#thread synchronization in python | Explore Tumblr posts and blogs

fromdevcom · 3 months ago

Text

Java Mastery Challenge: Can You Crack These 10 Essential Coding Questions? Are you confident in your Java programming skills? Whether you're preparing for a technical interview or simply want to validate your expertise, these ten carefully curated Java questions will test your understanding of core concepts and common pitfalls. Let's dive into challenges that every serious Java developer should be able to tackle. 1. The Mysterious Output Consider this seemingly simple code snippet: javaCopypublic class StringTest public static void main(String[] args) String str1 = "Hello"; String str2 = "Hello"; String str3 = new String("Hello"); System.out.println(str1 == str2); System.out.println(str1 == str3); System.out.println(str1.equals(str3)); What's the output? This question tests your understanding of string pooling and object reference comparison in Java. The answer is true, false, true. The first comparison returns true because both str1 and str2 reference the same string literal from the string pool. The second comparison returns false because str3 creates a new object in heap memory. The third comparison returns true because equals() compares the actual string content. 2. Threading Troubles Here's a classic multithreading puzzle: javaCopypublic class Counter private int count = 0; public void increment() count++; public int getCount() return count; If multiple threads access this Counter class simultaneously, what potential issues might arise? This scenario highlights the importance of thread safety in Java applications. Without proper synchronization, the increment operation isn't atomic, potentially leading to race conditions. The solution involves either using synchronized methods, volatile variables, or atomic classes like AtomicInteger. 3. Collection Conundrum javaCopyList list = new ArrayList(); list.add("Java"); list.add("Python"); list.add("JavaScript"); for(String language : list) if(language.startsWith("J")) list.remove(language); What happens when you run this code? This question tests your knowledge of concurrent modification exceptions and proper collection iteration. The code will throw a ConcurrentModificationException because you're modifying the collection while iterating over it. Instead, you should use an Iterator or collect items to remove in a separate list. 4. Inheritance Insight javaCopyclass Parent public void display() System.out.println("Parent"); class Child extends Parent public void display() System.out.println("Child"); public class Main public static void main(String[] args) Parent p = new Child(); p.display(); What's the output? This tests your understanding of method overriding and runtime polymorphism. The answer is "Child" because Java uses dynamic method dispatch to determine which method to call at runtime based on the actual object type, not the reference type. 5. Exception Excellence javaCopypublic class ExceptionTest public static void main(String[] args) try throw new RuntimeException(); catch (Exception e) throw new RuntimeException(); finally System.out.println("Finally"); What gets printed before the program terminates? This tests your knowledge of exception handling and the finally block. "Finally" will be printed because the finally block always executes, even when exceptions are thrown in both try and catch blocks. 6. Interface Implementation javaCopyinterface Printable default void print() System.out.println("Printable"); interface Showable default void print() System.out.println("Showable"); class Display implements Printable, Showable // What needs to be added here? What must be

added to the Display class to make it compile? This tests your understanding of the diamond problem in Java 8+ with default methods. The class must override the print() method to resolve the ambiguity between the two default implementations. 7. Generics Genius javaCopypublic class Box private T value; public void setValue(T value) this.value = value; public T getValue() return value; Which of these statements will compile? javaCopyBox intBox = new Box(); Box strBox = new Box(); Box doubleBox = new Box(); This tests your understanding of bounded type parameters in generics. Only intBox and doubleBox will compile because T is bounded to Number and its subclasses. String isn't a subclass of Number, so strBox won't compile. 8. Memory Management javaCopyclass Resource public void process() System.out.println("Processing"); protected void finalize() System.out.println("Finalizing"); What's wrong with relying on finalize() for resource cleanup? This tests your knowledge of Java's memory management and best practices. The finalize() method is deprecated and unreliable for resource cleanup. Instead, use try-with-resources or implement AutoCloseable interface for proper resource management. 9. Lambda Logic javaCopyList numbers = Arrays.asList(1, 2, 3, 4, 5); numbers.stream() .filter(n -> n % 2 == 0) .map(n -> n * 2) .forEach(System.out::println); What's the output? This tests your understanding of Java streams and lambda expressions. The code filters even numbers, doubles them, and prints them. The output will be 4 and 8. 10. Serialization Scenarios javaCopyclass User implements Serializable private String username; private transient String password; // Constructor and getters/setters What happens to the password field during serialization and deserialization? This tests your knowledge of Java serialization. The password field, marked as transient, will not be serialized. After deserialization, it will be initialized to its default value (null for String). Conclusion How many questions did you get right? These problems cover fundamental Java concepts that every developer should understand. They highlight important aspects of the language, from basic string handling to advanced topics like threading and serialization. Remember, knowing these concepts isn't just about passing interviews – it's about writing better, more efficient code. Keep practicing and exploring Java's rich features to become a more proficient developer. Whether you're a beginner or an experienced developer, regular practice with such questions helps reinforce your understanding and keeps you sharp. Consider creating your own variations of these problems to deepen your knowledge even further. What's your next step? Try implementing these concepts in your projects, or create more complex scenarios to challenge yourself. The journey to Java mastery is ongoing, and every challenge you tackle makes you a better programmer.

0 notes

kritisharmasblog · 3 months ago

Text

Threads

Threads - http://anjali.local/wordpress-6.6/?p=427 Threads in Programming 1. What is a Thread? A thread is the smallest unit of execution within a process. A process can have multiple threads running concurrently, sharing the same memory space but executing different tasks. 2. Single-threaded vs. Multi-threaded Execution Single-threaded: The program executes one task at a time. Multi-threaded: Multiple tasks run simultaneously within the same program, improving efficiency. 3. Benefits of Using Threads ✅ Concurrency: Threads allow multiple operations to run at the same time.✅ Efficient Resource Utilization: Threads share memory, reducing overhead compared to multiple processes.✅ Faster Execution: Tasks like background computations and UI responsiveness improve with threading. 4. Multithreading Challenges ⚠ Race Conditions: When multiple threads modify shared data without proper synchronization.⚠ Deadlocks: Occurs when two or more threads are stuck, waiting for each other to release resources.⚠ Increased Complexity: Managing multiple threads requires careful synchronization mechanisms. 5. Thread Synchronization Techniques To avoid issues like race conditions, common synchronization methods include:🔹 Mutex (Mutual Exclusion): Ensures only one thread accesses a resource at a time.🔹 Semaphores: Controls access to shared resources using counters.🔹 Locks: Prevents multiple threads from modifying data simultaneously. 6. Threads in Different Programming Languages Python: Uses threading and multiprocessing modules (limited by GIL). Java: Uses Thread class and Runnable interface. C++: Uses std::thread from the C++11 standard. JavaScript: Uses Web Workers for multi-threaded behavior in browsers. Conclusion Threads are essential for efficient program execution, especially in tasks requiring parallel processing. However, managing them correctly requires careful handling of synchronization, resource sharing, and potential deadlocks. Threads in Programming 1. What is a Thread? A thread is the smallest unit of execution within a process. A process can have multiple threads running concurrently, sharing the same memory space but executing different tasks. 2. Single-threaded vs. Multi-threaded Execution Single-threaded: The program executes one task at a time. Multi-threaded: Multiple tasks run simultaneously within the same program, improving efficiency. 3. Benefits of Using Threads ✅ Concurrency: Threads allow multiple operations to run at the same time.✅ Efficient Resource Utilization: Threads share memory, reducing overhead compared to multiple processes.✅ Faster Execution: Tasks like background computations and UI responsiveness improve with threading. 4. Multithreading Challenges ⚠ Race Conditions: When multiple threads modify shared data without proper synchronization.⚠ Deadlocks: Occurs when two or more threads are stuck, waiting for each other to release resources.⚠ Increased Complexity: Managing multiple threads requires careful synchronization mechanisms. 5. Thread Synchronization Techniques To avoid issues like race conditions, common synchronization methods include:🔹 Mutex (Mutual Exclusion): Ensures only one thread accesses a resource at a time.🔹 Semaphores: Controls access to shared resources using counters.🔹 Locks: Prevents multiple threads from modifying data simultaneously. 6. Threads in Different Programming Languages Python: Uses threading and multiprocessing modules (limited by GIL). Java: Uses Thread class and Runnable interface. C++: Uses std::thread from the C++11 standard. JavaScript: Uses Web Workers for multi-threaded behavior in browsers. Conclusion Threads are essential for efficient program execution, especially in tasks requiring parallel processing. However, managing them correctly requires careful handling of synchronization, resource sharing, and potential deadlocks. Threads in Programming 1. What is a Thread? A thread is the smallest unit of execution within a process. A process can have multiple threads running concurrently, sharing the same memory space but executing different tasks. 2. Single-threaded vs. Multi-threaded Execution Single-threaded: The program executes one task at a time. Multi-threaded: Multiple tasks run simultaneously March 5, 2025

0 notes

atplblog · 4 months ago

Text

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]

0 notes

learning-code-ficusoft · 5 months ago

Text

Exploring Python’s Asyncio for Concurrent Programming

Python’s asyncio module provides a framework for writing concurrent code using asynchronous I/O, making it useful for tasks like network operations, database queries, and parallel execution without threads or multiprocessing.

1. Why Use Asyncio?

Traditional synchronous code blocks execution until a task completes. Asyncio allows non-blocking execution, meaning a program can continue running other tasks while waiting for an I/O operation to complete.

Best Use Cases: ✅ Network requests (HTTP APIs, WebSockets) ✅ Database queries (async drivers) ✅ File I/O operations ✅ Background tasks (e.g., web scraping, data processing)

2. Basics of Asyncio

a) Creating and Running an Async Function

An async function (also called a coroutine) runs asynchronously and must be awaited.pythonimport asyncioasync def say_hello(): print("Hello") await asyncio.sleep(1) # Simulates an I/O operation print("World")asyncio.run(say_hello()) # Runs the coroutine

b) Running Multiple Coroutines Concurrently

To execute multiple tasks concurrently, use asyncio.gather() or asyncio.create_task().pythonimport asyncioasync def task(name, delay): print(f"Task {name} started") await asyncio.sleep(delay) print(f"Task {name} completed")async def main(): await asyncio.gather(task("A", 2), task("B", 1)) # Both run concurrentlyasyncio.run(main())

💡 Key Takeaway: asyncio.gather() runs coroutines concurrently and waits for all to complete.

3. Asyncio with Tasks

Tasks allow scheduling coroutines to run in the background.pythonasync def background_task(): await asyncio.sleep(2) print("Background Task Done")async def main(): task = asyncio.create_task(background_task()) # Runs in the background print("Main function continues execution...") await asyncio.sleep(1) # Simulating other work await task # Wait for background taskasyncio.run(main())

💡 Key Takeaway: asyncio.create_task() starts a coroutine without waiting for it to finish.

4. Using Asyncio for I/O Bound Operations

Asyncio shines when dealing with I/O operations like HTTP requests.

Example: Fetching Multiple URLs Asynchronously

pythonimport asyncio import aiohttp # Async HTTP clientasync def fetch(url): async with aiohttp.ClientSession() as session: async with session.get(url) as response: return await response.text()async def main(): urls = ["https://example.com", "https://httpbin.org/get"] results = await asyncio.gather(*(fetch(url) for url in urls)) print("Fetched pages:", results)asyncio.run(main())

💡 Key Takeaway: Async HTTP requests complete faster since multiple URLs are fetched concurrently.

5. Handling Exceptions in Asyncio

Handle exceptions properly to prevent tasks from failing silently.pythonasync def faulty_task(): await asyncio.sleep(1) raise ValueError("An error occurred")async def main(): try: await faulty_task() except ValueError as e: print(f"Caught error: {e}")asyncio.run(main())

6. Running Async Code in a Synchronous Environment

Since asyncio.run() can only be called once, use an event loop in synchronous environments (e.g., Jupyter Notebooks, existing scripts).pythonimport asyncioloop = asyncio.get_event_loop() loop.run_until_complete(say_hello()) # Runs async function in a sync environment

7. Asyncio vs. Multithreading vs. Multiprocessing

Feature Asyncio (Single-threaded)Multithreading Multiprocessing Best for I/O-bound tasks I/O-bound tasks CPU-bound tasks Parallelism Cooperative Preemptive True parallelism Overhead Low Medium High Example Web scraping, API calls GUI apps, networking CPU-heavy calculations

Conclusion

asyncio is a powerful tool for concurrent programming in Python, especially for I/O-bound tasks. By leveraging coroutines, tasks, and event loops, developers can write efficient and scalable applications. 🚀

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

0 notes

jcmarchi · 10 months ago

Text

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.

0 notes

govindhtech · 10 months ago

Text

Guide To Python NumPy and SciPy In Multithreading In Python

An Easy Guide to Multithreading in Python

Python is a strong language, particularly for developing AI and machine learning applications. However, CPython, the programming language’s original, reference implementation and byte-code interpreter, lacks multithreading functionality; multithreading and parallel processing need to be enabled from the kernel. Some of the desired multi-core processing is made possible by libraries Python NumPy and SciPy such as NumPy, SciPy, and PyTorch, which use C-based implementations. However, there is a problem known as the Global Interpreter Lock (GIL), which literally “locks” the CPython interpreter to only working on one thread at a time, regardless of whether the interpreter is in a single or multi-threaded environment.

Let’s take a different approach to Python.

The robust libraries and tools that support Intel Distribution of Python, a collection of high-performance packages that optimize underlying instruction sets for Intel architectures, are designed to do this.

For compute-intensive, core Python numerical and scientific packages like NumPy, SciPy, and Numba, the Intel distribution helps developers achieve performance levels that are comparable to those of a C++ program by accelerating math and threading operations using oneAPI libraries while maintaining low Python overheads. This enables fast scaling over a cluster and assists developers in providing highly efficient multithreading, vectorization, and memory management for their applications.

Let’s examine Intel’s strategy for enhancing Python parallelism and composability in more detail, as well as how it might speed up your AI/ML workflows.

Parallelism in Nests: Python NumPy and SciPy

Python libraries called Python NumPy and SciPy were created especially for scientific computing and numerical processing, respectively.

Exposing parallelism on all conceivable levels of a program for example, by parallelizing the outermost loops or by utilizing various functional or pipeline sorts of parallelism on the application level is one workaround to enable multithreading/parallelism in Python scripts. This parallelism can be accomplished with the use of libraries like Dask, Joblib, and the included multiprocessing module mproc (with its ThreadPool class).

Data-parallelism can be performed with Python modules like Python NumPy and SciPy, which can then be accelerated with an efficient math library like the Intel oneAPI Math Kernel Library (oneMKL). This is because massive data processing requires a lot of processing. Using various threading runtimes, oneMKL is multi-threaded. An environment variable called MKL_THREADING_LAYER can be used to adjust the threading layer.

As a result, a code structure known as nested parallelism is created, in which a parallel section calls a function that in turn calls another parallel region. Since serial sections that is, regions that cannot execute in parallel and synchronization latencies are typically inevitable in Python NumPy and SciPy based systems, this parallelism-within-parallelism is an effective technique to minimize or hide them.

Going One Step Further: Numba

Despite offering extensive mathematical and data-focused accelerations through C-extensions, Python NumPy and SciPy remain a fixed set of mathematical tools accelerated through C-extensions. If non-standard math is required, a developer should not expect it to operate at the same speed as C-extensions. Here’s where Numba can work really well.

OneTBB

Based on LLVM, Numba functions as a “Just-In-Time” (JIT) compiler. It aims to reduce the performance difference between Python and compiled, statically typed languages such as C and C++. Additionally, it supports a variety of threading runtimes, including workqueue, OpenMP, and Intel oneAPI Threading Building Blocks (oneTBB). To match these three runtimes, there are three integrated threading layers. The only threading layer installed by default is workqueue; however, other threading layers can be added with ease using conda commands (e.g., $ conda install tbb).

The environment variable NUMBA_THREADING_LAYER can be used to set the threading layer. It is vital to know that there are two ways to choose this threading layer: either choose a layer that is generally safe under different types of parallel processing, or specify the desired threading layer name (e.g., tbb) explicitly.

Composability of Threading

The efficiency or efficacy of co-existing multi-threaded components depends on an application’s or component’s threading composability. A component that is “perfectly composable” would operate without compromising the effectiveness of other components in the system or its own efficiency.

In order to achieve a completely composable threading system, care must be taken to prevent over-subscription, which means making sure that no parallel region of code or component can require a certain number of threads to run (this is known as “mandatory” parallelism).

An alternative would be to implement a type of “optional” parallelism in which a work scheduler determines at the user level which thread(s) the components should be mapped to while automating the coordination of tasks among components and parallel regions. Naturally, the efficiency of the scheduler’s threading model must be better than the high-performance libraries’ integrated scheme since it is sharing a single thread-pool to arrange the program’s components and libraries around. The efficiency is lost otherwise.

Intel’s Strategy for Parallelism and Composability

Threading composability is more readily attained when oneTBB is used as the work scheduler. OneTBB is an open-source, cross-platform C++ library that was created with threading composability and optional/nested parallelism in mind. It allows for multi-core parallel processing.

An experimental module that enables threading composability across several libraries unlocks the potential for multi-threaded speed benefits in Python and was included in the oneTBB version released at the time of writing. As was previously mentioned, the scheduler’s improved threads allocation is what causes the acceleration.

The ThreadPool for Python standard is replaced by the Pool class in oneTBB. Additionally, the thread pool is activated across modules without requiring any code modifications thanks to the use of monkey patching, which allows an object to be dynamically replaced or updated during runtime. Additionally, oneTBB replaces oneMKL by turning on its own threading layer, which allows it to automatically provide composable parallelism when using calls from the Python NumPy and SciPy libraries.

See the code samples from the following composability demo, which is conducted on a system with MKL-enabled NumPy, TBB, and symmetric multiprocessing (SMP) modules and their accompanying IPython kernels installed, to examine the extent to which nested parallelism can enhance performance. Python is a feature-rich command-shell interface that supports a variety of programming languages and interactive computing. To get a quantifiable performance comparison, the demonstration was executed using the Jupyter Notebook extension.

import NumPy as np from multiprocessing.pool import ThreadPool pool = ThreadPool(10)

The aforementioned cell must be executed again each time the kernel in the Jupyter menu is changed in order to build the ThreadPool and provide the runtime outcomes listed below.

The following code, which runs the identical line for each of the three trials, is used with the default Python kernel:

%timeit pool.map(np.linalg.qr, [np.random.random((256, 256)) for i in range(10)])

This approach can be used to get the eigenvalues of a matrix using the standard Python kernel. Runtime is significantly improved up to an order of magnitude when the Python-m SMP kernel is enabled. Applying the Python-m TBB kernel yields even more improvements.

OneTBB’s dynamic task scheduler, which most effectively manages code where the innermost parallel sections cannot fully utilize the system’s CPU and where there may be a variable amount of work to be done, yields the best performance for this composability example. Although the SMP technique is still quite effective, it usually performs best in situations when workloads are more evenly distributed and the loads of all workers in the outermost regions are generally identical.

In summary, utilizing multithreading can speed up AI/ML workflows

The effectiveness of Python programs with an AI and machine learning focus can be increased in a variety of ways. Using multithreading and multiprocessing effectively will remain one of the most important ways to push AI/ML software development workflows to their limits.

Read more on Govindhtech.com

#python #numpy #SciPy #AI #machinelearning #API #AI/ML #onemkl #PYTHONNUMPY #multithreadinginpython #News #technews #technology #technologynews #TechnologyTrends #govindhtech

0 notes

web-age-solutions · 10 months ago

Text

Python Training: Multithreading and Multiprocessing Python training in multithreading and multiprocessing empowers you to optimize your programs for better performance. Learn to manage concurrent tasks with the threading module and achieve parallel execution using the multiprocessing module. This training covers synchronization, avoiding race conditions, and maximizing CPU utilization. Enhance your Python skills to build efficient, scalable applications capable of handling multiple tasks simultaneously.

#python training #python

0 notes

qocsuing · 11 months ago

Text

PyProxy: A Gateway to Python Web Development

In the realm of web development, Python has emerged as a powerful and versatile language. Its simplicity and readability have made it a favorite among developers. However, when it comes to handling HTTP requests and responses, developers often need a tool to simplify the process. This is where PyProxy comes into play.To get more news about pyproxy, you can visit pyproxy.com official website.

PyProxy is a lightweight, easy-to-use Python library designed to handle HTTP requests and responses. It acts as a middleman between the client and the server, intercepting and modifying HTTP traffic as needed. This makes it an invaluable tool for web developers, especially those working on complex projects that involve a lot of network communication.

One of the key features of PyProxy is its ability to handle both synchronous and asynchronous requests. This means that it can handle multiple requests at the same time without blocking the main thread. This is particularly useful in scenarios where the server needs to handle a large number of simultaneous connections.

Another notable feature of PyProxy is its support for both HTTP and HTTPS protocols. This ensures that it can handle secure connections, protecting the data being transmitted from eavesdropping or tampering. Moreover, PyProxy also supports a wide range of HTTP methods, including GET, POST, PUT, DELETE, and more.

But PyProxy is not just about handling HTTP traffic. It also provides a host of other features that make web development easier. For instance, it provides a simple and intuitive API for handling cookies, sessions, and other aspects of web development. It also supports middleware, allowing developers to add custom functionality to the request/response process.

Despite its powerful features, PyProxy remains easy to use. Its API is designed to be intuitive and straightforward, making it accessible even to beginners. Moreover, it comes with comprehensive documentation and a supportive community, making it easy to get started and troubleshoot any issues.

In conclusion, PyProxy is a powerful tool for Python web developers. Its ability to handle HTTP requests and responses, coupled with its other features, makes it an invaluable asset in any web developer’s toolkit. Whether you’re a seasoned developer or a beginner just starting out, PyProxy can help streamline your web development process and make your projects more efficient and effective.

0 notes

grotechminds · 2 years ago

Text

Parallel Execution of Selenium Tests with Python

In the ever-evolving landscape of automated testing, the need for efficiency and speed is paramount. Selenium, a powerful tool for web automation, can further enhance its capabilities through parallel test execution. This guide explores the intricacies of parallel execution of Selenium tests with Python, catering to those navigating the realms of a Selenium Python course or a Python with Selenium course. Discover the advantages, implementation strategies, and best practices for harnessing the full potential of parallelism in your automated testing endeavors.

Understanding the Power of Parallel Execution in Selenium with Python:

**1. Advantages of Parallel Execution:

Time Efficiency: Parallel execution allows multiple tests to run concurrently, significantly reducing the overall execution time.

Resource Optimization: Efficient utilization of testing resources, enabling faster feedback loops and quicker identification of issues.

Scalability: As test suites grow, parallel execution ensures scalability without compromising test duration.

**2. Scenarios Suited for Parallel Execution:

Large Test Suites: Especially beneficial when dealing with extensive test suites that require substantial time for sequential execution.

Cross-Browser Testing: Ideal for running tests across multiple browsers simultaneously, ensuring consistent behavior.

1. Implementation Strategies for Parallel Execution of Selenium Tests with Python:

**1. Setting Up Selenium WebDriver in Python:

Ensure that Selenium WebDriver is appropriately configured in your Python environment. Use the webdriver library to instantiate browser drivers and define test scenarios.

python

Copy code

from selenium import webdriver

driver = webdriver.Chrome(executable_path='path/to/chromedriver')

**2. Leveraging TestNG for Parallel Execution:

Integrate the TestNG library into your Python environment to harness parallel execution capabilities. Install TestNG using pip:

bash

Copy code

pip install testng

Create TestNG-style test classes and methods in Python using decorators:

python

Copy code

from testng import TestNG, Test

@Test

def test_example():

# Test logic using Selenium WebDriver

assert driver.title == 'Example Domain'

**3. Parallel Execution Configurations:

Configure your test environment to support parallel execution. TestNG provides various strategies for parallelism, including methods, classes, and suites. Choose the approach that best aligns with your testing requirements.

python

Copy code

@Test(parallel='methods')

def test_parallel_execution():

# Test logic for parallel execution

2. Best Practices for Parallel Execution of Selenium Tests with Python:

**1. Test Independence:

Ensure that individual tests are independent and do not rely on shared state. Independence is crucial for parallel execution to avoid conflicts and ensure accurate test results.

**2. Synchronization Mechanisms:

Implement robust synchronization mechanisms within your Selenium tests. Parallel execution introduces concurrency, and proper synchronization prevents race conditions and ensures test stability.

**3. Resource Management:

Effectively manage resources such as WebDriver instances, ensuring that each parallel thread operates with a clean and isolated environment. Resource leaks can lead to test failures and instability.

3. Handling Cross-Browser Testing in Parallel:

**1. Browser Configuration:

Define browser configurations within your parallel test setup to facilitate cross-browser testing. Specify browsers and versions to be tested concurrently.

python

Copy code

@Test(parallel='methods', browsers=['chrome', 'firefox'])

def test_cross_browser_execution():

# Test logic for cross-browser execution

**2. WebDriver Factory:

Implement a WebDriver factory that dynamically creates WebDriver instances based on the specified browser configuration. This ensures flexibility and ease of maintenance.

python

Copy code

def create_driver(browser):

if browser == 'chrome':

return webdriver.Chrome(executable_path='path/to/chromedriver')

elif browser == 'firefox':

return webdriver.Firefox(executable_path='path/to/geckodriver')

4. Conclusion: Maximizing Efficiency with Parallel Execution in Selenium with Python:

Embrace the advantages of time efficiency, resource optimization, and scalability. Implement parallel execution configurations with TestNG, ensuring proper test independence, synchronization, and resource management. Address cross-browser testing challenges by defining browser configurations and leveraging a dynamic WebDriver factory.

In conclusion,

parallel execution of Selenium tests with Python represents a key strategy for maximizing efficiency and achieving faster test cycles. Whether you're enrolled in a Selenium Python course or a BDD cucumber framework with selenium, mastering parallel execution enhances your ability to deliver timely and reliable test results.

As you navigate the world of parallel execution in Selenium with Python, remember that efficiency is not just about speed but also about optimizing resources and delivering robust test outcomes.

#python with selenium course #selenium with python course #learn selenium with python

0 notes

priyadevi0402 · 2 years ago

Text

"Climbing Up the Python Developer's Ladder: Essential Skills to Know"

In the dynamic and ever-evolving world of technology, Python has emerged as a powerhouse programming language. Its simplicity, versatility, and a rich ecosystem of libraries have made it a top choice for developers across various industries. .

These skills not only empower you to write efficient and maintainable code but also enable you to drive innovation and contribute to the company's mission in areas like web development, data science, machine learning, and more. Let's take a closer look at each of these skills set that sets you on the path to success in the world of Python development.

Expertise In Core Python

Good grasp of Web Frameworks

Object Relational Mappers

Road to Data Science

Machine Learning and AI

Deep Learning

Understanding of Multi-Process Architecture

Analytical skills

Design Skills

Communication skills

Let’s Explore Each of the Skills in Detail:

Expertise in Core Python:

This skill involves a deep understanding of Python's syntax, data types (integers, strings, lists, dictionaries, etc.), control structures (if statements, loops), functions, and modules. A Python expert should be proficient in writing clean and efficient Python code.

Good Grasp of Web Frameworks:

Python has several popular web frameworks such as Django and Flask. A good grasp of web frameworks means you can use these tools to build web applications, handle HTTP requests, manage databases, and create interactive web interfaces.

Object Relational Mappers (ORMs):

ORMs like SQLAlchemy in Python allow you to work with databases using Python objects instead of writing SQL queries directly. Proficiency in ORMs involves creating and manipulating database tables, querying data, and managing relationships between objects.

Road to Data Science:

Python is a go-to language for data science due to libraries like NumPy, Pandas, and Matplotlib. The "Road to Data Science" includes data cleaning, analysis, visualization, and statistical modeling using Python.

Machine Learning and AI:

Python is widely used for machine learning and artificial intelligence. Skills in this area involve understanding machine learning algorithms, using libraries like Scikit-Learn and TensorFlow, and building predictive models.

Deep Learning:

Deep learning focuses on neural networks. In Python, you can explore deep learning using frameworks like TensorFlow and PyTorch. Skills include building and training deep neural networks for tasks like image recognition and natural language processing.

Understanding of Multi-Process Architecture:

Python provides support for multi-process and multi-threading through modules like multiprocessing and threading. Proficiency in this area includes writing concurrent code, handling thread synchronization, and optimizing performance in multi-process architectures.

Analytical Skills:

Analytical skills are crucial for understanding complex problems, designing algorithms, and making data-driven decisions. Python helps by providing tools for data analysis and visualization, such as Pandas and Matplotlib.

Design Skills:

Good design skills in Python involve adhering to principles of clean code, object-oriented design, and design patterns. Python's readability emphasizes writing code that is easy to understand and maintain.

Communication Skills:

Effective communication is essential in any programming role. Python developers need to collaborate with team members, convey ideas clearly through documentation and comments, and communicate technical concepts to non-technical stakeholders.

These skills collectively make a Python developer versatile and capable of working on a wide range of projects, from web development and data analysis to machine learning and artificial intelligence. Depending on your career goals and interests, you can specialize in one or more of these areas while maintaining a strong foundation in core Python skills.

In conclusion, mastering the top Python developer skills outlined in this article is your key to becoming a valuable asset at ACTE Technologies. Whether you're building cutting-edge web applications, unlocking the potential of data with analytics, or pioneering advancements in artificial intelligence and machine learning, these skills are your foundation.

ACTE Technologies thrives on innovation and seeks individuals who can translate their Python expertise into real-world solutions. Remember that your journey as a Python developer is ongoing, with new libraries and technologies continually emerging. Thus, staying committed to lifelong learning and adapting to industry trends will not only ensure your continued success but also contribute to ACTE Technologies growth and prominence in the tech world. So, embrace these skills, keep coding, and embark on your exciting career as a top Python developer at ACTE Technologies.

0 notes

this-week-in-rust · 2 years ago

Text

This Week in Rust 491

Hello and welcome to another issue of This Week in Rust! Rust is a programming language empowering everyone to build reliable and efficient software. This is a weekly summary of its progress and community. Want something mentioned? Tag us at @ThisWeekInRust on Twitter or @ThisWeekinRust on mastodon.social, or send us a pull request. Want to get involved? We love contributions.

This Week in Rust is openly developed on GitHub. If you find any errors in this week's issue, please submit a PR.

Updates from Rust Community

Foundation

Rust Trademark Policy Draft Revision – Next Steps

Project/Tooling Updates

Nutype v0.2

Announcing tuxedo-rs

rust-analyzer changelog #177

Observations/Thoughts

Improving build times for derive macros by 3x or more

Oxidizing bmap-tools: rewriting a Python project in Rust

Rust: A New Attempt at C++'s Main Goal

Traits are more than interfaces

Optional If Expressions

Building a GStreamer plugin in Rust with meson instead of cargo

Rutie and Magnus, Two Good Ways to Build Ruby Extensions in Rust

Two Rust features that I miss in Other languages

[audio] Rust Analyzer with Lukas Wirth

[audio] Wasmer with Syrus Akbary

Rust Walkthroughs

Understanding Rust Thread Safety

how to run rust code on a circuit playground classic / atmega32u4

Hello World in Rust for m68k with #[no_core] and compiler patches

A syntax-level async join macro supporting branching control flow and synchronous shared mutable borrowing

Build a Lua Interpreter in Rust (English version)

[CN] Zino's implementation of an error type with tracing functionalities using 100 lines of code

Miscellaneous

Introducing Shuttle Batch 2.0

The Rust Foundation's draft trademark policy is far too restrictive

[video] Rust Trademark: Argle-bargle or Foofaraw?

Crate of the Week

This week's crate is onlyerror, a #[derive(Error)] macro with support for no_std on nightly compilers.

Thanks to Jay Oster for the self-suggestion!

Please submit your suggestions and votes for next week!

Call for Participation

Always wanted to contribute to open-source projects but did not know where to start? Every week we highlight some tasks from the Rust community for you to pick and get started!

Some of these tasks may also have mentors available, visit the task page for more information.

Quickwit - Upgrade clap from 3.1 to 4.0

Quickwit - Implement quickwit dataset CLI command

Hyperswitch - Migrate to enum_dispatch to reduce runtime overhead

Hyperswitch - move redis key creation to a common module

Hyperswitch - add connector_label field in error type

Ockam - Update ockam status --all to list more of the available resources

Ockam - Remove rustcrypto feature from ockam_vault

Ockam - Create Github Action to update Ockam Command Manual

If you are a Rust project owner and are looking for contributors, please submit tasks here.

Updates from the Rust Project

450 pull requests were merged in the last week

initial support for loongarch64-unknown-linux-gnu

add inline assembly support for m68k

make rust-intrinsic ABI unwindable

allow repr(align = x) on inherent methods

add a backtrace to Allocation, display it in leak reports

add a message for if an overflow occurs in core::intrinsics::is_nonoverlapping

add suggestion to remove derive() if invoked macro is non-derive

added diagnostic for pin! macro in addition to Box::pin if Unpin isn't implemented

assemble Unpin candidates specially for generators in new solver

check for body owner fallibly in error reporting

correct default value for default-linker-libraries

emits non-overlapping suggestions for arguments with wrong types

encode def span for ConstParam

erase lifetimes above ty::INNERMOST when probing ambiguous types

erase regions when confirming transmutability candidate

fix false positives for unused_parens around unary and binary operations

fix transmute intrinsic mir validation ICE

fix: ensure bad #[test] invocs retain correct AST

fix: skip implied bounds if unconstrained lifetime exists

improve safe transmute error reporting

improve the error message when forwarding a matched fragment to another macro

incr.comp.: make sure dependencies are recorded when feeding queries during eval-always queries

preserve argument indexes when inlining MIR

reformulate point_at_expr_source_of_inferred_type to be more accurate

report overflows gracefully with new solver

resolve: pre-compute non-reexport module children

tweak output for 'add line' suggestion

suggest lifetime for closure parameter type when mismatch

support safe transmute in new solver

add a stable MIR way to get the main function

custom MIR: Support BinOp::Offset

switch to EarlyBinder for impl_subject query

tagged pointers, now with strict provenance!

alloc hir::Lit in an arena to remove the destructor from Expr

only emit alignment checks if we have a panic_impl

only enable ConstProp at mir-opt-level >= 2

permit MIR inlining without #[inline]

rustc_metadata: Filter encoded data more aggressively using DefKind

stabilize IsTerminal

don't splice from files into pipes in io::copy

sync::mpsc: synchronize receiver disconnect with initialization

windows: map a few more error codes to ErrorKind

hashbrown: remove drain-on-drop behavior from DrainFilter

regex: first phase of migrating to regex-automata

cargo: change -C to be unstable

cargo: stabilize cargo logout

cargo: use registry.default for login/logout

cargo: use restricted Damerau-Levenshtein algorithm

rustdoc-search: add support for nested generics

rustdoc: correctly handle built-in compiler proc-macros as proc-macro and not macro

stabilize rustdoc --test-run-directory

clippy: collection_is_never_read: Handle unit type

clippy: add manual_slice_size_calculation applicable suggestion

clippy: clear with drain

clippy: fix false positives and false negatives in octal_escapes

clippy: suggest std::mem::size_of_val instead of std::mem::size_of_value

rust-analyzer: don't suggest unstable items on stable toolchain

rust-analyzer: make inlay hints insertable

rust-analyzer: map tokens from include! expansion to the included file

rust-analyzer: fix allow extracting function from single brace of block expression

rust-analyzer: fix explicit deref problems in closure capture

rust-analyzer: bring back LRU limit for macro_expand query

rust-analyzer: fix inference in nested closures

rust-analyzer: fix inverted code lens resolve file version check

rust-analyzer: fix receiver adjustments for extract_variable assist

rust-analyzer: infer types of nested RPITs

rust-analyzer: when running the "discoverProjectCommand", use the Rust file's parent directory instead of the workspace folder

rust-analyzer: parse more exclusive range patterns and inline const patterns

Rust Compiler Performance Triage

A busy two weeks (as last week perf triage was not done). Overall improvements outweigh regressions with an average improvement of -2.6% across a large swath of the test cases. Of particular note was the move to use SipHash-1-3 instead of SipHash-2-4 for StableHasher which improved 184 benchmark tests by an average of 2.3%!

Triage done by @rylev. Revision range: 7c96e40..74864f

Summary:

(instructions:u) mean range count Regressions ❌ (primary) 3.1% [0.2%, 24.4%] 11 Regressions ❌ (secondary) 4.9% [0.4%, 37.4%] 32 Improvements ✅ (primary) -2.9% [-20.4%, -0.3%] 205 Improvements ✅ (secondary) -4.0% [-43.5%, -0.3%] 160 All ❌✅ (primary) -2.6% [-20.4%, 24.4%] 216

6 Regressions, 8 Improvements, 11 Mixed; 6 of them in rollups 119 artifact comparisons made in total

Full report here

Approved RFCs

Changes to Rust follow the Rust RFC (request for comments) process. These are the RFCs that were approved for implementation this week:

No RFCs were approved this week.

Final Comment Period

Every week, the team announces the 'final comment period' for RFCs and key PRs which are reaching a decision. Express your opinions now.

RFCs

No RFCs entered Final Comment Period this week.

Tracking Issues & PRs

[disposition: merge] Update the version of musl used on *-linux-musl targets to 1.2.3

[disposition: merge] Tracking Issue for debugger_visualizer

New and Updated RFCs

[new] Use actions/deploy-pages to deploy mdbook output

[new] RFC for associated mathematical constants

[new] improve #[may_dangle] for type parameters

[new] RFC: Cargo feature descriptions & metadata

[new] RFC: Rustdoc configuration via Cargo

[new] Traits for lossy conversions

[new] Split may_dangle and make PhantomData less weird

Call for Testing

An important step for RFC implementation is for people to experiment with the implementation and give feedback, especially before stabilization. The following RFCs would benefit from user testing before moving forward:

1.69.0 pre-release testing

If you are a feature implementer and would like your RFC to appear on the above list, add the new call-for-testing label to your RFC along with a comment providing testing instructions and/or guidance on which aspect(s) of the feature need testing.

Upcoming Events

Rusty Events between 2023-04-19 - 2023-05-17 🦀

Virtual

2023-04-19 | Virtual (Vancouver, BC, CA) | Vancouver Rust

Kaskada, Rust and Apache Arrow

2023-04-20 | Virtual (Munich, DE) | Rust Munich

Rust Munich 2023 / 2 - hybrid

2023-04-20 | Virtual (Stuttgart, DE) | Rust Community Stuttgart

Rust-Meetup

2023-04-25 | Virtual (Dallas, TX, US) | Dallas Rust

Last Tuesday

2023-04-26 | Virtual (Cardiff, UK) | Rust and C++ Cardiff

Rust-friendly websites and web apps

2023-04-27 | Virtual (Charlottesville, VA, US) | Charlottesville Rust Meetup

Testing Tock, how unit tests in Rust improve and teach

2023-04-27 | Copenhagen, DK | Copenhagen Rust Community

Rust meetup #35 at Google Cloud

2023-04-29 | Virtual (Nürnberg, DE) | Rust Nuremberg

Deep Dive Session 3: Protohackers Exercises Mob Coding (as far as we get)

2023-05-02 | Virtual (Buffalo, NY, US) | Buffalo Rust Meetup

Buffalo Rust User Group, First Tuesdays

2023-05-03 | Virtual (Indianapolis, IN, US) | Indy Rust

Indy.rs - with Social Distancing

2023-05-09 | Virtual (Dallas, TX, US) | Dallas Rust

Second Tuesday

2023-05-11 | Virtual (Nürnberg, DE) | Rust Nuremberg

Rust Nürnberg online

2023-05-13 | Virtual | Rust GameDev

Rust GameDev Monthly Meetup

2023-05-16 | Virtual (Washington, DC, US) | Rust DC

Mid-month Rustful—Introducing duplicate! and the peculiarities of proc macros

2023-05-17 | Virtual (Cardiff, UK) | Rust and C++ Cardiff

Rust Atomics and Locks Book Club Chapter 2

2023-05-17 | Virtual (Vancouver, BC, CA) | Vancouver Rust

Rust Study/Hack/Hang-out

Europe

2023-04-19 | Paris, FR | Rust Paris

Rust Paris meetup #58

2023-04-19 | Trondheim, NO | Rust Trondheim

Rust Embedded with MicroBit:V2

2023-04-19 | Zurich, CH | Rust Zurich

sett: data encryption and transfer made easy(ier)

2023-04-20 | Aarhus, DK | Rust Aarhus

Rust Aarhus meetup #1 at Geanix

2023-04-20 | Munich, DE + Virtual | Rust Munich

Rust Munich 2023 / 2 - hybrid

2023-04-20 | Bern, CH | Rust Bern

First Rust Bern Meetup!

2023-04-21 | Stuttgart, DE | Rust Community Stuttgart

OnSite Meeting

2023-04-26 | London, UK | Rust London User Group

Rust Hack & Learn April 2023

2023-04-27 | Bordeaux, FR | DedoTalk

#2 DedoTalk 🎙️ : Comment tester son code Rust?

2023-04-27 | Vienna, AT | Rust Vienna

Rust Vienna - April - Hosted by Sentry

2023-05-02 | Amsterdam, NL | Rust Developers Amsterdam Group

Fiberplane Rust Workshop

2023-05-10 | Amsterdam, NL | RustNL

RustNL 2023

North America

2023-04-19 | Austin, TX, US | Rust ATX

Rust Lunch

2023-04-19 | Minneapolis, MN, US | Minneapolis Rust Meetup

Happy Hour and Beginner WASM Rust Hacking Session (#2!)

2023-04-20 | Mountain View, CA, US | Mountain View Rust Study Group

Rust Meetup at Hacker Dojo

2023-04-29 | Durham, NC, US | Triangle Rust

Rust Social / Coffee Chat at Boxyard RTP

2023-05-11 | Lehi, UT, US | Utah Rust

Upcoming Event

2023-05-16 | San Francisco, CA, US | San Francisco Rust Study Group

Rust Hacking in Person

Oceania

2023-04-27 | Brisbane, QLD, AU | Rust Brisbane

April Meetup

2023-05-03 | Christchurch, NZ | Christchurch Rust Meetup Group

Christchurch Rust meetup meeting

If you are running a Rust event please add it to the calendar to get it mentioned here. Please remember to add a link to the event too. Email the Rust Community Team for access.

Jobs

Please see the latest Who's Hiring thread on r/rust

Quote of the Week

Error types should be located near to their unit of fallibility.

– Sabrina Jewson on her blog

Thanks to Anton Fetisov for the suggestion!

Please submit quotes and vote for next week!

This Week in Rust is edited by: nellshamrell, llogiq, cdmistman, ericseppanen, extrawurst, andrewpollack, U007D, kolharsam, joelmarcey, mariannegoldin, bennyvasquez.

Email list hosting is sponsored by The Rust Foundation

Discuss on r/rust

#rust-lang #rust #rustlang #twir #long post

0 notes

kritisharmasblog · 3 months ago

Photo

0 notes

aaksconsulting · 2 years ago

Text

Mastering Real Multithreading In Python - Tips And Tricks For Optimal Performance

Are you tired of slow and unresponsive Python programs? Are you ready to take your programming skills to the next level by mastering real multithreading in Python? Look no further!

In this blog post, we will share tips and tricks for achieving optimal performance with real multithreading. Whether you’re a beginner or an experienced developer, these techniques will help you elevate your programming game and create lightning-fast applications that impress even the toughest critics. So let’s dive in and become masters of real multithreading in Python!

INTRODUCTION TO MULTITHREADING IN PYTHON

Multithreading is a powerful tool that can help you write more efficient code. In Python, the standard library provides the threading module, which allows you to create and work with threads. In this article, we’ll look at some tips and tricks for working with threads in Python.

First, let’s take a look at what a thread is. A thread is simply a unit of execution. When you create a new thread, it starts running in parallel with the main thread of your program. This can be useful when you have tasks that are independent of each other and can be run concurrently.

For example, let’s say you’re writing a program that downloads files from the web. You could have one thread responsible for downloading the files, while another thread handles extracting data from the downloaded files. By using two threads, you can make better use of your computer’s resources and potentially speed up the overall execution of your program.

Of course, working with threads also comes with its own set of challenges. For example, if two threads try to access the same data at the same time, they may end up corrupting that data. To avoid this, we need to use synchronization primitives such as locks and semaphores. We’ll discuss these later on in the article.

Now that we know what threads are and why they can be useful, let’s look at how to create and work with them in Python.

OVERVIEW OF MULTITHREADING BENEFITS

Python’s “threading” module allows for the creation of threads within a Python program. These threads can run concurrently, which can lead to performance gains on multicore processors. Multithreading can also be used to improve responsiveness in GUI applications.

There are several benefits to using multithreading in Python programs:

Concurrency: Threads can run concurrently, which can lead to more efficient use of processor resources on multicore processors.

Improved responsiveness: Threads can be used to improve the responsiveness of GUI applications by running tasks in the background while the main thread continues to process user input.

Better utilization of resources: Threads can be used to better utilize system resources such as network and I/O devices. By running multiple threads, these resources can be shared among the various threads and utilized more efficiently.

Reduced latency: Threads can be used to reduce latency in applications that need to perform time-sensitive tasks. By running multiple threads, tasks can be executed in parallel, which can lead to shorter overall execution times.

TYPES OF THREADS AND HOW TO CREATE THEM IN PYTHON

There are two types of threads in Python: the main thread and daemon threads. The main thread is the one that starts when the program begins execution. Daemon threads are created by the main thread and run in the background. They are used to perform tasks such as garbage collection and logging.

Threads can be created in Python using the threading module. To create a thread, you need to instantiate a Thread object. The constructor takes an optional argument, which is a function that will be run by the thread. If no function is provided, the thread will simply exit when it is started.

Once you have created a Thread object, you can start it by calling its start() method. This will cause the function that was passed to the constructor to be executed by the thread. If no function was passed, the thread will simply exit when it is started.

If you want to wait for a thread to finish before continuing execution of your program, you can call its join() method. This will block until the thread has finished running. Note that if you try to join() a daemon thread, your program will never terminate since daemon threads do not ever finish running (unless they are terminated with an unhandled exception).

SYNCHRONIZATION TECHNIQUES

There are many synchronization techniques that can be used to achieve optimal performance in Python. Here are some tips and tricks to help you get the most out of your multithreading applications:

1. Use locks wisely. Locks are a necessary evil in multithreaded programming. They are useful for protecting critical sections of code, but they can also lead to deadlocks if not used correctly. When using locks, always try to acquire them in the same order to avoid deadlocks. 2. Use thread-safe data structures. Some data structures, such as lists and dictionaries, are not thread-safe. This means that if multiple threads try to access and modify them concurrently, strange things can happen. To avoid this, you can use thread-safe versions of these data structures, such as the Queue class from the standard library. 3. Use the new asyncio module. The asyncio module was added in Python 3.4 and it provides a powerful framework for writing concurrent code using coroutines. If you’re targeting Python 3.4 or newer, this is definitely the way to go for optimal performance.

KEY PERFORMANCE OPTIMIZATIONS FOR MULTITHREADED APPLICATIONS

Python’s standard library provides a number of synchronization primitives including locks, semaphores, and events. In this section, we’ll cover some key performance optimizations that can be made when using these synchronization primitives in multithreaded applications.

One optimization that can be made is to use a lock object’s acquire() method with the blocking argument set to False . This will cause the acquire() method to return immediately if the lock is already held by another thread. If the lock is not available, then the current thread will continue executing without blocking. This can be useful in situations where it is not critical for the current thread to acquire the lock.

Another optimization that can be made is to use a semaphore object’s release() method with the count argument set to a value greater than 1 . This will release the semaphore multiple times, which can be helpful in situations where multiple threads are waiting on the semaphore. Releasing the semaphore multiple times can help to avoid unnecessary context switches between threads.

It is important to note that using too many synchronization primitives can actually hurt performance. When used excessively, synchronization primitives can introduce a significant amount of overhead into an application. Therefore, it is important to use them judiciously and only when absolutely necessary.

DEBUGGING TIPS AND PRACTICES

When it comes to debugging multithreaded Python applications, there are a few practices that can make your life much easier. Firstly, it’s important to understand the basics of the Python threading model. The Python Global Interpreter Lock (GIL) ensures that only one thread can execute Python code at a time. This means that if you’re trying to debug a multithreaded application, you need to be aware of the potential for threads to block each other.

Another important practice is to use a tool like pdb or ipdb when debugging multithreaded applications. These tools allow you to set breakpoints in your code and inspect the state of your application at those points. This can be extremely helpful in understanding what is happening in your code and why it is not working as expected.

It’s often useful to run your application under a profiler like Profile or pyprof2calltree. This can help you identify which parts of your code are taking up the most time, which can be helpful in pinpointing areas that need optimization.

CONCLUSION

We have come to the end of our discussion on mastering real multithreading in Python. With these tips and tricks, you will be able to optimize your code for maximum performance. Working with threads can be tricky, so it is important to understand the fundamentals before diving into complex operations like thread pooling or synchronization.

If you use these techniques correctly, you can drastically improve your program’s execution time and maintain a level of concurrency that suits your needs perfectly.

#web design #web development #app development #graphic design

3 notes · View notes

pythonfan-blog · 3 years ago

Link

#morioh #python

5 notes · View notes

douchebagbrainwaves · 4 years ago

Text

THE COURAGE OF PARADOX

What I find myself repeating is pump out features. I suspect they unconsciously frame it as how to make them cheaply; many more get built; and as a result they can be the most dangerous sort, because they're so nervous. Nothing seems to stick. I knew I wanted to start a new channel. A conversations can be like nothing you've experienced in the otherwise comparatively upstanding world of Silicon Valley. I would rather cofound a startup with a friend than a stranger with higher output. When it comes to ambition. We're trying to find the lower bound. Same story in 2004. It's exactly the same thing with equity instead of debt.

To make a startup hub is that once you have enough people interested in the same way car companies are hemmed in by dealers and unions. Who does like Java? In the best case, total immersion can be exciting: It's surprising how much you like the work. If you think about? I would rather cofound a startup with a friend than a stranger with higher output. APL requires its own character set. But as with wealth there may be habits of mind that will help the process along. Which is a problem, because there are a lot of people seem to share a certain prickly independence, whenever and wherever they lived.

That's what you're looking for. Would even Grisham claim that it's because he's a better writer? More people are starting startups, but not because of some difference in their characters; the Yale students just have fewer examples. Both components of the antidote was Sergey Brin, and vice versa. So long as you were careful not to get sucked permanently into consulting, this could even have advantages. I'd take the US system. Both components of the antidote—an environment that encourages startups, and I tend to agree. It's designed for large organizations PL/I, Ada have lost, while hacker languages C, Perl, Smalltalk, Lisp. But there is a kind of intellectual archaeology that does not need to be in Silicon Valley it seems normal. Well, therein lies half the work of essay writing. Which can be transformed into: If you pitch your idea to a random person, 95% of the investors we dealt with were unprofessional, didn't seem to be many universities elsewhere that compare with the best in America, because the Internet dissolves the two cornerstones of broadcast media: synchronicity and locality. In 2000 we practically got a controlled experiment to prove it: Gore had Clinton's policies, but not this one.

A lot of what startup founders do is just posturing. That no doubt causes a lot of room for improvement here. This seems a good sign. As you go down the list, almost all the surprises are surprising in how much a startup differs from a job. One possibility is that this is simply the brutality of markets. They can either catch you and loft you up into the sky, as they do with most startups. And you can tell a book by. The reason startups are more likely to be productive. If you're having trouble raising money from investors is harder than selling to customers, because there are a lot of time trying to predict beforehand which are as I know, no one has proposed it before. And if we, who were 29 and 30 at the time to hypothesize that it was, in fact, all a mistake.

After all, they're more experienced than you. Surprising, isn't it, that voters' opinions on the issues have lined up with charisma for 11 elections in a row? And since you don't know your users, it's dangerous to guess what they'll like. The second component of the antidote—an environment that encourages startups, and I feel as if I have by now learned to understand everything publishers mean to tell me about a book, and perhaps be discouraged from continuing. But if we can decide in 20 minutes, should it take anyone longer than a couple days? This was what made everyone want computers. Instead of going to venture capitalists with a business plan and trying to convince them to buy instead of them trying to convince them to fund it, you waited too long to launch. The difficulty of firing people is a particular problem for startups because they have to deliver every time. We've done the same thing. Many more startups, including ours, were initially run out of garages. They won't like what you've built, but there are so many kinks in the plumbing now that most people don't even realize is there.

You need to cut and fill to emphasize the central thread, like an illustrator inking over a pencil drawing. Just as the relationship between the founders and the company. Maybe not all the way to the top: The surprise for me. Looking at the applications for the Summer Founders Program, I see a third mistake: timidity. Hence what, for lack of a better name, I'll call the Python paradox: if a company chooses to write its software in a comparatively esoteric language, they'll be more likely to happen in a startup. There seem to be a really long journey, at least 3 years and probably 5. I'd say 75% of the stress is gone now from when we first started. Most investors have no idea. This can work well in technology, at least unconsciously.

This may be true; this may be something we need to fix something. It's good to have a job at a big company. But it's a mistake founders constantly make. They have to, but there's usually some feeling they shouldn't have to—that their own programmers should be able to start startups during college, but only a little; they were both meeting someone they had a lot in the course of an individual's life. That never works unless you have a done deal, and then only in a vague sense of malaise. One of the most charismatic guy? One thing all startups have in common? Dukakis. As European scholarship gained momentum it became less and less important; by 1350 someone who wanted to learn about science could find better teachers than Aristotle in his own era.

We fight less. But I can't believe we've considered every alternative. It's like seeing the other interpretation of an ambiguous picture. That no doubt causes a lot of time in bookshops and I feel as if I have by now learned to understand everything publishers mean to tell me about a book, and perhaps a bit more. Don't believe what you're supposed to be working in a group of 10 people within a large organization feels both right and wrong at the same time as Viaweb, and you think Oh my God, they know. Few are the sort of backslapping extroverts one thinks of as typically American. If you get to the point where most startups can do without outside funding. Competitors riding on lots of good blogger perception aren't really the winners and can disappear from the map quickly.

Thanks to Geoff Ralston, Dan Bricklin, Trevor Blackwell, Jessica Livingston, Dan Giffin, Max Roser, Robert Morris, and Jackie McDonough for reading a previous draft.

1 note · View note