This tutorial was last given at SciPy 2020 which was a virtual conference. A video of the SciPy 2018 tutorial is available online.
Dask provides multi-core execution on larger-than-memory datasets.
We can think of dask at a high and a low level
- High level collections: Dask provides high-level Array, Bag, and DataFrame collections that mimic NumPy, lists, and Pandas but can operate in parallel on datasets that don't fit into main memory. Dask's high-level collections are alternatives to NumPy and Pandas for large datasets.
- Low Level schedulers: Dask provides dynamic
task schedulers that execute task graphs in parallel. These
execution engines power the high-level collections mentioned above
but can also power custom, user-defined workloads. These schedulers
are low-latency (around 1ms) and work hard to run computations in a
small memory footprint. Dask's schedulers are an alternative to
direct use of
multiprocessinglibraries in complex cases or other task scheduling systems like
Different users operate at different levels but it is useful to
understand both. This tutorial will interleave between high-level
(even sections) and low-level use of dask graphs and schedulers
You should clone this repository
git clone http://github.com/dask/dask-tutorial
and then install necessary packages.
a) Create a conda environment (preferred)
In the main repo directory
conda env create -f binder/environment.yml conda activate dask-tutorial jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter labextension install @bokeh/jupyter_bokeh
b) Install into an existing environment
You will need the following core libraries
conda install numpy pandas h5py pillow matplotlib scipy toolz pytables snakeviz scikit-image dask distributed -c conda-forge
You may find the following libraries helpful for some exercises
conda install python-graphviz -c conda-forge
Note that this options will alter your existing environment, potentially changing the versions of packages you already have installed.
c) Use Dockerfile
You can build a docker image out of the provided Dockerfile.
$ docker build . # This will build using the same env as in a)
Run a container, replacing the ID with the output of the previous command
$ docker run -it -p 8888:8888 -p 8787:8787 <container_id_or_tag>
The above command will give an URL (
127.0.0.1):8888/?token=<sometoken>) which can be used
to access the notebook from browser. You may need to replace the
given hostname with "localhost" or "127.0.0.1".
From the repo directory
This was already done for method c) and does not need repeating.
- Ask for help
Overview - dask's place in the universe.
Delayed - the single-function way to parallelize general python code.
1x. Lazy - some of the principles behind lazy execution, for the interested.
Bag - the first high-level collection: a generalized iterator for use with a functional programming style and to clean messy data.
Array - blocked numpy-like functionality with a collection of numpy arrays spread across your cluster.
Dataframe - parallelized operations on many pandas dataframes spread across your cluster.
Distributed - Dask's scheduler for clusters, with details of how to view the UI.
Advanced Distributed - further details on distributed computing, including how to debug.
Dataframe Storage - efficient ways to read and write dataframes to disc.
Machine Learning - applying dask to machine-learning problems.