In order to enable an iCal export link, your account needs to have an API key created. This key enables other applications to access data from within Indico even when you are neither using nor logged into the Indico system yourself with the link provided. Once created, you can manage your key at any time by going to 'My Profile' and looking under the tab entitled 'HTTP API'. Further information about HTTP API keys can be found in the Indico documentation.
Additionally to having an API key associated with your account, exporting private event information requires the usage of a persistent signature. This enables API URLs which do not expire after a few minutes so while the setting is active, anyone in possession of the link provided can access the information. Due to this, it is extremely important that you keep these links private and for your use only. If you think someone else may have acquired access to a link using this key in the future, you must immediately create a new key pair on the 'My Profile' page under the 'HTTP API' and update the iCalendar links afterwards.
Permanent link for public information only:
Permanent link for all public and protected information:
The Python programming language has become more and more popular in Scientific Computing. In this course, we will talk about the best practices in Python and in scientific computing in general. We will also introduce the concept of concurrent and parallel programming, distributed programming, and GPU programming. During the hands-on session, you will experience how these are done in Python on the Dutch National Supercomputer Cartesius. You will also learn how to use libraries such as numba, PyCUDA and mpi4py.
Introduction to efficient shared memory programming
This lecture focuses on issues like parallelism and concurrency and dives a little bit into compiler theory by explaining concepts like just in time and ahead of time compilation in the context of a interpreted language (Python).
Hands-on: Introduction to efficient Python CPU programming
This tutorial will teach how to apply certain theoretical concepts in Python by using the subprocess, multiprocessing, concurrent.futures, and asyncio libraries. Additionally, two examples are introduced illustrating the aot and jit concepts.
Shared Memory Programming in Python: Numba, Cython and OpenMP
The lecture is focused on introducing the three programming paradigms and contains Python code samples illustrating key concepts and performance.