Tracing Serverless Inference Responses

2025-01-21 14:52 UTC gpt-4o Open in ChatGPT ↗

Tracing your responses during serverless inference with Hugging Face involves setting up telemetry and debugging tools to monitor and understand how your LLM is generating responses. Here’s a step-by-step guide to achieving this:

1. Set Up a Hugging Face Account

Requirement: Free account is sufficient for basic serverless inference, but a paid account (Pro or Enterprise) is necessary for advanced features like logging and tracing.
Sign up or log in to your Hugging Face account.

2. Prepare Your Model and Codebase

Identify the Model:
- Select a model from the Hugging Face Model Hub.
- Ensure the model supports serverless inference (AutoModelFor* APIs or hosted on Inference Endpoints).
Install necessary dependencies:
Terminal window
```
pip install transformers huggingface-hub
```

3. Enable Serverless Inference

Use either Hugging Face’s Inference API or deploy an Inference Endpoint.

For Inference API:

Retrieve your API Token from the Settings page.

Example usage:

from huggingface_hub import InferenceApi

api = InferenceApi(repo_id="bigscience/bloom", token="your_api_token")
response = api(inputs="Your query here.")
print(response)

For Inference Endpoints:
- A paid account is required to deploy and manage endpoints.
- Deploy a custom model endpoint via the Inference Endpoints dashboard.

4. Enable Tracing

Tracing allows you to monitor requests and responses. This feature is part of the Pro or Enterprise plans.
Enable tracing in your Inference Endpoint settings:
1. Go to the Inference Endpoints page.
2. Select your endpoint.
3. Enable “Tracing” in the Monitoring tab.

5. Instrument Your Code for Logging

Add logging to your Python application to capture request details and response metadata.

Example:

import logging
from huggingface_hub import InferenceApi

# Set up logging
logging.basicConfig(level=logging.INFO, filename="trace.log", filemode="w")
logger = logging.getLogger()

# Initialize API
api = InferenceApi(repo_id="bigscience/bloom", token="your_api_token")

# Trace a request
prompt = "Explain the theory of relativity."
logger.info(f"Request prompt: {prompt}")
response = api(inputs=prompt)
logger.info(f"Response: {response}")
print(response)

6. Access Tracing Data

For Inference API:
- Logs are not directly provided; use your application logs for tracing.
For Inference Endpoints:
- Paid plans provide access to detailed tracing, including latency, input/output payloads, and errors.

7. Use Additional Monitoring Tools

If you’re using a cloud service, integrate with external monitoring systems like:
- AWS CloudWatch or Azure Monitor for cloud-based applications.
- Prometheus or Grafana for custom dashboards.

8. Debug with Hugging Face Logs

Paid plans provide detailed endpoint logs and traces in the dashboard:
- Navigate to the endpoint.
- Review request/response logs and latency metrics.

9. Test Locally Before Deployment

Test your inference code locally with tracing and debugging enabled.

Example:

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gpt2")
model = AutoModelForCausalLM.from_pretrained("gpt2")

prompt = "Hello, how are you?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

10. Upgrade to Pro or Enterprise Plan (Optional)

Features like custom Inference Endpoints, request tracing, and performance monitoring require a paid plan.
Visit the Pricing page to upgrade.

Summary of Paid Features

Feature	Free Plan	Paid Plans (Pro/Enterprise)
Inference API	✅	✅
Inference Endpoints	❌	✅
Tracing & Monitoring	❌	✅
Logs & Metrics Dashboard	❌	✅

Let me know if you’d like sample scripts or further guidance!

Got it! Since you have a paid Hugging Face plan and are using LangChain with LangSmith for monitoring, here’s how to set up and trace responses effectively while running inference on Hugging Face:

Step-by-Step Process

1. Install Necessary Libraries

Make sure you have all the necessary libraries installed:

pip install langchain langsmith huggingface-hub transformers

2. Set Up Hugging Face Inference with LangChain

Use Hugging Face’s integration in LangChain for serverless inference. You can use the HuggingFaceHub integration.

Example:

from langchain import HuggingFaceHub
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate

# Hugging Face Hub setup
HUGGINGFACE_API_KEY = "your_api_token"  # Ensure this is securely stored
llm = HuggingFaceHub(
    repo_id="bigscience/bloom",  # Replace with your model
    huggingfacehub_api_token=HUGGINGFACE_API_KEY
)

# Define a simple prompt
prompt = PromptTemplate(template="What is the meaning of life?")
chain = LLMChain(llm=llm, prompt=prompt)

# Run inference
response = chain.run()
print(response)

3. Integrate LangSmith for Tracing

LangSmith (by LangChain) is a great tool for monitoring and debugging your LangChain workflows.

Setup:

Log into LangSmith:
- Go to LangSmith dashboard.
- Get your API key from the dashboard.
Install LangSmith Client:
Terminal window
```
pip install langsmith
```

Initialize LangSmith in Your Code: Add the LangSmith Tracer to track chains and their outputs.

from langchain.tracing import tracing_enabled
from langchain import HuggingFaceHub, LLMChain
from langchain.prompts import PromptTemplate

# LangSmith API Key (from your dashboard)
LANGSMITH_API_KEY = "your_langsmith_api_key"

# Set up LangSmith tracing
tracing_enabled(api_key=LANGSMITH_API_KEY, project_name="HuggingFace Inference")

# Hugging Face LLM
llm = HuggingFaceHub(
    repo_id="bigscience/bloom",
    huggingfacehub_api_token="your_api_token"
)

# Define a prompt
prompt = PromptTemplate(template="What is the capital of France?")
chain = LLMChain(llm=llm, prompt=prompt)

# Trace a request
response = chain.run()
print(response)

All requests and responses will now be logged in the LangSmith dashboard.

4. Monitor Results in LangSmith

Dashboard Access:
- Open the LangSmith dashboard.
- Navigate to your project (e.g., “HuggingFace Inference”).
- View details of each traced request:
  - Inputs and outputs
  - Latency metrics
  - Debugging details

5. Advanced Debugging with LangSmith

You can enhance your LangSmith integration by:

Custom Metadata: Include additional metadata for each trace (e.g., user ID, request purpose).

from langchain.tracing import tracing_enabled

with tracing_enabled(
    api_key=LANGSMITH_API_KEY,
    project_name="HuggingFace Inference",
    metadata={"use_case": "FAQ response"}
):
    response = chain.run()
    print(response)

Asynchronous Tracing: Use async chains if required for high-throughput workloads.

6. Deploy Hugging Face Inference Endpoints

If you’re using custom Hugging Face endpoints (not the public Inference API), update the HuggingFaceHub integration:

llm = HuggingFaceHub(
    repo_id="your_custom_endpoint_repo_id",
    huggingfacehub_api_token="your_api_token",
    endpoint_url="https://your-endpoint-url"  # Optional for custom endpoints
)

7. Combine Logs from Hugging Face and LangSmith

LangSmith captures LangChain workflow-level traces, while Hugging Face monitoring (enabled in your Inference Endpoints settings) captures model-specific logs like input/output payloads and performance metrics.
Use both for comprehensive tracing:
- View LangSmith for chain-level debugging.
- View Hugging Face logs for low-level model performance.

8. Debug Locally

To ensure everything works locally before deploying:

Test Hugging Face Integration:

from huggingface_hub import InferenceApi

api = InferenceApi(repo_id="bigscience/bloom", token="your_api_token")
response = api(inputs="Translate 'hello' to French.")
print(response)

Test LangSmith Tracing:
- Run a local script with tracing enabled and confirm logs appear in the LangSmith dashboard.

Summary of Steps:

Install and configure Hugging Face and LangChain.
Use HuggingFaceHub to integrate Hugging Face models in LangChain.
Set up LangSmith tracing with tracing_enabled().
Monitor results in LangSmith and Hugging Face dashboards.
Debug locally before deployment.

Let me know if you need specific code snippets or more details on any step!