Aditya Mishra adimyth

Memory Growth in Python FastAPI Services: A Complete Guide

The Problem

Your Python/FastAPI service consumes RAM during request processing and doesn't release it back to the OS, even after the request completes. Over time (hours/days), memory usage grows from 500MB → 1GB → 2GB → eventually crashes.

Symptoms:

✅ Memory increases during processing (expected)
❌ Memory stays high after processing (problem)
❌ Each request adds a bit more memory (accumulation)

Inbox and Outbox Patterns: A Complete Guide

Core Differentiation Summary

Inbox Pattern: "Reliable Receiving"

Purpose: Ensure recipients can reliably consume messages
Location: Consumer/Recipient side
Problem Solved: Messages lost when recipient is unavailable
Use When: You need guaranteed message availability for consumers

Links to resources for creating Data Science Reports at different stages of the lifecycle.

Data Analysis Report

http://www.stat.cmu.edu/~brian/701/notes/paper-structure.pdf

Project Analysis Report

Stanford University's 2014 students final Data Science project reports
Kaggle Winning Solution Template - For technical audience only

Profling Tools

Setup

Airtel Dongle

https://opentechnics.com/how-to-get-airtel-4g-dongle-to-work-on-linux/

Arya Setup

Install Docker
- https://www.cyberciti.biz/faq/install-use-setup-docker-on-rhel7-centos7-linux/
- https://docs.docker.com/engine/install/centos/

	import os
	import aiohttp
	import asyncio
	import tempfile
	from pydub import AudioSegment
	from pydub.silence import split_on_silence


	async def split_audio_on_silence_and_save(audio_url: str, output_dir: str):
	# Create output directory if it doesn't exist

	CREATE TABLE sales (
	sale_date DATE,
	color VARCHAR(10),
	region VARCHAR(10),
	quantity INT,
	revenue DECIMAL(10, 2)
	);

	INSERT INTO sales VALUES
	('2016-01-01', 'Red', 'North', 1, 13.00),

	def is_illegal(input):
	words = input.split(" ")
	for word in words:
	if word in "ENGLISH_WORDS":
	return "ILLEGAL"
	for char in word:
	if char in [" ", "\n", "\t"]:
	return "ILLEGAL"
	return None

	#
	# This is the main Apache HTTP server configuration file. It contains the
	# configuration directives that give the server its instructions.
	# See <URL:http://httpd.apache.org/docs/2.4/> for detailed information.
	# In particular, see
	# <URL:http://httpd.apache.org/docs/2.4/mod/directives.html>
	# for a discussion of each configuration directive.
	#
	# Do NOT simply read the instructions in here without understanding
	# what they do. They're here only as hints or reminders. If you are unsure

	from glob import glob

	import pandas as pd
	import numpy as np
	import matplotlib.pyplot as plt
	from PIL import Image

	import pytorch_lightning as pl
	from pytorch_lightning.callbacks import ModelCheckpoint
	from pytorch_lightning.callbacks.early_stopping import EarlyStopping

	import numpy as np
	import pandas as pd
	from sklearn.model_selection import StratifiedKFold

	df = pd.read_csv(path_to_data)
	n_bins = 1+np.log2(df.shape[0]) # Sturge's rule
	df["bins"] = pd.cut(df.target, n_bins, labels=False)

	n_folds = 5
	skf = StratifiedKFold(n_splits=n_folds)