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* Add documentation for Mars on Ray * Update mars_on_ray.rst * refine according to comments Co-authored-by: hekaisheng <kaisheng.hks@alibaba-inc.com> Co-authored-by: Eric Liang <ekhliang@gmail.com>
56 lines
1.4 KiB
ReStructuredText
56 lines
1.4 KiB
ReStructuredText
Mars on Ray
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============
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.. _`issue on GitHub`: https://github.com/mars-project/mars/issues
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`Mars`_ Mars is a tensor-based unified framework for large-scale data computation which scales Numpy, Pandas and Scikit-learn.
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Mars on Ray makes it easy to scale your programs with a Ray cluster.
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.. note::
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This API is experimental in Mars. If you encounter any bugs, please file an `issue on GitHub`_.
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.. _`Mars`: https://docs.pymars.org
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Installation
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-------------
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You can simply install Mars via pip:
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.. code-block:: bash
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pip install pymars>=0.6.0a1
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Getting started
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----------------
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It's easy to run Mars jobs on a Ray cluster. Use ``from mars.session import new_session``
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and run ``new_session(backend='ray').as_default()``; this will create
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a Mars session for Ray as the default session, and then all Mars tasks will be
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submitted to Ray. Arguments will be passed to ``ray.init()`` when
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creating a Mars session like this:
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``new_session(backend='ray', address=<address>, num_cpus=<num_cpus>)``.
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.. code-block:: python
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from mars.session import new_session
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ray_session = new_session(backend='ray').as_default()
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import mars.dataframe as md
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import mars.tensor as mt
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t = mt.random.rand(100, 4, chunk_size=30)
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df = md.DataFrame(t, columns=list('abcd'))
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print(df.describe().execute())
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.. warning::
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`Mars remote API`_ is not available for now.
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.. _`Mars remote API`: https://docs.pymars.org/en/latest/getting_started/remote.html
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