Metadata-Version: 2.1
Name: metaloop-python-sdk
Version: 1.9.0
Summary: Deepglint Metaloop Python SDK
Home-page: http://data.deepglint.com/
Author: yuma
Author-email: yuma@deepglint.com
License: Apache License 2.0
Project-URL: Documentation, https://gitlab.deepglint.com/metaloop/metaloop-python-sdk
Project-URL: Source, https://gitlab.deepglint.com/metaloop/metaloop-python-sdk
Description: # Python SDK for Metaloop platform
        Metaloop Python SDK is a python library to access [Metaloop](http://data.deepglint.com/)
        and manage your datasets.
        It provides:
        
        -   A pythonic way to access your Metaloop resources by Metaloop OpenAPI [api](http://data.deepglint.com/api/v1/docs/).
        
        
        ## Installation
        
        ```console
        pip install metaloop-python-sdk
        ```
        
        ## Documentation
        
        More information can be found on the [examples](https://gitlab.deepglint.com/metaloop/metaloop-python-sdk/-/tree/dev/metaloop/examples)
        
        ## Usage
        生成API Token
        ```
          在登录平台后，点击页面右上角的用户名，在个人信息页面中，即可获取本用户的API Token
          正式环境将http://192.168.100.71:30301 替换为 http://data.deepglint.com
        ```
        操作数据集
        ```python
        import time
        
        from metaloop.client import MDS
        
        
        if __name__ == '__main__':
            # use MDS("0c02ca70e142b75a75ca4118ce33dbb0", "http://192.168.100.71:30301") when connecting test-server
            mds_client = MDS("0c02ca70e142b75a75ca4118ce33dbb0")
        
            date_time = time.strftime("%Y%m%d%H%M%S", time.localtime())
            dataset_name = f"test_dataset_{date_time}"
        
            # create new dataset
            dataset = mds_client.create_dataset(
                dataset_name,
                "image",
                ["screw"],
                comment="this is a test dataset for mds"
            )
            # print dataset info
            dataset.summary()
            
            # get existed dataset
            dataset = mds_client.get_dataset(dataset_name)
            dataset.summary()
        
            # create version
            dataset.create_version(comment="this is a test version for mds")
            dataset.summary()
        
            # switch version
            dataset.checkout(0)
            dataset.summary()
            
            # import local data to dataset
            dataset.import_data("import_test/sample_test")
            # accelerated when running on tencent cloud
            dataset.import_data("import_test/sample_test", storage_type='cos')
            # export data to local filesystem
            dataset.export_data("export_test")
            # delete version
            dataset.delete_version(0)
            dataset.summary()
            # iter objects
            for item in dataset:
                print(item)
                response = requests.get(item['obj_url'])
                image = Image.open(BytesIO(response.content)).convert("RGB")
                image.show()
            # delete dataset
            mds_client.delete_dataset(dataset_name)
            dataset.summary()
                
        ```
        
        ## Development
        - CI pipeline
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Python: >= 3.5
Description-Content-Type: text/markdown
