Deep Water

What it is

• Native implementation of Deep Learning models for GPU-optimized backends (MXNet, Caffe, TensorFlow, etc.)
• State-of-the-art Deep Learning models trained from the H2O Platform
• Train user-defined or pre-defined deeplearning models for image/text/H2OFrame classification from Flow, R, Python, Java, Scala or REST API
• Behaves just like any other H2O model (Flow, cross-validation, early stopping, hyper-parameter search, etc.)
• The next best thing after sliced bread
• Under development

What it is not

• An oil drilling platform

Python/R Jupyter Notebooks

Check out a sample of cool Deep Learning Jupyter notebooks!

Pre-Release Downloads

This latest release of Deep Water is based on H2O-3 3.10.3.1 "Tverberg-1" 1b263c79 - Jan 28 2017

The downloadable packages below are built for the following system specifications:

• Ubuntu 16.04 LTS
• Latest NVIDIA Display driver
• CUDA 8 (latest available) in /usr/local/cuda
• CUDNN 5.1 (placed inside of lib and include directories in /usr/local/cuda/)

To use the GPU, please set the following environment variables:

export CUDA_PATH=/usr/local/cuda
export LD_LIBRARY_PATH=$CUDA_PATH/lib64:$LD_LIRBARY_PATH

Python + Flow (most common)

• Required to run python Jupyter notebooks: H2O Deep Water enabled Python module -- install via pip install <file>
• To build custom networks from Python: Matching MXNet Python egg -- install via easy_install <file>

R + Flow (R users)

• Required to run R examples: H2O Deep Water enabled R package -- install via R CMD INSTALL <file>
• To build custom networks from R: Matching MXNet R package -- install via R CMD INSTALL <file>

Flow (Web UI)

• To run from Flow only: H2O Standalone h2o.jar -- launch via java -jar h2o.jar

If you are interested in running H2O Deep Water on a different infrastructure, see the DIY build instructions below.

Running GPU enabled Deep Water in H2O

(Optional) Launch H2O by hand and build Deep Water models from Flow (localhost:54321)

java -jar h2o.jar

Java example use cases

Example Java GPU-enabled unit tests.

Python example use cases

Example Python GPU-enabled unit tests. Check out a sample of cool Deep Learning Python Jupyter notebooks!

R example use cases

Example R GPU-enabled unit tests. Check out a sample of cool Deep Learning R Jupyter notebooks!

Scala / Sparkling Water example use cases

Coming soon.

Pre-Release Amazon AWS Image

For your convenience, here's a pre-built image for Amazon's EC2 environment, based off our recent H2O Open Tour Hands-On Deep Water workshop. (Recording coming soon.)

• AMI ID: ami-10bd9607
• AMI Name: deepwater-dallas-v4
• Recommended instance types: g2.2xlarge or p2.xlarge
• After launching the instance, you can connect to port 8888 (Jupyter Notebook) or port 54321 (H2O Flow).

Refer to the Deep Water Workshop on EC2 Amazon AMI document for additional information on how to run this AMI.

Roadmap, Architecture and Demo

Download the Deep Water overview slides.

DIY Build Instructions

If you want to use Deep Water in H2O-3, you'll need to have a .jar file that includes backend support for at least one of MXNet, Caffe or TensorFlow.

1. Build MXNet

Instructions to build MXNet

2. Build TensorFlow

Instructions to build TensorFlow

3. Build Caffe

Coming soon.

4. Build H2O Backend Connectors

From the top-level of the deepwater repository, do

./gradlew build -x test

This will create the following file: build/libs/deepwater-all.jar

5. Add DeepWater support to H2O-3

You need to check out the h2o-3. Copy the freshly created jar file build/libs/deepwater-all.jar from the previous step to H2O-3's library h2o-3/lib/deepwater-all.jar (create the directory if it's not there) and you're done!

Build H2O-3 as usual:

./gradlew build -x test

This H2O version will now have GPU Deep Learning support!

To use the GPU, please make sure to set your path to your CUDA installation:

export CUDA_PATH=/usr/local/cuda

Install the Python wheel:

sudo pip install h2o-3/h2o-py/dist/h2o-3.11.0.99999-py2.py3-none-any.whl

(Optional) Install the MXNet Python/R packages

If you want to build your own MXNet models from Python or R, install the MXNet wheel (which was built together with MXNet above):

sudo easy_install deepwater/thirdparty/mxnet/python/dist/mxnet-0.7.0-py2.7.egg
R CMD INSTALL deepwater/thirdparty/mxnet/mxnet_0.7.tar.gz

Releasing

The release process bundles all defined submodules and push them into Maven central via Sonatype repository provider. The released artifacts are Java 6 compatible.

The release can be invoked for all modules by:

./gradlew -PdoRelease -PbuildOnlyBackendApi -PdoJava6Bytecode=true -Prelease.useAutomaticVersion=true release

The process performs the following steps:

• Updates gradle.properties and removes SNAPSHOT and increases minor version (can be changed)
• Creates a new release commit and tags it with release tag. (See gradle/release.gradle file to override the default template.)
• Builds
• Verifies compatibility of used API with Java 6 API
• Bytecode rewrite to be compatible with Java 6
• Generation of artifact metadata
• Pushes artifacts into staging area at https://oss.sonatype.org/

The process needs to be finished manually by:

• Logging in to https://oss.sonatype.org/#stagingRepositories
• Performing the actions "Close" and "Release" for the ai.h2o staging area
  • Note: Be careful because the area can contain more artifacts from different H2O projects.

Note: The release process creates two new commits and a new tag with the release version. However, the process does not push it to a remote repository, so it is necessary to perform a remote update manually using git push --tags or update the gradle/release.gradle settings and remove the --dry-run option from the pushOptions field.