Building from source

Konduit Serving sources are hosted on GitHub. If you have git installed, clone the konduit-serving repository using the git clone command:

git clone https://github.com/KonduitAI/konduit-serving.git

Python module

To install the konduit Python module from source, in the python directory, after installing Cython, run

pip install .

To install all extensions needed for development run

pip install -e '.[tests,codegen,dev]'

The dev dependencies use black as a pre-commit hook to lint your code automatically. To activate this functionality, run pre-commit install on the command line first.

Running tests

Install test dependencies using pip install 'konduit[tests]' if you want to run tests.

On Windows, compiling the test dependencies requires Visual Studio Build Tools 14.0, which can be installed from here. You may also need to install the Windows 8.1 / 10 SDK. See Python's WindowsCompilers page for details.

The tests also require bert_mrpc_frozen.pb to be placed in the python/tests folder. Run the following code in python/tests:

curl https://deeplearning4jblob.blob.core.windows.net/testresources/bert_mrpc_frozen_v1.zip --output bert.zip
unzip bert.zip

The resulting JAR will be generated at the base of the konduit project. To copy that JAR into the tests folder and prepare the documentation (in the docs folder) to be tested within the testing framework, run:

cd tests
./prepare_doc_tests.sh

The tests are then run with pytest:

cd python/tests
python -m pytest .

To quickly run unit tests (recommended before each commit), or run the full set of integration tests, you can do:

pytest -m unit
pytest -m integration

To also run documentation tests with doctest for an individual file, simply run:

 python -m doctest ../konduit/server.py -v

JAR

A Java Archive (JAR) file is used to bundle a Java program.

Building the Konduit Serving JAR requires Maven and JDK 8.

Manual build

Run the following commands in the root directory of konduit-serving:

python build_jar.py --os <your-platform>

where <your-platform> is picked from windows-x86_64,linux-x86_64,linux-x86_64-gpu, macosx-x86_64, linux-armhf and windows-x86_64-gpu, depending on your operating system and architecture. Use the --help flag to view the full list of arguments.

An additional --spin argument provides the option to package Python (python), PMML (pmml), both (all) or neither (minimal). By default, both Python and PMML are packaged. Python bundling is not encouraged on ARM platforms, and PMML bundling is not encouraged if AGPL licensing is an issue.

Building with the command line interface

Once the konduit Python package is installed, you have access to a command line interface (CLI) tool called konduit.

The init command:

1. gets the latest Konduit Serving code, then

2. builds the Java dependencies needed forkonduit.

It assumes that you have git installed on your system and that python is available.

Run:

konduit init --os <your-platform>

where <your-platform> is picked from windows-x86_64, linux-x86_64, linux-x86_64-gpu, macosx-x86_64, linux-armhf and windows-x86_64-gpu, depending on your operating system and architecture.

An additional --spin argument provides the option to package Python (python), PMML (pmml), both (all) or neither (minimal). By default, both Python and PMML are packaged. Python bundling is not encouraged on ARM platforms, and PMML bundling is not encouraged if AGPL licensing is an issue.

To rebuild the Konduit Serving JAR without re-downloading sources, run build instead of init with the appropriate flags.

Known issues

• konduit init fails for linux-86_64-gpu (#115)

Linux builds

Generally, the Linux builds of Konduit Serving perform the following tasks on installation:

1. Copy Konduit Serving JAR file to /opt/konduit/serving/;

2. Create the necessary environment variables; and

3. Install a Konduit Serving-specific Conda distribution with install-python.sh.

RPM (CentOS, Redhat, etc.)

Konduit Serving RPM packages are generated using the RPM Maven Plugin.

First, install required packages with yum:

sudo yum install -y java-1.8.0-openjdk-devel which rpm-build redhat-rpm-config

This command installs the developer tools for developing Java programs using JDK 8, the which package to locate a program file's path, tools to build RPM files and Red Hat-specific RPM configuration files.

In the root folder of the konduit-serving project, run the following command to build RPM files using Maven Wrapper:

./mvnw clean package -Ppython,pmml,uberjar,tar,rpm -Dmaven.test.skip=true -Djavacpp.platform=linux-x86_64 -Dchip=cpu

The Maven Wrapper mvnw script allows Maven to be used even if mvn is not available on the system PATH. This command runs the Maven goals clean and install with the following arguments:

• maven.test.skip=true

• Profiles: uberjar,tar,rpm (ensure this is specified without spaces in between). The profiles python and pmml are optional.

• chip: cpu (use gpu to enable CUDA support)

• javacpp.platform: linux-x86_64

The clean install command first deletes previously compiled Java sources and resources; then compiles, tests and packages the Java project and copies it into the relevant folder. The path where the RPM file is saved depends on the spin.version (default custom) and the chip (cpu or gpu) .

Use the YUM command yum localinstallto install the RPM file.

# replace <spin.version> with the spin version specified
cd konduit-serving-rpm/target/rpm/konduit-serving-<spin.version>-cpu/RPMS/x86_64/
sudo yum localinstall -y *.rpm

DEB (for Ubuntu and other Debian-based systems)

Konduit Serving Debian packages are generated with the jdeb library.

Install JDK 8 using apt-get:

sudo apt-get install openjdk-8-jdk curl

In the root directory of the Konduit Serving project, run the mvnw script with parameters:

./mvnw clean package -Ppython,pmml,uberjar,tar,deb -Dmaven.test.skip=true -Djavacpp.platform=linux-x86_64 -Dchip=cpu

• maven.test.skip=true

• Enable the profiles uberjar,tar,deb (ensure this is specified without spaces in between). The python and pmml profiles are optional.

• chip: cpu (use gpu to enable CUDA support)

• javacpp.platform: linux-x86_64

The error java.io.IOException: This archives contains unclosed entries. usually indicates insufficient disk space (see tcurdt/jdeb#234).

Finally, use dpkg to install the built package:

sudo dpkg -i konduit-serving-deb/target/konduit-serving-custom-cpu_0.1.0-SNAPSHOT.deb

Note that dpkg does not support dependencies. If you run into missing dependencies, run

sudo apt-get install -f

to install dependencies. Alternately, use the gdebi package to install the local DEB package (see this StackExchange thread for details), or simply use apt-get install to install the local package (apt 1.1 and above):

cd konduit-serving-deb/target
sudo apt-get install ./*.deb

Tarball

Konduit Serving can also be built as a tarball, where the JAR file and associated scripts are packaged in a gzip-compressed tar file. To build a Konduit Serving tar file, run the following Maven Wrapper command in the root folder of the Konduit Serving project:

./mvnw clean package -Ppython,pmml,uberjar,tar -Dmaven.test.skip=true -Djavacpp.platform=linux-x86_64 -Dchip=cpu

This generates two compressed files in the target directory of the konduit-serving-tarfolder: a tar (.tar.gz) and a zip (.zip) file. In addition to the JAR file, the tar file contains a script to install a Conda distribution (ìnstall-python.sh) and a script to set environment variables (bin/konduit-serving).

After extracting the tar file, first run the konduit-servingshell script:

cd bin
chmod u+x konduit-serving # allow user to execute script
./konduit-serving

then the ìnstall-python.sh script:

cd .. 
chmod u+x install-python.sh 
./install-python.sh

Konduit Serving Conda distribution

The following packages are included in this Conda distribution:

Package	Version
NumPy	1.16.4
Jupyter	1.0.0
SciPy	1.3.3
requests	2.22.0
pandas	0.24.2
TensorFlow	1.15.0
Keras	2.2.4
konduit	0.1.3rc1
scikit-learn	0.22
matplotlib	3.1.2
PyTorch	1.3.1
torchvision	0.4.2
CUDA Toolkit	10.1
OpenJDK	8

Note that packages are sourced from the following Anaconda channels, in descending order of priority: pytorch, conda-forge, anaconda, konduitai.