MXNet-Scala Modules
Module | Package Generated | Dependencies | Description |
---|---|---|---|
|
|
| |
2. core | mxnet-core_2.11 |
| |
3. macros | mxnet-macros_2.11 |
| |
4. init | mxnet-init_2.11 | ||
5. init-native |
| mxnet-init_2.11 | |
6. native |
| mxnet-core_2.11 | |
7. infer | mxnet-infer_2.11 |
| |
8. spark | mxnet-spark_2.11 |
| |
9. examples | mxnet-examples_2.11 |
|
Publishing Scala Packages to Nexus/Maven
Currently 3 Packages from the above generated are published to Maven. `mxnet_full_2.11-{platform}`.
The following flags are used to build the MXNet Backend(derived from mxnet pip compiler flags)
FLAGS | MXNet-Scala OSX | MXNet-Scala Linux CPU | MXNet-Scala Linux GPU |
---|---|---|---|
USE_BLAS | apple | openblas | |
USE_LAPACK | 1 | 1 | |
USE_OPENCV | 1 | 1 | |
USE_S3 | 1 | 1 | |
USE_DIST_KVSTORE | 0 | 1 | |
USE_SIGNAL_HANDLER | 1 | 1 | |
USE_OPENMP | 1 | 1 | |
USE_CUDNN | 0 | 1 | |
USE_NCCL | 0 | 1 |
Build MXNet backend on OSX
- Clone MXNet repo and checkout the release branch
2. Install Lapack
brew install lapack
setup all the flags
echo "ADD_CFLAGS += -I/usr/local/opt/lapack/include" >> ~/$MXNET_LOC/config.mk echo "ADD_LDFLAGS += -L/usr/local/opt/lapack/lib" >> ~/$MXNET_LOC/config.mk echo "USE_OPENMP = 0" >> ~/$MXNET_LOC/config.mk
Build MXNet backend on Linux-CPU
To build for linux-cpu platform, we use the AWS Deep Learning Base AMI that comes built with all the necessary
libraries such as opencv/cudnn etc,.
Install LAPACK/BLAS
sudo apt-get install -y libopenblas-dev
setup all the flags
echo "USE_SIGNAL_HANDLER = 1" >> ~/$MXNET_LOC/config.mk echo "USE_BLAS = openblas" >> ~/$MXNET_LOC/config.mk echo "USE_DIST_KVSTORE = 1" >> ~/$MXNET_LOC/config.mk echo "USE_S3 = 1" >> ~/$MXNET_LOC/config.mk
Publishing to Maven
export GPG_TTY=$(tty) make scalapkg make scalarelease-dryrun
Set up your dev environment