Prefect: Production Workflows Who am I? The Problem I’m trying to solve How Dask helps Pain points when using Dask Technology we use around Dask Links Prefect: Production...
Diagnostics (local) Progress Bar Profiler ResourceProfiler CacheProfiler Example Custom Callbacks API Diagnostics (local) Profiling parallel code can be challenging, but...
User Interfaces High-Level Collections Low-Level Interfaces Combining High- and Low-Level Interfaces Laziness and Computing Persist into Distributed Memory Lazy vs Immediate D...
Worker Resources Example Resources are applied separately to each worker process Resources are Abstract Resources with collections Worker Resources Access to scarce resourc...
Full Spectrum: Credit and Banking Who am I? What problem am I trying to solve? How Dask helps Why I chose Dask originally Some of the pain points of using Dask in our problem ...
Setup Setup This page describes various ways to set up Dask on different hardware, eitherlocally on your own machine or on a distributed cluster. If you are justgetting started...
Diagnostics (distributed) Dashboard Progress bar External Documentation API Diagnostics (distributed) The Dask distributed scheduler provides live feedback in twoforms: ...
Parallel and Distributed Machine Learning Types of Scaling Scikit-Learn in 5 Minutes Hyperparameters Hyperparameter Optimization Single-machine parallelism with scikit-learn M...
IPython Integration Launch Dask from IPyParallel Launch IPython within Dask Workers Example with IPython Magics Example with qt-console IPython Integration Dask.distributed...
Task Graphs Motivation Example Schedulers Task Expectations Don’t Modify Data In-Place Avoid Holding the GIL Task Graphs Internally, Dask encodes algorithms in a simple f...