Jumpy: Python interface for nd4j

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Jumpy allows you to use nd4j from python via pointers (no network communication required like a lot of python libraries).

Installation

Jumpy requires an uber jar (a jar file that contains nd4j and all its dependencies) and the path to this file is expected to be found in the environment variable JUMPY_CLASS_PATH.

Easiest way to build the uber jar is by running mvn package on the pom.xml file:

git clone https://www.github.com/deeplearning4j/jumpy.git
cd jumpy
mvn package

This will create a jar file called dl4j-1.0.0-SNAPSHOT.jar in the target directory. Set JUMPY_CLASS_PATH environment variable to path of this file.

export JUMPY_CLASS_PATH='/...../jumpy/target/dl4j-1.0.0-SNAPSHOT.jar'

Finally, either install jumpy via pip:

pip install jumpy

Or from source:

python setup.py install

Using Jumpy

Creating arrays

Just like numpy, you can initialize an array using .zeros() or .ones()

import jumpy as jp

x = jp.zeros((32, 16))
y = jp.ones((32, 16))

Converting numpy array to jumpy array

A numpy ndarray instance can be converted to a jumpy ndarray instance (and vice-versa) without copying the data

import jumpy as jp
import numpy as np

x_np = np.random.random((100, 50))
x_jp = jp.array(x_np)

Converting jumpy array to numpy array

Simply call the .numpy() method of jumpy.ndarray.ndarray

import jumpy as jp

x_jp = jp.zeros((100,50))
x_np = x_jp.numpy()

Operations

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