本文介绍: 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。在conda中依然可以用pip freeze 来导出依赖。中指定的包,此时会提示哪些包是缺失的。还可以考虑借助脚本语言处理。的输出重定向到文件来导出。conda 无法提供。
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。
- 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。
- 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:
- 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
-
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： -
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。
-
pip 缓存:会提示使用本地缓存(
Using cached...):-
(d:condaPythonEnvstf2.11) PS C:UserscxxuDesktop> 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:condaPythonEnvspt2.0) PS C:Userscxxu.conda> conda list pytorch # packages in environment at d:condaPythonEnvspt2.0: # # 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
(d:condaPythonEnvspt_d2l) PS C:UserscxxuDesktop> 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:condaPythonEnvspt_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
conda clean@缓存清理
-
PS C:UserscxxuDesktop> 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)
从依赖列表中安装
pip 导出依赖
-
python – In requirements.txt, what does tilde equals (~=) mean? – Stack Overflow
-
PS D:reposblogs> 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
-
-
(d:condaPythonEnvstf2.5) PS D:reposCCSERemotion-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:condapythonenvstf2.5libsite-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:condaPythonEnvstf2.5) PS D:reposCCSERemotion-recognition-using-speech> -
(d:condaPythonEnvstf2.5) PS D:reposCCSERemotion-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:condapythonenvstf2.5libsite-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
conda导出依赖
conda export
-
-
(d:condaPythonEnvstf2.10) PS D:reposCCSERSER> 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:condaPythonEnvstf2.10
-
pip freeze
-
在conda中依然可以用pip freeze 来导出依赖
-
(base) PS D:reposblogs> 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
conda list
-
(d:condaPythonEnvstf2.5) PS D:reposCCSERemotion-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:condaPythonEnvstf2.5) PS D:reposCCSERemotion-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 --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:condaPythonEnvskeras2.8) PS D:reposCCSERser_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 -
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
-
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原文地址:https://blog.csdn.net/xuchaoxin1375/article/details/129561038
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