Metadata-Version: 2.1
Name: cogflow
Version: 1.9.31b1
Summary: COG modules
Home-page: UNKNOWN
Author: Sai_kireeti
Author-email: sai.kireeti@hiro-microdatacenters.nl
License: UNKNOWN
Description: 
        # CogFlow
        
        Cogflow module sets up a pipeline for handling datasets and machine learning models
        using multiple plugins. It includes functions for creating, registering, evaluating,
        and serving models, as well as managing datasets.
        
        **Key components include:**
        
        Mlflow Plugin: For model tracking, logging, and evaluation.
        Kubeflow Plugin: For pipeline management and serving models.
        Dataset Plugin: For dataset registration and management.
        Model Plugin: For saving model details.
        Configurations: Constants for configuration like tracking URIs, database credentials, etc.
        
        **Key Functions:**
        
        **Model Management**
        
        register_model: Register a new model.
        log_model: Log a model.
        load_model: Load a model.
        delete_registered_model: Delete a registered model.
        create_registered_model: Create a new registered model.
        create_model_version: Create a new version of a registered model.
        
        
        **Run Management**
        
        start_run: Start a new.
        end_run: End the current.
        log_param: Log a parameter to the current run.
        log_metric: Log a metric to the current run.
        
        
        **Evaluation and Autologging**
        
        evaluate: Evaluate a model.
        autolog: Enable automatic logging of parameters, metrics, and models.
        
        
        **Search and Query**
        
        search_registered_models: Search for registered models.
        search_model_versions: Search for model versions.
        get_model_latest_version: Get the latest version of a registered model.
        get_artifact_uri: Get the artifact URI of the current or specified run.
        
        
        **Dataset Management**
        
        link_model_to_dataset: Link a model to a dataset.
        save_dataset_details: Save dataset details.
        save_model_details_to_db: Save model details to the database.
        
        
        **Pipeline and Component Management**
        
        pipeline: Create a new Kubeflow pipeline.
        create_component_from_func: Create a Kubeflow component from a function.
        client: Get the Kubeflow client.
        load_component_from_url: Load a Kubeflow component from a URL.
        
        
        **Model Serving**
        
        serve_model_v1: Serve a model using Kubeflow V1.
        serve_model_v2: Serve a model using Kubeflow V2.
        get_model_url: Get the URL of a served model.
        delete_served_model: Delete a served model.
        
        
        **MinIO Operations**
        
        create_minio_client: Create a MinIO client.
        query_endpoint_and_download_file: Query an endpoint and download a file from MinIO.
        save_to_minio: Save file content to MinIO.
        delete_from_minio: Delete an object from MinIO.
        
        
        **Dataset Registration**
        
        register_dataset: Register a dataset.
        ## Getting Started
        
        To begin, import cogflow from the CogFlow module:
        
        ```python
        import cogflow
        
        ```
        
        ### Explore the Capabilities of `cogflow`
        
        - **List Attributes and Methods**: Understand the `cogflow` module better with:
            ```python
            print(dir(cogflow))
            ```
        
        - **Get Documentation**: For a comprehensive guide on the `cogflow`, use:
            ```python
            help(cogflow)
            ```
        
        ## 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: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
