Metadata-Version: 2.1
Name: cogflow
Version: 1.9.1
Summary: cog modules
Home-page: UNKNOWN
Author: Sai_kireeti
Author-email: sai.kireeti@hiro-microdatacenters.nl
License: UNKNOWN
Description: 
        # CogFlow
        
        CogFlow is a versatile framework designed to manage multiple plugins for cognitive and machine learning tasks. It includes several plugins such as `MlflowPlugin`, `KubeflowPlugin`, and `DatasetPlugin`, which can be activated as needed to extend the capabilities of the framework.
        
        ## Getting Started
        
        To begin, instantiate the `PluginManager` from the CogFlow module:
        
        ```python
        import cogflow
        
        ```
        
        ### Explore the Capabilities of `PluginManager`
        
        - **List Attributes and Methods**: Understand the `PluginManager` module better with:
            ```python
            print(dir(cogflow))
            ```
        
        - **Get Documentation**: For a comprehensive guide on the `PluginManager`, use:
            ```python
            help(cogflow)
            ```
        
        ### Managing Plugins
        
        By default, all plugins are in a deactivated state. Check the status of each plugin using:
        
        - **Check Plugin Status**:
            ```python
            cml.plugin_status()
            ```
        
        ### Activating Plugins
        
        To make use of a specific plugin, activate it for your session:
        
        - **Activate Plugins**:
            ```python
            mlp_activate = cml.activate_plugin("MlflowPlugin")
            kfp_activate = cml.activate_plugin("KubeflowPlugin")
            dsp_activate = cml.activate_plugin("DatasetPlugin")
            ```
        
        ### Retrieve Plugin Instances
        
        Once activated, you can retrieve the plugin instances:
        
        - **Retrieve Plugin Instances**:
            ```python
            mlp = cml.get_mlflow_plugin()
            kfp = cml.get_kflow_plugin()
            dsp = cml.get_dataset_plugin()
            ```
        
        With the plugins now active, you can leverage their functionalities for your machine learning and data management tasks.
        
        ## Environment Variables
        
        To maximize the functionality of CogFlow, set the following environment variables:
        
        - **Mlflow Configuration**:
            - `MLFLOW_TRACKING_URI`: The URI of the Mlflow tracking server.
            - `MLFLOW_S3_ENDPOINT_URL`: The endpoint URL for the AWS S3 service.
            - `ACCESS_KEY_ID`: The access key ID for AWS S3 authentication.
            - `SECRET_ACCESS_KEY`: The secret access key for AWS S3 authentication.
        
        - **Machine Learning Database**:
            - `ML_DB_USERNAME`: Username for connecting to the machine learning database.
            - `ML_DB_PASSWORD`: Password for connecting to the machine learning database.
            - `ML_DB_HOST`: Host address for the machine learning database.
            - `ML_DB_PORT`: Port number for the machine learning database.
            - `ML_DB_NAME`: Name of the machine learning database.
        
        - **CogFlow Database**:
            - `COGFLOW_DB_USERNAME`: Username for connecting to the CogFlow database.
            - `COGFLOW_DB_PASSWORD`: Password for connecting to the CogFlow database.
            - `COGFLOW_DB_HOST`: Host address for the CogFlow database.
            - `COGFLOW_DB_PORT`: Port number for the CogFlow database.
            - `COGFLOW_DB_NAME`: Name of the CogFlow database.
        
        - **MinIO Configuration**:
            - `MINIO_ENDPOINT_URL`: The endpoint URL for the MinIO service.
            - `MINIO_ACCESS_KEY`: The access key for MinIO authentication.
            - `MINIO_SECRET_ACCESS_KEY`: The secret access key for MinIO authentication.
        
        ---
        
        By setting the environment variables correctly, you can fully utilize the features and functionalities of the CogFlow framework for your cognitive and machine learning tasks.
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
