ND4J is a library for scientific computing on the JVM that provides NDArrays (multi-dimensional arrays) and interfaces to BLAS libraries. It aims to provide a familiar interface to NumPy and MATLAB while being portable across platforms like desktop, cloud, and mobile. ND4J supports Java, Scala, and Clojure and includes features like loss functions, adaptive learning rates, and solvers to facilitate machine learning.

1. ND4J
ND-ARRAYS FOR JAVA
SCIENTIFIC COMPUTING ON THE JVM

2. Why another scientific library?

3. Different libraries:
JVM:
● Jblas (not
maintained)
● Breeze (scala)
● Spire (scala)
● Core.matrix(clojure)
● Netlib blas
● N + 1 pure java
impls
Real scientific
environments (no
fragmentation!):
● octave/matlab
● numpy
● R
● Theano

4. Blas and NDArrays
BLAS - Basic Linear Algebra Sub programs. Underlies EVERY fast
matrix lib out there.
NDArrays - matrices, tensors, math is done on axes (eg: rows/cols)

5. Matlab/Octave
a = ones(5,5)
a =
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
1 1 1 1 1
octave:2> sum(a,1)
ans =
5 5 5 5 5

6. Numpy
>>> import numpy as np
>>> a = np.ones((5,5))
>>> np.sum(a,axis=1)
array([ 5., 5., 5., 5., 5.])

7. Nd4j java/scala
import org.nd4j.api.linalg.DSL._
import org.nd4j.linalg.api.ndarray.INDArray
import org.nd4j.linalg.factory.Nd4j
val a = Nd4j.ones(5,5)
println Nd4j.sum(a,1)
[5.0, 5.0, 5.0, 5.0, 5.0]

8. Blas Implementations
● Cublas (cuda)
● Openblas
● Intel MKL(cpu)
● Nvblas(cuda)
● Clblas(opencl)
● ATLAS(CPU)
● LAPACK(CPU)

9. ND4J
A DISTRIBUTED FRAMEWORK
FOR MATRIX MANIPULATION
NATIVE
JBLAS
GPU
JCUDA ??
WITH SWAPPABLE BACKENDS…

10. Core Concepts
● DataBuffer - A DataBuffer is a storage abstraction. A raw buffer
could be an nio buffer or a jcuda buffer. This allows for backend
optimal storage.
● FFTInstance - An instantiation of FFT. Every hardware maker has
their own impls.
● ConvolutionInstance - An instantiation of Convolution. Same as FFT
● Complex Numbers - (An abstraction over different libs)
● BlasWrapper - An interface to a blas backend (blas is a standard for
cripe sakes!)
● Op - Some sort of a mathematical operation (dot product, element
wise addition,negation,..)
● Garbage Collector (WIP) - Native resource handling where needed

11. Other Features
● Loss Functions
● Adaptive learning rates
● Solvers (LBFGS,Conjugate gradient,Hessian Free,SGD)
● RNG: (DIfferent distributions based on apache math)

12. ND4J
ND4J IS PORTABLE
& CROSS-PLATFORM
DESKTOP CLOUD MOBILE

13. ND4J
ND4J SUPPORTS
JAVA
SCAL
A CLOJURE

14. ND4J
FAMILIAR SEMANTICS
NUMPY
MATLA
B
SCIKIT-
LEARN

15. ND4J
ADAM GIBSON
adam@skymind.io