As the use of Large Language Models (LLMs) such as GPT-4, BERT, and others grows, monitoring their performance becomes increasingly crucial. With LLMs, monitoring provides insights into system performance, latencies, errors, and resource consumption, enabling engineers to maintain high availability, optimize resources, and troubleshoot issues effectively. SigNoz, an open-source APM ………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………………

Overview

As the use of Large Language Models (LLMs) such as GPT-4, BERT, and others grows, monitoring their performance becomes increasingly crucial. With LLMs, monitoring provides insights into system performance, latencies, errors, and resource consumption, enabling engineers to maintain high availability, optimize resources, and troubleshoot issues effectively. SigNoz, an open-source APM (Application Performance Monitoring) tool, is perfectly suited for this task.

In this blog, we will explore how to set up and use SigNoz to monitor LLMs, with code snippets and best practices for effective monitoring.

Why Monitoring LLMs Matters

LLMs are resource-intensive applications. Monitoring their performance provides benefits such as:

  • Latency Tracking: Measure how long it takes to process a request.
  • Error Reporting: Identify runtime errors and failures.
  • Resource Usage: Monitor CPU, memory, and GPU usage.
  • Throughput: Check how many requests are being processed over time.
  • Traffic Monitoring: Understand user behavior by tracking API requests.

What is SigNoz?

SigNoz is a full-stack open-source observability and monitoring platform. It provides metrics, traces, and logs that help DevOps teams monitor distributed applications. With built-in support for metrics, tracing, and logging, it’s a perfect tool for keeping tabs on the performance of LLMs.

Prerequisites

Before starting, ensure you have the following:

  1. A machine running Docker.
  2. Python installed with a pre-configured LLM (e.g., HuggingFace, OpenAI, etc.).
  3. Basic knowledge of Python and web frameworks like FastAPI or Flask.

Step 1: Setting Up SigNoz

1.1. Install SigNoz

SigNoz is available as a Docker image. To get started, you can install it with the following command:

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git clone https://github.com/SigNoz/signoz.git cd signoz/deploy/ ./install.sh

This will set up SigNoz locally with Grafana, OpenTelemetry, and Prometheus.

1.2. Verify Installation

Once SigNoz is installed, access the dashboard at http://localhost:3000 to verify that it’s working. You can see built-in sample data already being displayed.

Step 2: Monitoring LLMs with FastAPI

To demonstrate how to monitor LLMs, we’ll use a simple FastAPI server serving requests to an LLM (let’s say a HuggingFace model). We’ll integrate SigNoz with this server to track metrics.

2.1. Install Required Packages

You need FastAPI, OpenTelemetry (for tracing), and SigNoz-specific exporters. You can install them using pip:

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pip install fastapi uvicorn opentelemetry-api opentelemetry-sdk opentelemetry-instrumentation-fastapi opentelemetry-exporter-otlp

2.2. Create a FastAPI Application

Let’s create a simple FastAPI app that serves requests to an LLM.

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from fastapi import FastAPI from transformers import pipeline from opentelemetry.instrumentation.fastapi import FastAPIInstrumentor from opentelemetry.sdk.trace import TracerProvider from opentelemetry.sdk.resources import Resource from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter from opentelemetry.sdk.trace.export import SimpleSpanProcessor # Initialize tracing and FastAPI app = FastAPI() # Initialize HuggingFace LLM pipeline (e.g., sentiment analysis) model = pipeline('sentiment-analysis') # Set up OpenTelemetry Tracing resource = Resource(attributes={"service.name": "llm-monitoring"}) provider = TracerProvider(resource=resource) span_processor = SimpleSpanProcessor(OTLPSpanExporter(endpoint="http://localhost:4317", insecure=True)) provider.add_span_processor(span_processor) FastAPIInstrumentor.instrument_app(app, tracer_provider=provider) @app.get("/predict/") async def get_prediction(text: str):     result = model(text)     return {"prediction": result}

2.3. Run the FastAPI App

Run the app with Uvicorn:

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uvicorn main:app --host 0.0.0.0 --port 8000

Your FastAPI app is now serving predictions from the LLM model and sending traces to SigNoz.

Step 3: Visualizing Data in SigNoz

3.1. Open SigNoz Dashboard

Visit http://localhost:3000 and navigate to the tracing section. You should be able to see traces coming from the FastAPI application, showing you the latency, errors, and throughput of your LLM service.

3.2. Custom Metrics

SigNoz allows you to add custom metrics for monitoring. For instance, you can track how long each LLM model prediction takes:

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from opentelemetry import trace from time import time @app.get("/predict/") async def get_prediction(text: str):     tracer = trace.get_tracer(__name__)     start_time = time()     with tracer.start_as_current_span("predict_span"):         result = model(text)     prediction_time = time() - start_time     return {"prediction": result, "time_taken": prediction_time}

You can now visualize this custom metric in SigNoz by querying the data.

Step 4: Best Practices for Monitoring LLMs

Here are some tips to make the most of SigNoz for LLM monitoring:

  1. Alerting: Set up alerts for specific thresholds (e.g., latencies exceeding 1 second).
  2. Dashboards: Create dedicated dashboards for LLM-specific metrics like request latency, model load time, and API call volume.
  3. Logs: Combine tracing with logs for more detailed troubleshooting of your LLM models.
  4. Auto-scaling: Use SigNoz metrics to trigger auto-scaling when traffic spikes to your LLM model.

Conclusion

Monitoring LLMs is critical to maintaining their performance and availability. SigNoz provides a robust, open-source solution for observability, enabling you to monitor your models with ease. By following this guide, you can set up SigNoz to track performance metrics, trace LLM behavior, and ensure the smooth operation of your language model-powered applications.

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