text: Mars on Ray¶
  Mars is a tensor-based unified framework for large-scale data computation which scales Numpy, Pandas and Scikit-learn. Mars on Ray makes it easy to scale your programs with a Ray cluster.
  Note
  This API is experimental in Mars. If you encounter any bugs, please file an issue on GitHub.
  Getting started¶
  It’s easy to run Mars jobs on a Ray cluster. Use
  from mars.session import new_session
  and run
  new_session(backend='ray').as_default(); this will create
  a Mars session for Ray as the default session, and then all Mars tasks will be
  submitted to Ray. Arguments will be passed to
  ray.init() when
  creating a Mars session like this:
  new_session(backend='ray', address=<address>, num_cpus=<num_cpus>).
  from mars.session import new_session ray_session = new_session(backend='ray').as_default() import mars.dataframe as md import mars.tensor as mt t = mt.random.rand(100, 4, chunk_size=30) df = md.DataFrame(t, columns=list('abcd')) print(df.describe().execute())
  Warning
  Mars remote API is not available for now.