Future development of this binding has been moved to nexus-torch (https://github.com/ctongfei/nexus/tree/master/torch), serving as a computation backend of nexus, a typesafe deep learning engine.

JTorch: JVM bindings for Torch (PyTorch C/C++ core)

Corresponding PyTorch version: 0.4.0

🚧 Ongoing project 🚧 Status: Not ready for use 🚧

	Windows	Linux	MacOS
CPU			✓
CUDA			✓

Installation

Step 1: Install PyTorch from source.

Locate the headers (*.h) and shared libraries (*.so / *.dylib / *.dll)

Step 2: Build SWIG bindings and publish to local Ivy repository.

./build-{mac/linux}.sh

You probably need to modify something in the script to make it work.

[TODO] Will be refactored into a Makefile.

Step 3: Use!

    <dependency>
      <groupId>me.tongfei</groupId>
      <artifactId>jtorch-java</artifactId>
      <version>0.1-TH0.4-SNAPSHOT</version>
    </dependency>

When starting a JVM, add -Djava.library.path=/usr/local/lib (or where your libATen.dylib is), or set $LD_LIBRARY_PATH to include that directory, so that JVM can properly load native the binary.

Package mappings

Torch has several C packages, to which each is mapped to the following Java package:

Torch package	Mapped Java package
TH	jtorch
THNN	jtorch.nn
THS	jtorch.sparse
THC	jtorch.cuda
THCUNN	jtorch.cuda.nn
THCS	jtorch.cuda.sparse

Type mappings

Torch has the following Storage/Tensor types, which are mapped to the Java corresponding types on the right.

Torch type	Element type (C)	Bits	Mapped element type (Java)	Java type
THHalfTensor	THHalf	16	float	HalfTensor
THFloatTensor	float	32	float	FloatTensor
THDoubleTensor	double	64	double	DoubleTensor
THByteTensor	uint8_t	8	short	UByteTensor
THCharTensor	int8_t	8	byte	ByteTensor
THShortTensor	int16_t	16	short	ShortTensor
THIntTensor	int32_t	32	int	IntTensor
THLongTensor	int64_t	64	long	LongTensor