List of videos

"Understanding Numba - the Python and Numpy compiler [EuroPython 2019 - Talk - 2019-07-11 - PyCharm] [Basel, CH] By Christoph Deil Do you have numerical code written in Python and Numpy? Do you wish it ran faster, using the full potential of your CPU? Then you should try Numba, a JIT compiler that translates a subset of Python and Numpy code into fast machine code. This talk will explain how Numba works, and when and how to use it for numerical algorithms, focusing on how to get very good performance on the CPU. To understand this talk, only a basic knowledge of Python and Numpy is needed. You will learn how Python compiles functions to bytecode and how Numba compiles bytecode to machine code. Why algorithms implemented using Numpy sometimes don't yield great performance, and how to do better using Numba. You will learn about the @numba.jit and @numba.vectorize decorators and how to create functions that use the CPU well by using e.g. multi-threading (several CPU cores), vector instructions (single instruction multiple data) and fast math (trade float accuracy for speed). You will also learn when it does and doesn't make sense to use Numba, by contrasting it briefly with some other options for high-performance computing from Python: PyPy, C, C++, Cython, Numexpr, Dask, PyTorch, Tensorflow and Google JAX License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/

"Introduction to low-level profiling and tracing [EuroPython 2019 - Talk - 2019-07-11 - MongoDB] [Basel, CH] By Christian Heimes Python has built-in tracing and profiling facilities in form of callback hooks in the sys module. The settrace and setprofile callbacks have several drawbacks. They slow down the Python interpreter considerable and only allow tracing of Python code. Modern OS and CPUs come with a variety of APIs for efficient and low-level tracing down to system calls, Kernel space code, and hardware events. Some tools even create code that runs in Kernel space. This talks is an introduction and comparison of various low and high level tools for profiling and tracing as well as visualization tools like flame graphs. It covers ptrace, perf, SystemTap, and BCC/eBPF. Ptrace based commands like strace are easy to use but slow. Perf allows lightweight profiling of hardware events and CPU instructions. SystemTap is a powerful toolkit plus DSL to instrument probe points inside the Kernel as well as static SystemTap/DTrace markers in libs and languages like Java, PHP, and Python. CPython comes with a set of instrumentations for SystemTap. BCC is a collection of tools that run as JIT optimized eBPF code in Kernel space. The talk is an introduction to basic concepts of low-level tracing and profiling on Linux. The main goal is the show the potential of the tools. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/

Recruiting Session [EuroPython 2019 - - 2019-07-11 - Osaka / Samarkand [PyData track]] [Basel, Switzerland] License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/

"Tips for the scientific programmer [EuroPython 2019 - Talk - 2019-07-11 - PyCharm] [Basel, CH] By Michele Simionato This is a talk for people who need to perform large numeric calculations. They could be scientists, developers working in close contact with scientists, or even people working on finance and other quantitative fields. Such people are routinely confronted with issues like 1 parallelism: how to parallelize calculations efficiently 2 data: how to store and manage large amounts of data efficiently 3 memory: how to avoid running out of memory 4 performance: how to be fast The goal of the talk is to teach some lessons learned after several years of doing numeric simulations in a context were micro-optimizations are the least important factor, while overall architecture, design choices and good algorithms are of paramount importance. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/

"Deep Learning with TensorFlow 2.0 [EuroPython 2019 - Talk - 2019-07-11 - Osaka / Samarkand [PyData track] [Basel, CH] By Brad Miro Learn about the updates being made to TensorFlow in its 2.0 version. We’ll give an overview of what’s available in the new version as well as do a deep dive into an example using its central high-level API, Keras. You’ll walk away with a better understanding of how you can get started building machine learning models in Python with TensorFlow 2.0 as well as the other exciting available features! License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/

"Get up to speed with Cython 3.0 [EuroPython 2019 - Talk - 2019-07-11 - PyCharm] [Basel, CH] By Stefan Behnel Processing lots of data, in need of more speed, or struggling to make use of native code? The Cython compiler for Python solves all of these problems in a pythonic way, by compiling Python code to optimised C code. Cython [1] has a very long history of faithfully helping users to solve their Python performance problems and integrating native code into Python in production critical settings, while stimulating somewhat less confidence with its 0.x versioning scheme. For the next Cython version, a stable 3.0 release, we are planning to clean up several historically grown issues in the language to make the Cython compiler more friendly for modern Python users to work with. In this talk, I will show how Cython can be used for compiling and speeding up Python code, using fast native data structures and libraries. I will then give an overview of the changes that are on their way for the 3.0 release and how they will affect the user experience. [1] https://cython.org License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/

"AI in Contemporary Art [EuroPython 2019 - Keynote - 2019-07-11 - MongoDB] [Basel, CH] By Luba Elliott Over the past couple of years, there has been increasing interest in applying the latest advances in machine learning to creative projects in art and design. From DeepDream and style transfer to a GAN-generated painting selling for $430,000 at auction, AI art has moved beyond the world of research and academia and become a trend in its own right. Meanwhile, the contemporary art world's fascination with the social impact of facial recognition, recommendation systems and deep fakes has encouraged artists to explore AI critically as subject matter. This talk will give an overview of how artists and technologists are using and thinking about machine learning, its creative potential and societal impact. License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/

"Lightning Talks [EuroPython 2019 - - 2019-07-11 - MongoDB] [Basel, CH] License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/

"Python Performance: Past, Present and Future [EuroPython 2019 - Keynote - 2019-07-12 - MongoDB] [Basel, CH] By Victor Stinner Python Performance: Past, Present and Future License: This video is licensed under the CC BY-NC-SA 3.0 license: https://creativecommons.org/licenses/by-nc-sa/3.0/ Please see our speaker release agreement for details: https://ep2019.europython.eu/events/speaker-release-agreement/