Dask ExamplesΒΆ
These examples show how to use Dask in a variety of situations.
First, there are some high level examples about various Dask APIs like arrays, dataframes, and futures, then there are more in-depth examples about particular features or use cases.
You can run these examples in a live session here: 
Basic Examples
Machine Learning
- Blockwise Ensemble Methods
- Scale Scikit-Learn for Small Data Problems
- Score and Predict Large Datasets
- Batch Prediction with PyTorch
- Train Models on Large Datasets
- Incrementally Train Large Datasets
- Text Vectorization Pipeline
- Hyperparameter optimization with Dask
- Scale XGBoost
- Use Voting Classifiers
- Automate Machine Learning with TPOT
- Generalized Linear Models
- Singular Value Decomposition
Applications
- Analyze web-hosted JSON data
- Async/Await and Non-Blocking Execution
- Asynchronous Computation: Web Servers + Dask
- Embarrassingly parallel Workloads
- Handle Evolving Workflows
- Image Processing
- ETL Pipelines with Prefect
- Reading and manipulating tiled GeoTIFF datasets
- Stencil Computations with Numba
- Time Series Forecasting
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