cp37-abi3-manylinux_2_26_armv7l cp37-abi3-manylinux_2_25_armv7l cp37-abi3-manylinux_2_24_armv7l...cp37-abi3-manylinux_2_23_armv7l cp37-abi3-manylinux_2_22_armv7l cp37-abi3-manylinux_2_21_armv7l...cp37-abi3-manylinux_2_20_armv7l cp37-abi3-manylinux_2_19_armv7l cp37-abi3-manylinux_2_18_armv7l...cp36-abi3-manylinux_2_26_armv7l cp36-abi3-manylinux_2_25_armv7l cp36-abi3-manylinux_2_24_armv7l...-none-manylinux_2_21_armv7l py3-none-manylinux_2_20_armv7l py3-none-manylinux_2_19_armv7l py3-none-manylinux
tensorflow模块: 版本名称 tensorflow-2.7.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl tensorflow...-2.7.0-cp310-cp310-linux_aarch64.whl tensorflow-2.7.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_....manylinux2014_aarch64.whl tensorflow-2.7.0-cp38-cp38-linux_aarch64.whl tensorflow-2.7.0-cp37-cp37m-manylinux...-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl tensorflow-2.7.0-cp36-cp36m-linux_aarch64....whl tensorflow-2.6.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl tensorflow-2.6.0-cp39
【示例4】 mediapipe-0.10.15-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl 是一个 Python 包的安装文件，...manylinux_2_17_x86_64.manylinux2014_x86_64: manylinux_2_17_x86_64 和 manylinux2014_x86_64 是兼容性标签，表示该包可以在多个...Linux 发行版上运行，这些发行版符合 manylinux2014 或更新的 manylinux 标准（manylinux_2_17 是 manylinux2014 的一个更新或兼容版本，具体取决于实现和上下文...manylinux_2_17_aarch64.manylinux2014_aarch64: manylinux_2_17_aarch64 和 manylinux2014_aarch64 是兼容性标签...，表示该包可以在多个符合 manylinux2014 或更新的 manylinux 标准的 Linux 发行版上运行，并且是专门为 aarch64 架构（即 ARM 64 位架构）编译的。
cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl torch-1.10.0-cp310-cp310-linux_aarch64.whl...torch-1.10.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl torch-1.10.0-cp39-cp39-linux_aarch64....whl torch-1.10.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl torch-1.10.0-cp38-cp38-linux_aarch64....whl torch-1.10.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl torch-1.10.0-cp37-cp37m-linux_aarch64...-manylinux2014_aarch64.whl torch-1.9.0-cp38-cp38-linux_aarch64.whl torch-1.9.0-cp37-cp37m-manylinux2014
编写的部分，所以我们确保发布的二进制包可以支持主流的Linux操作系统，比如CentOS 6以上，Ubuntu 14.04以上，MacOS 10.12以上 PaddlePaddle发布的安装包会尽量对齐 manylinux1...标准（查看链接：https://www.python.org/dev/peps/pep-0513/#the-manylinux1-policy），通常使用CentOS 5作为编译环境。...另外最新的pip官方源中的安装包默认是manylinux1标准，需要使用最新的pip (>9.0.0) 才可以安装。...pypi安装包可以在该链接中找到（https://pypi.python.org/pypi/paddlepaddle/0.10.5） 如果系统支持的是 linux_x86_64 而安装包是 manylinux1..._x86_64 ，需要升级pip版本到最新； 如果系统支持 manylinux1_x86_64 而安装包（本地）是 linux_x86_64 ，可以重命名这个whl包为 manylinux1_x86_64
_x86_64.whl PillowPILPillow · PyPIPillow-8.4.0-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64...._x86_64.whl torchvisiontorchvisiontorchvision · PyPItorchvision-0.11.2-cp36-cp36m-manylinux1_x86_64.whl... torchtorchtorch · PyPItorch-1.10.1-cp36-cp36m-manylinux1_x86_64.whl 2、离线安装依赖库和pytorch pip3 install...numpy-1.19.5-cp36-cp36m-manylinux2010_x86_64.whl pip3 install Pillow-8.4.0-cp36-cp36m-manylinux_2_17_...-0.10.1-cp36-cp36m-manylinux1_x86_64.whl pip3 install torchvision-0.11.2-cp36-cp36m-manylinux1_x86_64
获取Anaconda3-4.3.1-Linux-x86_64.sh、netCDF4-1.2.7-cp36-cp36m-manylinux1_x86_64.whl、opencv_python-3.2.0.7...-cp36-cp36m-manylinux1_x86_64.whl 1、到/home/python/software目录下： 执行Anaconda3-4.3.1-Linux-x86_64.sh脚本， $...>>> 回车 >>> yes >>> 回车 >>> yes $ source /home/python/.bashrc $ pip install netCDF4-1.2.7-cp36-cp36m-manylinux1..._x86_64.whl $ pip install opencv_python-3.2.0.7-cp36-cp36m-manylinux1_x86_64.whl 2、使用nriet用户配置环境变量： 第一步
cp37m-win_amd64.whl下载地址：https://download.csdn.net/download/lwx666sl/88892622 gensim-4.0.0-cp38-cp38-manylinux1...cp38-win_amd64.whl下载地址：https://download.csdn.net/download/lwx666sl/88892628 gensim-4.0.1-cp36-cp36m-manylinux1...cp38-win_amd64.whl下载地址：https://download.csdn.net/download/lwx666sl/88892662 gensim-4.1.0-cp36-cp36m-manylinux...cp38-win_amd64.whl下载地址：https://download.csdn.net/download/lwx666sl/88892606 gensim-4.1.0-cp39-cp39-manylinux...cp38-win_amd64.whl下载地址：https://download.csdn.net/download/lwx666sl/88892655 gensim-4.1.1-cp39-cp39-manylinux
_internal.pep425tags.get_supported()) 此处会得到类似以下的回显： 记住第一个大括号内的 cp37m 和 manylinux1_x86_64 ，后面会用到。...前往这个页面下载 tensorflow 2.3.0 ，按照上一步中的结果，我们选择下载最接近的文件 tensorflow-2.3.0-cp37-cp37m-manylinux2010_x86_64.whl...-cp37m-manylinux1_x86_64.whl 进行安装。...之后前往这个页面下载 llvmlite ，按照和上一步中同样的方式，下载 llvmlite-0.36.0-cp37-cp37m-manylinux2010_x86_64.whl 并重命名为 .........manylinux1......
_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (81 kB) ━━━━━━━━━━━..._2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (897 kB) ━━━━━━━━━━..._2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (139 kB) ━━━━━━━━━━..._2_12_x86_64.manylinux2010_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (284 kB) ━━━━━━..._2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (1.3 MB) ━━━━━━━━━━
/gitee.com/ascend/pytorch/releases/download/v6.0.0.1-pytorch2.1.0/torch_npu- 2.1.0.post11-cp39-cp39-manylinux..._2_17_aarch64.manylinux2014_aarch64.whlpip3 install torch_npu-2.1.0.post11-cp39-cp39- manylinux_2_17_...aarch64.manylinux2014_aarch64.whl#安装其它依赖conda install -c bioconda -c conda-forge hhsuiteconda install
packages/16/89/f2d29c2eafc2eeafb17d5634340e06366af904d332341200a49d954bce85/tensorflow-2.3.0-cp37-cp37m-manylinux2010...200 OK Length: 320368291 (306M) [application/octet-stream] Saving to: 'tensorflow-2.3.0-cp37-cp37m-manylinux2010..._x86_64.whl' tensorflow-2.3.0-cp37-cp37m-manylinux2010_x86_64.whl 100%[===================...>] 305.53M 3.68MB/s in 2m 48s 2020-09-10 04:14:33 (1.82 MB/s) - 'tensorflow-2.3.0-cp37-cp37m-manylinux2010...64.whl' saved [320368291/320368291] 下载完成后，使用 pip install 即可 pip install tensorflow-2.3.0-cp37-cp37m-manylinux2010
_x86_64.whl bcrypt-3.1.7-cp34-abi3-manylinux1_x86_64.whl confluent_kafka-1.1.0-cp37-cp37m-manylinux1...安装已有whl文件 $ pip install bcrypt-3.1.7-cp34-abi3-manylinux1_x86_64.whl Processing ..../bcrypt-3.1.7-cp34-abi3-manylinux1_x86_64.whl Requirement already satisfied: cffi>=1.1 in /data/_software.../confluent_kafka-1.1.0-cp37-cp37m-manylinux1_x86_64.whl Installing collected packages: confluent-kafka.../cx_Oracle-8.0.0-cp37-cp37m-manylinux1_x86_64.whl Installing collected packages: cx-Oracle Successfully
/bin/psutil-5.8.0-cp38-cp38-manylinux2010_x86_64.whl Processing ..../bin/pyrsistent-0.18.0-cp38-cp38-manylinux1_x86_64.whl Processing ..../bin/google_crc32c-1.3.0-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl Processing ..../bin/multidict-5.2.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010.../bin/yarl-1.6.3-cp38-cp38-manylinux2014_x86_64.whl Processing .
1.1.0-py2.py3-none-any.whl (16 kB)Collecting MarkupSafe>=0.23 Downloading MarkupSafe-1.1.1-cp27-cp27mu-manylinux1.../MarkupSafe-1.1.1-cp27-cp27mu-manylinux1_x86_64.whlSuccessfully downloaded Flask click Jinja2 Werkzeug...-2.11.3-py2.py3-none-any.whl-rw-r--r-- 1 root root 24348 Jun 24 21:33 MarkupSafe-1.1.1-cp27-cp27mu-manylinux1...itsdangerous-1.1.0-py2.py3-none-any.whlJinja2-2.11.3-py2.py3-none-any.whlMarkupSafe-1.1.1-cp27-cp27mu-manylinux1...-2.11.3-py2.py3-none-any.whl-rw-r--r-- 1 root root 24348 Jun 24 2022 MarkupSafe-1.1.1-cp27-cp27mu-manylinux1
_x86_64.whlbcrypt-3.1.7-cp34-abi3-manylinux1_x86_64.whlconfluent_kafka-1.1.0-cp37-cp37m-manylinux1_x86...安装已有whl文件安装bcrypt-3.1.7-cp34-abi3-manylinux1_x86_64.whl$ pip install bcrypt-3.1.7-cp34-abi3-manylinux1...installed confluent-kafka-1.1.0安装PyNaCl-1.3.0-cp34-abi3-manylinux1_x86_64.whl先安装PyNaCl，再安装paramiko$...pip install PyNaCl-1.3.0-cp34-abi3-manylinux1_x86_64.whlProcessing ...._x86_64.whl$ pip install cx_Oracle-8.0.0-cp37-cp37m-manylinux1_x86_64.whlProcessing .
pypi/packages/13/ec/f727ddd3fbcdc6102eace62c9d5dd9d9ad8112d40eeb7de8783676aca24d/ray-1.4.1-cp36-cp36m-manylinux2014...packages/53/4e/e2db88d0bb0bda6a879eea62fddbaf813719ce3770d458bc5580512d9c95/protobuf-3.17.3-cp36-cp36m-manylinux..._2_5_x86_64.manylinux1_x86_64.whl (1.0 MB) |████████████████████████████████| 1.0 MB 136.1 MB/s...packages/7a/5b/bc0b5ab38247bba158504a410112b6c03f153c652734ece1849749e5f518/PyYAML-5.4.1-cp36-cp36m-manylinux1...packages/0c/0d/b1d9d32d03ce38ba5e2a37fbae850afd4530a14cc441e8335f1865a03705/msgpack-1.0.2-cp36-cp36m-manylinux1
_2_17_x86_64.manylinux2014_x86_64.whl (49.9 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 49.9/49.9..._2_17_x86_64.manylinux2014_x86_64.whl (14.9 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 14.9/14.9..._2_17_x86_64.manylinux2014_x86_64.whl (281 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 281.3/281.3..._2_17_x86_64.manylinux2014_x86_64.whl (2.5 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 2.5/2.5..._x86_64.manylinux_2_5_x86_64.whl (307 kB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 307.2/307.2 kB
/gitee.com/ascend/pytorch/releases/download/v6.0.rc3-pytorch2.1.0/torch_npu-2.1.0.post8-cp310-cp310-manylinux..._2_17_aarch64.manylinux2014_aarch64.whl --no-check-certificate安装pip3 install torch_npu-2.1.0.post8-cp310...-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl注：该环境已经提前安装过了，所以不会提示安装成功2.数据集下载2.1 下载原始数据集wget
-{python tag}-{abi tag}-{platform tag}.whl举个例子：numpy-1.24.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014...这个文件名告诉我们：- 包名：numpy- 版本：1.24.3- Python版本：cp310（CPython 3.10）- ABI标签：cp310（与CPython 3.10兼容的ABI）- 平台标签：manylinux..._2_17_x86_64.manylinux2014_x86_64（兼容特定版本的Linux）这种命名方式非常强大，它使pip能够确切知道哪个wheel适合你的系统，避免了下载不兼容包的问题。...此外，像manylinux、musllinux这样的平台标签使得创建跨Linux分发版的兼容wheel成为可能。总结wheel格式彻底改变了Python包的分发和安装方式。
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