Posted in 2020
2020 Dask User Survey
- Sep 22, 2020
This post presents the results of the 2020 Dask User Survey, which ran earlier this summer. Thanks to everyone who took the time to fill out the survey! These results help us better understand the Dask community and will guide future development efforts.
Announcing the DaskHub Helm Chart
- Aug 31, 2020
Today we’re announcing the release of the
daskhub
helm chart. This is a Helm chart to easily install
JupyterHub and Dask for multiple users on a
Kubernetes Cluster. If you’re managing deployment for many people that needs
interactive, scalable computing (say for a class of students, a data science
team, or a research lab) then dask/daskhub might be right for you.
Running tutorials
- Aug 21, 2020
For the last couple of months we’ve been running community tutorials every three weeks or so. The response from the community has been great and we’ve had 50-100 people at each 90 minute session.
Comparing Dask-ML and Ray Tune's Model Selection Algorithms
- Aug 06, 2020
Hyperparameter optimization is the process of deducing model parameters that can’t be learned from data. This process is often time- and resource-consuming, especially in the context of deep learning. A good description of this process can be found at “Tuning the hyper-parameters of an estimator,” and the issues that arise are concisely summarized in Dask-ML’s documentation of “Hyper Parameter Searches.”
Configuring a Distributed Dask Cluster
- Jul 30, 2020
Configuring a Dask cluster can seem daunting at first, but the good news is that the Dask project has a lot of built in heuristics that try its best to anticipate and adapt to your workload based on the machine it is deployed on and the work it receives. Possibly for a long time you can get away with not configuring anything special at all. That being said, if you are looking for some tips to move on from using Dask locally, or have a Dask cluster that you are ready to optimize with some more in-depth configuration, these tips and tricks will help guide you and link you to the best Dask docs on the topic!
The current state of distributed Dask clusters
- Jul 23, 2020
Dask enables you to build up a graph of the computation you want to perform and then executes it in parallel for you. This is great for making best use of your computer’s hardware. It is also great when you want to expand beyond the limits of a single machine.
Last Year in Review
- Jul 17, 2020
We recently enjoyed the 2020 SciPy conference from the comfort of our own homes this year. The 19th annual Scientific Computing with Python conference was a virtual conference this year due to the global pandemic. The annual SciPy Conference brought together over 1500 participants from industry, academia, and government to showcase their latest projects, learn from skilled users and developers, and collaborate on code development.
Dask Summit
- Apr 28, 2020
In late February members of the Dask community gathered together in Washington, DC. This was a mix of open source project maintainers and active users from a broad range of institutions. This post shares a summary of what happened at this workshop, including slides, images, and lessons learned.