Parallel Python: Analyzing Large Datasets
Students will walk away with a high-level understanding of both parallel problems and how to reason about parallel computing frameworks. They will also walk away with hands-on experience using a variety of frameworks easily accessible from Python.
Knowledge of Python and general familiarity with the Jupyter notebook are assumed. This is generally aimed at a beginning to intermediate audience.
For the first half we cover basic ideas and common patterns in parallel computing, including embarrassingly parallel map, unstructured asynchronous submit, and large collections.
For the second half we cover complications arising from distributed memory computing and exercise the lessons learned in the first section by running informative examples on provided clusters.
- Part one
- Parallel Map
- Asynchronous Futures
- High Level Datasets
- Part two
- Scaling cross validation parameter search
- Tabular data with map/submit
- Tabular data with dataframes
Download this repository:
git clone https://github.com/pydata/parallel-tutorial
or download as a zip file.
Create a new conda environment:
conda env create -f environment.yml source activate parallel # Linux OS/X activate parallel # Windows
If you want to use Spark (this is a large download):
conda install -c conda-forge pyspark
Test your installation:
python -c "import concurrent.futures, dask, jupyter"
We will generate a dataset for use locally. This will take up about 1GB of
space in a new local directory,
Part 1: Local Notebooks
Part one of this tutorial takes place on your laptop, using multiple cores.
Run Jupyter Notebook locally and navigate to the
The notebooks are ordered 1, 2, 3, so you can start with
Part 2: Remote Clusters
Part two of this tutorial takes place on a remote cluster.
Visit the following page to start an eight-node cluster: https://pydata-parallel.jovyan.org/
If at any point your cluster fails you can always start a new one by re-visiting this page.
Warning: your cluster will be deleted when you close out. If you want to save your work you will need to Download your notebooks explicitly.
Brief, high level slides exist at http://pydata.github.io/parallel-tutorial/.
Sponsored Cloud Provider
We thank Google for generously providing compute credits on Google Compute Engine.