Tracking Large Language Models (LLM) with MLflow : A Complete Guide

As Large Language Models (LLMs) grow in complexity and scale, tracking their performance, experiments, and deployments becomes increasingly challenging. This is where MLflow comes in – providing a comprehensive platform for managing the entire lifecycle of machine learning models, including LLMs.

In this in-depth guide, we’ll explore how to leverage MLflow for tracking, evaluating, and deploying LLMs. We’ll cover everything from setting up your environment to advanced evaluation techniques, with plenty of code examples and best practices along the way.

Setting Up Your Environment

Before we dive into tracking LLMs with MLflow, let’s set up our development environment. We’ll need to install MLflow and several other key libraries:

 pip install mlflow>=2.8.1 pip install openai pip install chromadb==0.4.15 pip install langchain==0.0.348 pip install tiktoken pip install 'mlflow[genai]' pip install databricks-sdk --upgrade 

After installation, it’s a good practice to restart your Python environment to ensure all libraries are properly loaded. In a Jupyter notebook, you can use:

 import mlflow import chromadb print(f"MLflow version: {mlflow.__version__}") print(f"ChromaDB version: {chromadb.__version__}") 

This will confirm the versions of key libraries we’ll be using.

Understanding MLflow’s LLM Tracking Capabilities

MLflow’s LLM tracking system builds upon its existing tracking capabilities, adding features specifically designed for the unique aspects of LLMs. Let’s break down the key components:

Runs and Experiments

In MLflow, a “run” represents a single execution of your model code, while an “experiment” is a collection of related runs. For LLMs, a run might represent a single query or a batch of prompts processed by the model.

Key Tracking Components

  1. Parameters: These are input configurations for your LLM, such as temperature, top_k, or max_tokens. You can log these using mlflow.log_param() or mlflow.log_params().
  2. Metrics: Quantitative measures of your LLM’s performance, like accuracy, latency, or custom scores. Use mlflow.log_metric() or mlflow.log_metrics() to track these.
  3. Predictions: For LLMs, it’s crucial to log both the input prompts and the model’s outputs. MLflow stores these as artifacts in CSV format using mlflow.log_table().
  4. Artifacts: Any additional files or data related to your LLM run, such as model checkpoints, visualizations, or dataset samples. Use mlflow.log_artifact() to store these.

Let’s look at a basic example of logging an LLM run:

This example demonstrates logging parameters, metrics, and the input/output as a table artifact.

 import mlflow import openai def query_llm(prompt, max_tokens=100):     response = openai.Completion.create(         engine="text-davinci-002",         prompt=prompt,         max_tokens=max_tokens     )     return response.choices[0].text.strip() with mlflow.start_run():     prompt = "Explain the concept of machine learning in simple terms."          # Log parameters     mlflow.log_param("model", "text-davinci-002")     mlflow.log_param("max_tokens", 100)          # Query the LLM and log the result     result = query_llm(prompt)     mlflow.log_metric("response_length", len(result))          # Log the prompt and response     mlflow.log_table("prompt_responses", {"prompt": [prompt], "response": [result]})          print(f"Response: {result}") 

Deploying LLMs with MLflow

MLflow provides powerful capabilities for deploying LLMs, making it easier to serve your models in production environments. Let’s explore how to deploy an LLM using MLflow’s deployment features.

Creating an Endpoint

First, we’ll create an endpoint for our LLM using MLflow’s deployment client:

 import mlflow from mlflow.deployments import get_deploy_client # Initialize the deployment client client = get_deploy_client("databricks") # Define the endpoint configuration endpoint_name = "llm-endpoint" endpoint_config = {     "served_entities": [{         "name": "gpt-model",         "external_model": {             "name": "gpt-3.5-turbo",             "provider": "openai",             "task": "llm/v1/completions",             "openai_config": {                 "openai_api_type": "azure",                 "openai_api_key": "{{secrets/scope/openai_api_key}}",                 "openai_api_base": "{{secrets/scope/openai_api_base}}",                 "openai_deployment_name": "gpt-35-turbo",                 "openai_api_version": "2023-05-15",             },         },     }], } # Create the endpoint client.create_endpoint(name=endpoint_name, config=endpoint_config) 

This code sets up an endpoint for a GPT-3.5-turbo model using Azure OpenAI. Note the use of Databricks secrets for secure API key management.

Testing the Endpoint

Once the endpoint is created, we can test it:

 
response = client.predict( endpoint=endpoint_name, inputs={"prompt": "Explain the concept of neural networks briefly.","max_tokens": 100,},) print(response)

This will send a prompt to our deployed model and return the generated response.

Evaluating LLMs with MLflow

Evaluation is crucial for understanding the performance and behavior of your LLMs. MLflow provides comprehensive tools for evaluating LLMs, including both built-in and custom metrics.

Preparing Your LLM for Evaluation

To evaluate your LLM with mlflow.evaluate(), your model needs to be in one of these forms:

  1. An mlflow.pyfunc.PyFuncModel instance or a URI pointing to a logged MLflow model.
  2. A Python function that takes string inputs and outputs a single string.
  3. An MLflow Deployments endpoint URI.
  4. Set model=None and include model outputs in the evaluation data.

Let’s look at an example using a logged MLflow model:

 import mlflow import openai with mlflow.start_run():     system_prompt = "Answer the following question concisely."     logged_model_info = mlflow.openai.log_model(         model="gpt-3.5-turbo",         task=openai.chat.completions,         artifact_path="model",         messages=[             {"role": "system", "content": system_prompt},             {"role": "user", "content": "{question}"},         ],     ) # Prepare evaluation data eval_data = pd.DataFrame({     "question": ["What is machine learning?", "Explain neural networks."],     "ground_truth": [         "Machine learning is a subset of AI that enables systems to learn and improve from experience without explicit programming.",         "Neural networks are computing systems inspired by biological neural networks, consisting of interconnected nodes that process and transmit information."     ] }) # Evaluate the model results = mlflow.evaluate(     logged_model_info.model_uri,     eval_data,     targets="ground_truth",     model_type="question-answering", ) print(f"Evaluation metrics: {results.metrics}") 

This example logs an OpenAI model, prepares evaluation data, and then evaluates the model using MLflow’s built-in metrics for question-answering tasks.

Custom Evaluation Metrics

MLflow allows you to define custom metrics for LLM evaluation. Here’s an example of creating a custom metric for evaluating the professionalism of responses:

 from mlflow.metrics.genai import EvaluationExample, make_genai_metric professionalism = make_genai_metric(     name="professionalism",     definition="Measure of formal and appropriate communication style.",     grading_prompt=(         "Score the professionalism of the answer on a scale of 0-4:n"         "0: Extremely casual or inappropriaten"         "1: Casual but respectfuln"         "2: Moderately formaln"         "3: Professional and appropriaten"         "4: Highly formal and expertly crafted"     ),     examples=[         EvaluationExample(             input="What is MLflow?",             output="MLflow is like your friendly neighborhood toolkit for managing ML projects. It's super cool!",             score=1,             justification="The response is casual and uses informal language."         ),         EvaluationExample(             input="What is MLflow?",             output="MLflow is an open-source platform for the machine learning lifecycle, including experimentation, reproducibility, and deployment.",             score=4,             justification="The response is formal, concise, and professionally worded."         )     ],     model="openai:/gpt-3.5-turbo-16k",     parameters={"temperature": 0.0},     aggregations=["mean", "variance"],     greater_is_better=True, ) # Use the custom metric in evaluation results = mlflow.evaluate(     logged_model_info.model_uri,     eval_data,     targets="ground_truth",     model_type="question-answering",     extra_metrics=[professionalism] ) print(f"Professionalism score: {results.metrics['professionalism_mean']}") 

This custom metric uses GPT-3.5-turbo to score the professionalism of responses, demonstrating how you can leverage LLMs themselves for evaluation.

Advanced LLM Evaluation Techniques

As LLMs become more sophisticated, so do the techniques for evaluating them. Let’s explore some advanced evaluation methods using MLflow.

Retrieval-Augmented Generation (RAG) Evaluation

RAG systems combine the power of retrieval-based and generative models. Evaluating RAG systems requires assessing both the retrieval and generation components. Here’s how you can set up a RAG system and evaluate it using MLflow:

 from langchain.document_loaders import WebBaseLoader from langchain.text_splitter import CharacterTextSplitter from langchain.embeddings import OpenAIEmbeddings from langchain.vectorstores import Chroma from langchain.chains import RetrievalQA from langchain.llms import OpenAI # Load and preprocess documents loader = WebBaseLoader(["https://mlflow.org/docs/latest/index.html"]) documents = loader.load() text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) texts = text_splitter.split_documents(documents) # Create vector store embeddings = OpenAIEmbeddings() vectorstore = Chroma.from_documents(texts, embeddings) # Create RAG chain llm = OpenAI(temperature=0) qa_chain = RetrievalQA.from_chain_type(     llm=llm,     chain_type="stuff",     retriever=vectorstore.as_retriever(),     return_source_documents=True ) # Evaluation function def evaluate_rag(question):     result = qa_chain({"query": question})     return result["result"], [doc.page_content for doc in result["source_documents"]] # Prepare evaluation data eval_questions = [     "What is MLflow?",     "How does MLflow handle experiment tracking?",     "What are the main components of MLflow?" ] # Evaluate using MLflow with mlflow.start_run():     for question in eval_questions:         answer, sources = evaluate_rag(question)                  mlflow.log_param(f"question", question)         mlflow.log_metric("num_sources", len(sources))         mlflow.log_text(answer, f"answer_{question}.txt")                  for i, source in enumerate(sources):             mlflow.log_text(source, f"source_{question}_{i}.txt")     # Log custom metrics     mlflow.log_metric("avg_sources_per_question", sum(len(evaluate_rag(q)[1]) for q in eval_questions) / len(eval_questions)) 

This example sets up a RAG system using LangChain and Chroma, then evaluates it by logging questions, answers, retrieved sources, and custom metrics to MLflow.

The way you chunk your documents can significantly impact RAG performance. MLflow can help you evaluate different chunking strategies:

This script evaluates different combinations of chunk sizes, overlaps, and splitting methods, logging the results to MLflow for easy comparison.

MLflow provides various ways to visualize your LLM evaluation results. Here are some techniques:

You can create custom visualizations of your evaluation results using libraries like Matplotlib or Plotly, then log them as artifacts:

This function creates a line plot comparing a specific metric across multiple runs and logs it as an artifact.

Alternatives to Open Source MLflow

There are numerous alternatives to open source MLflow for managing machine learning workflows, each offering unique features and integrations.

Managed MLflow by Databricks

Managed MLflow, hosted by Databricks, provides the core functionalities of open-source MLflow but with additional benefits such as seamless integration with Databricks’ ecosystem, advanced security features, and managed infrastructure. This makes it an excellent choice for organizations needing robust security and scalability.

Azure Machine Learning

Azure Machine Learning offers an end-to-end machine learning solution on Microsoft’s Azure cloud platform. It provides compatibility with MLflow components like the model registry and experiment tracker, though it is not based on MLflow.

Dedicated ML Platforms

Several companies provide managed ML products with diverse features:

  • neptune.ai: Focuses on experiment tracking and model management.
  • Weights & Biases: Offers extensive experiment tracking, dataset versioning, and collaboration tools.
  • Comet ML: Provides experiment tracking, model production monitoring, and data logging.
  • Valohai: Specializes in machine learning pipelines and orchestration.

Metaflow

Metaflow, developed by Netflix, is an open-source framework designed to orchestrate data workflows and ML pipelines. While it excels at managing large-scale deployments, it lacks comprehensive experiment tracking and model management features compared to MLflow.

Amazon SageMaker and Google’s Vertex AI

Both Amazon SageMaker and Google’s Vertex AI provide end-to-end MLOps solutions integrated into their respective cloud platforms. These services offer robust tools for building, training, and deploying machine learning models at scale.

Detailed Comparison

Managed MLflow vs. Open Source MLflow

Managed MLflow by Databricks offers several advantages over the open-source version, including:

  • Setup and Deployment: Seamless integration with Databricks reduces setup time and effort.
  • Scalability: Capable of handling large-scale machine learning workloads with ease.
  • Security and Management: Out-of-the-box security features like role-based access control (RBAC) and data encryption.
  • Integration: Deep integration with Databricks’ services, enhancing interoperability and functionality.
  • Data Storage and Backup: Automated backup strategies ensure data safety and reliability.
  • Cost: Users pay for the platform, storage, and compute resources.
  • Support and Maintenance: Dedicated support and maintenance provided by Databricks.

Conclusion

Tracking Large Language Models with MLflow provides a robust framework for managing the complexities of LLM development, evaluation, and deployment. By following the best practices and leveraging advanced features outlined in this guide, you can create more organized, reproducible, and insightful LLM experiments.

Remember that the field of LLMs is rapidly evolving, and new techniques for evaluation and tracking are constantly emerging. Stay updated with the latest MLflow releases and LLM research to continually refine your tracking and evaluation processes.

As you apply these techniques in your projects, you’ll develop a deeper understanding of your LLMs’ behavior and performance, leading to more effective and reliable language models.