Prerequisites
Should have experience programming in at least one language, ideally Python. If you do not have any experience programming in Python it is advisable to have a look at the Software Carpentry lesson on Python which introduces the basics of Python.
| Setup | Setup required for the lesson | ||
| Day 1 | 09:30 | 1. Introduction |
What is Dask?
How does it work? |
| 09:45 | 2. Python |
What is a virtual environment?
How can you time Python scripts? What is a simple way to run a Python script on a compute node? |
|
| 10:10 | 3. Dask Delayed |
How do you install Dask Delayed?
How can Dask Delayed be used to parallelize python code? How can I see what Dask Delayed is doing? |
|
| 10:50 | 4. Break | Break | |
| 11:05 | 5. Real Computations |
What happens when I use Dask for “real” computations?
How can I see CPU efficiency of a SLURM Job? |
|
| 11:30 | 6. Lunch Break | Break | |
| 13:00 | 7. Distributed Computations |
How can I avoid the GIL problem?
How can I run multiple Python interpreters at once on one problem? |
|
| 13:40 | 8. Cython |
How slow is Python?
Can I make my Python code faster? How can I turn my Python code into compiled C or C++ code? |
|
| 14:20 | 9. NumPy |
Why are NumPy arrays faster than lists?
How do you create NumPy arrays? What are some things I can do with NumPy arrays? |
|
| 14:35 | 10. Break | Break | |
| 14:50 | 11. Dask Array |
How can I process NumPy arrays in parallel?
Can I distribute NumPy arrays to avoid needing all the memory on one machine? |
|
| 15:15 | 12. Wrapping Up |
What did we learn?
What next? |
|
| 15:20 | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.