PyCon Taiwan 2016|一般演講 Talks
? 摘要 Abstract ?
In relation to Internet of Things, micro services, and big data, a developer is easily being expected to handle the stream of data flow. A growing fantasy of Reactive programming is being told that a paradigm can help people to face these challenges in theory and practice and to make life easier. Is it True? Or, does it SCALE?
Join the quest to to discover reactive design and data workflow implemented in Python. We’ll inspect their features and use cases of reactive programming, to name a few, Python built-in, PyFunctional, RxPy, Flexx, async and await (and asyncio), Promise, … etc., study their best practices, and discover the elegant part compared with commonly seen sequential chaining. We also want to know when it may complicate your code.
? 關於講者 About Speaker - Keith Yang ?
最近覺得邊騎室內腳踏車邊用電腦,離開臉書與 IG 的精神(神經?)生活很不賴。讓這隻小白鼠從大眾心理控制實驗學裡喘了一小口氣。
Recently he enjoys skateboard commute, still coffee-achemy, and indoor cycle while programming or gaming, an awesome mind vocation of leaving FB and IG. Keith is the founder and co-organizer of Taipei.py, largest Python user group in Taiwan, a Lead Software Engineer at iCHEF, and was Chairperson of PyCon APAC 2015. His work mostly focuses on web/backend/cloud services since 2006, and he hands on kernel tools of virtualization on hypervisors in 2016.
#python #pycontw #pycontw2016
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https://www.youtube.com/watch?v=Jdir84yZsVo
PyCon APAC 2022|一般演講 Talks|國泰金控 Cathay Financial Holdings / 美光科技 Micron 冠名贊助
✏️ 共筆 Note:https://hackmd.io/@pycontw/HkTp86XJs
?? Slido:https://app.sli.do/event/peUkfwVcPamsp56SBtkm8W
? 語言 Language:英文 English
? 層級 Level:中階 Intermediate
? 分類 Category:其他 Other
? 摘要 Abstract ?
By using Streamlit and streamlit-webrtc, we can create web-based real-time computer vision apps only with ~10 or 20 additional lines of Python code. It's a new easy way to turn computer vision models into web-based demos and has some advantages over the conventional ways which use OpenCV. In this talk, I will demonstrate the development process using these libraries and show a variety of examples so that we see how easy and useful they are and can make use of them in daily development and research.
? 說明 Description ?
Streamlit is a fast-growing framework with which developers can build web-based data apps only with Python code.
streamlit-webrtc is a custom component for Streamlit which allows to use real-time video/audio streams on Streamlit apps.
With a combination of these libraries, we can create web-based real-time video apps quickly.
The speaker is the author of streamlit-webrtc, and has been selected as a member of the Streamlit Creators program for the contributions including it.
A lightning talk about these technologies at PyCon JP 2021 is available: https://youtu.be/_LuLs8H1gJc
About development using these libraries, following articles have been published:
- Developing Web-Based Real-Time Video/Audio Processing Apps Quickly with Streamlit
- Real-Time Video Streams With Streamlit-WebRTC
As linked from the streamlit-webrtc repo, demo apps I have developed are available online:
- Demo showcase including real-time object detection: https://share.streamlit.io/whitphx/streamlit-webrtc-example/main/app.py
- Source code: https://github.com/whitphx/streamlit-webrtc-example/blob/main/app.py
- Real-time Speech-to-Text: https://share.streamlit.io/whitphx/streamlit-stt-app/main/app_deepspeech.py
- Source code: https://github.com/whitphx/streamlit-stt-app
- Real-time style transfer: https://share.streamlit.io/whitphx/style-transfer-web-app/main/app.py
- Source code: https://share.streamlit.io/whitphx/style-transfer-web-app/main/app.py
- Real-time Tokyo 2020 Pictogram: https://share.streamlit.io/whitphx/tokyo2020-pictogram-using-mediapipe/streamlit-app
- Source code: https://github.com/whitphx/Tokyo2020-Pictogram-using-MediaPipe
- Video chat: online demo is not available because it does not have an auth mechanism and is only for private use.
- Source code: https://github.com/whitphx/streamlit-video-chat-example
? 關於講者 About Speaker - Yuichiro Tachibana ?
Yuichiro works as a professional software developer and also loves contributing to OSS projects. As a Pythonista, he has participated in various projects including web development, multimedia streaming, data management, computer vision, and machine learning.
#pycontw #pyconapac2022 #python #opencv #streamlit #webrtc #computervision
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https://www.youtube.com/watch?v=ruSN7t2eor4
摘要 Abstract:
Unsupervised learning is how human learns. And generative adversarial networks is one of the most popular algorithms to do unsupervised learning. There are lots of GAN papers with amazing results. I would like to talk about issues of replicating GAN results and how I solved them.
PyCon Taiwan 2017 official: https://tw.pycon.org/2017/
PyCon Taiwan 2017 Facebook Fan Page: https://www.facebook.com/pycontw/
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https://www.youtube.com/watch?v=Ou-XwCH8f5w
Day 2, 13:00-13:45
Abstract
過往在使用Python開發API的時候,基於WSGI架構下,運算中遇到IO bound時,可以透過多線程去處理,但遇到cpu bound時,多線程並不是一個好的選擇,因為線程會受限於GIL,並不會有效提升效能,而當遇到運算請求量較大時,為了因應大量的運算請求,可以透過多進程來彌補多線程的不足,但同時也需要承擔過多的資源消耗以及考慮Inter-Process Communication overhead。 而這些問題在FastAPI得到了救贖,FastAPI是一個建立在ASGI架構下的Web框架,以Python所提出的非同步概念為基礎,透過Coroutine的方式,去提高CPU運算效率,去改善多線程、多進程對於cpu bound遇到的問題。 本次演講主要透過房屋估價模型服務程式碼的解說,分享如何從過去使用的Flask框架,轉換為FastAPI框架。透過非同步的設計,即使有GIL的限制,仍舊能夠透過單線程去達到類似多進程的運算效能,減少開啟多進程造成的資源耗費過多。此外,透過程式碼,說明這兩個框架開發上的不同之處,以及轉換過程中曾經遇到的問題,讓想使用FastAPI但還在觀望的人能夠有參考方向,減少未來使用時踩雷的機會!
Description
想透過本次演講告訴大家的內容
ASGI架構是什麼?FastAPI是什麼?是如何應用Async?
藉由實際痛點(mutli-thread、mutli-process issue)案例-房屋估價模型服務分析Flask與FastAPI開發上相異之處,以及這樣的相異之處對開發上的影響
非同步框架帶來好處的同時,使用上所需的注意事項
實際房屋估價模型服務轉換前轉後的數據比較
除了效能以外,FastAPI所帶來的好處
轉換上的心路歷程
本次使用的相關工具
FastAPI
Flask
Slides not uploaded by the speaker.
HackMD: https://hackmd.io/@pycontw/2021/%2F%40pycontw%2Frk6Kg4cfY
Speaker: 陳家丞
Intelligent System Engineer at E.SUN Bank. Like to study python Web Framework, e.g., Flask、FastAPI.
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https://www.youtube.com/watch?v=5bIJ1SwF2bk
PyCon Taiwan 2023|Talk 演講|Day 1, R2 11:35–12:05
? 說明 Description ?
Data cleaning and transformation are critical steps in any data-related project, including data analysis, machine learning, and business intelligence. However, these tasks can be challenging due to the various methods available and the lack of standardization in the process. In this speech, I will discuss the specific challenges I faced while cleaning and transforming video metadata. I will also introduce some solutions that I have found helpful in my work, such as the use of systematic data cleaning processes like Data Build Tool(DBT) and data pipeline quality monitoring tools like PipeRider. By combining these tools and techniques, we can improve the efficiency and reliability of data transformation and enhance the overall data application process. The goal of this speech is to show how DBT and PipeRider can help create a more efficient, scalable, and error-free data transformation pipeline.
? 投影片 Slides:https://docs.google.com/presentation/d/e/2PACX-1vSXyJm9M-BUzaZfAlfH6ZBwpr15Gw5OtlVTJFqBsW9-t0XN6Ns2A5xmS4a1d0c_IS5tOn2-uRTWkMBe/pub?start=false&loop=false&delayms=3000
? 講者介紹 About Speaker - Iris Chen ?
As a fresh graduate stepping into the professional world, I am filled with enthusiasm for every aspect of data.
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PyCon Taiwan 2023|Tutorial 專業課程|Day 1, R3 13:00–14:30
? 說明 Description ?
Explainable artificial intelligence (XAI) refers to AI systems that can clearly and understandably explain their decisions and actions. This is crucial for ensuring that AI systems are transparent, accountable, and trustworthy, and do not make biased or discriminatory decisions. Python offers various libraries that can be used to improve the interpretability of AI algorithms in a variety of situations, such as enhancing transparency in decision-making systems or facilitating communication between AI experts and non-experts. In this hands-on tutorial, you will learn how to use these tools to improve the interpretability of AI algorithms in Python, and explore key metrics and evaluation methods for measuring the effectiveness of XAI techniques. By the end of this tutorial, you will have a better understanding of how to make your AI systems more transparent and trustworthy.
? 講者介紹 About Speaker - Neeraj Pandey ?
Neeraj is a polyglot and open-source contributor. Over the years, he has spoken at multiple international Python conferences and has worked on a variety of full-stack software and data science applications, as well as computational arts and quantitative finance projects. He enjoys the challenge of creating new tools and applications.
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