Use Case

Machine Learning

Dask and Coiled accelerate pragmatic machine learning workflows by natively integrating into familiar libraries. Dask makes it easy to add ML to existing pipelines. No need to add a new tool like as with Spark.
Dask + Coiled

Docs

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Submit arbitrary functions for computation in a parallelized, eager, and non-blocking way.

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Dask Bag implements operations like map, filter, groupby and aggregations on collections of Python objects.

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What if you don’t have an array or dataframe? Instead of having blocks where the function is applied to each block, you can decorate functions with @delayed and have the functions themselves be lazy.

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Walkthrough with Matt Rocklin

Dask + Optuna Example

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What if you don’t have an array or dataframe? Instead of having blocks where the function is applied to each block, you can decorate functions with @delayed and have the functions themselves be lazy.

Explore
Learn More arrow
Heading

What if you don’t have an array or dataframe? Instead of having blocks where the function is applied to each block, you can decorate functions with @delayed and have the functions themselves be lazy.

Explore
Learn More arrow
Heading

What if you don’t have an array or dataframe? Instead of having blocks where the function is applied to each block, you can decorate functions with @delayed and have the functions themselves be lazy.

Explore
Learn More arrow

With GitHub, Google or email.

Use your AWS or GCP account.

Start scaling.

$ pip install coiled
$ coiled setup
$ ipython
>>> import coiled
>>> cluster = coiled.Cluster(n_workers=500)