What are the best practices for handling W&B Inference errors?

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  2 minute read  

Follow these best practices to handle W&B Inference errors gracefully and maintain reliable applications.

1. Always implement error handling

Wrap API calls in try-except blocks:

import openai

try:
    response = client.chat.completions.create(
        model="meta-llama/Llama-3.1-8B-Instruct",
        messages=messages
    )
except Exception as e:
    print(f"Error: {e}")
    # Handle error appropriately

2. Use retry logic with exponential backoff

import time
from typing import Optional

def call_inference_with_retry(
    client, 
    messages, 
    model: str,
    max_retries: int = 3,
    base_delay: float = 1.0
) -> Optional[str]:
    for attempt in range(max_retries):
        try:
            response = client.chat.completions.create(
                model=model,
                messages=messages
            )
            return response.choices[0].message.content
        except Exception as e:
            if attempt == max_retries - 1:
                raise
            
            # Calculate delay with exponential backoff
            delay = base_delay * (2 ** attempt)
            print(f"Attempt {attempt + 1} failed, retrying in {delay}s...")
            time.sleep(delay)
    
    return None

3. Monitor your usage

  • Track credit usage in the W&B Billing page
  • Set up alerts before hitting limits
  • Log API usage in your application

4. Handle specific error codes

def handle_inference_error(error):
    error_str = str(error)
    
    if "401" in error_str:
        # Invalid authentication
        raise ValueError("Check your API key and project configuration")
    elif "429" in error_str:
        if "quota" in error_str:
            # Out of credits
            raise ValueError("Insufficient credits")
        else:
            # Rate limited
            return "retry"
    elif "500" in error_str or "503" in error_str:
        # Server error
        return "retry"
    else:
        # Unknown error
        raise

5. Set appropriate timeouts

Configure reasonable timeouts for your use case:

# For longer responses
client = openai.OpenAI(
    base_url='https://api.inference.wandb.ai/v1',
    api_key="your-api-key",
    timeout=60.0  # 60 second timeout
)

Additional tips

  • Log errors with timestamps for debugging
  • Use async operations for better concurrency handling
  • Implement circuit breakers for production systems
  • Cache responses when appropriate to reduce API calls

Last modified August 6, 2025