Fixing AttributeError In Logfire With ProcessPoolExecutor

Hey guys! Ever run into a nasty AttributeError when using logfire with ProcessPoolExecutor in your FastAPI applications? It's a real head-scratcher, especially when you're trying to leverage multiprocessing for tasks like Optical Character Recognition (OCR). Let's dive deep into this common issue, understand why it pops up, and then explore effective solutions. I'll walk you through the problem, the root cause, and how to get things working smoothly. This is a common issue with logfire, multiprocessing and FastAPI. This will help you resolve the AttributeError issue when using ProcessPoolExecutor. We're going to use OCR as the example for the ProcessPoolExecutor.

Understanding the Problem: The AttributeError

The core of the problem lies in how Python's multiprocessing interacts with closures, especially when a logging library like logfire is involved. The traceback points directly to an AttributeError: Can't get local object. This error is a classic sign of the pickling process failing during multiprocessing. Pickling is how Python serializes objects so they can be passed between processes. When ProcessPoolExecutor tries to pickle a function that references a local object (in this case, setup_logfire.<locals>.exception_callback), it runs into trouble. This often happens because the local context isn't accessible in the child processes created by ProcessPoolExecutor. This is particularly tricky with libraries like logfire because they might internally use closures or refer to objects that are not easily serializable across process boundaries. In your provided code, the error arises when the RapidOCR engine, initialized in the main process, is attempted to be accessed within the child process through model_predict. The logfire integration with FastAPI, intended to capture and log errors, complicates things because it tries to access or serialize parts of the local context.

Root Cause: Pickling and Closures

The heart of the issue is pickling and closures. When you use ProcessPoolExecutor, your code gets executed in separate processes. These processes need to serialize the data to send it back to the parent process. The pickling library is used, and it needs to pickle the code to serialize it, but it cannot serialize closures, which are functions that reference variables from an enclosing scope. If your code, like the exception_callback in logfire, depends on a closure, then pickling fails, and you get the AttributeError. The RapidOCR engine in your code adds to this complexity because the initialization is outside the endpoint. The logfire library also has its hooks that create the exception_callback, which depends on the context of the running app. When you call the function model_predict in the child process, it does not have the context, and it fails to serialize. In this case, logfire's integration with FastAPI, intended to capture and log errors, tries to access the local context for logging purposes which is causing the problem.

Troubleshooting the Code

Let's break down the code you provided and see where the issues occur, and why the solutions can solve the problem. Let's analyze the code.

import asyncio
from concurrent.futures import ProcessPoolExecutor

import logfire
from fastapi import FastAPI, Request
from rapidocr import RapidOCR

logfire.configure(
  token='abc',
  environment='abc',
)
app = FastAPI()

logfire.instrument_fastapi(
  app,
  capture_headers=True,
  excluded_urls=['/health'],
)
engine = None

def create_model():
  global engine
  engine = RapidOCR()


# if you try to run all predicts concurrently, it will result in CPU trashing.
pool = ProcessPoolExecutor(max_workers=1, initializer=create_model)

def model_predict():
  return engine(
    'https://rapidai.github.io/RapidOCRDocs/main/images/vis_det_cls_rec.jpg'
  )


@app.get('/ocr')
async def ocr_recognize(request: Request):
  loop = asyncio.get_event_loop()
  # worker should be initialized outside endpoint to avoid cold start
  result = await loop.run_in_executor(pool, model_predict)
  return {}

1. Logfire Configuration: The logfire.configure call initializes logfire. The logfire.instrument_fastapi instruments the FastAPI app. The problematic aspect is the interaction with ProcessPoolExecutor.
2. OCR Initialization: The RapidOCR engine is initialized using the create_model function as the initializer for the ProcessPoolExecutor. This is done to avoid cold starts. While this helps with initialization, it still doesn't solve the pickling problem since logfire still needs to serialize the environment to send data.
3. model_predict Function: It is a regular function that uses the initialized engine. This is where the error likely occurs since it will run in a child process that has no context. It might fail to serialize the environment.
4. FastAPI Endpoint: The ocr_recognize endpoint uses loop.run_in_executor to run model_predict in a separate process. The loop is the event loop for the FastAPI app, and the pool is the ProcessPoolExecutor. The child process will not have the context, which raises the exception.

Solution 1: Initialize within the Process

The first thing is to ensure that the engine and the logfire are configured inside the child process. The key here is to move the initialization of RapidOCR inside the model_predict function. This avoids the need to pickle the engine object and context. Also, configure logfire inside the model_predict function. This is the simplest way to solve this problem.

import asyncio
from concurrent.futures import ProcessPoolExecutor

import logfire
from fastapi import FastAPI, Request
from rapidocr import RapidOCR

app = FastAPI()


@app.get('/ocr')
async def ocr_recognize(request: Request):
  async def model_predict():
    # Configure logfire inside the child process
    logfire.configure(token='abc', environment='abc')
    engine = RapidOCR()
    return engine(
        'https://rapidai.github.io/RapidOCRDocs/main/images/vis_det_cls_rec.jpg'
    )

  loop = asyncio.get_event_loop()
  result = await loop.run_in_executor(pool, model_predict)
  return {}

# if you try to run all predicts concurrently, it will result in CPU trashing.
pool = ProcessPoolExecutor(max_workers=1)

In this solution, the logfire and the RapidOCR are initialized within the model_predict function. This makes sure that there is no need to pickle it. The main process only sends the function to the child process for running, and there is no context involved.

Solution 2: Using functools.partial and Avoiding Closures

Another approach is to use functools.partial to create a callable that can be pickled. This is great for passing arguments to a function without relying on closures. This way, you don't need to pickle an object that relies on an outside context. While the first solution solves the problem, this solution makes the code more robust and flexible.

import asyncio
from concurrent.futures import ProcessPoolExecutor
from functools import partial

import logfire
from fastapi import FastAPI, Request
from rapidocr import RapidOCR

app = FastAPI()

# Configure logfire outside but ensure it doesn't cause pickling issues.
logfire.configure(token='abc', environment='abc')


def initialize_ocr():
  return RapidOCR()


# Create a partial function to be executed in the executor
def model_predict(ocr_engine, image_url):
  return ocr_engine(image_url)


@app.get('/ocr')
async def ocr_recognize(request: Request):
  loop = asyncio.get_event_loop()
  ocr_engine = initialize_ocr()
  predict_partial = partial(model_predict, ocr_engine, 'https://rapidai.github.io/RapidOCRDocs/main/images/vis_det_cls_rec.jpg')
  result = await loop.run_in_executor(pool, predict_partial)
  return {}

# if you try to run all predicts concurrently, it will result in CPU trashing.
pool = ProcessPoolExecutor(max_workers=1)

In this code:

1. We have moved the configuration of logfire outside the endpoint, but in a way that it does not affect the pickling issue.
2. We defined initialize_ocr, which will initialize the RapidOCR object. We will pass this to the partial function.
3. We defined the model_predict function. It takes two parameters: ocr_engine and image_url
4. Inside the endpoint, we create ocr_engine and pass it to the partial function.
5. We call the partial function with the pool to execute.

This approach ensures that everything that needs to run in the child process is passed as an argument. The partial function is picklable, and the child process will run correctly.

Solution 3: Re-architecting for Asynchronous Operations

If possible, consider refactoring your code to use asynchronous operations within your FastAPI application instead of relying on ProcessPoolExecutor. This is particularly useful if the OCR task is I/O-bound (waiting for network or disk operations), as it can improve overall performance. This is generally the best solution for FastAPI applications.

import asyncio

import logfire
from fastapi import FastAPI, Request
from rapidocr import RapidOCR

app = FastAPI()

# Configure logfire outside but ensure it doesn't cause pickling issues.
logfire.configure(token='abc', environment='abc')
engine = RapidOCR()


async def model_predict(image_url):
  # No need for process pool.  Just use await for async operations
  return engine(image_url)


@app.get('/ocr')
async def ocr_recognize(request: Request):
  result = await model_predict('https://rapidai.github.io/RapidOCRDocs/main/images/vis_det_cls_rec.jpg')
  return {}

Here, instead of using ProcessPoolExecutor, we're making the model_predict function asynchronous and calling it directly using await. If RapidOCR supports asynchronous operations (e.g., uses asyncio internally for network requests), this can be a very efficient solution, as it prevents the overhead of creating and managing separate processes. If RapidOCR does not support asynchronous operations, consider using a different library or wrapping the operations to run in an asyncio.to_thread to prevent blocking the event loop.

Conclusion: Choosing the Right Approach

So, guys, the AttributeError when using ProcessPoolExecutor with logfire in FastAPI stems from pickling issues. The best solution depends on your needs. Here's a quick recap:

• Initialize within the Process: The simplest fix, especially if you can initialize dependencies within the worker function.
• Use functools.partial: More robust, especially if you need to pass multiple arguments without relying on closures.
• Re-architect for Asynchronous Operations: The preferred solution if the OCR task is I/O-bound. This usually gives the best performance.

I hope this helps you guys! Let me know if you run into any more issues or have any other questions. Keep coding, and keep those errors at bay!