tag:engineers.sg,2005:/episodes?page=171Engineers.SG2024-03-19T07:28:15Ztag:engineers.sg,2005:Episode/8732016-07-06T04:04:59Z2023-12-27T14:00:58ZLightning Talk: Python Decorators - PyConSG 2016<iframe width="560" height="315" src="https://www.youtube.com/embed/Ei_mm2UIUnY" frameborder="0" allowfullscreen></iframe><p>Speaker: Graham Dumpleton</p>
<p>Event Page: <a href="https://pycon.sg">https://pycon.sg</a></p>
<p>Produced by Engineers.SG</p>
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<p><a href="http://amara.org/v/P6Rt/">http://amara.org/v/P6Rt/</a></p>Graham Dumpletontag:engineers.sg,2005:Episode/8722016-07-06T04:04:54Z2023-12-27T14:00:58ZLightning Talk: Pycon Malaysia - PyConSG 2016<iframe width="560" height="315" src="https://www.youtube.com/embed/Pj_-DeGVllA" frameborder="0" allowfullscreen></iframe><p>Speaker: Swee Meng</p>
<p>Event Page: <a href="https://pycon.sg">https://pycon.sg</a></p>
<p>Produced by Engineers.SG</p>
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<p><a href="http://amara.org/v/P6Ru/">http://amara.org/v/P6Ru/</a></p>Swee Mengtag:engineers.sg,2005:Episode/8712016-07-06T04:04:50Z2023-12-27T14:00:58ZLightning Talk: Openshift - PyConSG 2016<iframe width="560" height="315" src="https://www.youtube.com/embed/9nPZ0dFj_30" frameborder="0" allowfullscreen></iframe><p>Speaker: Graham Dumpleton</p>
<p>Event Page: <a href="https://pycon.sg">https://pycon.sg</a></p>
<p>Produced by Engineers.SG</p>
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<p><a href="http://amara.org/v/P6Rv/">http://amara.org/v/P6Rv/</a></p>Graham Dumpletontag:engineers.sg,2005:Episode/8702016-07-06T04:04:46Z2024-03-17T02:00:28ZLightning Talk: Bitcoin mining on Python - PyConSG 2016<iframe width="560" height="315" src="https://www.youtube.com/embed/jVEszkzbyCw" frameborder="0" allowfullscreen></iframe><p>Speaker: Yves J Hilpisch</p>
<p>Event Page: <a href="https://pycon.sg">https://pycon.sg</a></p>
<p>Produced by Engineers.SG</p>
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<p><a href="http://amara.org/v/P6Rw/">http://amara.org/v/P6Rw/</a></p>Dr. Yves J. Hilpischtag:engineers.sg,2005:Episode/8692016-07-06T04:04:33Z2024-03-11T07:00:44Zmusic2vec: A smart music recommendation and discovery - PyConSG 2016<iframe width="560" height="315" src="https://www.youtube.com/embed/4gagrwwMJcA" frameborder="0" allowfullscreen></iframe><p>Speaker: Laam Pham</p>
<p>Description
<br>Using million playlists as sequences of songs, music2vec (written in Python) can map a song/playlist to a vector. It turns out these vectors make sense and keep the hidden structure. Imagine you like the song A, and dislike song B, but a playlist X. So the math is easy: your findings = song A - song B + playlist X. Voila, this is music2vec.</p>
<p>Abstract
<br>Music2vec is a recommendation to explore music for you. We combine several state of the art in NLP domain e.g. LDA, word2vec/paragraph2vec. It achieves quite impressive semantic result and can be learnable and smarter day by day. We'll present some basic concepts and hidden technologies behind the scenes for building this application. At the end of the talk, the demo will come out.</p>
<p>Here is the first look and feel "music2vec demo" (<a href="https://pbs.twimg.com/tweet_video/Cc5q69PUMAAvBun.mp4">https://pbs.twimg.com/tweet_video/Cc5q69PUMAAvBun.mp4</a>)</p>
<p>Event Page: <a href="https://pycon.sg">https://pycon.sg</a></p>
<p>Produced by Engineers.SG</p>
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<p><a href="http://amara.org/v/P6Rx/">http://amara.org/v/P6Rx/</a></p>Laam Phamtag:engineers.sg,2005:Episode/8682016-07-06T04:04:25Z2024-03-15T13:00:30ZCustomer Segmentation in Python - PyConSG 2016<iframe width="560" height="315" src="https://www.youtube.com/embed/4NDORb4HBkw" frameborder="0" allowfullscreen></iframe><p>Speaker: Mao Ting</p>
<p>Description
<br>By segmenting customers into groups with distinct patterns, businesses can target them more effectively with customized marketing and product features. I'll dive into a few machine learning and statistical techniques to extract insights from customer data, and demonstrate how to execute them on real data using Python and open-source libraries.</p>
<p>Abstract
<br>I will go through clustering and decision tree analysis using sciki-learn and two-sample t test using scipy. We will learn the intuition for each technique, the math behind them, and how to implement them and evaluate the results using Python. I will be using open-source data for the demonstration, and show what insights you can extract from actual data using these techniques.</p>
<p>Event Page: <a href="https://pycon.sg">https://pycon.sg</a></p>
<p>Produced by Engineers.SG</p>
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<p><a href="http://amara.org/v/P6SD/">http://amara.org/v/P6SD/</a></p>Mao Tingtag:engineers.sg,2005:Episode/8672016-07-06T04:04:21Z2024-03-17T16:00:50ZKeynote by Dr. Yves J. Hilpisch - PyConSG 2016<iframe width="560" height="315" src="https://www.youtube.com/embed/jSY1OQqrdfk" frameborder="0" allowfullscreen></iframe><p>How Open Source, Open Data and the Cloud are Reshaping Finance Education and the Financial Industry</p>
<p>Speaker: Dr. Yves J. Hilpisch</p>
<p>Event Page: <a href="https://pycon.sg">https://pycon.sg</a></p>
<p>Produced by Engineers.SG</p>
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<p><a href="http://amara.org/v/P6SA/">http://amara.org/v/P6SA/</a></p>Dr. Yves J. Hilpischtag:engineers.sg,2005:Episode/8662016-07-06T04:04:01Z2023-11-26T20:00:53ZPython Robotics for Education - PyConSG 2016<iframe width="560" height="315" src="https://www.youtube.com/embed/1tLhx2044vI" frameborder="0" allowfullscreen></iframe><p>Speaker: Max Ong Zong Bao</p>
<p>Description
<br>Raspberry Pi is a credit card size, low cost computer that could be used to build things ranging from supercomputers, to game consoles and robots. In this tutorial we will learn how to program a smart remote control for a robot. This is great for educators who would like to include robotics and computer programming into their curriculum.</p>
<p>Abstract
<br>In this tutorial we will show how to program the robot called Pi2Go. Students will learn robotics control fundamental using Python:</p>
<p>Movement of the Robot Use of on-board sensors to detect obstacles Line sensing using the Robot line sensors Duration: 1 hour 30 minutes</p>
<p>Company: Smart Nation Coding Academy, <a href="http://snca.com.sg/courses.html">http://snca.com.sg/courses.html</a></p>
<p>Website - <a href="http://snca.com.sg/courses.html">http://snca.com.sg/courses.html</a></p>
<p>Faccebook Page - <a href="https://www.facebook.com/snca.com.sg/">https://www.facebook.com/snca.com.sg/</a></p>
<p>Event Page: <a href="https://pycon.sg">https://pycon.sg</a></p>
<p>Produced by Engineers.SG</p>
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<p><a href="http://amara.org/v/P6SG/">http://amara.org/v/P6SG/</a></p>Max Ong Zong Baotag:engineers.sg,2005:Episode/8652016-07-06T04:03:54Z2023-12-06T11:01:37ZBuilding a simple-to-use Database Management tool - PyConSG 2016<iframe width="560" height="315" src="https://www.youtube.com/embed/AS9fxvZiMd8" frameborder="0" allowfullscreen></iframe><p>Speaker: Chinab Chugh</p>
<p>Description
<br>Jublia is a business matching SaaS solution for the events industry. A common challenge faced by us and this industry is managing different dynamic datasets of participant databases. That’s why we built Jublia DATASYNC - an easy-to-use data management tool via Google Sheets. I will be sharing how you can also build a simple-to-use tools for both techies and non-techies alike to manage databases.</p>
<p>Abstract
<br>In this talk, I will be sharing about the bread and butter of Jublia's Data Management Tool, which we call DATASYNC. It will focus on how we leverage on Google Sheets to build our own user-friendly database which maps to your SQL database. To do this, we decided to leverage on Google sheets because of its ubiquity and developed our own ‘middle layer’ using Google Sheets API to ensure our database is always in sync with the sheets. In addition to exploring the functionality of Sheets using the API, I will also share how we used Server-sent events on AWS DynamoDB to build a loading bar to communicate to the users when a 'sync' between the Sheets and database is happening.</p>
<p>Event Page: <a href="https://pycon.sg">https://pycon.sg</a></p>
<p>Produced by Engineers.SG</p>
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<p><a href="http://amara.org/v/P6SH/">http://amara.org/v/P6SH/</a></p>Chinab Chughtag:engineers.sg,2005:Episode/8642016-07-06T04:03:39Z2024-03-12T16:00:21ZPython Pants - A build system for large codebases with multiple dependencies - PyConSG 2016<iframe width="560" height="315" src="https://www.youtube.com/embed/Q6sLN8rqkDE" frameborder="0" allowfullscreen></iframe><p>Speaker: Angad Singh</p>
<p>Description
<br>This talk is geared towards Infrastructure and system engineers who are interested in learning about structuring a large monorepo codebase, consisting of multiple micro services that share many dependencies. This talk will introduce Pants as a build system for such large monolithic codebase and how it ties with today’s container ecosystem principles.</p>
<p>Abstract
<br>Pants was developed at Twitter as the need grew for developers to be able to build large code bases, really fast. A simple way to speed up code builds is to split the code into hundreds of small repositories (with a one-to-one mapping between each micro service and its repository). But that solution does not scale as you end up with hundreds of non-standard repositories with shared code and dependency management issues. A large unified codebase promotes better engineering collaboration. Pants was developed as a build system for such large codebases.</p>
<p>In web companies of all sorts and sizes, Python is a popular choice for writing infrastructure/operations scripts as well as large scale web projects. I have worked at two companies, Twitter and Viki, where Python is a core language for Infrastructure, used for automation, job scheduling, deployments, infrastructure provisioning and operational dashboards. Across both companies, we have been using Pants as a build system for our Python projects.</p>
<p>With the rise of the container ecosystems, the importance of strong build systems has increased even more. Golang with its statically linked binaries is a beautiful example of “drop your binary into a container and run”. Pants introduces a similar concept of packaging the dependencies along with the target for python projects. It can run your tests and build standalone python executables called PEX files. It gives benefits similar to a virtualenv and can even give you a REPL for your python project. All of this is helpful as we ship python executables in our Docker containers, which is used in both development and production environments.</p>
<p>There are other python build systems as well such as Bazel, <a href="http://bazel.io/docs/be/python.html">http://bazel.io/docs/be/python.html</a> and Buck <a href="https://buckbuild.com/">https://buckbuild.com/</a> with all of them providing almost similar benefits. The goal is to encourage the usage of a build system to ensure standardisation and increased productivity of the development team.</p>
<p>The talk will include a quick overview of the Pants command line as well as how we can ship a simple Flask application, which has multiple library dependencies as a Python executable inside a Docker container.</p>
<p>Event Page: <a href="https://pycon.sg">https://pycon.sg</a></p>
<p>Produced by Engineers.SG</p>
<p>Help us caption & translate this video!</p>
<p><a href="http://amara.org/v/P6SI/">http://amara.org/v/P6SI/</a></p>Angad Singhtag:engineers.sg,2005:Episode/8632016-07-06T04:03:31Z2024-03-18T06:00:42ZDeep Learning With Python & Tensorflow - PyConSG 2016<iframe width="560" height="315" src="https://www.youtube.com/embed/U0ACP9J8vOU" frameborder="0" allowfullscreen></iframe><p>Speaker: Ian Lewis</p>
<p>Description
<br>Python has lots of scientific, data analysis, and machine learning libraries. But there are many problems when starting out on a machine learning project. Which library do you use? How can you use a model that has been trained in your production app? In this talk I will discuss how you can use TensorFlow to create Deep Learning applications and how to deploy them into production.</p>
<p>Abstract
<br>Python has lots of scientific, data analysis, and machine learning libraries. But there are many problems when starting out on a machine learning project. Which library do you use? How do they compare to each other? How can you use a model that has been trained in your production application?</p>
<p>TensorFlow is a new Open-Source framework created at Google for building Deep Learning applications. Tensorflow allows you to construct easy to understand data flow graphs in Python which form a mathematical and logical pipeline. Creating data flow graphs allow easier visualization of complicated algorithms as well as running the training operations over multiple hardware GPUs in parallel.</p>
<p>In this talk I will discuss how you can use TensorFlow to create Deep Learning applications. I will discuss how it compares to other Python machine learning libraries like Theano or Chainer. Finally, I will discuss how trained TensorFlow models could be deployed into a production system using TensorFlow Serve.</p>
<p>Event Page: <a href="https://pycon.sg">https://pycon.sg</a></p>
<p>Produced by Engineers.SG</p>
<p>Help us caption & translate this video!</p>
<p><a href="http://amara.org/v/P6SE/">http://amara.org/v/P6SE/</a></p>Ian Lewistag:engineers.sg,2005:Episode/8622016-07-06T04:03:24Z2024-03-19T01:00:55ZUsing Python to Build a GIS Data Pipeline for Rural-Urban Classification - PyConSG 2016<iframe width="560" height="315" src="https://www.youtube.com/embed/YXtScKuH7VU" frameborder="0" allowfullscreen></iframe><p>Speaker: Tung Whye Loon</p>
<p>Description
<br>This talk will introduce the use of Python and several related modules (GDAL, Shapely, Fiona etc) to build a GIS-based data processing pipeline to retrieve the road network information required to build a rural-urban classification scheme for a region of interest. The speaker will highlight the main concepts used to build the data pipeline and run a simple demo to illustrate these concepts.</p>
<p>Abstract
<br>Overview: This talk will introduce the use of Python and several related modules (GDAL, Shapely, Fiona etc) to build a GIS-based data processing pipeline to retrieve the road network information required to build a rural-urban classification scheme for a region of interest. The speaker will highlight the main concepts used to build the data pipeline and run a simple demo to illustrate these concepts.</p>
<p>Background: A promising source of information for rural-urban classification alternative to population census is remote sensing data, particularly multispectral satellite imagery. A rural-urban classifier based on remote sensing data has the advantage of data consistency and data timeliness. However, from a cost perspective, the proposed methodology has limited practical applicability. The main drawback is that, for geographically diverse countries, the yearly acquisition cost of multispectral satellite images from satellite data provider is prohibitively expensive. Another drawback is that the quality of the satellite images is heavily dependent on the imaging conditions.</p>
<p>In the presence of adverse imaging conditions such as extensive cloud coverage, satellite images of a region may become unclear or unavailable for classification. Even when satellite images are readily available, pre-classification image processing is still necessary and onerous to execute, as the existing classification methodology is sensitive to variations in tone and color of the acquired satellite images.</p>
<p>To address these limitations, a novel solution to the rural-urban classification problem is crafted using data features extracted from road network information. Primarily, rural and urban areas are characterized by distinctive road network profiles. Rural areas generally feature long stretches of road with few intersections and branchings, whereas in urban areas the road networks are often highly inter-connected, resulting in short road segments and a large number of intersections.</p>
<p>Event Page: <a href="https://pycon.sg">https://pycon.sg</a></p>
<p>Produced by Engineers.SG</p>
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<p><a href="http://amara.org/v/P6SJ/">http://amara.org/v/P6SJ/</a></p>Tung Whye Loontag:engineers.sg,2005:Episode/8612016-07-06T04:03:16Z2023-12-20T09:00:28ZDeep Learning Hands-on Workshop - PyConSG 2016<iframe width="560" height="315" src="https://www.youtube.com/embed/1KjJPikAodk" frameborder="0" allowfullscreen></iframe><p>Speaker: Martin Andrews</p>
<p>Description
<br>Deep Learning is a hot topic, but has a steep initial learning curve. To ease the pain, a pre-configured virtual machine will be handed out, so that participants can run it on their own laptops using cross-platform open-source VirtualBox. In order to get the most out of the talk, participants should install VirtualBox on their laptops beforehand.</p>
<p>Abstract
<br>The workshop will start from the very basics (with a little mathematics), and quickly progress to getting hands-on with open source software including the training of deep networks on simple problems.</p>
<p>This will be followed by a more in-depth portion, using a pre-built Virtual Machine, run within VirtualBox (which participants should have installed on their laptops before the workshop). This section includes an introduction to Deep Reinforcement Learning : Specifically training a deep neural network to play 'Bubble Breaker' using 'Q-learning', similar in principle to Google's AlphaGo.</p>
<p>While parts of this are very technical, the subject can be appreciated at different levels - and the models (inside the Virtual Machine) are all in Jupyter (fka iPython) notebooks, making interaction straightforward.</p>
<p>Event Page: <a href="https://pycon.sg">https://pycon.sg</a></p>
<p>Produced by Engineers.SG</p>
<p>Help us caption & translate this video!</p>
<p><a href="http://amara.org/v/P6SO/">http://amara.org/v/P6SO/</a></p>Martin Andrewstag:engineers.sg,2005:Episode/8602016-07-06T04:03:10Z2024-01-02T23:00:33ZSentiment Analysis, its techniques and applications - PyConSG 2016<iframe width="560" height="315" src="https://www.youtube.com/embed/rhVhR22t2IE" frameborder="0" allowfullscreen></iframe><p>Speaker: Mimansa Jaiswal</p>
<p>Description
<br>I aim to cover the following aspects under the talk: 1. Using nltk with python (Overview of modules and data) 2. Basics of natural language processing (tokenisation, stemming, wordnet, pos tagging) 3. Sentiment Analysis (overview of classification methods, binary versus fuzzy classification) 4. Directions of sentiment analysis 5. Applications in discerning human emotions.</p>
<p>Abstract
<br>The workshop would aim to provide a general overview of the concepts that are used in conducting a Sentiment Analysis on textual data.</p>
<p>The beginning 5 minutes of the talk would deal with how nltk is used in python, what corpus it provides, the stemmers inbuilt, sentence tokenisation and pickled models. I would then move to using this nltk toolkit for sentence tokenisation and pos tagging and how NER (Named-Entity Recognition can be useful for Aspect based sentiment analysis) which would take around 10 minutes.</p>
<p>I would then proceed to discuss about the classification methods like bag-of-words, random forests etc. and where and when they should be used. In here, I would also explain the bias induced in dataset regarding the industry it is dealing with. I would also touch briefly on binary classification (positive, negative) or probability value vector in case of multi-label classification. This would take 10 minutes.</p>
<p>I would then discuss about the various directions in which sentiment analysis is used, namely, stance detection, aspect based sentiment analysis etc. I would go over the various ares that sentiment analysis can be used (product reviews, social media posts) and how that information about sentiment can be used. And then I would conclude by discussing about the projects that I have worked upon, that is, giving AI the benefit of recognising and empathising with emotions and how it would be helpful.</p>
<p>Event Page: <a href="https://pycon.sg">https://pycon.sg</a></p>
<p>Produced by Engineers.SG</p>
<p>Help us caption & translate this video!</p>
<p><a href="http://amara.org/v/P6SN/">http://amara.org/v/P6SN/</a></p>Mimansa Jaiswaltag:engineers.sg,2005:Episode/8592016-07-06T04:02:57Z2024-02-05T01:00:56ZVisualising Machine Learning Models - PyConSG 2016<iframe width="560" height="315" src="https://www.youtube.com/embed/_XFphDKCzBY" frameborder="0" allowfullscreen></iframe><p>Speaker: Amit Kapoor</p>
<p>Description
<br>We rarely use the power of visualisation to understand our models better. Model evaluation is largely limited to numerical summaries. Visualising models helps us better understand - shape of the model, impact of parameters on the model, impact of different input data , model fit and where it can be improved. This talk summarizes the learnings and key takeaways when communicating model results</p>
<p>Abstract
<br>For a data scientist building predictive models, the following are important:</p>
<p>How good is the model ?
<br>How good is it compared to competing/alternate models?
<br>Is there a way to identify what worked in the models built so far, to leverage it to build something even better?
<br>The stakeholder/end-user who finally uses the output from the model, for whom the ML process is mostly black-box, is concerned with the following: 1. How to trust the model output? 2. How to understand the drivers? 3. How to do what-if analysis?</p>
<p>The unifying theme that could answer most of the above questions is visualization. The biggest challenge is to find a way to visualize the model, the model fitting process and the impact of drivers. This talk summarizes the learnings and key takeaways when communicating model results.</p>
<p>Even though exploratory data analysis (EDA) is an integral part of the data science pipeline and helps us understand the portrait of the data, we rarely leverage the power of visualisation for understanding our models better. Model evaluation is still largely restricted through numerical summaries. Visualising models can help us better understand - the shape of the model, the impact of parameter on the model, the impact of different input data on the model, the fit of the model and where it can be improved.</p>
<p>Inspired by "Visualising Statistical Models" by Hadley Wickham et.al, several visualisation techniques were tried and presented. In this talk we look at the practical examples of the methods that can help us better understand the model. This includes showing model in data space, plotting multiple models as opposed to just one and exploring the process of fitting (as opposed to final result). This talk summarises the learning and key takeaways.</p>
<p>Most of the visualisations were done using matplotlib, seaborn and bokeh.</p>
<p>Event Page: <a href="https://pycon.sg">https://pycon.sg</a></p>
<p>Produced by Engineers.SG</p>
<p>Help us caption & translate this video!</p>
<p><a href="http://amara.org/v/P6SL/">http://amara.org/v/P6SL/</a></p>Amit Kapoortag:engineers.sg,2005:Episode/8582016-07-06T04:02:44Z2023-03-26T05:01:29ZUsing Artificial Life Simulation to Gain Insights into Contradictory Field Evidence - PyConSG 2016<iframe width="560" height="315" src="https://www.youtube.com/embed/zz2Ox25eNxo" frameborder="0" allowfullscreen></iframe><p>Speaker: Maurice Ling</p>
<p>Description
<br>Antibiotics resistance is a serious biomedical issue and there are contradictory results on the prevalence of resistance following disuse of the specific antibiotics. However, it is not ethically possible to carry out such field experiments. Hence, digital organisms are used as proxy to gain insights into contradictory field evidence in attempt to provide crucial information into this debate.</p>
<p>Abstract
<br>Antibiotics resistance is a serious biomedical issue as formally susceptible organisms gain resistance under its selective pressure. There have been contradictory results regarding the prevalence of resistance following withdrawal and disuse of the specific antibiotics.</p>
<p>In the first experiment, we use experimental evolution in “digital organisms” to examine the rate of gain and loss of resistance under the assumption that there is no fitness cost for maintaining resistance. Our results show that selective pressure is likely to result in maximum resistance with respect to the selective pressure. During de-selection as a result of disuse of the specific antibiotics, a large initial loss and prolonged stabilization of resistance are observed but resistance is not lost to the stage of pre-selection. This suggests that a pool of partial resistant organisms persist long after withdrawal of selective pressure at a relatively constant proportion.</p>
<p>In the second experiment, fitness costs incurred in maintaining resistance, in the form of deviation from GC-content, is added. However, our results showed that GC-content based fitness cost during de-selection by removal of antibiotic-induced selective pressure portrayed similar trends in resistance compared to that of no fitness cost, at all stages of initial selection, repeated de-selection and re-introduction of selective pressure. Paired t-test suggested that prolonged stabilization of resistance after initial loss is not statistically significant for its difference to that of no fitness cost.</p>
<p>Hence, contradictory results regarding the prevalence of resistance following withdrawal and disuse of the specific antibiotics may be a statistical variation about constant proportion. Our results also show that subsequent re-introduction of the same selective pressure results in rapid re-gain of maximal resistance. Thus, our simulation results suggest that complete elimination of specific antibiotics resistance is unlikely after the disuse of antibiotics, once a resistant pool of micro-organism has been established, which has important implications towards responsible use of antibiotics.</p>
<p>This work demonstrates that the use of digital organisms has the potential to provide insights into otherwise ethically and practically inaccessible areas.</p>
<p>Event Page: <a href="https://pycon.sg">https://pycon.sg</a></p>
<p>Produced by Engineers.SG</p>
<p>Help us caption & translate this video!</p>
<p><a href="http://amara.org/v/P6SR/">http://amara.org/v/P6SR/</a></p>Maurice Lingtag:engineers.sg,2005:Episode/8572016-07-06T04:02:25Z2024-03-16T00:01:17ZDesign and implement a scalable and high performance tcp server with tornado - PyConSG 2016<iframe width="560" height="315" src="https://www.youtube.com/embed/lfJdSyAE-w4" frameborder="0" allowfullscreen></iframe><p>Speaker: Nhu Dinh Tuan</p>
<p>Description
<br>Developing and maintaining a robust, scalable, high performance tcp server is usually quite tricky, even more so with Python where worker threads running on multiple cpu cores are absent due to GPI (Global Interpreter Lock). This talk covers the design and implementation of tcp/ip communication components, the way how to handle requests and assign the tasks efficiently.</p>
<p>Abstract
<br>Developing and maintaining a robust, scalable, high performance tcp server is usually quite tricky, even more so with Python where worker threads running on multiple cpu cores are absent due to GPI (Global Interpreter Lock). This talk covers the design and implementation of tcp/ip communication components, the way how to handle requests and assign the tasks efficiently. A scalable, high-performance and easy-to-use framework based on Tornado is also introduced to help you to set up a tcp server in short time, so you can just focus on writing the logic part.</p>
<p>Contents</p>
<p>1/ Design aspects - tcp servers - scale vs performance - design targets - sync vs async vs coroutines. - what options for network library in Python</p>
<p>2/ System Architecture and flow</p>
<p>3/ Implementation overview - Network components - CPython restrictions - Partial data transmission - Socket Tips - worker processor manager</p>
<p>4/ Demo - small instant message application.</p>
<p>Event Page: <a href="https://pycon.sg">https://pycon.sg</a></p>
<p>Produced by Engineers.SG</p>
<p>Help us caption & translate this video!</p>
<p><a href="http://amara.org/v/P6SM/">http://amara.org/v/P6SM/</a></p>Nhu Dinh Tuantag:engineers.sg,2005:Episode/8562016-07-06T04:01:49Z2024-01-22T05:01:12ZWarpdrive, making Python web application deployment magically easy - PyConSG 2016<iframe width="560" height="315" src="https://www.youtube.com/embed/OW-tU0j52Ys" frameborder="0" allowfullscreen></iframe><p>Speaker: Graham Dumpleton</p>
<p>Description
<br>Deploying Python web applications is too hard. You either have to understand some arcane configuration syntax, or have to dig through an encyclopaedic volume of options. It shouldn't have to be this hard. In this talk you will see how 'warpdrive', with the right sort of magic, can make Python web application deployment easy.</p>
<p>Abstract
<br>Ask a beginner to deploy a Python web application and they will often complain it is too hard. Although we have standards for how a Python web application should interface with a web server, the web servers for Python all work differently, with a myriad of options and being difficult to set up properly.</p>
<p>In this talk you will be given a preview of a project called 'warpdrive', a project being developed to simplify the process of deploying a Python web application.</p>
<p>The 'warpdrive' project makes it easy to run your Python web application on your own system, but it can also create a Docker image for your application, providing you with an easy path to deploying it on a Docker service.</p>
<p>How 'warpdrive' works is also compatible with next generation Platform as a Service (PaaS) offerings such as the latest OpenShift, which has been reimplemented around Docker and Kubernetes.</p>
<p>Come see how working on and deploying your Python web application could be made so much easier using 'warpdrive'.</p>
<p>Event Page: <a href="https://pycon.sg">https://pycon.sg</a></p>
<p>Produced by Engineers.SG</p>
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<p><a href="http://amara.org/v/P6SK/">http://amara.org/v/P6SK/</a></p>Graham Dumpleton