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2020-06-26 Scalable analytics with Python (DASK) (09:00-17:00)
Exploit the full power of your computer or a cluster using Dask in Python for distributed computing.
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About this training
Scalable analytics with Python (DASK)
Sometimes one has to work with large datasets such as large data tables or images. One faces then two problems: how to use all the cores of a computer or a cluster to decrease computational time (parallelization) and how to process data that maybe don't even fit into RAM. The Dask package offers a powerfull and simple to use solution to those problems by offering data structures very similar to classic Numpy arrays or Pandas dataframes hiding much of the distributed computational complexity from the user used to such structures.
Learning outcomes - participants can/know
Sometimes one has to work with large datasets such as large data tables or images. One faces then two problems: how to use all the cores of a computer or a cluster to decrease computational time (parallelization) and how to process data that maybe don't even fit into RAM. The Dask package offers a powerfull and simple to use solution to those problems by offering data structures very similar to classic Numpy arrays or Pandas dataframes hiding much of the distributed computational complexity from the user used to such structures.
Learning outcomes - participants can/know
- Discover the dask data structures
- Understand the Dask dashboard to monitor computing
- Exploiting multi-core computing on personal computer
- Runnning Dask on a cluster
- Use Dask with dataframes
- Use Dask with images
- UNIBE staff members, students and potential users involved in scientific research
- Participants must bring own laptops. They need to be experienced with the classic Python scientific stack, in particular Numpy. Experience with Pandas dataframe is not required.
- Students have access to the Notebooks used during the lecture and can live experiment with them during the presentation and in pauses between presentation units.
- A certificate will be delivered to participants who have attended the whole training.
- The coaches are local or external experts
Practical information (time, location ...)
Time : 2020-06-26 09:00 - 17:00
Location : Uni Mittelstrasse, Room 124, Mittelstrasse 43, University of Bern
Training language: English
Participants : Max 25
Registraion : Mandatory
Coaches : Dr. Guillaume Witz
Prerequisites : Laptop, good scientific Python knowledge
Certificate : Certificate for full training attendance
Course material : You don't have to prepare anything for the course. You will be given access to a Jupyter environment that runs in your default browser and that is pre-loaded with the course material. In order to have a full experience, it is recommmended to install Jupyter, JupyterLab and Dask on your own laptop.
Location : Uni Mittelstrasse, Room 124, Mittelstrasse 43, University of Bern
Training language: English
Participants : Max 25
Registraion : Mandatory
Coaches : Dr. Guillaume Witz
Prerequisites : Laptop, good scientific Python knowledge
Certificate : Certificate for full training attendance
Course material : You don't have to prepare anything for the course. You will be given access to a Jupyter environment that runs in your default browser and that is pre-loaded with the course material. In order to have a full experience, it is recommmended to install Jupyter, JupyterLab and Dask on your own laptop.
About ScITS
The Science IT Support is there to boost your research by supporting you solving computing challenges.
Your code doesn't compile, you need more computing power, more storage, a data mangament plan and
so on - scits.math.unibe.ch.
Your code doesn't compile, you need more computing power, more storage, a data mangament plan and
so on - scits.math.unibe.ch.
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