函数计算 使用fun install安装第三方依赖

fun install -h

Usage: fun install [-f|--function <[service/]function>] [-r|--runtime <runtime>] [-p|--package-type <type>] [--save] [-e|--env key=val ...] [packageNames...]

install dependencies which are described in fun.yml file.

Options:
  -f, --function <[service/]function>  Specify which function to execute installation task.
  -e, --env <env>                      environment variable, ex. -e PATH=/code/bin (default: [])
  -d, --use-docker                     Use docker container to install function dependencies
  -r, --runtime <runtime>              function runtime, avaliable choice is: nodejs6, nodejs8, nodejs10,
                                       nodejs12, python2.7, python3, java8, php7.2, dotnetcore2.1, custom
  -p, --package-type <type>            avaliable package type option: pip, apt, npm.
  --save                               add task to fun.yml file.
  --registry <npm-url>                 Configure npm to use any compatible registry, and even run your own
                                       registry.
  --index-url <pip-url>                Base URL of Python Package Index (default https://pypi.org/simple). This should point to a repository compliant with PEP 503 (the simple repository API) or a local
                                       directory laid out in the same format.
  -h, --help                           display help for command

Commands:
  init                                 initialize fun.yml file.
  env                                  print environment varables.
  sbox [options]                       Start a local sandbox for installation dependencies or
                                       configuration

fun install --runtime python2.7 --package-type pip tensorflow  

预期输出：

skip pulling image aliyunfc/runtime-python2.7:build-1.2.0...
Task => [UNNAMED]
     => PYTHONUSERBASE=/code/.fun/python pip install --user tensorflow 

• --runtime：指定运行的环境，您可以按需修改相应的运行环境。如果fun.yml里已经声明了运行环境，说明文件已经被初始化，您可以省略该选项。
• --package-type：指定安装依赖的类型。
• tensorflow：自定义PIP包的文件名称，本文以tensorflow为例。
• PYTHONUSERBASE=/code/.fun/python pip install --user tensorflow：命令在FC-Docker提供的容器内部执行，执行的命令会逐行打印出来。
  注意 APT包依赖的安装方式及其参数和PIP包依赖类似，如果您需要安装APT包的依赖，您需要将
  pip修改为
  apt，APT的包名使用
  apt-get可以安装的DEB格式的包名即可。

• .fun/python/bin：可执行文件被放置的目录。
• .fun/python/lib/python2.7/site-packages：库文件放置的目录。

文件存放结构如下所示：

.fun
└── python
    ├── bin
    │   ├── freeze_graph
    │   ├── markdown_py
    │   ├── pbr
    │   ├── saved_model_cli
    │   ├── tensorboard
    │   ├── tflite_convert
    │   ├── toco
    │   └── toco_from_protos
    └── lib
        └── python2.7
            └── site-packages
                ├── tensorboard
                ├── tensorboard-1.12.2.dist-info
                ├── tensorflow
                ├── tensorflow-1.12.0.dist-info
                ├── termcolor-1.1.0.dist-info
                ...            

• 存放位置更有组织性。
• 依赖文件和代码文件分离开，便于借助OSS或NAS初始化依赖文件。

LD_LIBRARY_PATH=/code/.fun/root/usr/local/lib:/code/.fun/root/usr/lib:/code/.fun/root/usr/lib/x86_64-linux-gnu:/code/.fun/root/usr/lib64:/code/.fun/root/lib:/code/.fun/root/lib/x86_64-linux-gnu:/code/.fun/root/python/lib/python2.7/site-packages:/code/.fun/root/python/lib/python3.6/site-packages:/code:/code/lib:/usr/local/lib
PATH=/code/.fun/root/usr/local/bin:/code/.fun/root/usr/local/sbin:/code/.fun/root/usr/bin:/code/.fun/root/usr/sbin:/code/.fun/root/sbin:/code/.fun/root/bin:/code:/code/node_modules/.bin:/code/.fun/python/bin:/code/.fun/node_modules/.bin:/usr/local/bin:/usr/local/sbin:/usr/bin:/usr/sbin:/sbin:/bin
NODE_PATH=/code/node_modules:/usr/local/lib/node_modules
PYTHONUSERBASE=/code/.fun/python