Files
modelscope/docs
wenmeng.zwm 8e51a073a6 [to #42966122] requirements enchanment and self-host repo support
* add self-hosted repo:
* add extra requirements for different field and reduce necessary requirements
* update docker file with so required by audio
* add requirements checker which will be used later when implement lazy import
* remove repeated requirements and replace opencv-python-headless with opencv-python

example usage:
```shell
pip install model_scope[all] -f https://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/release/maas/repo.html
pip install model_scope[cv] -f https://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/release/maas/repo.html
pip install model_scope[nlp] -f https://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/release/maas/repo.html
pip install model_scope[audio] -f https://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/release/maas/repo.html
pip install model_scope[multi-modal] -f https://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/release/maas/repo.html

```
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/9211383
2022-07-01 16:38:06 +08:00
..

maintain docs

  1. build docs

    # in root directory:
    make docs
    
  2. doc string format

    We adopt the google style docstring format as the standard, please refer to the following documents.

    1. Google Python style guide docstring link
    2. Google docstring example link
    3. sampletorch.nn.modules.conv link
    4. load function as an example
    def load(file, file_format=None, **kwargs):
        """Load data from json/yaml/pickle files.
    
        This method provides a unified api for loading data from serialized files.
    
        Args:
            file (str or :obj:`Path` or file-like object): Filename or a file-like
                object.
            file_format (str, optional): If not specified, the file format will be
                inferred from the file extension, otherwise use the specified one.
                Currently supported formats include "json", "yaml/yml".
    
        Examples:
            >>> load('/path/of/your/file')  # file is storaged in disk
            >>> load('https://path/of/your/file')  # file is storaged in Internet
            >>> load('oss://path/of/your/file')  # file is storaged in petrel
    
        Returns:
            The content from the file.
        """