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Dask.distributed — Dask.distributed 1.15.0+26.g228f7bb documentation

Data Streams with Queues. Shared Futures With Channels. Launch Tasks from Tasks. Journey of a Task. Daskdistributed is a lightweight library for distributed computing in Python. It extends both the. APIs to moderate sized clusters. Serves to complement the existing PyData analysis stack. In particular it meets the following needs:. Each task suffers about 1ms of overhead. A small computation and network roundtrip can complete in less than 10ms. Compatible with the concurrent.futures. On your own cluster.

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Dask.distributed — Dask.distributed 1.15.0+26.g228f7bb documentation | distributed.readthedocs.io Reviews

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Data Streams with Queues. Shared Futures With Channels. Launch Tasks from Tasks. Journey of a Task. Daskdistributed is a lightweight library for distributed computing in Python. It extends both the. APIs to moderate sized clusters. Serves to complement the existing PyData analysis stack. In particular it meets the following needs:. Each task suffers about 1ms of overhead. A small computation and network roundtrip can complete in less than 10ms. Compatible with the concurrent.futures. On your own cluster.

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Dask.distributed — Dask.distributed 1.15.0+26.g228f7bb documentation | distributed.readthedocs.io Reviews

https://distributed.readthedocs.io

Data Streams with Queues. Shared Futures With Channels. Launch Tasks from Tasks. Journey of a Task. Daskdistributed is a lightweight library for distributed computing in Python. It extends both the. APIs to moderate sized clusters. Serves to complement the existing PyData analysis stack. In particular it meets the following needs:. Each task suffers about 1ms of overhead. A small computation and network roundtrip can complete in less than 10ms. Compatible with the concurrent.futures. On your own cluster.

INTERNAL PAGES

distributed.readthedocs.io

1

Setup Network — Dask.distributed 1.16.0+36.g2d869bf documentation

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

Using the Command Line. Using a Shared Network File System and a Job Scheduler. Using the Python API. DRMAA (SGE, SLURM, Torque, etc.). Data Streams with Queues. Shared Futures With Channels. Launch Tasks from Tasks. Journey of a Task. Network consists of one. Nodes One can set these up in a variety of ways. Using the Command Line. Executable in one process and the. Executable in several processes, possibly on different machines. Dask-scheduler Start scheduler at 192.168.0.1:8786. Or you can specify a ho...

2

Managing Memory — Dask.distributed 1.16.0+36.g2d869bf documentation

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

Difference with dask.compute. Data Streams with Queues. Shared Futures With Channels. Launch Tasks from Tasks. Journey of a Task. A client holds a future pointing to this task. The data should stay in RAM so that the client can gather the data on demand. The task is necessary for ongoing computations that are working to produce the final results pointed to by futures. These tasks will be removed once no ongoing tasks require them. The following functions produce Futures. Func, *args, * kwargs). Collectio...

3

Web Interface — Dask.distributed 1.16.0+36.g2d869bf documentation

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

Connecting to Web Interface. Data Streams with Queues. Shared Futures With Channels. Launch Tasks from Tasks. Journey of a Task. Information about the current state of the network helps to track progress, identify performance issues, and debug failures. Daskdistributed includes a web interface to help deliver this information over a normal web page in real time. This web interface is launched by default wherever the scheduler is launched if the scheduler machine has Bokeh. Main Scheduler pages at. These ...

4

Data Locality — Dask.distributed 1.16.0+36.g2d869bf documentation

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

Specify workers with Compute/Persist. Data Streams with Queues. Shared Futures With Channels. Launch Tasks from Tasks. Journey of a Task. Data movement often needlessly limits performance. This is especially true for analytic computations. Dask.distributed minimizes data movement when possible and enables the user to take control when necessary. This document describes current scheduling policies and user API around data locality. That requires some data. Function, which depends on the. Keyword argument ...

5

Managing Computation — Dask.distributed 1.16.0+36.g2d869bf documentation

http://distributed.readthedocs.io/en/latest/manage-computation.html

Dask Collections to Concrete Values. Dask Collections to Futures. Concrete Values to Futures. Futures to Concrete Values. Futures to Dask Collections. Data Streams with Queues. Shared Futures With Channels. Launch Tasks from Tasks. Journey of a Task. Data and Computation in Dask.distributed are always in one of three states. Concrete values in local memory. Example include the integer. Or a numpy array in the local process. Lazy computations in a dask graph, perhaps stored in a. You can asynchronously su...

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What’s New — xarray 0.9.1 documentation

http://xarray.pydata.org/en/stable/whats-new.html

V091 (30 January 2017). V090 (25 January 2017). V082 (18 August 2016). V081 (5 August 2016). V080 (2 August 2016). V072 (13 March 2016). V071 (16 February 2016). V070 (21 January 2016). V061 (21 October 2015). V060 (21 August 2015). V052 (16 July 2015). V051 (15 June 2015). V05 (1 June 2015). V041 (18 March 2015). V04 (2 March, 2015). V032 (23 December, 2014). V031 (22 October, 2014). V03 (21 September 2014). V02 (14 August 2014). V011 (20 May 2014). V01 (2 May 2014). Why is pandas not enough? Repr (adde...

dask.readthedocs.io

Use Cases — dask 0.14.0 documentation

http://dask.readthedocs.io/en/latest/use-cases.html

Directory of CSV or tabular HDF files. Directory of CSV files on HDFS. Directories of custom format files. Scikit-Learn or Joblib User. Dask is a versatile tool that supports a variety of workloads. This page contains brief and illustrative examples for how people use Dask in practice. This page emphasizes breadth and hopefully inspires readers to find new ways that Dask can serve them beyond their original intent. Dask use cases can be roughly divided in the following two categories:. Single machine par...

dask.readthedocs.io

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...

dask.readthedocs.io

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:.

dask.readthedocs.io

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.

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Dask.distributed — Dask.distributed 1.15.0+26.g228f7bb documentation

Data Streams with Queues. Shared Futures With Channels. Launch Tasks from Tasks. Journey of a Task. Daskdistributed is a lightweight library for distributed computing in Python. It extends both the. APIs to moderate sized clusters. Serves to complement the existing PyData analysis stack. In particular it meets the following needs:. Each task suffers about 1ms of overhead. A small computation and network roundtrip can complete in less than 10ms. Compatible with the concurrent.futures. On your own cluster.

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