: Travis E. Oliphant et al.
	Author-email:
	License: BSD
	Location: d:\condapythonenvs\tf2.5\lib\site-packages
	Requires:
	Required-by: Bottleneck, h5py, Keras-Preprocessing, librosa, matplotlib, mkl-fft, mkl-random, numba, numexpr, opt-einsum, pandas, resampy, scikit-learn, scipy, tensorboard, tensorflow

	(d:\condaPythonEnvs\tf2.10) PS D:\repos\CCSER\SER> conda env export
	name: tf2.10
	channels:
	- conda-forge
	- defaults
	dependencies:
	- _tflow_select=2.1.0=gpu
	- abseil-cpp=20210324.2=hd77b12b_0
	- absl-py=1.3.0=py39haa95532_0
	...(省略篇幅)
	- flit-core=3.6.0=pyhd3eb1b0_0 - yarl=1.8.1=py39h2bbff1b_0
	- zeromq=4.3.4=hd77b12b_0
	- zipp=3.11.0=py39haa95532_0
	- zlib=1.2.13=h8cc25b3_0
	- zstd=1.5.0=h6255e5f_0
	- pip:
	- keras==2.10.0
	- libclang==15.0.6.1
	- pyside6==6.4.2
	- pyside6-addons==6.4.2
	- pyside6-essentials==6.4.2
	- shiboken6==6.4.2
	- soundfile==0.9.0
	- tensorboard==2.10.1
	- tensorflow==2.10.0
	- tensorflow-estimator==2.10.0
	- tensorflow-io-gcs-filesystem==0.31.0
	prefix: d:\condaPythonEnvs\tf2.10

	(base) PS D:\repos\blogs> cat .\requirements.txt
	anyio==3.6.2
	argon2-cffi==21.3.0
	argon2-cffi-bindings==21.2.0
	arrow==1.2.3
	asttokens==2.2.1
	attrs==22.2.0
	backcall==0.2.0
	beautifulsoup4==4.11.1
	bleach==5.0.1
	Bottleneck @ file:///C:/Windows/Temp/abs_3198ca53-903d-42fd-87b4-03e6d03a8381yfwsuve8/croots/recipe/bottleneck_1657175565403/work
	brotlipy==0.7.0
	certifi @ file:///C:/b/abs_85o_6fm0se/croot/certifi_1671487778835/work/certifi
	cffi @ file:///C:/b/abs_49n3v2hyhr/croot/cffi_1670423218144/work

	(d:\condaPythonEnvs\tf2.5) PS D:\repos\CCSER\emotion-recognition-using-speech> conda list -h
	usage: conda-script.py list [-h] [-n ENVIRONMENT \| -p PATH] [--json] [-v] [-q] [--show-channel-urls] [-c]
	[-f] [--explicit] [--md5] [-e] [-r] [--no-pip]
	[regex]

	List installed packages in a conda environment.

	Options:

	positional arguments:
	regex List only packages matching this regular expression.

	optional arguments:
	-h, --help Show this help message and exit.
	--show-channel-urls Show channel urls. Overrides the value given by `conda config --show
	show_channel_urls`.
	-c, --canonical Output canonical names of packages only.
	-f, --full-name Only search for full names, i.e., ^<regex>$. --full-name NAME is identical to regex
	'^NAME$'.
	--explicit List explicitly all installed conda packages with URL (output may be used by conda
	create --file).
	--md5 Add MD5 hashsum when using --explicit.
	-e, --export Output explicit, machine-readable requirement strings instead of human-readable
	lists of packages. This output may be used by conda create --file.
	-r, --revisions List the revision history.
	--no-pip Do not include pip-only installed packages.

	Target Environment Specification:
	-n ENVIRONMENT, --name ENVIRONMENT
	Name of environment.
	-p PATH, --prefix PATH
	Full path to environment location (i.e. prefix).

	Output, Prompt, and Flow Control Options:
	--json Report all output as json. Suitable for using conda programmatically.
	-v, --verbose Use once for info, twice for debug, three times for trace.
	-q, --quiet Do not display progress bar.

	Examples:

	List all packages in the current environment::

	conda list

	List all packages installed into the environment 'myenv'::

	conda list -n myenv

	List all packages that begin with the letters "py", using regex::

	conda list ^py

	Save packages for future use::

	conda list --export > package-list.txt

	Reinstall packages from an export file::

	conda create -n myenv --file package-list.txt

	(d:\condaPythonEnvs\tf2.5) PS D:\repos\CCSER\emotion-recognition-using-speech> conda list --export
	# This file may be used to create an environment using:
	# $ conda create --name <env> --file <this file>
	# platform: win-64
	_tflow_select=2.2.0=eigen
	absl-py=1.3.0=py37haa95532_0
	aiohttp=3.8.3=py37h2bbff1b_0
	aiosignal=1.2.0=pyhd3eb1b0_0
	anyio=3.5.0=py37haa95532_0
	argon2-cffi=21.3.0=pyhd3eb1b0_0
	argon2-cffi-bindings=21.2.0=py37h2bbff1b_0
	astunparse=1.6.3=py_0
	async-timeout=4.0.2=py37haa95532_0
	asynctest=0.13.0=py_0

	(d:\condaPythonEnvs\keras2.8) PS D:\repos\CCSER\ser_cnn_svm_mlp> conda install --file .\requirements.txt
	Collecting package metadata (current_repodata.json): done
	...
	PackagesNotFoundError: The following packages are not available from current channels:

	- tensorflow==2.8.0
	- scipy==1.8.0
	- librosa==0.9.1