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Dask — dask 0.13.0+11.gc68f277 documentation

Dask is a flexible parallel computing library for analytic computing. Dask is composed of two components:. Optimized for computation. This is similar to. Airflow, Luigi, Celery, or Make. But optimized for interactive computational workloads. 8220;Big Data” collections. Like parallel arrays, dataframes, and lists that extend common interfaces like. NumPy, Pandas, or Python iterators. To larger-than-memory or distributed environments. These parallel collections run on top of the dynamic task schedulers.

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Dask — dask 0.13.0+11.gc68f277 documentation | dask.readthedocs.io Reviews

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Dask is a flexible parallel computing library for analytic computing. Dask is composed of two components:. Optimized for computation. This is similar to. Airflow, Luigi, Celery, or Make. But optimized for interactive computational workloads. 8220;Big Data” collections. Like parallel arrays, dataframes, and lists that extend common interfaces like. NumPy, Pandas, or Python iterators. To larger-than-memory or distributed environments. These parallel collections run on top of the dynamic task schedulers.

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Dask — dask 0.13.0+11.gc68f277 documentation | dask.readthedocs.io Reviews

https://dask.readthedocs.io

Dask is a flexible parallel computing library for analytic computing. Dask is composed of two components:. Optimized for computation. This is similar to. Airflow, Luigi, Celery, or Make. But optimized for interactive computational workloads. 8220;Big Data” collections. Like parallel arrays, dataframes, and lists that extend common interfaces like. NumPy, Pandas, or Python iterators. To larger-than-memory or distributed environments. These parallel collections run on top of the dynamic task schedulers.

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dask.readthedocs.io

1

Choosing between Schedulers — dask 0.14.0 documentation

http://dask.readthedocs.io/en/latest/scheduler-choice.html

Distributed Scheduler on a Cluster. Distributed Scheduler on a Single Machine. Dask enables you to run your computations on a variety of schedulers that use different technologies, ranging from a single thread, to multiple processes, to a distributed cluster. This document helps explain the differences between the different choices so that users can make decisions that improve performance. Briefly, the current options are as follows:. And sometimes useful with. Pure Python code (like JSON parsing) does n...

2

Scheduler Overview — dask 0.14.0 documentation

http://dask.readthedocs.io/en/latest/scheduler-overview.html

After we create a dask graph, we use a scheduler to run it. Dask currently implements a few different schedulers:. A scheduler backed by a thread pool. A scheduler backed by a process pool. A synchronous scheduler, good for debugging. A distributed scheduler for executing graphs. On multiple machines. This lives in the external distributed. The entry point for all schedulers is a. Function. This takes a dask graph, and a key or list of keys to compute:. Method that calculates the output of the collection:.

3

Install Dask — dask 0.14.0 documentation

http://dask.readthedocs.io/en/latest/install.html

You can install dask with. Or by installing from source. To install the latest version of Dask from the conda-forge. This installs dask and all common dependencies, including Pandas and NumPy. To install Dask with. There are a few options, depending on which dependencies you would like to keep up to date:. Install dask and numpy. Install dask and cloudpickle. Install dask, numpy, and pandas. To install dask from source, clone the repository from github. You can view the list of all dependencies within the.

4

Distributed Scheduling — dask 0.14.0 documentation

http://dask.readthedocs.io/en/latest/distributed.html

Dask can run on a cluster of hundreds of machines and thousands of cores. Technical documentation for the distributed system is located on a separate website located here:. Https:/ distributed.readthedocs.io/en/latest/. Provided by Read the Docs. On Read the Docs. Free document hosting provided by Read the Docs.

5

Contact and Support — dask 0.14.0 documentation

http://dask.readthedocs.io/en/latest/support.html

Where to ask for help. Where to ask for help. Dask conversation happens in the following places:. For new features or established bugs. For real-time discussion among developers. For usage questions and bug reports we strongly prefer the use of StackOverflow and Github issues respectively over Gitter chat. Github and StackOverflow are more easily searchable by future users and so is more efficient for everyone’s time. Gitter chat is strictly reserved for developer and community discussion.

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Dask — dask 0.13.0+11.gc68f277 documentation

Dask is a flexible parallel computing library for analytic computing. Dask is composed of two components:. Optimized for computation. This is similar to. Airflow, Luigi, Celery, or Make. But optimized for interactive computational workloads. 8220;Big Data” collections. Like parallel arrays, dataframes, and lists that extend common interfaces like. NumPy, Pandas, or Python iterators. To larger-than-memory or distributed environments. These parallel collections run on top of the dynamic task schedulers.

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