cogflow_lite

Cogflow_lite 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:

import cogflow_lite

Explore the Capabilities of cogflow_lite

• List Attributes and Methods: Understand the cogflow_lite module better with:

  print(dir(cogflow_lite))
  

• Get Documentation: For a comprehensive guide on the cogflow_lite, use:

  help(cogflow_lite)
  

Environment Variables

To maximize the functionality of CogFlow_lite, 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.

• 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.