Mars is a tensor-based unified framework for large-scale data computation.
Mars tensor provides a familiar interface like Numpy.
import numpy as np a = np.random.rand(1000, 2000) (a + 1).sum(axis=1)
import mars.tensor as mt a = mt.random.rand(1000, 2000) (a + 1).sum(axis=1).execute()
Easy to scale in and scale out
Mars can scale in to a single machine, and scale out to a cluster with hundreds of machines. Both the local and distributed version share the same piece of code, it's fairly simple to migrate from a single machine to a cluster due to the increase of data.
.. toctree:: :maxdepth: 2 :caption: Getting Started :hidden: install kubernetes
.. toctree:: :maxdepth: 2 :caption: Tensor Interface :hidden: tensor/overview tensor/datasource tensor/ufunc tensor/routines tensor/sparse tensor/execution tensor/eager-mode
.. toctree:: :maxdepth: 2 :caption: Distributed Scheduling :hidden: distributed/architecture distributed/prepare distributed/schedule-policy distributed/states distributed/worker-schedule distributed/fault-tolerance
.. toctree:: :maxdepth: 2 :caption: Contribution Guide :hidden: contributing