SystemML Release Process

• Release Candidate Build and Deployment
  • Release Documentation
• Release Candidate Checklist
  • All Artifacts and Checksums Present
  • Release Candidate Build
  • Test Suite Passes
  • All Binaries Execute
  • Check LICENSE and NOTICE Files
  • Src Artifact Builds and Tests Pass
  • Single-Node Standalone
  • Single-Node Spark
  • Single-Node Hadoop
  • Notebooks
  • Performance Suite
• Voting
• Release
  • Release Deployment

Release Candidate Build and Deployment

To be written. (Describe how the release candidate is built, including checksums. Describe how the release candidate is deployed to servers for review.)

Release Documentation

The SYSTEMML_VERSION value in docs/_config.yml should be updated to the correct release version. The documentation site should be built. The SystemML documentation site should be deployed to a docs version folder within the main website project (using svn). As an example, the documentation site for SystemML version 0.11.0 should be available at http://systemml.apache.org/docs/0.11.0.

The Javadocs should be generated for the project and should be deployed to a docs version folder, such as http://systemml.apache.org/docs/0.11.0/api/java. Any other docs, such as Scaladocs if they are available, should be deployed to corresponding locations. Note that the version number specified in the Javadocs is determined by the project version number in the project pom.xml file.

Additionally, the Javadocs should be deployed to http://systemml.apache.org/docs/latest/api/java if the Javadocs have not already been deployed to this location.

Release Candidate Checklist

All Artifacts and Checksums Present

Up to Checklist

Verify that each expected artifact is present at https://dist.apache.org/repos/dist/dev/incubator/systemml/ and that each artifact has accompanying checksums (such as .asc and .md5).

Release Candidate Build

Up to Checklist

The release candidate should build on Windows, OS X, and Linux. To do this cleanly, the following procedure can be performed.

Clone the Apache SystemML GitHub repository to an empty location. Next, check out the release tag. Following this, build the distributions using Maven. This should be performed with an empty local Maven repository.

Here is an example:

$ git clone https://github.com/apache/incubator-systemml.git
$ cd incubator-systemml
$ git tag -l
$ git checkout tags/0.11.0-incubating-rc1 -b 0.11.0-incubating-rc1
$ mvn -Dmaven.repo.local=$HOME/.m2/temp-repo clean package -P distribution

Test Suite Passes

Up to Checklist

The entire test suite should pass on Windows, OS X, and Linux. The test suite can be run using:

$ mvn clean verify

All Binaries Execute

Up to Checklist

Validate that all of the binary artifacts can execute, including those artifacts packaged in other artifacts (in the tgz and zip artifacts).

The build artifacts should be downloaded from https://dist.apache.org/repos/dist/dev/incubator/systemml/ and these artifacts should be tested, as in this OS X example.

# download artifacts
wget -r -nH -nd -np -R index.html* https://dist.apache.org/repos/dist/dev/incubator/systemml/0.11.0-incubating-rc1/

# verify standalone tgz works
tar -xvzf systemml-0.11.0-incubating-standalone.tgz
cd systemml-0.11.0-incubating-standalone
echo "print('hello world');" > hello.dml
./runStandaloneSystemML.sh hello.dml
cd ..

# verify main jar works
mkdir lib
cp -R systemml-0.11.0-incubating-standalone/lib/* lib/
rm lib/systemml-0.11.0-incubating.jar
java -cp ./lib/*:systemml-0.11.0-incubating.jar org.apache.sysml.api.DMLScript -s "print('hello world');"

# verify src works
tar -xvzf systemml-0.11.0-incubating-src.tgz
cd systemml-0.11.0-incubating-src
mvn clean package -P distribution
cd target/
java -cp ./lib/*:systemml-0.11.0-incubating.jar org.apache.sysml.api.DMLScript -s "print('hello world');"
java -cp ./lib/*:SystemML.jar org.apache.sysml.api.DMLScript -s "print('hello world');"
cd ..
cd ..

# verify distrib tgz works
tar -xvzf systemml-0.11.0-incubating.tgz
cd systemml-0.11.0-incubating
java -cp ../lib/*:SystemML.jar org.apache.sysml.api.DMLScript -s "print('hello world');"

# verify spark batch mode
export SPARK_HOME=/Users/deroneriksson/spark-1.5.1-bin-hadoop2.6
$SPARK_HOME/bin/spark-submit SystemML.jar -s "print('hello world');" -exec hybrid_spark

# verify hadoop batch mode
hadoop jar SystemML.jar -s "print('hello world');"

Here is an example of doing a basic sanity check on OS X after building the artifacts manually.

# build distribution artifacts
mvn clean package -P distribution

cd target

# verify main jar works
java -cp ./lib/*:systemml-0.11.0-incubating.jar org.apache.sysml.api.DMLScript -s "print('hello world');"

# verify SystemML.jar works
java -cp ./lib/*:SystemML.jar org.apache.sysml.api.DMLScript -s "print('hello world');"

# verify src works
tar -xvzf systemml-0.11.0-incubating-src.tgz
cd systemml-0.11.0-incubating-src
mvn clean package -P distribution
cd target/
java -cp ./lib/*:systemml-0.11.0-incubating.jar org.apache.sysml.api.DMLScript -s "print('hello world');"
java -cp ./lib/*:SystemML.jar org.apache.sysml.api.DMLScript -s "print('hello world');"
cd ..
cd ..

# verify standalone tgz works
tar -xvzf systemml-0.11.0-incubating-standalone.tgz
cd systemml-0.11.0-incubating-standalone
echo "print('hello world');" > hello.dml
./runStandaloneSystemML.sh hello.dml
cd ..

# verify distrib tgz works
tar -xvzf systemml-0.11.0-incubating.tgz
cd systemml-0.11.0-incubating
java -cp ../lib/*:SystemML.jar org.apache.sysml.api.DMLScript -s "print('hello world');"

# verify spark batch mode
export SPARK_HOME=/Users/deroneriksson/spark-1.5.1-bin-hadoop2.6
$SPARK_HOME/bin/spark-submit SystemML.jar -s "print('hello world');" -exec hybrid_spark

# verify hadoop batch mode
hadoop jar SystemML.jar -s "print('hello world');"

Check LICENSE and NOTICE Files

Up to Checklist

Each artifact must contain LICENSE and NOTICE files. These files must reflect the contents of the artifacts. If the project dependencies (ie, libraries) have changed since the last release, the LICENSE and NOTICE files must be updated to reflect these changes.

Each artifact should contain a DISCLAIMER file.

For more information, see:

1. http://incubator.apache.org/guides/releasemanagement.html
2. http://www.apache.org/dev/licensing-howto.html

Src Artifact Builds and Tests Pass

Up to Checklist

The project should be built using the src (tgz and zip) artifacts. In addition, the test suite should be run using an src artifact and the tests should pass.

tar -xvzf systemml-0.11.0-incubating-src.tgz
cd systemml-0.11.0-incubating-src
mvn clean package -P distribution
mvn verify

Single-Node Standalone

Up to Checklist

The standalone tgz and zip artifacts contain runStandaloneSystemML.sh and runStandaloneSystemML.bat files. Verify that one or more algorithms can be run on a single node using these standalone distributions.

Here is an example based on the Standalone Guide demonstrating the execution of an algorithm (on OS X).

$ tar -xvzf systemml-0.11.0-incubating-standalone.tgz
$ cd systemml-0.11.0-incubating-standalone
$ wget -P data/ http://archive.ics.uci.edu/ml/machine-learning-databases/haberman/haberman.data
$ echo '{"rows": 306, "cols": 4, "format": "csv"}' > data/haberman.data.mtd
$ echo '1,1,1,2' > data/types.csv
$ echo '{"rows": 1, "cols": 4, "format": "csv"}' > data/types.csv.mtd
$ ./runStandaloneSystemML.sh scripts/algorithms/Univar-Stats.dml -nvargs X=data/haberman.data TYPES=data/types.csv STATS=data/univarOut.mtx CONSOLE_OUTPUT=TRUE

Single-Node Spark

Up to Checklist

Verify that SystemML runs algorithms on Spark locally.

Here is an example of running the Univar-Stats.dml algorithm on random generated data.

$ tar -xvzf systemml-0.11.0-incubating.tgz
$ cd systemml-0.11.0-incubating
$ export SPARK_HOME=/Users/deroneriksson/spark-1.5.1-bin-hadoop2.6
$ $SPARK_HOME/bin/spark-submit SystemML.jar -f scripts/datagen/genRandData4Univariate.dml -exec hybrid_spark -args 1000000 100 10 1 2 3 4 uni.mtx
$ echo '1' > uni-types.csv
$ echo '{"rows": 1, "cols": 1, "format": "csv"}' > uni-types.csv.mtd
$ $SPARK_HOME/bin/spark-submit SystemML.jar -f scripts/algorithms/Univar-Stats.dml -exec hybrid_spark -nvargs X=uni.mtx TYPES=uni-types.csv STATS=uni-stats.txt CONSOLE_OUTPUT=TRUE

Single-Node Hadoop

Up to Checklist

Verify that SystemML runs algorithms on Hadoop locally.

Based on the “Single-Node Spark” setup above, the Univar-Stats.dml algorithm could be run as follows:

$ hadoop jar SystemML.jar -f scripts/algorithms/Univar-Stats.dml -nvargs X=uni.mtx TYPES=uni-types.csv STATS=uni-stats.txt CONSOLE_OUTPUT=TRUE

Notebooks

Up to Checklist

Verify that SystemML can be executed from Jupyter and Zeppelin notebooks. For examples, see the Spark MLContext Programming Guide.

Performance Suite

Up to Checklist

Verify that the performance suite located at scripts/perftest/ executes on Spark and Hadoop. Testing should include 80MB, 800MB, 8GB, and 80GB data sizes.

Voting

Following a successful release candidate vote by SystemML PMC members on the SystemML mailing list, the release candidate is voted on by Incubator PMC members on the general incubator mailing list. If this vote succeeds, the release candidate has been approved.

Release

Release Deployment

To be written. (What steps need to be done? How is the release deployed to the central maven repo? What updates need to happen to the main website, such as updating the Downloads page? Where do the release notes for the release go?)