fosforml 1.1.0

A Python package for registering machine learning models directly to the Snowflake Model Registry, leveraging Snowflake ML capabilities.

Fosforml

Overview

The fosforml package is designed to facilitate the registration, management, and deployment of machine learning models with a focus on integration with Snowflake. It provides tools for managing datasets, model metadata, and the lifecycle of models within a Snowflake environment.

Features

• Model Registration: Register models to the Snowflake Model registry with detailed metadata, including descriptions, types, and dependencies.
• Dataset Management: Handle datasets within Snowflake, including creation, versioning, and deletion of dataset objects.
• Metadata Management: Update model registry with descriptions and tags for better organization and retrieval.
• Snowflake Session Management: Manage Snowflake sessions for executing operations within the Snowflake environment.

Installation

To install the fosforml package, ensure you have Python installed on your system and run the following command:

pip install fosforml

Usage

Register a model with the Snowflake Model Registry using the register_model function. The function supports both Snowflake and Pandas dataframes, catering to different data handling preferences.

Requirements

• Snowflake DataFrame: If you are using Snowflake as your data warehouse, you must provide a Snowflake DataFrame (snowflake.snowpark.dataframe.DataFrame) that includes model feature names, labels, and output column names.

• Pandas DataFrame: For users preferring local or in-memory data processing, you must upload the following as Pandas DataFrames (pandas.DataFrame):

  • x_train: Training data with feature columns.
  • y_train: Training data labels.
  • x_test: Test data with feature columns.
  • y_test: Test data labels.
  • y_pred: Predicted labels for the test data.
  • prob: Predicted probabilities for the test data classes.

• Numpy data arrays are not allowed as input datasets to register the model

• dataset_name: Name fo dataset on which model trained.

• dataset_source: Name fo source from where dataset is pulled/created.

• source: Model environment name where model getting developed Ex: Notebook/Experiment.

Supported Model Flavours

Currently, the framework supports the following model flavours:

• Snowflake Models (snowflake): Models that are directly integrated with Snowflake, leveraging Snowflake's data processing capabilities.
• Scikit-Learn Models (sklearn): Models built using the Scikit-Learn library, a widely used library for machine learning in Python.

Registering a Model

To register a model with the fosforml package, you need to provide the model object, session, and other relevant details such as the model name, description, and type.

For Snowflake Models :

from fosforml import register_model

register_model(
  model_obj=pipeline,
  session=my_session,
  name="MyModel",
  snowflake_df=pred_df,
  dataset_name="HR_CHURN",
  dataset_source="Dataset",
  source="Notebook",
  description="This is a Snowflake model",
  flavour="snowflake",
  model_type="classification",
  conda_dependencies=["scikit-learn==1.3.2"]
)

For Scikit-Learn Models :

from fosforml import register_model

register_model(
  model_obj=model,
  session=session,
  x_train=x_train,
  y_train=y_train,
  x_test=x_test,
  y_test=y_test,
  y_pred=y_pred,
  source="Notebook",
  dataset_name="HR_CHURN",
  dataset_source="InMemory",
  name="MyModel",
  description="This is a sklearn model",
  flavour="sklearn",
  model_type="classification",
  conda_dependencies=["scikit-learn==1.3.2"]
)

Snowflake Session Management

The SnowflakeSession class is used to manage connections to Snowflake, facilitating the execution of operations within the Snowflake environment.

from fosforml.model_manager.snowflakesession import get_session

my_session = get_session()

Managing Datasets

The DatasetManager class allows for the creation, uploading, and removal of datasets associated with models.

from fosforml.model_manager import DatasetManager

dataset_manager = DatasetManager(model_name="MyModel", version_name="v1", session=my_session)
dataset_manager.upload_datasets(session=my_session, datasets={"x_train": x_train_df, "y_train": y_train_df})

Dependencies

• pandas
• snowflake-ml-python
• requests

Ensure these dependencies are installed in your environment to use the fosforml package effectively.

For issues and contributions, please refer to the project's GitHub repository.

Additional Resources

For further assistance and examples on how to register models using fosforml, please refer to the example folder in the project repository. This folder contains Jupyter notebooks that provide step-by-step guidance on model registration and other operations.

Visit www.fosfor.com for more information.