#asyncio

[jcmarchi]

Asynchronous LLM API Calls in Python: A Comprehensive Guide

New Post has been published on https://thedigitalinsider.com/asynchronous-llm-api-calls-in-python-a-comprehensive-guide/

Asynchronous LLM API Calls in Python: A Comprehensive Guide

As developers and dta scientists, we often find ourselves needing to interact with these powerful models through APIs. However, as our applications grow in complexity and scale, the need for efficient and performant API interactions becomes crucial. This is where asynchronous programming shines, allowing us to maximize throughput and minimize latency when working with LLM APIs.

In this comprehensive guide, we’ll explore the world of asynchronous LLM API calls in Python. We’ll cover everything from the basics of asynchronous programming to advanced techniques for handling complex workflows. By the end of this article, you’ll have a solid understanding of how to leverage asynchronous programming to supercharge your LLM-powered applications.

Before we dive into the specifics of async LLM API calls, let’s establish a solid foundation in asynchronous programming concepts.

Asynchronous programming allows multiple operations to be executed concurrently without blocking the main thread of execution. In Python, this is primarily achieved through the asyncio module, which provides a framework for writing concurrent code using coroutines, event loops, and futures.

Key concepts:

Coroutines: Functions defined with async def that can be paused and resumed.

Event Loop: The central execution mechanism that manages and runs asynchronous tasks.

Awaitables: Objects that can be used with the await keyword (coroutines, tasks, futures).

Here’s a simple example to illustrate these concepts:

import asyncio async def greet(name): await asyncio.sleep(1) # Simulate an I/O operation print(f"Hello, name!") async def main(): await asyncio.gather( greet("Alice"), greet("Bob"), greet("Charlie") ) asyncio.run(main())

In this example, we define an asynchronous function greet that simulates an I/O operation with asyncio.sleep(). The main function uses asyncio.gather() to run multiple greetings concurrently. Despite the sleep delay, all three greetings will be printed after approximately 1 second, demonstrating the power of asynchronous execution.

The Need for Async in LLM API Calls

When working with LLM APIs, we often encounter scenarios where we need to make multiple API calls, either in sequence or parallel. Traditional synchronous code can lead to significant performance bottlenecks, especially when dealing with high-latency operations like network requests to LLM services.

Consider a scenario where we need to generate summaries for 100 different articles using an LLM API. With a synchronous approach, each API call would block until it receives a response, potentially taking several minutes to complete all requests. An asynchronous approach, on the other hand, allows us to initiate multiple API calls concurrently, dramatically reducing the overall execution time.

Setting Up Your Environment

To get started with async LLM API calls, you’ll need to set up your Python environment with the necessary libraries. Here’s what you’ll need:

Python 3.7 or higher (for native asyncio support)

aiohttp: An asynchronous HTTP client library

openai: The official OpenAI Python client (if you’re using OpenAI’s GPT models)

langchain: A framework for building applications with LLMs (optional, but recommended for complex workflows)

You can install these dependencies using pip:

pip install aiohttp openai langchain <div class="relative flex flex-col rounded-lg">

Basic Async LLM API Calls with asyncio and aiohttp

Let’s start by making a simple asynchronous call to an LLM API using aiohttp. We’ll use OpenAI’s GPT-3.5 API as an example, but the concepts apply to other LLM APIs as well.

import asyncio import aiohttp from openai import AsyncOpenAI async def generate_text(prompt, client): response = await client.chat.completions.create( model="gpt-3.5-turbo", messages=["role": "user", "content": prompt] ) return response.choices[0].message.content async def main(): prompts = [ "Explain quantum computing in simple terms.", "Write a haiku about artificial intelligence.", "Describe the process of photosynthesis." ] async with AsyncOpenAI() as client: tasks = [generate_text(prompt, client) for prompt in prompts] results = await asyncio.gather(*tasks) for prompt, result in zip(prompts, results): print(f"Prompt: promptnResponse: resultn") asyncio.run(main())

In this example, we define an asynchronous function generate_text that makes a call to the OpenAI API using the AsyncOpenAI client. The main function creates multiple tasks for different prompts and uses asyncio.gather() to run them concurrently.

This approach allows us to send multiple requests to the LLM API simultaneously, significantly reducing the total time required to process all prompts.

Advanced Techniques: Batching and Concurrency Control

While the previous example demonstrates the basics of async LLM API calls, real-world applications often require more sophisticated approaches. Let’s explore two important techniques: batching requests and controlling concurrency.

Batching Requests: When dealing with a large number of prompts, it’s often more efficient to batch them into groups rather than sending individual requests for each prompt. This reduces the overhead of multiple API calls and can lead to better performance.

import asyncio from openai import AsyncOpenAI async def process_batch(batch, client): responses = await asyncio.gather(*[ client.chat.completions.create( model="gpt-3.5-turbo", messages=["role": "user", "content": prompt] ) for prompt in batch ]) return [response.choices[0].message.content for response in responses] async def main(): prompts = [f"Tell me a fact about number i" for i in range(100)] batch_size = 10 async with AsyncOpenAI() as client: results = [] for i in range(0, len(prompts), batch_size): batch = prompts[i:i+batch_size] batch_results = await process_batch(batch, client) results.extend(batch_results) for prompt, result in zip(prompts, results): print(f"Prompt: promptnResponse: resultn") asyncio.run(main())

Concurrency Control: While asynchronous programming allows for concurrent execution, it’s important to control the level of concurrency to avoid overwhelming the API server or exceeding rate limits. We can use asyncio.Semaphore for this purpose.

import asyncio from openai import AsyncOpenAI async def generate_text(prompt, client, semaphore): async with semaphore: response = await client.chat.completions.create( model="gpt-3.5-turbo", messages=["role": "user", "content": prompt] ) return response.choices[0].message.content async def main(): prompts = [f"Tell me a fact about number i" for i in range(100)] max_concurrent_requests = 5 semaphore = asyncio.Semaphore(max_concurrent_requests) async with AsyncOpenAI() as client: tasks = [generate_text(prompt, client, semaphore) for prompt in prompts] results = await asyncio.gather(*tasks) for prompt, result in zip(prompts, results): print(f"Prompt: promptnResponse: resultn") asyncio.run(main())

In this example, we use a semaphore to limit the number of concurrent requests to 5, ensuring we don’t overwhelm the API server.

Error Handling and Retries in Async LLM Calls

When working with external APIs, it’s crucial to implement robust error handling and retry mechanisms. Let’s enhance our code to handle common errors and implement exponential backoff for retries.

import asyncio import random from openai import AsyncOpenAI from tenacity import retry, stop_after_attempt, wait_exponential class APIError(Exception): pass @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10)) async def generate_text_with_retry(prompt, client): try: response = await client.chat.completions.create( model="gpt-3.5-turbo", messages=["role": "user", "content": prompt] ) return response.choices[0].message.content except Exception as e: print(f"Error occurred: e") raise APIError("Failed to generate text") async def process_prompt(prompt, client, semaphore): async with semaphore: try: result = await generate_text_with_retry(prompt, client) return prompt, result except APIError: return prompt, "Failed to generate response after multiple attempts." async def main(): prompts = [f"Tell me a fact about number i" for i in range(20)] max_concurrent_requests = 5 semaphore = asyncio.Semaphore(max_concurrent_requests) async with AsyncOpenAI() as client: tasks = [process_prompt(prompt, client, semaphore) for prompt in prompts] results = await asyncio.gather(*tasks) for prompt, result in results: print(f"Prompt: promptnResponse: resultn") asyncio.run(main())

This enhanced version includes:

A custom APIError exception for API-related errors.

A generate_text_with_retry function decorated with @retry from the tenacity library, implementing exponential backoff.

Error handling in the process_prompt function to catch and report failures.

Optimizing Performance: Streaming Responses

For long-form content generation, streaming responses can significantly improve the perceived performance of your application. Instead of waiting for the entire response, you can process and display chunks of text as they become available.

import asyncio from openai import AsyncOpenAI async def stream_text(prompt, client): stream = await client.chat.completions.create( model="gpt-3.5-turbo", messages=["role": "user", "content": prompt], stream=True ) full_response = "" async for chunk in stream: if chunk.choices[0].delta.content is not None: content = chunk.choices[0].delta.content full_response += content print(content, end='', flush=True) print("n") return full_response async def main(): prompt = "Write a short story about a time-traveling scientist." async with AsyncOpenAI() as client: result = await stream_text(prompt, client) print(f"Full response:nresult") asyncio.run(main())

This example demonstrates how to stream the response from the API, printing each chunk as it arrives. This approach is particularly useful for chat applications or any scenario where you want to provide real-time feedback to the user.

Building Async Workflows with LangChain

For more complex LLM-powered applications, the LangChain framework provides a high-level abstraction that simplifies the process of chaining multiple LLM calls and integrating other tools. Let’s look at an example of using LangChain with async capabilities:

This example shows how LangChain can be used to create more complex workflows with streaming and asynchronous execution. The AsyncCallbackManager and StreamingStdOutCallbackHandler enable real-time streaming of the generated content.

import asyncio from langchain.llms import OpenAI from langchain.prompts import PromptTemplate from langchain.chains import LLMChain from langchain.callbacks.manager import AsyncCallbackManager from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler async def generate_story(topic): llm = OpenAI(temperature=0.7, streaming=True, callback_manager=AsyncCallbackManager([StreamingStdOutCallbackHandler()])) prompt = PromptTemplate( input_variables=["topic"], template="Write a short story about topic." ) chain = LLMChain(llm=llm, prompt=prompt) return await chain.arun(topic=topic) async def main(): topics = ["a magical forest", "a futuristic city", "an underwater civilization"] tasks = [generate_story(topic) for topic in topics] stories = await asyncio.gather(*tasks) for topic, story in zip(topics, stories): print(f"nTopic: topicnStory: storyn'='*50n") asyncio.run(main())

Serving Async LLM Applications with FastAPI

To make your async LLM application available as a web service, FastAPI is an great choice due to its native support for asynchronous operations. Here’s an example of how to create a simple API endpoint for text generation:

from fastapi import FastAPI, BackgroundTasks from pydantic import BaseModel from openai import AsyncOpenAI app = FastAPI() client = AsyncOpenAI() class GenerationRequest(BaseModel): prompt: str class GenerationResponse(BaseModel): generated_text: str @app.post("/generate", response_model=GenerationResponse) async def generate_text(request: GenerationRequest, background_tasks: BackgroundTasks): response = await client.chat.completions.create( model="gpt-3.5-turbo", messages=["role": "user", "content": request.prompt] ) generated_text = response.choices[0].message.content # Simulate some post-processing in the background background_tasks.add_task(log_generation, request.prompt, generated_text) return GenerationResponse(generated_text=generated_text) async def log_generation(prompt: str, generated_text: str): # Simulate logging or additional processing await asyncio.sleep(2) print(f"Logged: Prompt 'prompt' generated text of length len(generated_text)") if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=8000)

This FastAPI application creates an endpoint /generate that accepts a prompt and returns generated text. It also demonstrates how to use background tasks for additional processing without blocking the response.

Best Practices and Common Pitfalls

As you work with async LLM APIs, keep these best practices in mind:

Use connection pooling: When making multiple requests, reuse connections to reduce overhead.

Implement proper error handling: Always account for network issues, API errors, and unexpected responses.

Respect rate limits: Use semaphores or other concurrency control mechanisms to avoid overwhelming the API.

Monitor and log: Implement comprehensive logging to track performance and identify issues.

Use streaming for long-form content: It improves user experience and allows for early processing of partial results.

[uwumacaronitime]

possibly the best joke I've ever made

[rock-cake-with-a-pin-in-it]

this is shamefully lacking in Python offerings:

there are two main ones, snorblpy and pysnorbl. tutorials online have a roughly equal mix of what they do. the APIs are not compatible and both require some learning to figure out the basic concepts.

one of them is a wrapper around libsnorble-2-dev, the other is a partial reimplementation of snorblite which is missing some major features from snorblite because the original maintainer got a job at a defence contractor and it's now unmaintained. but it does have at least one very useful extra feature that is hard to port into any of the other similar languages because of architectural features of how the other implementations were done.

pysprongloxide is also getting pretty popular. you can probably guess what that one is.

oh and there's also django-snorbl, which has possibly the cleanest API of the lot and several very useful features but is incredibly hard to separate from the web framework in question, for some reason.

every software is like. your mission-critical app requires you to use the scrimble protocol to squeeb some snorble files for sprongle expressions. do you use:

libsnorble-2-dev, a C library that the author only distributes as source code and therefore must be compiled from source using CMake

Squeeb.js, which sort of has most of the features you want, but requires about a gigabyte of Node dependencies and has only been in development for eight months and has 4.7k open issues on Github

Squeeh.js, a typosquatting trojan that uses your GPU to mine crypto if you install it by mistake

Sprongloxide, a Rust crate beloved by its fanatical userbase, which has been in version 0.9.* for about four years, and is actually just a thin wrapper for libsnorble-2-dev

GNU Scrimble, a GPLv3-licensed command-line tool maintained by the Free Software Foundation, which has over a hundred different flags, and also comes with an integrated Lisp interpreter for scripting, and also a TUI-based Pong implementation as an "easter egg", and also supports CSV, XML, JSON, PDF, XLSX, and even HTML files, but does not actually come with support for squeebing snorble files for ideological reasons. it does have a boomeresque drawing of a grinning meerkat as its logo, though

Microsoft Scrimble Framework Core, a .NET library that has all the features you need and more, but costs $399 anually and comes with a proprietary licensing agreement that grants Microsoft the right to tattoo advertisements on the inside of your eyelids

snorblite, a full-featured Perl module which is entirely developed and maintained by a single guy who is completely insane and constantly makes blog posts about how much he hates the ATF and the "woke mind-virus", but everyone uses it because it has all the features you need and is distributed under the MIT license

Google Squeebular (deprecated since 2017)

[utopicwork]

Of course I figure this out right after but PierMesh was weirdly hanging on some tasks and I realized it was because I was awaiting tasks I should have just been adding as asyncio tasks so after I push the next update things should run a lot smoother

[cleverhottubmiracle]

[ad_1] In this tutorial, we demonstrate how to harness Crawl4AI, a modern, Python‑based web crawling toolkit, to extract structured data from web pages directly within Google Colab. Leveraging the power of asyncio for asynchronous I/O, httpx for HTTP requests, and Crawl4AI’s built‑in AsyncHTTPCrawlerStrategy, we bypass the overhead of headless browsers while still parsing complex HTML via JsonCssExtractionStrategy. With just a few lines of code, you install dependencies (crawl4ai, httpx), configure HTTPCrawlerConfig to request only gzip/deflate (avoiding Brotli issues), define your CSS‑to‑JSON schema, and orchestrate the crawl through AsyncWebCrawler and CrawlerRunConfig. Finally, the extracted JSON data is loaded into pandas for immediate analysis or export.  What sets Crawl4AI apart is its unified API, which seamlessly switches between browser-based (Playwright) and HTTP-only strategies, its robust error-handling hooks, and its declarative extraction schemas. Unlike traditional headless-browser workflows, Crawl4AI allows you to choose the most lightweight and performant backend, making it ideal for scalable data pipelines, on-the-fly ETL in notebooks, or feeding LLMs and analytics tools with clean JSON/CSV outputs. !pip install -U crawl4ai httpx First, we install (or upgrade) Crawl4AI, the core asynchronous crawling framework, alongside HTTPX. This high-performance HTTP client provides all the building blocks we need for lightweight, asynchronous web scraping directly in Colab. import asyncio, json, pandas as pd from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, HTTPCrawlerConfig from crawl4ai.async_crawler_strategy import AsyncHTTPCrawlerStrategy from crawl4ai.extraction_strategy import JsonCssExtractionStrategy We bring in Python’s core async and data‑handling modules, asyncio for concurrency, json for parsing, and pandas for tabular storage, alongside Crawl4AI’s essentials: AsyncWebCrawler to drive the crawl, CrawlerRunConfig and HTTPCrawlerConfig to configure extraction and HTTP settings, AsyncHTTPCrawlerStrategy for a browser‑free HTTP backend, and JsonCssExtractionStrategy to map CSS selectors into structured JSON. http_cfg = HTTPCrawlerConfig( method="GET", headers= "User-Agent": "crawl4ai-bot/1.0", "Accept-Encoding": "gzip, deflate" , follow_redirects=True, verify_ssl=True ) crawler_strategy = AsyncHTTPCrawlerStrategy(browser_config=http_cfg) Here, we instantiate an HTTPCrawlerConfig to define our HTTP crawler’s behavior, using a GET request with a custom User-Agent, gzip/deflate encoding only, automatic redirects, and SSL verification. We then plug that into AsyncHTTPCrawlerStrategy, allowing Crawl4AI to drive the crawl via pure HTTP calls rather than a full browser. schema = "name": "Quotes", "baseSelector": "div.quote", "fields": [ "name": "quote", "selector": "span.text", "type": "text", "name": "author", "selector": "small.author", "type": "text", "name": "tags", "selector": "div.tags a.tag", "type": "text" ] extraction_strategy = JsonCssExtractionStrategy(schema, verbose=False) run_cfg = CrawlerRunConfig(extraction_strategy=extraction_strategy) We define a JSON‑CSS extraction schema targeting each quote block (div.quote) and its child elements (span.text, small.author, div.tags a.tag), then initializes a JsonCssExtractionStrategy with that schema, and wraps it in a CrawlerRunConfig so Crawl4AI knows exactly what structured data to pull on each request. async def crawl_quotes_http(max_pages=5): all_items = [] async with AsyncWebCrawler(crawler_strategy=crawler_strategy) as crawler: for p in range(1, max_pages+1): url = f" try: res = await crawler.arun(url=url, config=run_cfg) except Exception as e: print(f"❌ Page p failed outright: e") continue if not res.extracted_content: print(f"❌ Page p returned no content, skipping") continue try: items = json.loads(res.extracted_content) except Exception as e: print(f"❌ Page p JSON‑parse error: e") continue print(f"✅ Page p: len(items) quotes") all_items.extend(items) return pd.DataFrame(all_items) Now, this asynchronous function orchestrates the HTTP‑only crawl: it spins up an AsyncWebCrawler with our AsyncHTTPCrawlerStrategy, iterates through each page URL, and safely awaits crawler.arun(), handles any request or JSON parsing errors and collects the extracted quote records into a single pandas DataFrame for downstream analysis. df = asyncio.get_event_loop().run_until_complete(crawl_quotes_http(max_pages=3)) df.head() Finally, we kick off the crawl_quotes_http coroutine on Colab’s existing asyncio loop, fetching three pages of quotes, and then display the first few rows of the resulting pandas DataFrame to verify that our crawler returned structured data as expected. In conclusion, by combining Google Colab’s zero-config environment with Python’s asynchronous ecosystem and Crawl4AI’s flexible crawling strategies, we have now developed a fully automated pipeline for scraping and structuring web data in minutes. Whether you need to spin up a quick dataset of quotes, build a refreshable news‑article archive, or power a RAG workflow, Crawl4AI’s blend of httpx, asyncio, JsonCssExtractionStrategy, and AsyncHTTPCrawlerStrategy delivers both simplicity and scalability. Beyond pure HTTP crawls, you can instantly pivot to Playwright‑driven browser automation without rewriting your extraction logic, underscoring why Crawl4AI stands out as the go‑to framework for modern, production‑ready web data extraction. Here is the Colab Notebook. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. Don’t Forget to join our 90k+ ML SubReddit. 🔥 [Register Now] miniCON Virtual Conference on AGENTIC AI: FREE REGISTRATION + Certificate of Attendance + 4 Hour Short Event (May 21, 9 am- 1 pm PST) + Hands on Workshop Nikhil is an intern consultant at Marktechpost. He is pursuing an integrated dual degree in Materials at the Indian Institute of Technology, Kharagpur. Nikhil is an AI/ML enthusiast who is always researching applications in fields like biomaterials and biomedical science. With a strong background in Material Science, he is exploring new advancements and creating opportunities to contribute. [ad_2] Source link

[souhaillaghchimdev]

Introduction to Server and Network Programming

Server and network programming is essential for building applications that communicate over the internet or local networks. From creating web servers to building chat apps or IoT solutions, understanding networking fundamentals is key for any modern developer.

What is Server and Network Programming?

Server and network programming involves writing code that enables applications to communicate with each other over a network. This includes:

Creating and managing servers

Establishing network connections

Sending and receiving data (HTTP, TCP/IP, UDP)

Managing client-server interactions

Common Use Cases

Web servers and APIs

Chat applications

Multiplayer games

IoT device communication

File transfer services

Key Concepts in Network Programming

IP Address: Identifies a device on a network

Port: Endpoint for communication on a device

Client-Server Model: One device requests (client), another responds (server)

Protocols: Rules for data exchange (TCP, UDP, HTTP, FTP, etc.)

Sockets: Programming interface for network communication

Popular Languages for Network Programming

Python: Great for rapid prototyping and learning (socket, asyncio, Flask)

JavaScript/Node.js: Ideal for real-time apps (Express, WebSockets)

Java: Enterprise-grade networking (ServerSocket, RMI)

C/C++: Low-level networking with high performance (raw sockets)

Go: Fast and efficient concurrency (net/http, goroutines)

1. Creating a Simple Server in Python

import socket server = socket.socket(socket.AF_INET, socket.SOCK_STREAM) server.bind(('localhost', 8080)) server.listen(1) print("Waiting for a connection...") conn, addr = server.accept() print(f"Connected to {addr}") conn.send(b"Hello from the server!") conn.close()

2. Making a Request (Client Side)

import socket client = socket.socket(socket.AF_INET, socket.SOCK_STREAM) client.connect(('localhost', 8080)) message = client.recv(1024) print("Received:", message.decode()) client.close()

3. RESTful API with Node.js

// Install Express first: npm install express const express = require('express'); const app = express(); app.get('/', (req, res) => res.send('Hello from Node server!')); app.listen(3000, () => console.log('Server running on port 3000'));

4. Real-Time Communication with WebSockets

Use WebSockets for two-way communication:

Socket.io (Node.js)

ws library (JavaScript)

WebSocket library (Python)

5. Network Security Basics

Use HTTPS to encrypt web traffic

Sanitize inputs to avoid injection attacks

Use authentication tokens or API keys

Implement firewalls and access control

6. Tools and Protocol Analyzers

Wireshark: Analyze network packets

Postman: Test HTTP APIs

Netcat: Debug and scan ports

Ping/traceroute: Diagnose connectivity

Conclusion

Server and network programming are crucial for building scalable, efficient, and connected applications. Whether you're interested in building a simple REST API or a real-time multiplayer game, a strong grasp of networking concepts will take your skills to the next level.

[fraoula1]

𝐓𝐨𝐩 5 𝐏𝐲𝐭𝐡𝐨𝐧 𝐒𝐤𝐢𝐥𝐥𝐬 𝐭𝐨 𝐌𝐚𝐬𝐭𝐞𝐫 𝐢𝐧 2025 | 𝐁𝐨𝐨𝐬𝐭 𝐘𝐨𝐮𝐫 𝐏𝐲𝐭𝐡𝐨𝐧 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠

𝐓𝐨𝐩 5 𝐏𝐲𝐭𝐡𝐨𝐧 𝐒𝐤𝐢𝐥𝐥𝐬 𝐭𝐨 𝐌𝐚𝐬𝐭𝐞𝐫 𝐢𝐧 2025 | 𝐁𝐨𝐨𝐬𝐭 𝐘𝐨𝐮𝐫 𝐏𝐲𝐭𝐡𝐨𝐧 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 Ready to take your Python skills to the next level in 2025? In this video, we break down the Top 5 Python Skills you need to master for better performance, scalability, and flexibility in your coding projects. Top 5 Python Skills: Object-Oriented Programming (OOP) – Learn about classes, objects, inheritance, polymorphism, and encapsulation. Python Memory Management & Performance Optimization – Master garbage collection, memory profiling, and optimize code performance with generators and multiprocessing. Asynchronous Programming – Handle concurrent tasks efficiently using asyncio, threading, and multiprocessing. Exception Handling & Debugging – Learn to write robust code with try-except blocks and debug using tools like pdb and PyCharm. Advanced Python Typing & Decorators – Use type hints, dataclasses, and decorators to write cleaner and more maintainable code.

By mastering these skills, you'll be well on your way to becoming a Python expert! Don’t forget to like, comment, and subscribe for more programming tips and tutorials. Watch complete video https://lnkd.in/gF6nwnKf

[codezup]

Leverage Python Concurrency: Master Asyncio & Multiprocessing

1. Introduction Concurrency is a powerful technique that allows your program to perform multiple tasks simultaneously, improving responsiveness and throughput. In Python, concurrency can be achieved through various methods, including asyncio for asynchronous programming and the multiprocessing module for parallel execution. This guide will walk you through the basics and advanced usage of these…

[wingedmiraclesublime]

蜘蛛池需要哪些代码？

蜘蛛池，通常指的是用于自动化抓取网页数据的工具或平台。在构建一个有效的蜘蛛池时，你需要考虑多种编程语言和框架，以确保能够高效、稳定地抓取数据。下面是一些关键的代码和技术要点，它们是构建蜘蛛池的基础。

1. 网络请求库

首先，你需要一个强大的网络请求库来发起HTTP请求。Python中的`requests`库是一个非常流行的选择，它简单易用且功能强大。对于更复杂的场景，如处理JavaScript渲染的页面，可以使用`Selenium`这样的库。

```python

import requests

response = requests.get('http://example.com')

print(response.text)

```

2. 数据解析库

抓取到的数据通常是HTML格式，因此需要一个解析库来提取有用的信息。`BeautifulSoup`是Python中常用的HTML解析库，它可以方便地从HTML文档中提取数据。

```python

from bs4 import BeautifulSoup

soup = BeautifulSoup(html_content, 'html.parser')

data = soup.find_all('div', {'class': 'content'})

```

3. 异步处理

为了提高效率，异步处理是必不可少的。Python的`asyncio`库提供了异步I/O的支持，可以显著提升爬虫的性能。

```python

import asyncio

import aiohttp

async def fetch(session, url):

async with session.get(url) as response:

return await response.text()

async def main():

async with aiohttp.ClientSession() as session:

html = await fetch(session, 'http://example.com')

print(html)

loop = asyncio.get_event_loop()

loop.run_until_complete(main())

```

4. 数据存储

抓取到的数据需要���储起来以便后续分析。常见的存储方式包括数据库（如MySQL、MongoDB）和文件系统。Python有丰富的库支持这些操作，如`pymysql`和`pymongo`。

```python

import pymysql

conn = pymysql.connect(host='localhost', user='root', password='password', db='spider_data')

cursor = conn.cursor()

cursor.execute("INSERT INTO data (url, content) VALUES (%s, %s)", ('http://example.com', html))

conn.commit()

```

5. 错误处理与重试机制

在实际应用中，网络请求可能会遇到各种问题，如超时、服务器错误等。因此，良好的错误处理和重试机制是必要的。

```python

from requests.exceptions import RequestException

def get_page(url):

try:

response = requests.get(url)

if response.status_code == 200:

return response.text

else:

return None

except RequestException:

return None

```

结语

构建一个高效的蜘蛛池需要综合运用多种技术和工具。以上介绍的只是一些基础的代码示例，实际应用中可能还需要考虑更多因素，如IP代理、反爬虫策略等。希望本文能为你的蜘蛛池开发提供一些启示。你认为在构建蜘蛛池时还应该注意哪些方面？欢迎在评论区分享你的观点！

加飞机@yuantou2048

SEO优化

谷歌留痕

[hersongfan]

如何优化蜘蛛池源码的性能？

在互联网技术领域，蜘蛛池（Spider Pool）是一种常见的爬虫技术应用，它通过模拟多个浏览器请求来提高数据抓取的效率。然而，随着网络环境的复杂化和技术要求的提升，如何优化蜘蛛池源码的性能成为了一个重要的课题。本文将从几个关键方面探讨如何有效提升蜘蛛池的性能。

1. 选择合适的编程语言

首先，选择一种适合高并发处理的编程语言是至关重要的。Python、Java 和 Go 等语言因其各自的特点，在爬虫开发中都有广泛的应用。例如，Go 语言以其出色的并发处理能力而闻名，非常适合用于构建高性能的蜘蛛池系统。

2. 优化网络请求

在网络请求方面，可以采用异步非阻塞的方式进行处理。这可以通过使用如 Python 的 asyncio 库或 Node.js 的 async/await 语法来实现。这样可以显著减少等待时间，提高整体的响应速度。

3. 使用代理和 IP 池

为了防止被目标网站封禁，使用代理服务器和 IP 池是一个有效的策略。通过轮换使用不同的 IP 地址，可以降低被检测到的风险，同时也能提高数据抓取的成功率。

4. 数据库优化

合理地设计数据库结构和索引，可以极大地提升数据存储和检索的效率。此外，使用缓存机制（如 Redis）也可以减轻数据库的压力，加快数据访问的速度。

5. 分布式部署

对于大规模的数据抓取任务，分布式部署是必不可少的。通过将任务分配到多台机器上并行执行，可以显著提高处理能力。常用的分布式框架包括 Apache Hadoop 和 Spark。

6. 监控与调试

最后，建立一套完善的监控和调试系统，可以帮助开发者及时发现和解决问题。通过日志记录、性能分析工具等手段，可以持续优化系统的性能表现。

结语

优化蜘蛛池源码的性能是一个系统性工程，需要从多个角度综合考虑。希望本文提供的建议能够帮助你构建出更加高效、稳定的蜘蛛池系统。欢迎读者们分享自己的经验和见解，共同推动这一领域的技术进步。

如果你有任何关于蜘蛛池优化的问题或经验分享，欢迎在评论区留言讨论！

加飞机@yuantou2048

BCH Miner

CESUR Mining

[atplblog]

Price: [price_with_discount] (as of [price_update_date] - Details) [ad_1] Master efficient parallel programming to build powerful applications using PythonKey FeaturesDesign and implement efficient parallel softwareMaster new programming techniques to address and solve complex programming problemsExplore the world of parallel programming with this book, which is a go-to resource for different kinds of parallel computing tasks in Python, using examples and topics covered in great depthBook DescriptionThis book will teach you parallel programming techniques using examples in Python and will help you explore the many ways in which you can write code that allows more than one process to happen at once. Starting with introducing you to the world of parallel computing, it moves on to cover the fundamentals in Python. This is followed by exploring the thread-based parallelism model using the Python threading module by synchronizing threads and using locks, mutex, semaphores queues, GIL, and the thread pool.Next you will be taught about process-based parallelism where you will synchronize processes using message passing along with learning about the performance of MPI Python Modules. You will then go on to learn the asynchronous parallel programming model using the Python asyncio module along with handling exceptions. Moving on, you will discover distributed computing with Python, and learn how to install a broker, use Celery Python Module, and create a worker.You will understand anche Pycsp, the Scoop framework, and disk modules in Python. Further on, you will learnGPU programming withPython using the PyCUDA module along with evaluating performance limitations.What you will learnSynchronize multiple threads and processes to manage parallel tasksImplement message passing communication between processes to build parallel applicationsProgram your own GPU cards to address complex problemsManage computing entities to execute distributed computational tasksWrite efficient programs by adopting the event-driven programming modelExplore the cloud technology with DJango and Google App EngineApply parallel programming techniques that can lead to performance improvementsWho this book is forPython Parallel Programming Cookbook is intended for software developers who are well versed with Python and want to use parallel programming techniques to write powerful and efficient code. This book will help you master the basics and the advanced of parallel computing. Publisher ‏ : ‎ Packt Pub Ltd (29 August 2015) Language ‏ : ‎ English Paperback ‏ : ‎ 286 pages ISBN-10 ‏ : ‎ 1785289586 ISBN-13 ‏ : ‎ 978-1785289583 Item Weight ‏ : ‎ 500 g Dimensions ‏ : ‎ 23.5 x 19.1 x 1.53 cm Country of Origin ‏ : ‎ India [ad_2]

[utopicwork]

Looking at uvloop again and it's a much more reasonable way to get gains for PierMesh then PyPy since I can change ~3 lines of code and ostensibly get 2-4x faster performance. I'll test this soon

[cleverhottubmiracle]

[ad_1] In this Colab‑ready tutorial, we demonstrate how to integrate Google’s Gemini 2.0 generative AI with an in‑process Model Context Protocol (MCP) server, using FastMCP. Starting with an interactive getpass prompt to capture your GEMINI_API_KEY securely, we install and configure all necessary dependencies: the google‑genai Python client for calling the Gemini API, fastmcp for defining and hosting our MCP tools in‑process, httpx for making HTTP requests to the Open‑Meteo weather API, and nest_asyncio to patch Colab’s already‑running asyncio event loop. The workflow proceeds by spinning up a minimal FastMCP “weather” server with two tools, get_weather(latitude, longitude) for a three‑day forecast and get_alerts(state) for state‑level weather alerts, then creating a FastMCPTransport to connect an MCP client to that server. Finally, using the Gemini function‑calling feature, we send a natural‑language prompt to Gemini, have it emit a function call based on our explicit JSON schemas, and then execute that call via the MCP client, returning structured weather data into our notebook. from getpass import getpass import os api_key = getpass("Enter your GEMINI_API_KEY: ") os.environ["GEMINI_API_KEY"] = api_key We securely prompt you to enter your Gemini API key (without displaying it on the screen) and then store it in the GEMINI_API_KEY environment variable, allowing the rest of your notebook to authenticate with Google’s API. !pip install -q google-genai mcp fastmcp httpx nest_asyncio We install all the core dependencies needed for our Colab notebook in one go—google‑genai for interacting with the Gemini API, mcp and fastmcp for building and hosting our Model Context Protocol server and client, httpx for making HTTP requests to external APIs, and nest_asyncio to patch the event loop so our async code runs smoothly. We apply the nest_asyncio patch to the notebook’s existing event loop, allowing us to run asyncio coroutines (like our MCP client interactions) without encountering “event loop already running” errors. from fastmcp import FastMCP import httpx mcp_server = FastMCP("weather") @mcp_server.tool() def get_weather(latitude: float, longitude: float) -> str: """3‑day min/max temperature forecast via Open‑Meteo.""" url = ( f" f"?latitude=latitude&longitude=longitude" "&daily=temperature_2m_min,temperature_2m_max&timezone=UTC" ) resp = httpx.get(url, timeout=10) daily = resp.json()["daily"] return "\n".join( f"date: low mn°C, high mx°C" for date, mn, mx in zip( daily["time"], daily["temperature_2m_min"], daily["temperature_2m_max"], ) ) @mcp_server.tool() def get_alerts(state: str) -> str: """Dummy US‑state alerts.""" return f"No active weather alerts for state.upper()." We create an in‑process FastMCP server named “weather” and register two tools: get_weather(latitude, longitude), which fetches and formats a 3‑day temperature forecast from the Open‑Meteo API using httpx, and get_alerts(state), which returns a placeholder message for U.S. state weather alerts. import asyncio from google import genai from google.genai import types from fastmcp import Client as MCPClient from fastmcp.client.transports import FastMCPTransport We import the core libraries for our MCP‑Gemini integration: asyncio to run asynchronous code, google‑genai and its types module for calling Gemini and defining function‑calling schemas, and FastMCP’s Client (aliased as MCPClient) with its FastMCPTransport to connect our in‑process weather server to the MCP client. client = genai.Client(api_key=os.getenv("GEMINI_API_KEY")) MODEL = "gemini-2.0-flash" transport = FastMCPTransport(mcp_server) We initialize the Google Gemini client using the GEMINI_API_KEY from your environment, specify the gemini-2.0-flash model for function‑calling, and set up a FastMCPTransport that connects the in‑process mcp_server to the MCP client. function_declarations = [ "name": "get_weather", "description": "Return a 3‑day min/max temperature forecast for given coordinates.", "parameters": "type": "object", "properties": "latitude": "type": "number", "description": "Latitude of target location." , "longitude": "type": "number", "description": "Longitude of target location." , "required": ["latitude", "longitude"] , "name": "get_alerts", "description": "Return any active weather alerts for a given U.S. state.", "parameters": "type": "object", "properties": "state": "type": "string", "description": "Two‑letter U.S. state code, e.g. 'CA'." , "required": ["state"] ] tool_defs = types.Tool(function_declarations=function_declarations) We manually define the JSON schema specifications for our two MCP tools, get_weather (which accepts latitude and longitude as numeric inputs) and get_alerts (which accepts a U.S. state code as a string), including names, descriptions, required properties, and data types. It then wraps these declarations in types. Tool object (tool_defs), which informs Gemini how to generate and validate the corresponding function calls. async def run_gemini(lat: float, lon: float): async with MCPClient(transport) as mcp_client: prompt = f"Give me a 3‑day weather forecast for latitude=lat, longitude=lon." response = client.models.generate_content( model=MODEL, contents=[prompt], config=types.GenerateContentConfig( temperature=0, tools=[tool_defs] ) ) call = response.candidates[0].content.parts[0].function_call if not call: print("No function call; GPT said:", response.text) return print("🔧 Gemini wants:", call.name, call.args) result = await mcp_client.call_tool(call.name, call.args) print("\n📋 Tool result:\n", result) asyncio.get_event_loop().run_until_complete(run_gemini(37.7749, -122.4194)) Finally, this async function run_gemini opens an MCP client session over our in‑process transport, sends a natural‑language prompt to Gemini asking for a 3‑day forecast at the given coordinates, captures the resulting function call (if any), invokes the corresponding MCP tool, and prints out the structured weather data, all of which is kicked off by running it in the notebook’s event loop with run_until_complete. In conclusion, we have a fully contained pipeline that showcases how to define custom MCP tools in Python, expose them via FastMCP, and seamlessly integrate them with Google’s Gemini 2.0 model using the google‑genai client. The key frameworks, FastMCP for MCP hosting, FastMCPTransport and MCPClient for transport and invocation, httpx for external API access, and nest_asyncio for Colab compatibility, work together to enable real‑time function calling without external processes or stdio pipes. This pattern simplifies local development and testing of MCP integrations in Colab and provides a template for building more advanced agentic applications that combine LLM reasoning with specialized domain tools. Here is the Colab Notebook. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. Don’t Forget to join our 90k+ ML SubReddit. 🔥 [Register Now] miniCON Virtual Conference on AGENTIC AI: FREE REGISTRATION + Certificate of Attendance + 4 Hour Short Event (May 21, 9 am- 1 pm PST) + Hands on Workshop Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences. [ad_2] Source link

[one-earbuds1]

Mastering Event-Driven Programming in Python

Me learn Python magic! Make fire with asyncio, make big noise with Tkinter, talk fast with WebSockets, and hunt big data with FastAPI. Me test, me debug, me become Python master! Join now, make code dance!

Yo, fam! 🔥 Get ready to SLAY event-driven programming in Python! 🚀- **asyncio** - Code like a boss without blocking! - **GUI** - Whip up some dope apps with Tkinter! - **WebSockets** - Keep it LIT with real-time updates! - **FastAPI** - Build some next-level microservices! - **Debugging** - Fix your code like a PRO! Grab this book, dive in, and LEVEL UP your Python game! 💪

Book -> Free

[learning-code-ficusoft]

Explain asynchronous programming and its benefits.

Asynchronous Programming in Python

Python provides built-in support for asynchronous programming through the asyncio module, which enables efficient handling of I/O-bound operations like network requests, file handling, and database interactions.

1. Basic Example: Asynchronous Function with async/await

Here’s a simple example demonstrating an asynchronous function:pythonimport asyncioasync def fetch_data(): print("Fetching data...") await asyncio.sleep(2) # Simulating a network request print("Data received!")async def main(): print("Start") await fetch_data() # Waits for fetch_data() to complete print("End")asyncio.run(main()) # Run the async function

🔹 Explanation:

async def defines an asynchronous function.

await asyncio.sleep(2) simulates a delay without blocking execution.

asyncio.run(main()) runs the asynchronous event loop.

💡 Output:sqlStart Fetching data... (Data fetches after 2 seconds) Data received! End

2. Running Multiple Tasks Concurrently

Instead of waiting for one task to finish before starting another, we can execute multiple tasks at the same time using asyncio.gather().pythonimport asyncioasync def fetch_data(id): print(f"Fetching data for Task {id}...") await asyncio.sleep(2) print(f"Data received for Task {id}!")async def main(): tasks = [fetch_data(1), fetch_data(2), fetch_data(3)] # Multiple tasks await asyncio.gather(*tasks) # Run tasks concurrentlyasyncio.run(main())

💡 Output:arduinoFetching data for Task 1... Fetching data for Task 2... Fetching data for Task 3... (Data fetches after 2 seconds) Data received for Task 1! Data received for Task 2! Data received for Task 3!

🔹 Explanation:

asyncio.gather(*tasks) runs multiple asynchronous tasks simultaneously.

This approach significantly improves performance when handling multiple tasks.

3. Using Asynchronous HTTP Requests (aiohttp)

For real-world applications, network requests are a common use case for async programming. The aiohttp library enables non-blocking HTTP requests.pythonimport asyncio import aiohttpasync def fetch_url(url): async with aiohttp.ClientSession() as session: async with session.get(url) as response: print(f"Fetched {url} with status {response.status}")async def main(): urls = [ "https://example.com", "https://httpbin.org/get", "https://jsonplaceholder.typicode.com/posts/1" ] tasks = [fetch_url(url) for url in urls] await asyncio.gather(*tasks) # Fetch multiple URLs concurrentlyasyncio.run(main())

🔹 Explanation:

aiohttp makes non-blocking HTTP requests.

async with ensures proper resource management.

await asyncio.gather(*tasks) fetches multiple URLs concurrently.

When to Use Asynchronous Programming in Python?

✅ Handling I/O-bound tasks — Web scraping, API calls, database operations. ✅ Real-time applications — Chat apps, notifications, stock market monitoring. ✅ Large file processing — Reading/writing large files without freezing execution.

WEBSITE: https://www.ficusoft.in/python-training-in-chennai/

[lacyc3]

Néha úgy érzem, hogy a tudatalattim szándékosan talál ki újabb és újabb feladatokat, hogy nehogy egyszer véletlenül elkészüljön a pingvin.

Most is, csinálom egy ideje a workflow service-t hogy jobban látható és követhető lehessen, mi történik a szinte teljesen aszinkron rendszerben.

Egy - egy workflow tarthat viszonylag sokáig is a rendszerben, miközben jó lenne ha más is hozzáférne. Uccu neki, akkor párhuzamosítás.

Elsősorban nem több számítási kapacitásra van szükség, hanem arra hogy párhuzamosan több feladat futhasson. Uccu neki, akkor legyen már asyncio (coroutine-ok) de oh wait, ezzel csak minimális tapasztalatom van. Jó, akkor ássuk bele magunkat.

Persze az sqlalchemy async változata kehel, nyilván nem plug & play, a régi dolgokkal nem kompatibilis.

És még csak ezután jön majd a Redis.

És ennek az egésznek mi lesz a felhasználó által látható eredménye? Semmi.

Jó, ha talán egyszer valamikor elkészül a comment engine, akkor lesz, de addig nettó nulla.

Azzal kéne foglalkozni, hogy végre kint legyen az 1.0, de neeem, miért is olyan dologgal tölteném a korlátos időmet és kreatív időszakomat, amit az olvasók is látnak.. ugyan.

Persze, tapasztalat meg ilyesmi az kell, meg érdekes, meg izgat az újdonság ereje.

De mégis.

Ha kész lesz az 1.0, akkor talán menni fog a release early, release often.

De addig... bocsi.