If you’d like to delve deeper into the notebooks in this guide, they can be viewed or downloaded directly below.
In this tutorial, we’ll explore the nuances of deploying advanced Large Language Models (LLMs) with MLflow, particularly focusing on models that can’t be readily managed with MLflow’s built-in functionality. We’ll highlight the necessity of custom pyfunc definitions when dealing with such complex models, emphasizing its role in managing intricate model behaviors and dependencies. By the end, you’ll understand the intricacies of deploying an LLM model using the MPT-7B instruct transformer, wrapped efficiently using a custom pyfunc.
LLM Deployment Challenges: Recognize the complexities and challenges associated with deploying advanced LLMs in MLflow.
Custom PyFuncs for LLMs: Understand the need and process of creating a custom pyfunc to effectively manage LLMs, particularly when default flavors fall short.
Prompt Management in Deployment: Delve into how custom pyfunc allows manipulation of interface data to generate prompts, simplifying end-user interactions in a RESTful environment.
Leveraging Custom PyFunc for Enhanced Flexibility: Witness how custom pyfunc definitions provide the flexibility needed for advanced model behaviors and dependencies.
Deploying advanced LLMs isn’t straightforward. Models like the MPT-7B instruct transformer have specific requirements and behaviors that don’t align with traditional MLflow flavors. This section highlights the challenges faced and the importance of custom pyfunc definitions in addressing these challenges.
Venturing into the solution, we’ll craft a custom pyfunc to efficiently wrap and manage our LLM. This custom definition serves as a bridge, ensuring our LLM can be deployed seamlessly while retaining its original capabilities and adhering to MLflow’s standards.
LLM Introduction: Understand the MPT-7B instruct transformer, its importance, and its intricacies.
Challenges with Traditional Deployment: Recognize the difficulties when attempting to deploy such an LLM using MLflow’s default capabilities.
Designing the Custom `pyfunc`: Create a custom pyfunc that addresses the LLM’s requirements and behaviors.
Deploying the LLM: Integrate with MLflow to deploy the LLM using the crafted custom pyfunc.
Interface Simplification: Examine how the custom pyfunc simplifies user interactions, particularly in RESTful deployments.
With the complexities of advanced LLM deployment unraveled, this tutorial showcases the indispensable role of custom pyfunc in MLflow. Through a detailed, hands-on approach, you’ll appreciate how custom pyfunc definitions can make seemingly insurmountable deployment challenges manageable and streamlined.View the Notebook
If you’d like to run a copy of the notebooks locally in your environment, you can download them by clicking the respective links:Download the LLM Custom Pyfunc notebook
To execute the notebooks, ensure you either have a local MLflow Tracking Server running or adjust the
mlflow.set_tracking_uri() to point to an active MLflow Tracking Server instance.
To engage with the MLflow UI, ensure you’re either running the UI server locally or have a configured, accessible, deployed MLflow UI server.