Resolving Memory Leak in a Pandas DataFrame While Training a Model with FastAPI
During development of a machine learning API using FastAPI and Pandas, I noticed a memory leak that occurs during the training phase of my model. The API is supposed to handle multiple requests for training with different datasets, but with each request, the memory usage keeps increasing instead of stabilizing after the process completes. In my setup, I'm using a version of Python 3.9 along with FastAPI 0.68.0 and Pandas 1.3.3. My typical workflow for handling the data looks like this: ```python import pandas as pd from fastapi import FastAPI app = FastAPI() @app.post("/train") def train_model(data: list): df = pd.DataFrame(data) # Assume some preprocessing here model.fit(df) return {"status": "model trained"} ``` I’ve tried explicitly deleting the DataFrame after training: ```python del df ``` This had no effect on the memory consumption. Using `gc.collect()` also didn’t help. The application remains responsive but after several training calls, the memory usage becomes unsustainable, forcing me to restart the server frequently. I am also utilizing a generator to stream data to the model, aiming to lower memory usage, but it doesn’t seem to alleviate the problem. Here’s a simplified version of the generator: ```python def data_generator(data): for batch in data: yield batch ``` Attempting to switch to a Batch training strategy using Scikit-learn's `partial_fit` method didn’t yield better results either. I read that the issue could be related to how Pandas manages memory in conjunction with FastAPI's async capabilities. Any recommendations on how to effectively manage memory in this setup? Is there a better approach to prevent memory leaks when handling multiple training requests?