MARS: a tensor-based unified framework for large-scale data computation.

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Project description

https://raw.githubusercontent.com/mars-project/mars/master/docs/source/images/mars-logo-title.png

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Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and many other libraries.

Documentation, 中文文档

Installation

Mars is easy to install by

pip install pymars

Installation for Developers

When you want to contribute code to Mars, you can follow the instructions below to install Mars for development:

git clone https://github.com/mars-project/mars.git
cd mars
pip install -e ".[dev]"

More details about installing Mars can be found at installation section in Mars document.

Architecture Overview

https://raw.githubusercontent.com/mars-project/mars/master/docs/source/images/architecture.png

Getting Started

Starting a new runtime locally via:

>>> import mars
>>> mars.new_session()

Or connecting to a Mars cluster which is already initialized.

>>> import mars
>>> mars.new_session('http://<web_ip>:<ui_port>')

Mars Tensor

Mars tensor provides a familiar interface like Numpy.

Numpy

Mars tensor

 
import numpy as np
N = 200_000_000
a = np.random.uniform(-1, 1, size=(N, 2))
print((np.linalg.norm(a, axis=1) < 1)
      .sum() * 4 / N)
 
import mars.tensor as mt
N = 200_000_000
a = mt.random.uniform(-1, 1, size=(N, 2))
print(((mt.linalg.norm(a, axis=1) < 1)
        .sum() * 4 / N).execute())
 
3.14174502
CPU times: user 11.6 s, sys: 8.22 s,
           total: 19.9 s
Wall time: 22.5 s
 
3.14161908
CPU times: user 966 ms, sys: 544 ms,
           total: 1.51 s
Wall time: 3.77 s

Mars can leverage multiple cores, even on a laptop, and could be even faster for a distributed setting.

Mars DataFrame

Mars DataFrame provides a familiar interface like pandas.

Pandas

Mars DataFrame

 
import numpy as np
import pandas as pd
df = pd.DataFrame(
    np.random.rand(100000000, 4),
    columns=list('abcd'))
print(df.sum())
 
import mars.tensor as mt
import mars.dataframe as md
df = md.DataFrame(
    mt.random.rand(100000000, 4),
    columns=list('abcd'))
print(df.sum().execute())
 
CPU times: user 10.9 s, sys: 2.69 s,
           total: 13.6 s
Wall time: 11 s
 
CPU times: user 1.21 s, sys: 212 ms,
           total: 1.42 s
Wall time: 2.75 s

Mars Learn

Mars learn provides a familiar interface like scikit-learn.

Scikit-learn

Mars learn

 
from sklearn.datasets import make_blobs
from sklearn.decomposition import PCA
X, y = make_blobs(
    n_samples=100000000, n_features=3,
    centers=[[3, 3, 3], [0, 0, 0],
             [1, 1, 1], [2, 2, 2]],
    cluster_std=[0.2, 0.1, 0.2, 0.2],
    random_state=9)
pca = PCA(n_components=3)
pca.fit(X)
print(pca.explained_variance_ratio_)
print(pca.explained_variance_)
 
from mars.learn.datasets import make_blobs
from mars.learn.decomposition import PCA
X, y = make_blobs(
    n_samples=100000000, n_features=3,
    centers=[[3, 3, 3], [0, 0, 0],
              [1, 1, 1], [2, 2, 2]],
    cluster_std=[0.2, 0.1, 0.2, 0.2],
    random_state=9)
pca = PCA(n_components=3)
pca.fit(X)
print(pca.explained_variance_ratio_)
print(pca.explained_variance_)

Mars learn also integrates with many libraries:

Mars remote

Mars remote allows users to execute functions in parallel.

Vanilla function calls

Mars remote

 
import numpy as np


def calc_chunk(n, i):
    rs = np.random.RandomState(i)
    a = rs.uniform(-1, 1, size=(n, 2))
    d = np.linalg.norm(a, axis=1)
    return (d < 1).sum()

def calc_pi(fs, N):
    return sum(fs) * 4 / N

N = 200_000_000
n = 10_000_000

fs = [calc_chunk(n, i)
      for i in range(N // n)]
pi = calc_pi(fs, N)
print(pi)
 
import numpy as np
import mars.remote as mr

def calc_chunk(n, i):
    rs = np.random.RandomState(i)
    a = rs.uniform(-1, 1, size=(n, 2))
    d = np.linalg.norm(a, axis=1)
    return (d < 1).sum()

def calc_pi(fs, N):
    return sum(fs) * 4 / N

N = 200_000_000
n = 10_000_000

fs = [mr.spawn(calc_chunk, args=(n, i))
      for i in range(N // n)]
pi = mr.spawn(calc_pi, args=(fs, N))
print(pi.execute().fetch())
 
3.1416312
CPU times: user 32.2 s, sys: 4.86 s,
           total: 37.1 s
Wall time: 12.4 s
 
3.1416312
CPU times: user 616 ms, sys: 307 ms,
           total: 923 ms
Wall time: 3.99 s

DASK on Mars

Refer to DASK on Mars for more information.

Eager Mode

Mars supports eager mode which makes it friendly for developing and easy to debug.

Users can enable the eager mode by options, set options at the beginning of the program or console session.

>>> from mars.config import options
>>> options.eager_mode = True

Or use a context.

>>> from mars.config import option_context
>>> with option_context() as options:
>>>     options.eager_mode = True
>>>     # the eager mode is on only for the with statement
>>>     ...

If eager mode is on, tensor, DataFrame etc will be executed immediately by default session once it is created.

>>> import mars.tensor as mt
>>> import mars.dataframe as md
>>> from mars.config import options
>>> options.eager_mode = True
>>> t = mt.arange(6).reshape((2, 3))
>>> t
array([[0, 1, 2],
       [3, 4, 5]])
>>> df = md.DataFrame(t)
>>> df.sum()
0    3
1    5
2    7
dtype: int64

Mars on Ray

Mars also has deep integration with Ray and can run on Ray efficiently and interact with the large ecosystem of machine learning and distributed systems built on top of the core Ray.

Starting a new Mars on Ray runtime locally via:

import mars
mars.new_session(backend='ray')
# Perform compute

Interact with Ray Dataset:

import mars.tensor as mt
import mars.dataframe as md
df = md.DataFrame(
    mt.random.rand(1000_0000, 4),
    columns=list('abcd'))
# Convert mars dataframe to ray dataset
ds = md.to_ray_dataset(df)
print(ds.schema(), ds.count())
ds.filter(lambda row: row["a"] > 0.5).show(5)
# Convert ray dataset to mars dataframe
df2 = md.read_ray_dataset(ds)
print(df2.head(5).execute())

Refer to Mars on Ray for more information.

Easy to scale in and scale out

Mars can scale in to a single machine, and scale out to a cluster with thousands of machines. It’s fairly simple to migrate from a single machine to a cluster to process more data or gain a better performance.

Bare Metal Deployment

Mars is easy to scale out to a cluster by starting different components of mars distributed runtime on different machines in the cluster.

A node can be selected as supervisor which integrated a web service, leaving other nodes as workers. The supervisor can be started with the following command:

mars-supervisor -h <host_name> -p <supervisor_port> -w <web_port>

Workers can be started with the following command:

mars-worker -h <host_name> -p <worker_port> -s <supervisor_endpoint>

After all mars processes are started, users can run

>>> sess = new_session('http://<web_ip>:<ui_port>')
>>> # perform computation

Kubernetes Deployment

Refer to Run on Kubernetes for more information.

Yarn Deployment

Refer to Run on Yarn for more information.

Getting involved

Thank you in advance for your contributions!

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